publishedAt timestamp[ns]date 2023-02-13 12:55:54 2026-04-15 20:00:00 ⌀ | title stringlengths 6 206 ⌀ | thumbnail stringlengths 77 77 ⌀ | numComments int64 0 143 ⌀ | submittedBy dict | isAuthorParticipating bool 2
classes | mediaUrls listlengths 0 15 ⌀ | paper_id stringlengths 10 10 ⌀ | paper_authors listlengths 1 3.3k ⌀ | paper_publishedAt timestamp[ns]date 2023-02-13 17:55:54 2026-04-16 00:00:00 ⌀ | paper_title stringlengths 6 206 ⌀ | paper_summary stringlengths 165 1.92k ⌀ | paper_upvotes int64 0 673 ⌀ | paper_discussionId stringlengths 24 24 ⌀ | paper_projectPage stringlengths 15 247 ⌀ | paper_githubRepo stringlengths 25 132 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2025-02-20T22:33:22.039000 | SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features | 7 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | true | null | 2502.14786 | [
{
"_id": "67b7ed0d58f6b70b18dda7b4",
"hidden": false,
"name": "Michael Tschannen",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-21T10:23:44.125Z",
"user": {
"_id": "6489893e1ec8356ba5bb9777",
"avatarUrl": "/avatars/54354c1e5774cadd1d83d42054e9d96b.svg",
"full... | 2025-02-20T18:08:29 | SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic
Understanding, Localization, and Dense Features | We introduce SigLIP 2, a family of new multilingual vision-language encoders
that build on the success of the original SigLIP. In this second iteration, we
extend the original image-text training objective with several prior,
independently developed techniques into a unified recipe -- this includes
captioning-based pre... | 124 | 67b7ed0e58f6b70b18dda7f4 | null | null | |
2025-02-20T22:30:51.542000 | RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers | 2 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.14377 | [
{
"_id": "67b7f350357c2729ac216494",
"hidden": false,
"name": "Ke Cao",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-21T15:08:00.737Z",
"user": {
"_id": "66e4077369d1083dd97c7cd8",
"avatarUrl": "/avatars/0dad41e3e2f38f89b7b21c12d673f432.svg",
"fullname": "Ke ... | 2025-02-20T09:10:05 | RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers | The Diffusion Transformer plays a pivotal role in advancing text-to-image and
text-to-video generation, owing primarily to its inherent scalability. However,
existing controlled diffusion transformer methods incur significant parameter
and computational overheads and suffer from inefficient resource allocation due
to t... | 12 | 67b7f354357c2729ac216582 | null | null | |
2025-02-20T22:19:05.902000 | Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning | 5 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.14768 | [
{
"_id": "67b7f08c357c2729ac20a81b",
"hidden": false,
"name": "Tian Xie",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b7f08c357c2729ac20a81c",
"hidden": false,
"name": "Zitian Gao",
"status": "admin_assigned",
"statusLastChangedAt": "2025-... | 2025-02-20T17:49:26 | Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement
Learning | Inspired by the success of DeepSeek-R1, we explore the potential of
rule-based reinforcement learning (RL) in large reasoning models. To analyze
reasoning dynamics, we use synthetic logic puzzles as training data due to
their controllable complexity and straightforward answer verification. We make
some key technical co... | 44 | 67b7f08e357c2729ac20a88f | null | null | |
2025-02-20T22:15:33.133000 | SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines | 10 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | true | null | 2502.14739 | [
{
"_id": "67b7efc26348a1df80a8ae53",
"hidden": false,
"name": "M-A-P Team",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b7efc26348a1df80a8ae54",
"hidden": false,
"name": "Xinrun Du",
"status": "claimed_verified",
"statusLastChangedAt": "20... | 2025-02-20T17:05:58 | SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines | Large language models (LLMs) have demonstrated remarkable proficiency in
mainstream academic disciplines such as mathematics, physics, and computer
science. However, human knowledge encompasses over 200 specialized disciplines,
far exceeding the scope of existing benchmarks. The capabilities of LLMs in
many of these sp... | 94 | 67b7efc66348a1df80a8afc8 | null | null | |
2025-02-20T22:11:45.130000 | AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO | 2 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.14669 | [
{
"_id": "67b7eeddaf9f1b1bd95b878b",
"hidden": false,
"name": "Alan Dao",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-21T15:03:59.165Z",
"user": {
"_id": "62d7b2339b629105a5d6888a",
"avatarUrl": "/avatars/c3f164fde6b8f9a671890e08ce8a3e75.svg",
"fullname": "A... | 2025-02-20T16:05:18 | AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via
GRPO | Large Language Models (LLMs) have demonstrated impressive capabilities in
language processing, yet they often struggle with tasks requiring genuine
visual spatial reasoning. In this paper, we introduce a novel two-stage
training framework designed to equip standard LLMs with visual reasoning
abilities for maze navigati... | 11 | 67b7eeddaf9f1b1bd95b87c8 | null | null | |
2025-02-20T22:08:38.225000 | MLGym: A New Framework and Benchmark for Advancing AI Research Agents | 3 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.14499 | [
{
"_id": "67b7ee1dfedfe971271dcca0",
"hidden": false,
"name": "Deepak Nathani",
"status": "extracted_confirmed",
"statusLastChangedAt": "2025-02-21T07:20:46.836Z",
"user": {
"_id": "6114c9fae7a2566ae7d1a1a7",
"avatarUrl": "/avatars/c71ab1850322fcf5ef239cb8d31cb137.svg",
"fu... | 2025-02-20T12:28:23 | MLGym: A New Framework and Benchmark for Advancing AI Research Agents | We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for
evaluating and developing LLM agents on AI research tasks. This is the first
Gym environment for machine learning (ML) tasks, enabling research on
reinforcement learning (RL) algorithms for training such agents. MLGym-bench
consists of 13 divers... | 171 | 67b7ee1ffedfe971271dcd3a | null | null | |
2025-02-20T22:04:42.635000 | S*: Test Time Scaling for Code Generation | 3 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | true | null | 2502.14382 | [
{
"_id": "67b7ed3e58f6b70b18ddb4bc",
"hidden": false,
"name": "Dacheng Li",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-21T14:45:13.558Z",
"user": {
"_id": "63715b25ffc0489ed7d1f415",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63715b25f... | 2025-02-20T09:18:53 | S*: Test Time Scaling for Code Generation | Increasing test-time compute for LLMs shows promise across domains but
remains underexplored in code generation, despite extensive study in math. In
this paper, we propose S*, the first hybrid test-time scaling framework that
substantially improves the coverage and selection accuracy of generated code.
S* extends the e... | 59 | 67b7ed3f58f6b70b18ddb510 | null | null | |
2025-02-20T21:25:09.725000 | On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective | 2 | {
"_id": "639d94ab7145123e0d44e48a",
"avatarUrl": "/avatars/5bb6a65b306d1383c4a8bcd9334b470a.svg",
"followerCount": 2,
"fullname": "Yue Huang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "HowieHwong",
"type": "user"
} | true | null | 2502.14296 | [
{
"_id": "67b7e371f17ca6989faa9884",
"hidden": false,
"name": "Yue Huang",
"status": "extracted_pending",
"statusLastChangedAt": "2025-02-21T02:22:45.907Z",
"user": {
"_id": "639d94ab7145123e0d44e48a",
"avatarUrl": "/avatars/5bb6a65b306d1383c4a8bcd9334b470a.svg",
"fullname"... | 2025-02-20T06:20:36 | On the Trustworthiness of Generative Foundation Models: Guideline,
Assessment, and Perspective | Generative Foundation Models (GenFMs) have emerged as transformative tools.
However, their widespread adoption raises critical concerns regarding
trustworthiness across dimensions. This paper presents a comprehensive
framework to address these challenges through three key contributions. First,
we systematically review ... | 45 | 67b7e375f17ca6989faa9a28 | null | null | |
2025-02-20T21:13:28.792000 | Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above | 2 | {
"_id": "62a3f93fe2b7740fe2a94c86",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/62a3f93fe2b7740fe2a94c86/ZiaPqiVqXI2ANIyWQY_hT.png",
"followerCount": 6,
"fullname": "Nishant Balepur",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "nbalepur",
"type": "user"
} | true | null | 2502.14127 | [
{
"_id": "67b7e12b92b9b5b8184c6580",
"hidden": false,
"name": "Nishant Balepur",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-21T09:59:02.019Z",
"user": {
"_id": "62a3f93fe2b7740fe2a94c86",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/62... | 2025-02-19T22:11:52 | Which of These Best Describes Multiple Choice Evaluation with LLMs? A)
Forced B) Flawed C) Fixable D) All of the Above | Multiple choice question answering (MCQA) is popular for LLM evaluation due
to its simplicity and human-like testing, but we argue for its reform. We first
reveal flaws in MCQA's format, as it struggles to: 1) test
generation/subjectivity; 2) match LLM use cases; and 3) fully test knowledge.
We instead advocate for gen... | 2 | 67b7e12c92b9b5b8184c65a5 | null | null | |
2025-02-20T16:00:25.426000 | REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation | 2 | {
"_id": "6142ec5a7215c6d505bafd4e",
"avatarUrl": "/avatars/ae0387b672435c5a4cf16ff6764ce597.svg",
"followerCount": null,
"fullname": "Dong-Ho Lee",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "danny911kr",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/6142ec5a7215c6d505bafd4e/8ZXPnL7UdgpvHkiP0HHDI.png"
] | 2502.13270 | [
{
"_id": "67b7975d10a9714460c03882",
"hidden": false,
"name": "Dong-Ho Lee",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-21T09:59:30.731Z",
"user": {
"_id": "6142ec5a7215c6d505bafd4e",
"avatarUrl": "/avatars/ae0387b672435c5a4cf16ff6764ce597.svg",
"fullname... | 2025-02-18T20:29:01 | REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation | Long-term, open-domain dialogue capabilities are essential for chatbots
aiming to recall past interactions and demonstrate emotional intelligence (EI).
Yet, most existing research relies on synthetic, LLM-generated data, leaving
open questions about real-world conversational patterns. To address this gap,
we introduce ... | 6 | 67b7975e10a9714460c038bb | null | null | |
2025-02-20T14:34:52.849000 | From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions | 3 | {
"_id": "62645f88c39850dc093d6105",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1650745211725-noauth.png",
"followerCount": 51,
"fullname": "Mohammed Hamdy",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "mmhamdy",
"type": "user"
} | true | null | 2502.13791 | [
{
"_id": "67b7838bb41e5f760f8bd1b0",
"hidden": false,
"name": "Nathanaël Carraz Rakotonirina",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-24T15:51:38.471Z",
"user": {
"_id": "6195d3199b7166aedc74247f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/product... | 2025-02-19T14:58:04 | From Tools to Teammates: Evaluating LLMs in Multi-Session Coding
Interactions | Large Language Models (LLMs) are increasingly used in working environments
for a wide range of tasks, excelling at solving individual problems in
isolation. However, are they also able to effectively collaborate over
long-term interactions? To investigate this, we introduce MemoryCode, a
synthetic multi-session dataset... | 5 | 67b7838cb41e5f760f8bd209 | null | null | |
2025-02-20T13:47:47.134000 | Judging the Judges: A Collection of LLM-Generated Relevance Judgements | 2 | {
"_id": "64108fc514215c0775e13f5e",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64108fc514215c0775e13f5e/pHWr8TlBnYrulo2owIrrv.jpeg",
"followerCount": null,
"fullname": "Hossein A. (Saeed) Rahmani",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "rahmanidashti",
"ty... | true | null | 2502.13908 | [
{
"_id": "67b75ce1fedef65ff99cf5f8",
"hidden": false,
"name": "Hossein A. Rahmani",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T16:57:36.417Z",
"user": {
"_id": "64108fc514215c0775e13f5e",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads... | 2025-02-19T17:40:32 | Judging the Judges: A Collection of LLM-Generated Relevance Judgements | Using Large Language Models (LLMs) for relevance assessments offers promising
opportunities to improve Information Retrieval (IR), Natural Language
Processing (NLP), and related fields. Indeed, LLMs hold the promise of allowing
IR experimenters to build evaluation collections with a fraction of the manual
human labor c... | 4 | 67b75ce2fedef65ff99cf623 | null | null | |
2025-02-20T12:26:53.898000 | MMTEB: Massive Multilingual Text Embedding Benchmark | 3 | {
"_id": "5f1eb362eec0ad2a071ad6e2",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/5f1eb362eec0ad2a071ad6e2/IXMYkYKuTwn6kBdWnQeeY.png",
"followerCount": 120,
"fullname": "Niklas Muennighoff",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Muennighoff",
"type": "user"
... | true | null | 2502.13595 | [
{
"_id": "67b6fa9cb544aa153178a60b",
"hidden": false,
"name": "Kenneth Enevoldsen",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T15:52:45.751Z",
"user": {
"_id": "5ff5943752c26e9bc240bada",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads... | 2025-02-19T10:13:43 | MMTEB: Massive Multilingual Text Embedding Benchmark | Text embeddings are typically evaluated on a limited set of tasks, which are
constrained by language, domain, and task diversity. To address these
limitations and provide a more comprehensive evaluation, we introduce the
Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale,
community-driven expansion o... | 31 | 67b6fa9db544aa153178a69c | null | null | |
2025-02-20T12:23:27.067000 | AIDE: AI-Driven Exploration in the Space of Code | 6 | {
"_id": "65f7927e7bc58032aa5bda58",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65f7927e7bc58032aa5bda58/JxUSj-J7YBwtgj6rqQtjn.jpeg",
"followerCount": null,
"fullname": "Dex Dixing Xu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "dexhunter",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/65f7927e7bc58032aa5bda58/bkhW4LYUeFqT9_aqPd3Om.jpeg"
] | 2502.13138 | [
{
"_id": "67b6e0829b29983879ad2312",
"hidden": false,
"name": "Zhengyao Jiang",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T17:38:24.557Z",
"user": {
"_id": "630384837b50dd9d0a3328dc",
"avatarUrl": "/avatars/17097a93ef403592bc07c0ff6712faf3.svg",
"fullnam... | 2025-02-18T18:57:21 | AIDE: AI-Driven Exploration in the Space of Code | Machine learning, the foundation of modern artificial intelligence, has
driven innovations that have fundamentally transformed the world. Yet, behind
advancements lies a complex and often tedious process requiring labor and
compute intensive iteration and experimentation. Engineers and scientists
developing machine lea... | 7 | 67b6e0839b29983879ad2346 | null | null | |
2025-02-20T12:09:53.761000 | MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching | 2 | {
"_id": "64c8c2d87d0ea4e7f12995c6",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64c8c2d87d0ea4e7f12995c6/h8eWJrz8kqavemy8vQ2NK.jpeg",
"followerCount": 3,
"fullname": "Fabian David Schmidt",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "fdschmidt93",
"type": "user"... | true | null | 2502.12852 | [
{
"_id": "67b5b31f5a17526b55c3ccde",
"hidden": false,
"name": "Fabian David Schmidt",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T15:53:15.852Z",
"user": {
"_id": "64c8c2d87d0ea4e7f12995c6",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploa... | 2025-02-18T13:40:05 | MVL-SIB: A Massively Multilingual Vision-Language Benchmark for
Cross-Modal Topical Matching | Existing multilingual vision-language (VL) benchmarks often only cover a
handful of languages. Consequently, evaluations of large vision-language models
(LVLMs) predominantly target high-resource languages, underscoring the need for
evaluation data for low-resource languages. To address this limitation, we
introduce MV... | 3 | 67b5b3205a17526b55c3cd40 | null | null | |
2025-02-20T12:07:02.880000 | Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval | 2 | {
"_id": "63e972f1ccae1fe5c6211759",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63e972f1ccae1fe5c6211759/AfKPgMdAraUtvbtJpoHFY.jpeg",
"followerCount": 2,
"fullname": "Luis Lara",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "ludolara",
"type": "user"
} | true | null | 2502.13369 | [
{
"_id": "67b7610afedfe97127f75374",
"hidden": false,
"name": "Aditya Sharma",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T17:37:33.974Z",
"user": {
"_id": "66d959e4fb6d15635f2b9d76",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth... | 2025-02-19T02:08:13 | Reducing Hallucinations in Language Model-based SPARQL Query Generation
Using Post-Generation Memory Retrieval | The ability to generate SPARQL queries from natural language questions is
crucial for ensuring efficient and accurate retrieval of structured data from
knowledge graphs (KG). While large language models (LLMs) have been widely
adopted for SPARQL query generation, they are often susceptible to
hallucinations and out-of-... | 2 | 67b7610bfedfe97127f7539c | null | null | |
2025-02-20T10:53:49.049000 | High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion | 2 | {
"_id": "657dc1576dc01435cd9029d8",
"avatarUrl": "/avatars/3bba11ac7659fce61aeaedf40e2057a8.svg",
"followerCount": 2,
"fullname": "Xiang Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "XiangZ",
"type": "user"
} | true | null | 2502.12752 | [
{
"_id": "67b74fbdbb87b88059a9c5d3",
"hidden": false,
"name": "Xiang Zhang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T16:06:01.193Z",
"user": {
"_id": "657dc1576dc01435cd9029d8",
"avatarUrl": "/avatars/3bba11ac7659fce61aeaedf40e2057a8.svg",
"fullname... | 2025-02-18T11:13:06 | High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion | Despite recent advances in Novel View Synthesis (NVS), generating
high-fidelity views from single or sparse observations remains a significant
challenge. Existing splatting-based approaches often produce distorted geometry
due to splatting errors. While diffusion-based methods leverage rich 3D priors
to achieve improve... | 3 | 67b74fc7bb87b88059a9c75d | null | null | |
2025-02-20T10:46:55.281000 | TESS 2: A Large-Scale Generalist Diffusion Language Model | 3 | {
"_id": "62608fc2ffe8827cb1d89f9f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1654027835241-62608fc2ffe8827cb1d89f9f.png",
"followerCount": 11,
"fullname": "Hamish Ivison",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "hamishivi",
"type": "user"
} | true | null | 2502.13917 | [
{
"_id": "67b698422c8b2ef925e03f4f",
"hidden": false,
"name": "Jaesung Tae",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b698422c8b2ef925e03f50",
"hidden": false,
"name": "Hamish Ivison",
"status": "extracted_confirmed",
"statusLastChanged... | 2025-02-19T17:50:31 | TESS 2: A Large-Scale Generalist Diffusion Language Model | We introduce TESS 2, a general instruction-following diffusion language model
that outperforms contemporary instruction-tuned diffusion models, as well as
matches and sometimes exceeds strong autoregressive (AR) models. We train TESS
2 by first adapting a strong AR model via continued pretraining with the usual
cross-e... | 6 | 67b698432c8b2ef925e03fb4 | null | null | |
2025-02-20T07:25:12.795000 | REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models | 2 | {
"_id": "6540fbf9cb7fffd683942b43",
"avatarUrl": "/avatars/d4a64fbde511d0949e1c339179586850.svg",
"followerCount": 2,
"fullname": "DongGeon Lee",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "oneonlee",
"type": "user"
} | true | null | 2502.13622 | [
{
"_id": "67b69cf4573aa8417aec103c",
"hidden": false,
"name": "DongGeon Lee",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T09:35:55.480Z",
"user": {
"_id": "6540fbf9cb7fffd683942b43",
"avatarUrl": "/avatars/d4a64fbde511d0949e1c339179586850.svg",
"fullnam... | 2025-02-19T10:59:05 | REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
Language Models | Hallucinations in large language model (LLM) outputs severely limit their
reliability in knowledge-intensive tasks such as question answering. To address
this challenge, we introduce REFIND (Retrieval-augmented Factuality
hallucINation Detection), a novel framework that detects hallucinated spans
within LLM outputs by ... | 4 | 67b69cf7573aa8417aec10bf | null | null | |
2025-02-20T06:45:40.507000 | Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models | 2 | {
"_id": "63fcb42c987f631186e554f2",
"avatarUrl": "/avatars/5cf87e9fa21c088c0bd8577d651d01f6.svg",
"followerCount": null,
"fullname": "Jun Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "junzhang98",
"type": "user"
} | true | null | 2502.13533 | [
{
"_id": "67b68f883cd5860d8597eace",
"hidden": false,
"name": "Jun Zhang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T09:36:13.757Z",
"user": {
"_id": "63fcb42c987f631186e554f2",
"avatarUrl": "/avatars/5cf87e9fa21c088c0bd8577d651d01f6.svg",
"fullname":... | 2025-02-19T08:39:15 | Train Small, Infer Large: Memory-Efficient LoRA Training for Large
Language Models | Large Language Models (LLMs) have significantly advanced natural language
processing with exceptional task generalization capabilities. Low-Rank Adaption
(LoRA) offers a cost-effective fine-tuning solution, freezing the original
model parameters and training only lightweight, low-rank adapter matrices.
However, the mem... | 9 | 67b68f8b3cd5860d8597eb97 | null | null | |
2025-02-20T05:38:39.430000 | Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective | 2 | {
"_id": "668bb3b14c25c09b01815a55",
"avatarUrl": "/avatars/5d46301dd5d7641e3da05b0ad560efee.svg",
"followerCount": null,
"fullname": "Yuan Yao",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "yyyaoyuan",
"type": "user"
} | true | null | 2502.13573 | [
{
"_id": "67b70459ea22340afaaf416f",
"hidden": false,
"name": "Yuan Yao",
"status": "extracted_pending",
"statusLastChangedAt": "2025-02-20T10:30:51.477Z",
"user": {
"_id": "668bb3b14c25c09b01815a55",
"avatarUrl": "/avatars/5d46301dd5d7641e3da05b0ad560efee.svg",
"fullname":... | 2025-02-19T09:27:03 | Noise May Contain Transferable Knowledge: Understanding Semi-supervised
Heterogeneous Domain Adaptation from an Empirical Perspective | Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning
across domains with distinct feature representations and distributions, where
source samples are labeled while most target samples are unlabeled, with only a
small fraction labeled. Moreover, there is no one-to-one correspondence between
source a... | 2 | 67b7045bea22340afaaf41fd | null | null | |
2025-02-20T05:19:11.890000 | GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking | 2 | {
"_id": "62dfd54798815401141c47fe",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/62dfd54798815401141c47fe/ct2OA_K0Wwpshy8DCswxy.png",
"followerCount": 6,
"fullname": "Flo Schneider",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "floschne",
"type": "user"
} | true | null | 2502.13766 | [
{
"_id": "67b6faf5a96bf2b8ff8c235c",
"hidden": false,
"name": "Florian Schneider",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T10:49:37.443Z",
"user": {
"_id": "62dfd54798815401141c47fe",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/... | 2025-02-19T14:27:40 | GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge
Benchmarking | Large Vision-Language Models (LVLMs) have recently gained attention due to
their distinctive performance and broad applicability. While it has been
previously shown that their efficacy in usage scenarios involving non-Western
contexts falls short, existing studies are limited in scope, covering just a
narrow range of c... | 3 | 67b6faf8a96bf2b8ff8c2422 | null | null | |
2025-02-20T04:32:22.011000 | InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning | 2 | {
"_id": "618c1ad1c74578e0a4a4d074",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/618c1ad1c74578e0a4a4d074/8u_AkeHt4d6xtQ8hzaffU.jpeg",
"followerCount": 60,
"fullname": "Drishti Sharma",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "DrishtiSharma",
"type": "user"
} | false | null | 2502.11573 | [
{
"_id": "67b6f629d9da6999328e38f5",
"hidden": false,
"name": "Congkai Xie",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T17:12:49.025Z",
"user": {
"_id": "6719f1ad725123d503b5ef3c",
"avatarUrl": "/avatars/08e1be1f4afa1b6e1501a15cdb786a47.svg",
"fullname":... | 2025-02-17T09:07:32 | InfiR : Crafting Effective Small Language Models and Multimodal Small
Language Models in Reasoning | Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs)
have made significant advancements in reasoning capabilities. However, they
still face challenges such as high computational demands and privacy concerns.
This paper focuses on developing efficient Small Language Models (SLMs) and
Multimodal Smal... | 8 | 67b6f62ad9da6999328e3955 | null | null | |
2025-02-20T03:56:54.121000 | ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation | 3 | {
"_id": "64a62c2f500beb50968e5c9c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/wfL3ojJmXqyzGUmCblPf4.jpeg",
"followerCount": 5,
"fullname": "Yupeng Hou",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "hyp1231",
"type": "user"
} | true | null | 2502.13581 | [
{
"_id": "67b6ee04412c9eccae5151f5",
"hidden": false,
"name": "Yupeng Hou",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T09:35:14.498Z",
"user": {
"_id": "64a62c2f500beb50968e5c9c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/... | 2025-02-19T09:45:29 | ActionPiece: Contextually Tokenizing Action Sequences for Generative
Recommendation | Generative recommendation (GR) is an emerging paradigm where user actions are
tokenized into discrete token patterns and autoregressively generated as
predictions. However, existing GR models tokenize each action independently,
assigning the same fixed tokens to identical actions across all sequences
without considerin... | 5 | 67b6ee04412c9eccae515223 | null | null | |
2025-02-20T02:40:09.567000 | MoM: Linear Sequence Modeling with Mixture-of-Memories | 2 | {
"_id": "6246bb33da617c00b48e4d92",
"avatarUrl": "/avatars/0304a9f6eb7f5dee4d933d03222f94e9.svg",
"followerCount": 3,
"fullname": "Weigao Sun",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "weigao266",
"type": "user"
} | true | null | 2502.13685 | [
{
"_id": "67b6dc1ba7567156c6547880",
"hidden": false,
"name": "Jusen Du",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T16:08:01.601Z",
"user": {
"_id": "65003e857804f04a163328d9",
"avatarUrl": "/avatars/fe32150aabfde8d283b38ccebcf6982e.svg",
"fullname": "J... | 2025-02-19T12:53:55 | MoM: Linear Sequence Modeling with Mixture-of-Memories | Linear sequence modeling methods, such as linear attention, state space
modeling, and linear RNNs, offer significant efficiency improvements by
reducing the complexity of training and inference. However, these methods
typically compress the entire input sequence into a single fixed-size memory
state, which leads to sub... | 33 | 67b6dc1ca7567156c65478b8 | null | https://github.com/OpenSparseLLMs/MoM | |
2025-02-20T01:20:46.431000 | Presumed Cultural Identity: How Names Shape LLM Responses | 2 | {
"_id": "60c50f18754747f54fa37114",
"avatarUrl": "/avatars/648ae58b81806dbd93a68546666047e3.svg",
"followerCount": 1,
"fullname": "Siddhesh",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "sidicity",
"type": "user"
} | false | null | 2502.11995 | [
{
"_id": "67b65bbe0d878eff1a6b111d",
"hidden": false,
"name": "Siddhesh Pawar",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T16:11:39.727Z",
"user": {
"_id": "661e2ac200798c2e33cc49a5",
"avatarUrl": "/avatars/8e5e1672b36f86bb4ad7a7e22e8d4f4d.svg",
"fullnam... | 2025-02-17T16:35:15 | Presumed Cultural Identity: How Names Shape LLM Responses | Names are deeply tied to human identity. They can serve as markers of
individuality, cultural heritage, and personal history. However, using names as
a core indicator of identity can lead to over-simplification of complex
identities. When interacting with LLMs, user names are an important point of
information for perso... | 10 | 67b65bbf0d878eff1a6b1174 | null | null | |
2025-02-20T01:07:44.785000 | SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation | 2 | {
"_id": "64b4eec4faa3181a5eab9c46",
"avatarUrl": "/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg",
"followerCount": 16,
"fullname": "Jiaqi Wang",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "myownskyW7",
"type": "user"
} | true | null | 2502.13128 | [
{
"_id": "67b6c696e9b901edeaf320d5",
"hidden": false,
"name": "Zihan Liu",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T16:07:49.211Z",
"user": {
"_id": "65f33b1c9f7970ccc0234cbf",
"avatarUrl": "/avatars/99fbab303912e3674663251c04279907.svg",
"fullname": "... | 2025-02-18T18:52:21 | SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song
Generation | Text-to-song generation, the task of creating vocals and accompaniment from
textual inputs, poses significant challenges due to domain complexity and data
scarcity. Existing approaches often employ multi-stage generation procedures,
resulting in cumbersome training and inference pipelines. In this paper, we
propose Son... | 37 | 67b6c698e9b901edeaf321a7 | null | null | |
2025-02-19T23:54:57.669000 | Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety Mechanisms Tend to Be Anchored in The Template Region | 2 | {
"_id": "631326d6289cf15634c52369",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/631326d6289cf15634c52369/lmPWGHLsQ36H556cqcXjT.jpeg",
"followerCount": 7,
"fullname": "Cooper Leong",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "cooperleong00",
"type": "user"
} | true | null | 2502.13946 | [
{
"_id": "67b6b416b4ad845374143c31",
"hidden": false,
"name": "Chak Tou Leong",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-24T09:25:12.631Z",
"user": {
"_id": "631326d6289cf15634c52369",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/631... | 2025-02-19T18:42:45 | Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety
Mechanisms Tend to Be Anchored in The Template Region | The safety alignment of large language models (LLMs) remains vulnerable, as
their initial behavior can be easily jailbroken by even relatively simple
attacks. Since infilling a fixed template between the input instruction and
initial model output is a common practice for existing LLMs, we hypothesize
that this template... | 9 | 67b6b416b4ad845374143c5b | null | null | |
2025-02-19T23:35:06.194000 | Qwen2.5-VL Technical Report | 7 | {
"_id": "63451cf0a05b51f7ded25505",
"avatarUrl": "/avatars/dec4bbee4a82b773fc58dfc2dce9dbeb.svg",
"followerCount": 14,
"fullname": "shuai bai",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "bluelike",
"type": "user"
} | true | null | 2502.13923 | [
{
"_id": "67b6b0688b56622e70b9e83e",
"hidden": false,
"name": "Shuai Bai",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T15:54:00.062Z",
"user": {
"_id": "63451cf0a05b51f7ded25505",
"avatarUrl": "/avatars/dec4bbee4a82b773fc58dfc2dce9dbeb.svg",
"fullname": "... | 2025-02-19T18:00:14 | Qwen2.5-VL Technical Report | We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language
series, which demonstrates significant advancements in both foundational
capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap
forward in understanding and interacting with the world through enhanced visual
recognition, p... | 154 | 67b6b0688b56622e70b9e875 | null | null | |
2025-02-19T23:34:43.424000 | Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering | 4 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | true | null | 2502.13962 | [
{
"_id": "67b691751f861500916ecd5d",
"hidden": false,
"name": "William Jurayj",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T09:36:09.674Z",
"user": {
"_id": "6372bc95c4267fd7cd77f4d0",
"avatarUrl": "/avatars/17a24af68f45487e601687d777b352b6.svg",
"fulln... | 2025-02-19T18:58:31 | Is That Your Final Answer? Test-Time Scaling Improves Selective Question
Answering | Scaling the test-time compute of large language models has demonstrated
impressive performance on reasoning benchmarks. However, existing evaluations
of test-time scaling make the strong assumption that a reasoning system should
always give an answer to any question provided. This overlooks concerns about
whether a mod... | 28 | 67b691761f861500916ecd8e | null | null | |
2025-02-19T23:31:36.410000 | Thinking Preference Optimization | 4 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.13173 | [
{
"_id": "67b6b014f7e569081326494f",
"hidden": false,
"name": "Wang Yang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b6b014f7e5690813264950",
"hidden": false,
"name": "Hongye Jin",
"status": null,
"statusLastChangedAt": null,
"user":... | 2025-02-17T19:56:21 | Thinking Preference Optimization | Supervised Fine-Tuning (SFT) has been a go-to and effective method for
enhancing long chain-of-thought (CoT) reasoning in relatively small LLMs by
fine-tuning them with long CoT responses from larger LLMs. To continually
improve reasoning abilities, we can either collect new high-quality long CoT
reasoning SFT data or ... | 17 | 67b6b015f7e56908132649a0 | null | null | |
2025-02-19T23:18:32.647000 | NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation | 2 | {
"_id": "6310a3cd531cc21f9e06de6a",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6310a3cd531cc21f9e06de6a/aTGMx3O41lUARK9s3dAik.jpeg",
"followerCount": 3,
"fullname": "Zhiyuan Liu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "acharkq",
"type": "user"
} | true | null | 2502.12638 | [
{
"_id": "67b6acdb3a3df2f965e7af0b",
"hidden": false,
"name": "Zhiyuan Liu",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T17:43:04.070Z",
"user": {
"_id": "6310a3cd531cc21f9e06de6a",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6310a3cd... | 2025-02-18T08:40:13 | NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule
Generation | 3D molecule generation is crucial for drug discovery and material design.
While prior efforts focus on 3D diffusion models for their benefits in modeling
continuous 3D conformers, they overlook the advantages of 1D SELFIES-based
Language Models (LMs), which can generate 100% valid molecules and leverage the
billion-sca... | 8 | 67b6acdd3a3df2f965e7af85 | null | null | |
2025-02-19T23:07:01.367000 | AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence | 2 | {
"_id": "6529f79e802e3d1a4f8ec662",
"avatarUrl": "/avatars/d05320c370a6497d8792ef5acb563dd5.svg",
"followerCount": 2,
"fullname": "Yuliang Liu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "yuliang03181",
"type": "user"
} | true | null | 2502.13943 | [
{
"_id": "67b6a9a7c721bee91cac2888",
"hidden": false,
"name": "Yuliang Liu",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T17:11:40.282Z",
"user": {
"_id": "6529f79e802e3d1a4f8ec662",
"avatarUrl": "/avatars/d05320c370a6497d8792ef5acb563dd5.svg",
"fullname":... | 2025-02-19T18:35:55 | AdaptiveStep: Automatically Dividing Reasoning Step through Model
Confidence | Current approaches for training Process Reward Models (PRMs) often involve
breaking down responses into multiple reasoning steps using rule-based
techniques, such as using predefined placeholder tokens or setting the
reasoning step's length into a fixed size. These approaches overlook the fact
that specific words do no... | 7 | 67b6a9a8c721bee91cac28e7 | null | null | |
2025-02-19T22:57:23.298000 | Craw4LLM: Efficient Web Crawling for LLM Pretraining | 2 | {
"_id": "6135eeeb5bc6ecdf86b60f0d",
"avatarUrl": "/avatars/43cedcf20ab6b0801a662787400e1384.svg",
"followerCount": 7,
"fullname": "Shi Yu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "yushi",
"type": "user"
} | true | null | 2502.13347 | [
{
"_id": "67b6a7e83ef3656c48f149b9",
"hidden": false,
"name": "Shi Yu",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T09:35:47.487Z",
"user": {
"_id": "6135eeeb5bc6ecdf86b60f0d",
"avatarUrl": "/avatars/43cedcf20ab6b0801a662787400e1384.svg",
"fullname": "S... | 2025-02-19T00:31:43 | Craw4LLM: Efficient Web Crawling for LLM Pretraining | Web crawl is a main source of large language models' (LLMs) pretraining data,
but the majority of crawled web pages are discarded in pretraining due to low
data quality. This paper presents Crawl4LLM, an efficient web crawling method
that explores the web graph based on the preference of LLM pretraining.
Specifically, ... | 27 | 67b6a7e93ef3656c48f149f1 | null | null | |
2025-02-19T22:42:06.502000 | Autellix: An Efficient Serving Engine for LLM Agents as General Programs | 2 | {
"_id": "654037be97949fd2304aab7f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/654037be97949fd2304aab7f/2cSME81gcwYa2OTeVlq5Q.jpeg",
"followerCount": 3,
"fullname": "Michael Luo",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "michaelzhiluo",
"type": "user"
} | true | null | 2502.13965 | [
{
"_id": "67b6a3fa09841367596a1db5",
"hidden": false,
"name": "Michael Luo",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-24T09:25:24.729Z",
"user": {
"_id": "654037be97949fd2304aab7f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/654037... | 2025-02-19T18:59:30 | Autellix: An Efficient Serving Engine for LLM Agents as General Programs | Large language model (LLM) applications are evolving beyond simple chatbots
into dynamic, general-purpose agentic programs, which scale LLM calls and
output tokens to help AI agents reason, explore, and solve complex tasks.
However, existing LLM serving systems ignore dependencies between programs and
calls, missing si... | 18 | 67b6a3fb09841367596a1e06 | null | null | |
2025-02-19T22:27:22.403000 | SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering? | 2 | {
"_id": "64beb6b6140491ca9f803ebf",
"avatarUrl": "/avatars/0daa2e813a13668b8b708cd8c12763d9.svg",
"followerCount": null,
"fullname": "Yucheng SHi",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "YuchengShi",
"type": "user"
} | true | null | 2502.13233 | [
{
"_id": "67b689aeba514d2c2c969289",
"hidden": false,
"name": "Yucheng Shi",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-20T09:36:18.925Z",
"user": {
"_id": "64beb6b6140491ca9f803ebf",
"avatarUrl": "/avatars/0daa2e813a13668b8b708cd8c12763d9.svg",
"fullname... | 2025-02-18T19:12:15 | SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
Answering? | Large Language Models (LLMs) have shown remarkable capabilities in general
domains but often struggle with tasks requiring specialized knowledge.
Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve
external information from static knowledge bases, which can be outdated or
incomplete, missing... | 13 | 67b689aeba514d2c2c9692b9 | null | null | |
2025-02-19T22:13:49.764000 | RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning | 2 | {
"_id": "6536187bd34e9f02b9df1c3b",
"avatarUrl": "/avatars/0b34d62868b93053b0a05062a018b5bd.svg",
"followerCount": 1,
"fullname": "Hao Gao",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Hao605",
"type": "user"
} | true | null | 2502.13144 | [
{
"_id": "67b55c7fba22c1ddbb8d5746",
"hidden": false,
"name": "Hao Gao",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:00:48.944Z",
"user": {
"_id": "6536187bd34e9f02b9df1c3b",
"avatarUrl": "/avatars/0b34d62868b93053b0a05062a018b5bd.svg",
"fullname": "... | 2025-02-18T18:59:21 | RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
Reinforcement Learning | Existing end-to-end autonomous driving (AD) algorithms typically follow the
Imitation Learning (IL) paradigm, which faces challenges such as causal
confusion and the open-loop gap. In this work, we establish a 3DGS-based
closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS
techniques, we constr... | 36 | 67b55c80ba22c1ddbb8d579c | null | null | |
2025-02-19T21:38:13.468000 | Small Models Struggle to Learn from Strong Reasoners | 6 | {
"_id": "653df1323479e9ebbe3eb6cc",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/653df1323479e9ebbe3eb6cc/K_g-r1iMRNKj99LXPuYF3.jpeg",
"followerCount": 11,
"fullname": "Zhangchen Xu",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "flydust",
"type": "user"
} | true | null | 2502.12143 | [
{
"_id": "67b4d05a9f8a8ab661450397",
"hidden": false,
"name": "Yuetai Li",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b4d05a9f8a8ab661450398",
"hidden": false,
"name": "Xiang Yue",
"status": null,
"statusLastChangedAt": null,
"user": ... | 2025-02-17T18:56:15 | Small Models Struggle to Learn from Strong Reasoners | Large language models (LLMs) excel in complex reasoning tasks, and distilling
their reasoning capabilities into smaller models has shown promise. However, we
uncover an interesting phenomenon, which we term the Small Model Learnability
Gap: small models (leq3B parameters) do not consistently benefit from long
chain-of-... | 28 | 67b4d05b9f8a8ab6614503cb | null | null | |
2025-02-19T21:35:20.931000 | LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization | 2 | {
"_id": "645475e2548f22be59847604",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/645475e2548f22be59847604/EhSurrZ25u31qQ2TVXQXt.jpeg",
"followerCount": 1,
"fullname": "Chen",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Guanzheng",
"type": "user"
} | true | null | 2502.13922 | [
{
"_id": "67b6948dbef24bad725b5d4b",
"hidden": false,
"name": "Guanzheng Chen",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-20T16:08:30.816Z",
"user": {
"_id": "645475e2548f22be59847604",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64547... | 2025-02-19T17:59:03 | LongPO: Long Context Self-Evolution of Large Language Models through
Short-to-Long Preference Optimization | Large Language Models (LLMs) have demonstrated remarkable capabilities
through pretraining and alignment. However, superior short-context LLMs may
underperform in long-context scenarios due to insufficient long-context
alignment. This alignment process remains challenging due to the impracticality
of human annotation f... | 25 | 67b6948ebef24bad725b5d84 | null | null | |
2025-02-19T20:37:51.607000 | The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1 | 2 | {
"_id": "64679a226192d39142245e5e",
"avatarUrl": "/avatars/05abee0b6317f100923936ca2099e9eb.svg",
"followerCount": 4,
"fullname": "Xin Eric Wang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "xw-eric",
"type": "user"
} | false | null | 2502.12659 | [
{
"_id": "67b68700ce3055c9c0fc2987",
"hidden": false,
"name": "Kaiwen Zhou",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b68700ce3055c9c0fc2988",
"hidden": false,
"name": "Chengzhi Liu",
"status": null,
"statusLastChangedAt": null,
"us... | 2025-02-18T09:06:07 | The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1 | The rapid development of large reasoning models, such as OpenAI-o3 and
DeepSeek-R1, has led to significant improvements in complex reasoning over
non-reasoning large language models~(LLMs). However, their enhanced
capabilities, combined with the open-source access of models like DeepSeek-R1,
raise serious safety concer... | 6 | 67b68701ce3055c9c0fc29e4 | null | null | |
2025-02-19T18:20:05.946000 | Scaling Autonomous Agents via Automatic Reward Modeling And Planning | 2 | {
"_id": "654e024de113b04ba5c71e2f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/654e024de113b04ba5c71e2f/WH6S_gpQU6OXqDaiPpheK.jpeg",
"followerCount": 1,
"fullname": "Rui Sun",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "ThreeSR",
"type": "user"
} | true | null | 2502.12130 | [
{
"_id": "67b657d6a267b1a747a7fed6",
"hidden": false,
"name": "Zhenfang Chen",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b657d6a267b1a747a7fed7",
"hidden": false,
"name": "Delin Chen",
"status": null,
"statusLastChangedAt": null,
"us... | 2025-02-17T18:49:25 | Scaling Autonomous Agents via Automatic Reward Modeling And Planning | Large language models (LLMs) have demonstrated remarkable capabilities across
a range of text-generation tasks. However, LLMs still struggle with problems
requiring multi-step decision-making and environmental feedback, such as online
shopping, scientific reasoning, and mathematical problem-solving. Unlike pure
text da... | 2 | 67b657d7a267b1a747a7ff1a | null | null | |
2025-02-19T13:39:32.672000 | YOLOv12: Attention-Centric Real-Time Object Detectors | 2 | {
"_id": "5f1158120c833276f61f1a84",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1608042047613-5f1158120c833276f61f1a84.jpeg",
"followerCount": 777,
"fullname": "Niels Rogge",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "nielsr",
"type": "user"
} | false | null | 2502.12524 | [
{
"_id": "67b608ca13df25808fbc22ae",
"hidden": false,
"name": "Yunjie Tian",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b608ca13df25808fbc22af",
"hidden": false,
"name": "Qixiang Ye",
"status": null,
"statusLastChangedAt": null,
"user... | 2025-02-18T04:20:14 | YOLOv12: Attention-Centric Real-Time Object Detectors | Enhancing the network architecture of the YOLO framework has been crucial for
a long time, but has focused on CNN-based improvements despite the proven
superiority of attention mechanisms in modeling capabilities. This is because
attention-based models cannot match the speed of CNN-based models. This paper
proposes an ... | 10 | 67b608cb13df25808fbc2308 | null | null | |
2025-02-19T10:33:08.946000 | Harnessing Vision Models for Time Series Analysis: A Survey | 2 | {
"_id": "67b5efbe38c175486e2869b9",
"avatarUrl": "/avatars/64a698259033bb8ac324e57c557a9aa9.svg",
"followerCount": null,
"fullname": "Jingchao Ni",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "nijingchao",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/67b5efbe38c175486e2869b9/iBIxlNXQX2KDabTTeqWL0.png",
"https://cdn-uploads.huggingface.co/production/uploads/67b5efbe38c175486e2869b9/cGTQawzFrVI21iLfRjpFt.png",
"https://cdn-uploads.huggingface.co/production/uploads/67b5efbe38c175486e2869b9/j-lNPZ3OqCUHj6vh... | 2502.08869 | [
{
"_id": "67b5f3e30e7fed1190f29f80",
"hidden": false,
"name": "Jingchao Ni",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T15:14:31.563Z",
"user": {
"_id": "67b5efbe38c175486e2869b9",
"avatarUrl": "/avatars/64a698259033bb8ac324e57c557a9aa9.svg",
"fullname... | 2025-02-13T00:42:11 | Harnessing Vision Models for Time Series Analysis: A Survey | Time series analysis has witnessed the inspiring development from traditional
autoregressive models, deep learning models, to recent Transformers and Large
Language Models (LLMs). Efforts in leveraging vision models for time series
analysis have also been made along the way but are less visible to the
community due to ... | 2 | 67b5f3e30e7fed1190f29fb7 | null | null | |
2025-02-19T08:03:59.885000 | Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options | 2 | {
"_id": "643837ef581e6bf0fa9c72f8",
"avatarUrl": "/avatars/5b95d2509d1c7640d77a3405ebd53eaf.svg",
"followerCount": null,
"fullname": "Lakshmi Nair",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "lnair",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/643837ef581e6bf0fa9c72f8/HhevzVLx8wGDy7sD0zSAj.png"
] | 2502.12929 | [
{
"_id": "67b546dc2b2ec6908f00c771",
"hidden": false,
"name": "Lakshmi Nair",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T14:37:39.033Z",
"user": {
"_id": "643837ef581e6bf0fa9c72f8",
"avatarUrl": "/avatars/5b95d2509d1c7640d77a3405ebd53eaf.svg",
"fullnam... | 2025-02-18T15:11:46 | Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
Through Options | We present a novel reasoning approach called Flow-of-Options (FoO), designed
to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs
to systematically explore a diverse range of possibilities in their reasoning,
as demonstrated by an FoO-based agentic system for autonomously solving Machine
Learni... | 7 | 67b546dd2b2ec6908f00c7f6 | null | null | |
2025-02-19T07:53:04.918000 | Text2World: Benchmarking Large Language Models for Symbolic World Model Generation | 2 | {
"_id": "6237df4a5ab9df625fb70c1a",
"avatarUrl": "/avatars/c5d1a52895cb6515f28019a8e7e3e855.svg",
"followerCount": 1,
"fullname": "Mengkang Hu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "MengkangHu",
"type": "user"
} | true | null | 2502.13092 | [
{
"_id": "67b5473109afe1f3029835cb",
"hidden": false,
"name": "Mengkang Hu",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:01:15.592Z",
"user": {
"_id": "6237df4a5ab9df625fb70c1a",
"avatarUrl": "/avatars/c5d1a52895cb6515f28019a8e7e3e855.svg",
"fullname... | 2025-02-18T17:59:48 | Text2World: Benchmarking Large Language Models for Symbolic World Model
Generation | Recently, there has been growing interest in leveraging large language models
(LLMs) to generate symbolic world models from textual descriptions. Although
LLMs have been extensively explored in the context of world modeling, prior
studies encountered several challenges, including evaluation randomness,
dependence on in... | 12 | 67b5473209afe1f302983600 | null | null | |
2025-02-19T06:51:04.672000 | Atom of Thoughts for Markov LLM Test-Time Scaling | 3 | {
"_id": "6402e8fb06c715b93407442d",
"avatarUrl": "/avatars/12b67f0632be5a53b56d8a68586a7f98.svg",
"followerCount": 2,
"fullname": "Fengwei Teng",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "leavendough",
"type": "user"
} | true | null | 2502.12018 | [
{
"_id": "67b5c4ed85107d20148ae710",
"hidden": false,
"name": "Fengwei Teng",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T11:49:34.612Z",
"user": {
"_id": "6402e8fb06c715b93407442d",
"avatarUrl": "/avatars/12b67f0632be5a53b56d8a68586a7f98.svg",
"fullnam... | 2025-02-17T16:52:42 | Atom of Thoughts for Markov LLM Test-Time Scaling | Large Language Models (LLMs) achieve superior performance through
training-time scaling, and test-time scaling further enhances their
capabilities by conducting effective reasoning during inference. However, as
the scale of reasoning increases, existing test-time scaling methods suffer
from accumulated historical infor... | 15 | 67b5c4ee85107d20148ae73d | null | null | |
2025-02-19T06:13:51.101000 | Eager Updates For Overlapped Communication and Computation in DiLoCo | 2 | {
"_id": "622792366303bf1dc304f49f",
"avatarUrl": "/avatars/975c1cc3eb2f97cf8e848162056d5bea.svg",
"followerCount": 4,
"fullname": "Arthur Douillard",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "ArthurDouillard",
"type": "user"
} | true | null | 2502.12996 | [
{
"_id": "67b5bcd091132877cf330179",
"hidden": false,
"name": "Satyen Kale",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b5bcd091132877cf33017a",
"hidden": false,
"name": "Arthur Douillard",
"status": "admin_assigned",
"statusLastChangedAt... | 2025-02-18T16:16:14 | Eager Updates For Overlapped Communication and Computation in DiLoCo | Distributed optimization methods such as DiLoCo have been shown to be
effective in training very large models across multiple distributed workers,
such as datacenters. These methods split updates into two parts: an inner
optimization phase, where the workers independently execute multiple
optimization steps on their ow... | 7 | 67b5bcd191132877cf3301aa | null | null | |
2025-02-19T04:54:27.788000 | FinMTEB: Finance Massive Text Embedding Benchmark | 2 | {
"_id": "647d834618274bce03013cc2",
"avatarUrl": "/avatars/a95c7df96dc4fb6a96193f6dd5068227.svg",
"followerCount": 2,
"fullname": "yixuan",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "yixuantt",
"type": "user"
} | true | null | 2502.10990 | [
{
"_id": "67b3ee6c1e80a69e79c3155a",
"hidden": false,
"name": "Yixuan Tang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:04:50.969Z",
"user": {
"_id": "647d834618274bce03013cc2",
"avatarUrl": "/avatars/a95c7df96dc4fb6a96193f6dd5068227.svg",
"fullname... | 2025-02-16T04:23:52 | FinMTEB: Finance Massive Text Embedding Benchmark | Embedding models play a crucial role in representing and retrieving
information across various NLP applications. Recent advances in large language
models (LLMs) have further enhanced the performance of embedding models. While
these models are often benchmarked on general-purpose datasets, real-world
applications demand... | 3 | 67b3ee6d1e80a69e79c3158f | null | null | |
2025-02-19T04:43:42.973000 | Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity | 4 | {
"_id": "639c6e978a34ed9a404c6a7b",
"avatarUrl": "/avatars/c98ca8c9f9ed8509c2f1bb6aa994fd57.svg",
"followerCount": 7,
"fullname": "MIKHAIL BURTSEV",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "mbur",
"type": "user"
} | true | null | 2502.13063 | [
{
"_id": "67b5a7896f72266cb765e744",
"hidden": false,
"name": "Yuri Kuratov",
"status": "extracted_pending",
"statusLastChangedAt": "2025-02-19T09:42:34.422Z",
"user": {
"_id": "618b9540682ec1c38327e586",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/618b... | 2025-02-18T17:08:45 | Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the
Limits of Embedding Space Capacity | A range of recent works addresses the problem of compression of sequence of
tokens into a shorter sequence of real-valued vectors to be used as inputs
instead of token embeddings or key-value cache. These approaches allow to
reduce the amount of compute in existing language models. Despite relying on
powerful models as... | 64 | 67b5a78a6f72266cb765e779 | null | null | |
2025-02-19T03:03:51.930000 | You Do Not Fully Utilize Transformer's Representation Capacity | 3 | {
"_id": "63ed5676684767daecac6f8a",
"avatarUrl": "/avatars/d0e4a715f9c3fb6d74c183bab751ec35.svg",
"followerCount": 4,
"fullname": "Yaroslav Aksenov",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "yaraksen",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/63ed5676684767daecac6f8a/tZDsnW0gjHoYCpbZ-wwJi.png"
] | 2502.09245 | [
{
"_id": "67b57a993d4f319f1fa9424b",
"hidden": false,
"name": "Gleb Gerasimov",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T10:10:30.547Z",
"user": {
"_id": "65db0871ab2f64915ce05e73",
"avatarUrl": "/avatars/77e03f493196c5413cd2a02270e93660.svg",
"fulln... | 2025-02-13T12:00:50 | You Do Not Fully Utilize Transformer's Representation Capacity | In contrast to RNNs, which compress previous tokens into a single hidden
state, Transformers can attend to all previous tokens directly. However,
standard Transformers only use representations from the immediately preceding
layer. In this paper, we show that this design choice causes representation
collapse and leads t... | 34 | 67b57a9a3d4f319f1fa94274 | null | null | |
2025-02-19T02:56:09.510000 | Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey | 2 | {
"_id": "65407ba7a38390065750233f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65407ba7a38390065750233f/1_IPMZbk-S9u2t18PQgMp.jpeg",
"followerCount": 1,
"fullname": "Zirui Song",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Ziruibest",
"type": "user"
} | true | null | 2502.10708 | [
{
"_id": "67b58e32e972a2806a9a0451",
"hidden": false,
"name": "Zirui Song",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:00:38.943Z",
"user": {
"_id": "65407ba7a38390065750233f",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65407ba... | 2025-02-15T07:43:43 | Injecting Domain-Specific Knowledge into Large Language Models: A
Comprehensive Survey | Large Language Models (LLMs) have demonstrated remarkable success in various
tasks such as natural language understanding, text summarization, and machine
translation. However, their general-purpose nature often limits their
effectiveness in domain-specific applications that require specialized
knowledge, such as healt... | 4 | 67b58e33e972a2806a9a04b8 | null | null | |
2025-02-19T02:47:33.654000 | Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research | 2 | {
"_id": "63024676056ec3a2a8714b24",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1661093436322-noauth.jpeg",
"followerCount": 5,
"fullname": "Xiang Liu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Dominic789654",
"type": "user"
} | true | null | 2502.12669 | [
{
"_id": "67b58c806e53744c2a373351",
"hidden": false,
"name": "Xiang Liu",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T09:34:03.429Z",
"user": {
"_id": "63024676056ec3a2a8714b24",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1661093436... | 2025-02-18T09:19:24 | Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite
Solar Cell Research | The rapid advancement of perovskite solar cells (PSCs) has led to an
exponential growth in research publications, creating an urgent need for
efficient knowledge management and reasoning systems in this domain. We present
a comprehensive knowledge-enhanced system for PSCs that integrates three key
components. First, we... | 2 | 67b58c826e53744c2a3733c2 | null | null | |
2025-02-19T02:27:36.940000 | OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning | 3 | {
"_id": "60f5f68fa7fd83d025749234",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/60f5f68fa7fd83d025749234/gCeJAZfzaANAcEvI6v5-P.jpeg",
"followerCount": 8,
"fullname": "Pan Lu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "lupantech",
"type": "user"
} | true | null | 2502.11271 | [
{
"_id": "67b4322c217ec18a40587bec",
"hidden": false,
"name": "Pan Lu",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:04:43.677Z",
"user": {
"_id": "60f5f68fa7fd83d025749234",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/60f5f68fa7f... | 2025-02-16T21:18:47 | OctoTools: An Agentic Framework with Extensible Tools for Complex
Reasoning | Solving complex reasoning tasks may involve visual understanding, domain
knowledge retrieval, numerical calculation, and multi-step reasoning. Existing
methods augment large language models (LLMs) with external tools but are
restricted to specialized domains, limited tool types, or require additional
training data. In ... | 16 | 67b4322d217ec18a40587c27 | null | null | |
2025-02-19T01:24:26.365000 | Pre-training Auto-regressive Robotic Models with 4D Representations | 2 | {
"_id": "667c5764186b27ef806636d3",
"avatarUrl": "/avatars/5c08f0109bc0e350624112c0aff544f6.svg",
"followerCount": null,
"fullname": "Roei Herzig",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "roeiherz",
"type": "user"
} | true | null | 2502.13142 | [
{
"_id": "67b5790132be608036ee94e5",
"hidden": false,
"name": "Dantong Niu",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T09:12:28.457Z",
"user": {
"_id": "65c3fdf79d062be813813e45",
"avatarUrl": "/avatars/52528a61abe5bbbef4a4a431944973cd.svg",
"fullname":... | 2025-02-18T18:59:01 | Pre-training Auto-regressive Robotic Models with 4D Representations | Foundation models pre-trained on massive unlabeled datasets have
revolutionized natural language and computer vision, exhibiting remarkable
generalization capabilities, thus highlighting the importance of pre-training.
Yet, efforts in robotics have struggled to achieve similar success, limited by
either the need for co... | 4 | 67b5790832be608036ee9638 | null | null | |
2025-02-19T01:21:54.836000 | PAFT: Prompt-Agnostic Fine-Tuning | 8 | {
"_id": "65ed3051492a7f35db21fea2",
"avatarUrl": "/avatars/4fc0ccc21aa88e4e8ff74a6f850570b8.svg",
"followerCount": null,
"fullname": "Chenxing Wei",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "kittttttt",
"type": "user"
} | true | null | 2502.12859 | [
{
"_id": "67b576aa489d68b981e086ad",
"hidden": false,
"name": "Chenxing Wei",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T10:23:00.016Z",
"user": {
"_id": "65ed3051492a7f35db21fea2",
"avatarUrl": "/avatars/4fc0ccc21aa88e4e8ff74a6f850570b8.svg",
"fullname"... | 2025-02-18T13:46:47 | PAFT: Prompt-Agnostic Fine-Tuning | While Large Language Models (LLMs) adapt well to downstream tasks after
fine-tuning, this adaptability often compromises prompt robustness, as even
minor prompt variations can significantly degrade performance. To address this,
we propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach
that dynamical... | 15 | 67b576aa489d68b981e08708 | null | null | |
2025-02-19T00:22:36.628000 | Soundwave: Less is More for Speech-Text Alignment in LLMs | 2 | {
"_id": "66975b9f8031bf92b428e138",
"avatarUrl": "/avatars/3254281a7bac1c8ddde1d6bc7e518b2f.svg",
"followerCount": null,
"fullname": "Yuhao Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Yoohao",
"type": "user"
} | true | null | 2502.12900 | [
{
"_id": "67b54851b986e35c41e063da",
"hidden": false,
"name": "Yuhao Zhang",
"status": "extracted_pending",
"statusLastChangedAt": "2025-02-19T02:56:18.848Z",
"user": {
"_id": "66975b9f8031bf92b428e138",
"avatarUrl": "/avatars/3254281a7bac1c8ddde1d6bc7e518b2f.svg",
"fullnam... | 2025-02-18T14:36:39 | Soundwave: Less is More for Speech-Text Alignment in LLMs | Existing end-to-end speech large language models (LLMs) usually rely on
large-scale annotated data for training, while data-efficient training has not
been discussed in depth. We focus on two fundamental problems between speech
and text: the representation space gap and sequence length inconsistency. We
propose Soundwa... | 76 | 67b54852b986e35c41e06426 | null | null | |
2025-02-18T23:51:36.910000 | Magma: A Foundation Model for Multimodal AI Agents | 6 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | true | null | 2502.13130 | [
{
"_id": "67b5625fb27eb6046b2ceec5",
"hidden": false,
"name": "Jianwei Yang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b5625fb27eb6046b2ceec6",
"hidden": false,
"name": "Reuben Tan",
"status": "admin_assigned",
"statusLastChangedAt": "2... | 2025-02-18T18:55:21 | Magma: A Foundation Model for Multimodal AI Agents | We present Magma, a foundation model that serves multimodal AI agentic tasks
in both the digital and physical worlds. Magma is a significant extension of
vision-language (VL) models in that it not only retains the VL understanding
ability (verbal intelligence) of the latter, but is also equipped with the
ability to pla... | 54 | 67b56265b27eb6046b2cf08f | null | null | |
2025-02-18T23:37:46.756000 | Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities? | 2 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.12215 | [
{
"_id": "67b56007fa141a55e51d9d78",
"hidden": false,
"name": "Zhiyuan Zeng",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b56007fa141a55e51d9d79",
"hidden": false,
"name": "Qinyuan Cheng",
"status": null,
"statusLastChangedAt": null,
"... | 2025-02-17T07:21:11 | Revisiting the Test-Time Scaling of o1-like Models: Do they Truly
Possess Test-Time Scaling Capabilities? | The advent of test-time scaling in large language models (LLMs), exemplified
by OpenAI's o1 series, has advanced reasoning capabilities by scaling
computational resource allocation during inference. While successors like QwQ,
Deepseek-R1 (R1) and LIMO replicate these advancements, whether these models
truly possess tes... | 16 | 67b56007fa141a55e51d9da7 | null | null | |
2025-02-18T23:23:34.214000 | SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models | 2 | {
"_id": "64ad5f59b7e4b2c1ce47eb43",
"avatarUrl": "/avatars/1f13ebe21a90d8c99920aa2c8cd9ac45.svg",
"followerCount": 4,
"fullname": "Seanie Lee",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Seanie-lee",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/64ad5f59b7e4b2c1ce47eb43/ZEq_vSLjsXuPX3O-TWIpE.png"
] | 2502.12464 | [
{
"_id": "67b55b2cc92c4aa82c13562d",
"hidden": false,
"name": "Seanie Lee",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:00:53.341Z",
"user": {
"_id": "64ad5f59b7e4b2c1ce47eb43",
"avatarUrl": "/avatars/1f13ebe21a90d8c99920aa2c8cd9ac45.svg",
"fullname"... | 2025-02-18T02:51:17 | SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety
Guardrails in Large Language Models | Deploying large language models (LLMs) in real-world applications requires
robust safety guard models to detect and block harmful user prompts. While
large safety guard models achieve strong performance, their computational cost
is substantial. To mitigate this, smaller distilled models are used, but they
often underpe... | 27 | 67b55b2dc92c4aa82c13568b | null | null | |
2025-02-18T22:59:16.530000 | MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections | 2 | {
"_id": "62d77440bad37ef354028365",
"avatarUrl": "/avatars/df0dea879e06fa814867e9aad03d1e68.svg",
"followerCount": null,
"fullname": "Da Xiao",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "xiaoda99",
"type": "user"
} | false | null | 2502.12170 | [
{
"_id": "67b5434f2b2ec6908fffe75e",
"hidden": false,
"name": "Da Xiao",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b5434f2b2ec6908fffe75f",
"hidden": false,
"name": "Qingye Meng",
"status": "admin_assigned",
"statusLastChangedAt": "2025-... | 2025-02-13T10:26:27 | MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway
Dynamic Dense Connections | We propose MUltiway Dynamic Dense (MUDD) connections, a simple yet effective
method to address the limitations of residual connections and enhance
cross-layer information flow in Transformers. Unlike existing dense connection
approaches with static and shared connection weights, MUDD generates connection
weights dynami... | 12 | 67b543502b2ec6908fffe788 | null | null | |
2025-02-18T22:46:16.586000 | Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages | 2 | {
"_id": "6430bdd8cd31d174a9f900fb",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Y9SPnRfpKSbYc7MhNdP-H.jpeg",
"followerCount": 2,
"fullname": "Ziyin Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Geralt-Targaryen",
"type": "user"
} | true | null | 2502.10852 | [
{
"_id": "67b55321f703732d151de666",
"hidden": false,
"name": "Zeli Su",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b55321f703732d151de667",
"hidden": false,
"name": "Ziyin Zhang",
"status": "admin_assigned",
"statusLastChangedAt": "2025-... | 2025-02-15T16:53:10 | Multilingual Encoder Knows more than You Realize: Shared Weights
Pretraining for Extremely Low-Resource Languages | While multilingual language models like XLM-R have advanced multilingualism
in NLP, they still perform poorly in extremely low-resource languages. This
situation is exacerbated by the fact that modern LLMs such as LLaMA and Qwen
support far fewer languages than XLM-R, making text generation models
non-existent for many... | 2 | 67b55322f703732d151de69d | null | null | |
2025-02-18T22:43:02.567000 | Continuous Diffusion Model for Language Modeling | 4 | {
"_id": "65e5bd4568234ef5d6decadc",
"avatarUrl": "/avatars/c41095a946c0176b949c0b3566136c05.svg",
"followerCount": 4,
"fullname": "Jaehyeong Jo",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "harryjo97",
"type": "user"
} | true | null | 2502.11564 | [
{
"_id": "67b40f93aba9e111862052ab",
"hidden": false,
"name": "Jaehyeong Jo",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:27.544Z",
"user": {
"_id": "65e5bd4568234ef5d6decadc",
"avatarUrl": "/avatars/c41095a946c0176b949c0b3566136c05.svg",
"fullnam... | 2025-02-17T08:54:29 | Continuous Diffusion Model for Language Modeling | Diffusion models have emerged as a promising alternative to autoregressive
models in modeling discrete categorical data. Yet diffusion models that
directly work on discrete data space do not fully exploit the power of
iterative refinement, as the signals are lost during the transition between
discrete states. Existing ... | 50 | 67b40f94aba9e111862052d5 | null | null | |
2025-02-18T22:35:23.066000 | HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation | 2 | {
"_id": "65fc18edfb66882aba4d548e",
"avatarUrl": "/avatars/f70d47fe4aba98b5a5cd64f7e002dfd2.svg",
"followerCount": null,
"fullname": "wenqiao",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "wannature",
"type": "user"
} | true | null | 2502.09838 | [
{
"_id": "67b55078a64445f58c771d84",
"hidden": true,
"name": "Tianwei Lin",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b55078a64445f58c771d85",
"hidden": false,
"name": "Wenqiao Zhang",
"status": "admin_assigned",
"statusLastChangedAt": "... | 2025-02-14T00:42:36 | HealthGPT: A Medical Large Vision-Language Model for Unifying
Comprehension and Generation via Heterogeneous Knowledge Adaptation | We present HealthGPT, a powerful Medical Large Vision-Language Model
(Med-LVLM) that integrates medical visual comprehension and generation
capabilities within a unified autoregressive paradigm. Our bootstrapping
philosophy is to progressively adapt heterogeneous comprehension and generation
knowledge to pre-trained la... | 10 | 67b5507aa64445f58c771df9 | null | null | |
2025-02-18T22:08:27.750000 | Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation | 2 | {
"_id": "6577073fc2bf55b1f6bafb49",
"avatarUrl": "/avatars/58803398b1a918b7570db17893e65122.svg",
"followerCount": 4,
"fullname": "liao",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "LegendBC",
"type": "user"
} | true | null | 2502.13145 | [
{
"_id": "67b54b04bd51b4e46e39d287",
"hidden": false,
"name": "Bencheng Liao",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:01:00.934Z",
"user": {
"_id": "6577073fc2bf55b1f6bafb49",
"avatarUrl": "/avatars/58803398b1a918b7570db17893e65122.svg",
"fullna... | 2025-02-18T18:59:57 | Multimodal Mamba: Decoder-only Multimodal State Space Model via
Quadratic to Linear Distillation | Recent Multimodal Large Language Models (MLLMs) have achieved remarkable
performance but face deployment challenges due to their quadratic computational
complexity, growing Key-Value cache requirements, and reliance on separate
vision encoders. We propose mmMamba, a framework for developing
linear-complexity native mul... | 36 | 67b54b05bd51b4e46e39d2bb | null | null | |
2025-02-18T22:06:19.200000 | FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading | 2 | {
"_id": "63b58ed5889aa6707f0bb0f4",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63b58ed5889aa6707f0bb0f4/znl74_aMswlV8VtHrfj3G.jpeg",
"followerCount": 15,
"fullname": "Jimin Huang",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "jiminHuang",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/63b58ed5889aa6707f0bb0f4/2C9mhT-1Qz14hik7sxjf2.png"
] | 2502.11433 | [
{
"_id": "67b54a644508bd0617598c21",
"hidden": false,
"name": "Guojun Xiong",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T10:14:19.641Z",
"user": {
"_id": "67b54cbcd9f66be7f6f3f7de",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth... | 2025-02-17T04:45:53 | FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
for Financial Trading | Large language models (LLMs) fine-tuned on multimodal financial data have
demonstrated impressive reasoning capabilities in various financial tasks.
However, they often struggle with multi-step, goal-oriented scenarios in
interactive financial markets, such as trading, where complex agentic
approaches are required to i... | 31 | 67b54a654508bd0617598c7e | null | null | |
2025-02-18T21:59:45.466000 | Rethinking Diverse Human Preference Learning through Principal Component Analysis | 3 | {
"_id": "64d45451c34a346181b130dd",
"avatarUrl": "/avatars/9bb8205b889337df5d321539c9b5d69d.svg",
"followerCount": 6,
"fullname": "Rui Yang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Ray2333",
"type": "user"
} | true | null | 2502.13131 | [
{
"_id": "67b5461d29cc269e5a4eb823",
"hidden": false,
"name": "Feng Luo",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b5461d29cc269e5a4eb824",
"hidden": true,
"name": "Rui Yang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-0... | 2025-02-18T18:55:26 | Rethinking Diverse Human Preference Learning through Principal Component
Analysis | Understanding human preferences is crucial for improving foundation models
and building personalized AI systems. However, preferences are inherently
diverse and complex, making it difficult for traditional reward models to
capture their full range. While fine-grained preference data can help,
collecting it is expensive... | 35 | 67b5461f29cc269e5a4eb8bc | null | null | |
2025-02-18T21:57:00.289000 | HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading | 2 | {
"_id": "64cb48f7667f4f808535107e",
"avatarUrl": "/avatars/8f77f378ad665b246e1ea3aaba2153ae.svg",
"followerCount": 1,
"fullname": "chengluo",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "wdlctc",
"type": "user"
} | true | null | 2502.12574 | [
{
"_id": "67b547f555d0424a31b9c384",
"hidden": false,
"name": "Cheng Luo",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T09:40:25.130Z",
"user": {
"_id": "64cb48f7667f4f808535107e",
"avatarUrl": "/avatars/8f77f378ad665b246e1ea3aaba2153ae.svg",
"fullname": "... | 2025-02-18T06:26:05 | HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading | Transformer-based large language models (LLMs) demonstrate impressive
performance in long context generation. Extending the context length has
disproportionately shifted the memory footprint of LLMs during inference to the
key-value cache (KV cache). In this paper, we propose HEADINFER, which offloads
the KV cache to C... | 11 | 67b547f755d0424a31b9c3e5 | null | null | |
2025-02-18T21:56:39.407000 | Phantom: Subject-consistent video generation via cross-modal alignment | 2 | {
"_id": "63a950ac3453852ef5394178",
"avatarUrl": "/avatars/48a5e537b10e2247a17e63502e3201a6.svg",
"followerCount": 1,
"fullname": "Lijie Liu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "liulj13",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/63a950ac3453852ef5394178/HuVZ5d9xTlI4R1onRv_F5.mp4"
] | 2502.11079 | [
{
"_id": "67b40141ad717fe02e188c1a",
"hidden": false,
"name": "Lijie Liu",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:42.570Z",
"user": {
"_id": "63a950ac3453852ef5394178",
"avatarUrl": "/avatars/48a5e537b10e2247a17e63502e3201a6.svg",
"fullname":... | 2025-02-16T11:02:50 | Phantom: Subject-consistent video generation via cross-modal alignment | The continuous development of foundational models for video generation is
evolving into various applications, with subject-consistent video generation
still in the exploratory stage. We refer to this as Subject-to-Video, which
extracts subject elements from reference images and generates
subject-consistent video throug... | 52 | 67b40144ad717fe02e188cb2 | null | null | |
2025-02-18T21:55:26.822000 | Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge | 2 | {
"_id": "62a42f22c683d02f5b63320c",
"avatarUrl": "/avatars/bc611abe9c4ef8d378123cb8ac9fdbf2.svg",
"followerCount": null,
"fullname": "Qiyuan Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "DonJoey",
"type": "user"
} | true | null | 2502.12501 | [
{
"_id": "67b547ffc9071a3e97139532",
"hidden": false,
"name": "Qiyuan Zhang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:01:10.215Z",
"user": {
"_id": "62a42f22c683d02f5b63320c",
"avatarUrl": "/avatars/bc611abe9c4ef8d378123cb8ac9fdbf2.svg",
"fullnam... | 2025-02-18T03:31:06 | Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for
LLM-as-a-Judge | LLM-as-a-Judge, which generates chain-of-thought (CoT) judgments, has become
a widely adopted auto-evaluation method. However, its reliability is
compromised by the CoT reasoning's inability to capture comprehensive and
deeper details, often leading to incomplete outcomes. Existing methods mainly
rely on majority votin... | 6 | 67b54800c9071a3e9713956c | null | null | |
2025-02-18T21:52:22.326000 | RealSyn: An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm | 2 | {
"_id": "63e202f352b7578dba448ab5",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63e202f352b7578dba448ab5/8itVBLcv14m7OVsoF8h1o.jpeg",
"followerCount": 4,
"fullname": "Yang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Kaichengalex",
"type": "user"
} | true | null | 2502.12513 | [
{
"_id": "67b545fd88527668fa8bcc14",
"hidden": false,
"name": "Tiancheng Gu",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T10:19:15.243Z",
"user": {
"_id": "6508712e7ee07e274b0f4c94",
"avatarUrl": "/avatars/23fe5593b0bce36c2167c3142e57e0e9.svg",
"fullname"... | 2025-02-18T03:58:38 | RealSyn: An Effective and Scalable Multimodal Interleaved Document
Transformation Paradigm | After pre-training on extensive image-text pairs, Contrastive Language-Image
Pre-training (CLIP) demonstrates promising performance on a wide variety of
benchmarks. However, a substantial volume of non-paired data, such as
multimodal interleaved documents, remains underutilized for vision-language
representation learni... | 15 | 67b545fe88527668fa8bcc65 | null | null | |
2025-02-18T21:51:33.957000 | SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation | 2 | {
"_id": "63c3e8abc7d7f4c63a515a02",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63c3e8abc7d7f4c63a515a02/npMHnVP2hHLbvoUGe7C4O.jpeg",
"followerCount": 2,
"fullname": "Zekun Qi",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "qizekun",
"type": "user"
} | true | null | 2502.13143 | [
{
"_id": "67b546c0d8a1eac02c605f6a",
"hidden": false,
"name": "Zekun Qi",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:01:21.001Z",
"user": {
"_id": "63c3e8abc7d7f4c63a515a02",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63c3e8abc... | 2025-02-18T18:59:02 | SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and
Object Manipulation | Spatial intelligence is a critical component of embodied AI, promoting robots
to understand and interact with their environments. While recent advances have
enhanced the ability of VLMs to perceive object locations and positional
relationships, they still lack the capability to precisely understand object
orientations-... | 29 | 67b546c5d8a1eac02c606090 | null | null | |
2025-02-18T21:18:22.741000 | Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs | 4 | {
"_id": "6214e4ee1e35c843d42d1f88",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6214e4ee1e35c843d42d1f88/fj-9wuIdPhvogh3BrcXTB.jpeg",
"followerCount": 15,
"fullname": "Longxu Dou",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "dreamerdeo",
"type": "user"
} | true | null | 2502.12982 | [
{
"_id": "67b53f572b2ec6908ffef365",
"hidden": false,
"name": "Longxu Dou",
"status": "extracted_pending",
"statusLastChangedAt": "2025-02-19T02:17:59.980Z",
"user": {
"_id": "6214e4ee1e35c843d42d1f88",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6214e4... | 2025-02-18T16:04:57 | Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs | Sailor2 is a family of cutting-edge multilingual language models for
South-East Asian (SEA) languages, available in 1B, 8B, and 20B sizes to suit
diverse applications. Building on Qwen2.5, Sailor2 undergoes continuous
pre-training on 500B tokens (400B SEA-specific and 100B replay tokens) to
support 13 SEA languages whi... | 14 | 67b53f572b2ec6908ffef3c9 | null | null | |
2025-02-18T20:05:09.186000 | ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability | 2 | {
"_id": "6538e649f940c8a0358aa8b8",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6538e649f940c8a0358aa8b8/veNw6QJuZu8anWCXtOXxu.jpeg",
"followerCount": null,
"fullname": "Ryuto Koike",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "ryuryukke",
"type": "user"
} | false | [
"https://cdn-uploads.huggingface.co/production/uploads/6538e649f940c8a0358aa8b8/LTS6uI3uy5AxEeoD9-oMX.png"
] | 2502.11336 | [
{
"_id": "67b52de36007d463b988b202",
"hidden": false,
"name": "Ryuto Koike",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:03:41.013Z",
"user": {
"_id": "6538e649f940c8a0358aa8b8",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6538e6... | 2025-02-17T01:15:07 | ExaGPT: Example-Based Machine-Generated Text Detection for Human
Interpretability | Detecting texts generated by Large Language Models (LLMs) could cause grave
mistakes due to incorrect decisions, such as undermining student's academic
dignity. LLM text detection thus needs to ensure the interpretability of the
decision, which can help users judge how reliably correct its prediction is.
When humans ve... | 0 | 67b52de46007d463b988b279 | null | null | |
2025-02-18T18:58:34.838000 | Diffusion Models without Classifier-free Guidance | 2 | {
"_id": "6372f265112fb535baf254c4",
"avatarUrl": "/avatars/9b821bc533175c7dded48cdb3a3e1a12.svg",
"followerCount": 2,
"fullname": "tzco",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "tzco",
"type": "user"
} | true | null | 2502.12154 | [
{
"_id": "67b400719ff3ff79dae14701",
"hidden": false,
"name": "Zhicong Tang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:45.361Z",
"user": {
"_id": "6372f265112fb535baf254c4",
"avatarUrl": "/avatars/9b821bc533175c7dded48cdb3a3e1a12.svg",
"fullnam... | 2025-02-17T18:59:50 | Diffusion Models without Classifier-free Guidance | This paper presents Model-guidance (MG), a novel objective for training
diffusion model that addresses and removes of the commonly used Classifier-free
guidance (CFG). Our innovative approach transcends the standard modeling of
solely data distribution to incorporating the posterior probability of
conditions. The propo... | 4 | 67b400789ff3ff79dae147ee | null | null | |
2025-02-18T14:56:45.613000 | EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling | 2 | {
"_id": "661ba524bd9243bf7e598355",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/661ba524bd9243bf7e598355/i77yD4XgJn2vUbn_mIsT8.jpeg",
"followerCount": 2,
"fullname": "Ioannis Kakogeorgiou",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "gkakogeorgiou",
"type": "use... | true | [
"https://cdn-uploads.huggingface.co/production/uploads/661ba524bd9243bf7e598355/9XkVow22TY84dDgXm-Duc.gif"
] | 2502.09509 | [
{
"_id": "67b4e4259beded220ad14729",
"hidden": false,
"name": "Theodoros Kouzelis",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-21T16:15:22.578Z",
"user": {
"_id": "6383aa17834d3558a3955186",
"avatarUrl": "/avatars/1f6aed0a762379df334bc6a734d42f86.svg",
"f... | 2025-02-13T17:21:51 | EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
Image Modeling | Latent generative models have emerged as a leading approach for high-quality
image synthesis. These models rely on an autoencoder to compress images into a
latent space, followed by a generative model to learn the latent distribution.
We identify that existing autoencoders lack equivariance to semantic-preserving
trans... | 7 | 67b4e4289beded220ad147c7 | null | null | |
2025-02-18T13:59:31.380000 | Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation | 2 | {
"_id": "64ba58d377dd483716aba098",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64ba58d377dd483716aba098/6VASAUkFpDC-PR01yUJWj.png",
"followerCount": 3,
"fullname": "Mahdi Abootorabi",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "aboots",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/64ba58d377dd483716aba098/N0fZ0I60EfZjITEnf6gPc.png",
"https://cdn-uploads.huggingface.co/production/uploads/64ba58d377dd483716aba098/CtLxMqUEhWr6d9ztU1YZq.jpeg",
"https://cdn-uploads.huggingface.co/production/uploads/64ba58d377dd483716aba098/HczPPOjzArOwgdw... | 2502.08826 | [
{
"_id": "67b303f18bd6e9a5cad8bc4d",
"hidden": false,
"name": "Mohammad Mahdi Abootorabi",
"status": "extracted_confirmed",
"statusLastChangedAt": "2025-02-17T09:40:27.588Z",
"user": {
"_id": "64ba58d377dd483716aba098",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/producti... | 2025-02-12T22:33:41 | Ask in Any Modality: A Comprehensive Survey on Multimodal
Retrieval-Augmented Generation | Large Language Models (LLMs) struggle with hallucinations and outdated
knowledge due to their reliance on static training data. Retrieval-Augmented
Generation (RAG) mitigates these issues by integrating external dynamic
information enhancing factual and updated grounding. Recent advances in
multimodal learning have led... | 17 | 67b303f28bd6e9a5cad8bc85 | null | null | |
2025-02-18T13:21:05.722000 | IHEval: Evaluating Language Models on Following the Instruction Hierarchy | 2 | {
"_id": "63bf9695da08ed054400205e",
"avatarUrl": "/avatars/b6fca49559a61cf66628088c60d26c10.svg",
"followerCount": 1,
"fullname": "Zhihan Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "zhihz0535",
"type": "user"
} | true | null | 2502.08745 | [
{
"_id": "67b4cf1994ec5e365fb7995d",
"hidden": false,
"name": "Zhihan Zhang",
"status": "extracted_confirmed",
"statusLastChangedAt": "2025-02-18T18:19:31.455Z",
"user": {
"_id": "63bf9695da08ed054400205e",
"avatarUrl": "/avatars/b6fca49559a61cf66628088c60d26c10.svg",
"full... | 2025-02-12T19:35:28 | IHEval: Evaluating Language Models on Following the Instruction
Hierarchy | The instruction hierarchy, which establishes a priority order from system
messages to user messages, conversation history, and tool outputs, is essential
for ensuring consistent and safe behavior in language models (LMs). Despite its
importance, this topic receives limited attention, and there is a lack of
comprehensiv... | 18 | 67b4cf1a94ec5e365fb799c1 | null | null | |
2025-02-18T13:04:04.423000 | Data Valuation using Neural Networks for Efficient Instruction Fine-Tuning | 2 | {
"_id": "6391e4e984afa726d66180b9",
"avatarUrl": "/avatars/e437e2820745b522a868b8da27d9a11f.svg",
"followerCount": 0,
"fullname": "Ishika Agarwal",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "ishikaa",
"type": "user"
} | true | null | 2502.09969 | [
{
"_id": "67b4cb6c777b7676c8b3c43d",
"hidden": false,
"name": "Ishika Agarwal",
"status": "extracted_confirmed",
"statusLastChangedAt": "2025-02-18T18:06:42.786Z",
"user": {
"_id": "6391e4e984afa726d66180b9",
"avatarUrl": "/avatars/e437e2820745b522a868b8da27d9a11f.svg",
"fu... | 2025-02-14T07:55:47 | Data Valuation using Neural Networks for Efficient Instruction
Fine-Tuning | Influence functions provide crucial insights into model training, but
existing methods suffer from large computational costs and limited
generalization. Particularly, recent works have proposed various metrics and
algorithms to calculate the influence of data using language models, which do
not scale well with large mo... | 1 | 67b4cb6d777b7676c8b3c45c | null | null | |
2025-02-18T11:57:43.538000 | Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents | 2 | {
"_id": "6556717676fe5cfa6a115405",
"avatarUrl": "/avatars/570dd8f4eb6baaff12d7ebe11dde6348.svg",
"followerCount": 1,
"fullname": "Vardaan Pahuja",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "vardaan123",
"type": "user"
} | true | null | 2502.11357 | [
{
"_id": "67b3f1f1f5bd60d66133e1f3",
"hidden": false,
"name": "Vardaan Pahuja",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:47.969Z",
"user": {
"_id": "6556717676fe5cfa6a115405",
"avatarUrl": "/avatars/570dd8f4eb6baaff12d7ebe11dde6348.svg",
"fulln... | 2025-02-17T02:13:48 | Explorer: Scaling Exploration-driven Web Trajectory Synthesis for
Multimodal Web Agents | Recent success in large multimodal models (LMMs) has sparked promising
applications of agents capable of autonomously completing complex web tasks.
While open-source LMM agents have made significant advances in offline
evaluation benchmarks, their performance still falls substantially short of
human-level capabilities ... | 9 | 67b3f1f1f5bd60d66133e24b | null | null | |
2025-02-18T11:42:58.976000 | ILIAS: Instance-Level Image retrieval At Scale | 2 | {
"_id": "66a3ae59f33ff23e1c027ccd",
"avatarUrl": "/avatars/216717d547bf785a2b1696171e5f4b11.svg",
"followerCount": 1,
"fullname": "Vladan Stojnic",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "stojnvla",
"type": "user"
} | true | null | 2502.11748 | [
{
"_id": "67b465600e5142133055d7c1",
"hidden": false,
"name": "Giorgos Kordopatis-Zilos",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:04:38.791Z",
"user": {
"_id": "673c934bdf13003bd11746fd",
"avatarUrl": "/avatars/1aec1157549be85963b39eb54845b695.svg",
... | 2025-02-17T12:42:38 | ILIAS: Instance-Level Image retrieval At Scale | This work introduces ILIAS, a new test dataset for Instance-Level Image
retrieval At Scale. It is designed to evaluate the ability of current and
future foundation models and retrieval techniques to recognize particular
objects. The key benefits over existing datasets include large scale, domain
diversity, accurate gro... | 4 | 67b465680e5142133055d97d | null | null | |
2025-02-18T08:59:34.204000 | Can a Single Model Master Both Multi-turn Conversations and Tool Use? CALM: A Unified Conversational Agentic Language Model | 2 | {
"_id": "63888d3fd68e37abd599f428",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63888d3fd68e37abd599f428/YaNyxG_oM6IgrHTkFZ6Eq.jpeg",
"followerCount": 12,
"fullname": "emre can",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "emrecanacikgoz",
"type": "user"
} | true | null | 2502.08820 | [
{
"_id": "67aece59f2e8a2ee35b5affd",
"hidden": false,
"name": "Emre Can Acikgoz",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:34:01.421Z",
"user": {
"_id": "63888d3fd68e37abd599f428",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6... | 2025-02-12T22:18:34 | Can a Single Model Master Both Multi-turn Conversations and Tool Use?
CALM: A Unified Conversational Agentic Language Model | Large Language Models (LLMs) with API-calling capabilities enabled building
effective Language Agents (LA), while also revolutionizing the conventional
task-oriented dialogue (TOD) paradigm. However, current approaches face a
critical dilemma: TOD systems are often trained on a limited set of target
APIs, requiring new... | 4 | 67aece5af2e8a2ee35b5b03e | null | null | |
2025-02-18T07:33:17.294000 | The Mirage of Model Editing: Revisiting Evaluation in the Wild | 2 | {
"_id": "64e4090f222b232f03fe5f63",
"avatarUrl": "/avatars/1e97328de374d726f64bf16528d36ca4.svg",
"followerCount": null,
"fullname": "Wanli Yang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "WenDingY",
"type": "user"
} | false | null | 2502.11177 | [
{
"_id": "67b47dd2e638b35196b8e014",
"hidden": false,
"name": "Wanli Yang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b47dd2e638b35196b8e015",
"hidden": false,
"name": "Fei Sun",
"status": null,
"statusLastChangedAt": null,
"user": n... | 2025-02-16T15:57:55 | The Mirage of Model Editing: Revisiting Evaluation in the Wild | Despite near-perfect results in artificial evaluations, the effectiveness of
model editing in real-world applications remains unexplored. To bridge this
gap, we propose to study model editing in question answering (QA) by
establishing a rigorous evaluation practice to assess the effectiveness of
editing methods in corr... | 10 | 67b47dd2e638b35196b8e03a | null | null | |
2025-02-18T07:16:07.632000 | Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning | 2 | {
"_id": "6668687caee0993c95b0eb81",
"avatarUrl": "/avatars/301fe1f395e0a129b1c9785868fa9858.svg",
"followerCount": 2,
"fullname": "Egor Cherepanov",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "avanturist",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/6668687caee0993c95b0eb81/zl6FgeOWq-7PC7PRLEyzW.qt"
] | 2502.10550 | [
{
"_id": "67b478517fa6ecaa21d1498d",
"hidden": false,
"name": "Egor Cherepanov",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T16:39:34.993Z",
"user": {
"_id": "6668687caee0993c95b0eb81",
"avatarUrl": "/avatars/301fe1f395e0a129b1c9785868fa9858.svg",
"full... | 2025-02-14T20:46:19 | Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with
Reinforcement Learning | Memory is crucial for enabling agents to tackle complex tasks with temporal
and spatial dependencies. While many reinforcement learning (RL) algorithms
incorporate memory, the field lacks a universal benchmark to assess an agent's
memory capabilities across diverse scenarios. This gap is particularly evident
in tableto... | 5 | 67b478557fa6ecaa21d14a24 | null | null | |
2025-02-18T06:33:31.888000 | Dyve: Thinking Fast and Slow for Dynamic Process Verification | 2 | {
"_id": "6608fa4f5baec84322ec85ea",
"avatarUrl": "/avatars/13bdaff931676b065fa1efef06fef922.svg",
"followerCount": 1,
"fullname": "Zhong",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Jianyuan1",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/6608fa4f5baec84322ec85ea/iiYwe_FlXRwT1RjPvzF-b.png"
] | 2502.11157 | [
{
"_id": "67b44baa5fd91177ed7760a2",
"hidden": false,
"name": "Jianyuan Zhong",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:30:45.385Z",
"user": {
"_id": "6608fa4f5baec84322ec85ea",
"avatarUrl": "/avatars/13bdaff931676b065fa1efef06fef922.svg",
"fulln... | 2025-02-16T15:11:19 | Dyve: Thinking Fast and Slow for Dynamic Process Verification | We present Dyve, a dynamic process verifier that enhances reasoning error
detection in large language models by integrating fast and slow thinking,
inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate
token-level confirmation System 1 for straightforward steps and comprehensive
analysis System 2 for... | 6 | 67b44bab5fd91177ed7760ca | null | null | |
2025-02-18T06:07:36.212000 | Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention | 9 | {
"_id": "645e054ff7a55f0d780a8ff7",
"avatarUrl": "/avatars/9614510443bee3bd5d6266efd1c39fc1.svg",
"followerCount": 5,
"fullname": "Chunjiang Ge",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "HelloJiang",
"type": "user"
} | true | null | 2502.11089 | [
{
"_id": "67b43211d3c5f50aa9c03a2d",
"hidden": false,
"name": "Jingyang Yuan",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b43211d3c5f50aa9c03a2e",
"hidden": false,
"name": "Huazuo Gao",
"status": "admin_assigned",
"statusLastChangedAt": "... | 2025-02-16T11:53:44 | Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse
Attention | Long-context modeling is crucial for next-generation language models, yet the
high computational cost of standard attention mechanisms poses significant
computational challenges. Sparse attention offers a promising direction for
improving efficiency while maintaining model capabilities. We present NSA, a
Natively train... | 139 | 67b43212d3c5f50aa9c03a5c | null | null | |
2025-02-18T05:28:54.029000 | Better Embeddings with Coupled Adam | 3 | {
"_id": "66867e1675f10ce7ef96180e",
"avatarUrl": "/avatars/ac85c00ba9d4dc48887b8864a0626743.svg",
"followerCount": null,
"fullname": "Felix Stollenwerk",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "flxst",
"type": "user"
} | true | null | 2502.08441 | [
{
"_id": "67b30311a2b3622dd42a51ff",
"hidden": false,
"name": "Felix Stollenwerk",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:32:36.770Z",
"user": {
"_id": "66867e1675f10ce7ef96180e",
"avatarUrl": "/avatars/ac85c00ba9d4dc48887b8864a0626743.svg",
"fu... | 2025-02-12T14:32:17 | Better Embeddings with Coupled Adam | Despite their remarkable capabilities, LLMs learn word representations that
exhibit the undesirable yet poorly understood feature of anisotropy. In this
paper, we argue that the second moment in Adam is a cause of anisotropic
embeddings, and suggest a modified optimizer called Coupled Adam to mitigate
the problem. Our ... | 1 | 67b30312a2b3622dd42a522d | null | null | |
2025-02-18T04:37:21.573000 | Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking | 2 | {
"_id": "6698cffdb2ebada9f4a7e7d7",
"avatarUrl": "/avatars/e66d946c14595d3b008185f2be8d2f57.svg",
"followerCount": 2,
"fullname": "Greta Warren",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "gretawarren",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/6698cffdb2ebada9f4a7e7d7/55xAEeg9Xsk87DXHTH9gM.png"
] | 2502.09083 | [
{
"_id": "67b30726d4665a0448e6436d",
"hidden": false,
"name": "Greta Warren",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:32:34.585Z",
"user": {
"_id": "6698cffdb2ebada9f4a7e7d7",
"avatarUrl": "/avatars/e66d946c14595d3b008185f2be8d2f57.svg",
"fullnam... | 2025-02-13T08:56:25 | Show Me the Work: Fact-Checkers' Requirements for Explainable Automated
Fact-Checking | The pervasiveness of large language models and generative AI in online media
has amplified the need for effective automated fact-checking to assist
fact-checkers in tackling the increasing volume and sophistication of
misinformation. The complex nature of fact-checking demands that automated
fact-checking systems provi... | 4 | 67b30727d4665a0448e6438d | null | null | |
2025-02-18T04:34:15.786000 | MagicArticulate: Make Your 3D Models Articulation-Ready | 2 | {
"_id": "64fb31a34c8924c4fe7498bc",
"avatarUrl": "/avatars/6c8e4a66e1b8b3c786a4000210089392.svg",
"followerCount": 4,
"fullname": "Chaoyue Song",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "chaoyue7",
"type": "user"
} | true | null | 2502.12135 | [
{
"_id": "67b4028237db78705fb256e1",
"hidden": false,
"name": "Chaoyue Song",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:40.771Z",
"user": {
"_id": "64fb31a34c8924c4fe7498bc",
"avatarUrl": "/avatars/6c8e4a66e1b8b3c786a4000210089392.svg",
"fullnam... | 2025-02-17T18:53:27 | MagicArticulate: Make Your 3D Models Articulation-Ready | With the explosive growth of 3D content creation, there is an increasing
demand for automatically converting static 3D models into articulation-ready
versions that support realistic animation. Traditional approaches rely heavily
on manual annotation, which is both time-consuming and labor-intensive.
Moreover, the lack ... | 8 | 67b4028437db78705fb25726 | null | null | |
2025-02-18T04:33:41.120000 | I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models | 3 | {
"_id": "6354bda206d707b33249c4c2",
"avatarUrl": "/avatars/bbd9f76274ac52214df92084d50bc7b5.svg",
"followerCount": 1,
"fullname": "Zhenxing Mi",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Mifucius",
"type": "user"
} | true | null | 2502.10458 | [
{
"_id": "67b3ea0f4dd7ea0538ce589d",
"hidden": false,
"name": "Zhenxing Mi",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:52.837Z",
"user": {
"_id": "6354bda206d707b33249c4c2",
"avatarUrl": "/avatars/bbd9f76274ac52214df92084d50bc7b5.svg",
"fullname... | 2025-02-12T05:30:08 | I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning
in Diffusion Models | This paper presents ThinkDiff, a novel alignment paradigm that empowers
text-to-image diffusion models with multimodal in-context understanding and
reasoning capabilities by integrating the strengths of vision-language models
(VLMs). Existing multimodal diffusion finetuning methods largely focus on
pixel-level reconstr... | 30 | 67b3ea124dd7ea0538ce592d | https://mizhenxing.github.io/ThinkDiff | https://github.com/MiZhenxing/ThinkDiff | |
2025-02-18T04:20:25.916000 | Intuitive physics understanding emerges from self-supervised pretraining on natural videos | 2 | {
"_id": "5f1158120c833276f61f1a84",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1608042047613-5f1158120c833276f61f1a84.jpeg",
"followerCount": 777,
"fullname": "Niels Rogge",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "nielsr",
"type": "user"
} | false | null | 2502.11831 | [
{
"_id": "67b450cf315f7b69956df3d6",
"hidden": false,
"name": "Quentin Garrido",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T15:28:09.217Z",
"user": {
"_id": "63049022412a1b9d381b9dcb",
"avatarUrl": "/avatars/7382c0a0e3f5609b754ec09a309d33f6.svg",
"fullna... | 2025-02-17T14:27:14 | Intuitive physics understanding emerges from self-supervised pretraining
on natural videos | We investigate the emergence of intuitive physics understanding in
general-purpose deep neural network models trained to predict masked regions in
natural videos. Leveraging the violation-of-expectation framework, we find that
video prediction models trained to predict outcomes in a learned representation
space demonst... | 18 | 67b450d0315f7b69956df3f9 | null | https://github.com/facebookresearch/jepa-intuitive-physics | |
2025-02-18T04:16:28.219000 | Towards Data-Efficient Pretraining for Atomic Property Prediction | 3 | {
"_id": "642b51385bf2355d02a23d15",
"avatarUrl": "/avatars/87985347643b2647555f2453fa4d94fb.svg",
"followerCount": 4,
"fullname": "Hasan Abed Al Kader Hammoud",
"isHf": false,
"isMod": false,
"isPro": true,
"name": "hammh0a",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/642b51385bf2355d02a23d15/bLvTbh56AkUmcmRst8mT3.png"
] | 2502.11085 | [
{
"_id": "67b44f44620ae0bad17d6699",
"hidden": false,
"name": "Yasir Ghunaim",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b44f44620ae0bad17d669a",
"hidden": false,
"name": "Hasan Abed Al Kader Hammoud",
"status": "claimed_verified",
"stat... | 2025-02-16T11:46:23 | Towards Data-Efficient Pretraining for Atomic Property Prediction | This paper challenges the recent paradigm in atomic property prediction that
links progress to growing dataset sizes and computational resources. We show
that pretraining on a carefully selected, task-relevant dataset can match or
even surpass large-scale pretraining, while using as little as 1/24th of the
computationa... | 3 | 67b44f45620ae0bad17d66b0 | null | null | |
2025-02-18T03:53:47.570000 | PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning | 2 | {
"_id": "6602548a68d519ed324b47c5",
"avatarUrl": "/avatars/5ab411f87440cc2a98c7a1c6a3ed5548.svg",
"followerCount": 4,
"fullname": "ChengyouJia",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "ChengyouJia",
"type": "user"
} | true | null | 2502.12054 | [
{
"_id": "67b44a6888813676da9f8239",
"hidden": false,
"name": "Xinyu Zhang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b44a6888813676da9f823a",
"hidden": false,
"name": "Yuxuan Dong",
"status": null,
"statusLastChangedAt": null,
"use... | 2025-02-17T17:24:14 | PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning | Large language models demonstrate remarkable capabilities across various
domains, especially mathematics and logic reasoning. However, current
evaluations overlook physics-based reasoning - a complex task requiring physics
theorems and constraints. We present PhysReason, a 1,200-problem benchmark
comprising knowledge-b... | 5 | 67b44a6988813676da9f82d0 | null | null | |
2025-02-18T02:26:18.856000 | Large Language Models and Mathematical Reasoning Failures | 3 | {
"_id": "6033e34a9aa44495c80dd043",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1614079701740-6033e34a9aa44495c80dd043.jpeg",
"followerCount": 39,
"fullname": "Birger Moell",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "birgermoell",
"type": "user"
} | true | null | 2502.11574 | [
{
"_id": "67b435c29e5685b308a8edac",
"hidden": false,
"name": "Johan Boye",
"status": "extracted_pending",
"statusLastChangedAt": "2025-02-18T07:24:50.956Z",
"user": {
"_id": "65bcbc01d6d0ffbceb8b2e6e",
"avatarUrl": "/avatars/73edb2d6b7b11208439ac88b365079e8.svg",
"fullname... | 2025-02-17T09:07:32 | Large Language Models and Mathematical Reasoning Failures | This paper investigates the mathematical reasoning capabilities of large
language models (LLMs) using 50 newly constructed high-school-level word
problems. Unlike prior studies that focus solely on answer correctness, we
rigorously analyze both final answers and solution steps to identify reasoning
failures. Evaluating... | 3 | 67b435c29e5685b308a8edf1 | null | null | |
2025-02-18T02:23:29.869000 | Language Complexity Measurement as a Noisy Zero-Shot Proxy for Evaluating LLM Performance | 2 | {
"_id": "6033e34a9aa44495c80dd043",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1614079701740-6033e34a9aa44495c80dd043.jpeg",
"followerCount": 39,
"fullname": "Birger Moell",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "birgermoell",
"type": "user"
} | true | null | 2502.11578 | [
{
"_id": "67b435475bff5f34c1ebee1b",
"hidden": false,
"name": "Birger Moell",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:30:52.639Z",
"user": {
"_id": "6033e34a9aa44495c80dd043",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/16140... | 2025-02-17T09:09:58 | Language Complexity Measurement as a Noisy Zero-Shot Proxy for
Evaluating LLM Performance | Large Language Models (LLMs) have made significant strides in natural
language generation but often face challenges in tasks requiring precise
calculations and structural analysis. This paper investigates the performance
of state-of-the-art LLMs on language complexity measurement tasks, through the
computation of the L... | 0 | 67b435485bff5f34c1ebee52 | null | null | |
2025-02-18T01:45:36.359000 | System Message Generation for User Preferences using Open-Source Models | 2 | {
"_id": "64587be872b60ae7a3817858",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/64587be872b60ae7a3817858/BbdOOxOCEzWTvEpkWp8MM.png",
"followerCount": 3,
"fullname": "Minbyul Jeong",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Minbyul",
"type": "user"
} | true | null | 2502.11330 | [
{
"_id": "67b42c5632929e97a92dee90",
"hidden": false,
"name": "Minbyul Jeong",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-19T09:04:45.723Z",
"user": {
"_id": "64587be872b60ae7a3817858",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6458... | 2025-02-17T01:05:31 | System Message Generation for User Preferences using Open-Source Models | System messages play a crucial role in interactions with large language
models (LLMs), often serving as prompts to initiate conversations. Through
system messages, users can assign specific roles, perform intended tasks,
incorporate background information, specify various output formats and
communication styles. Despit... | 15 | 67b42c5732929e97a92deed7 | null | null | |
2025-02-18T01:02:25.236000 | How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training | 6 | {
"_id": "620b3bbb0668e435407c8d0a",
"avatarUrl": "/avatars/e0fccbb2577d76088e09f054c35cffbc.svg",
"followerCount": 19,
"fullname": "Ningyu Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Ningyu",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/620b3bbb0668e435407c8d0a/_LGnwvwslWc3YDIirfOKS.png"
] | 2502.11196 | [
{
"_id": "67b42223c2fe54b8d43efed6",
"hidden": false,
"name": "Yixin Ou",
"status": "admin_assigned",
"statusLastChangedAt": "2025-02-19T15:22:40.840Z",
"user": {
"_id": "6241749cf80bd930bd99f3dd",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/16692102433... | 2025-02-16T16:55:43 | How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on
Continual Pre-Training | Despite exceptional capabilities in knowledge-intensive tasks, Large Language
Models (LLMs) face a critical gap in understanding how they internalize new
knowledge, particularly how to structurally embed acquired knowledge in their
neural computations. We address this issue through the lens of knowledge
circuit evoluti... | 22 | 67b42225c2fe54b8d43eff9b | null | null | |
2025-02-18T01:01:24.331000 | SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors | 2 | {
"_id": "650267e7e751d03da933a24a",
"avatarUrl": "/avatars/f047a047d1de304cd97027463541bdf3.svg",
"followerCount": 1,
"fullname": "Bohan22",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Bohan22",
"type": "user"
} | true | null | 2502.11167 | [
{
"_id": "67b4221bbc387d2eda6f8637",
"hidden": false,
"name": "Bohan Lyu",
"status": "claimed_verified",
"statusLastChangedAt": "2025-02-18T09:31:06.388Z",
"user": {
"_id": "650267e7e751d03da933a24a",
"avatarUrl": "/avatars/f047a047d1de304cd97027463541bdf3.svg",
"fullname":... | 2025-02-16T15:38:19 | SURGE: On the Potential of Large Language Models as General-Purpose
Surrogate Code Executors | Large language models (LLMs) have demonstrated remarkable capabilities in
code-related tasks, such as code understanding and code generation. However, an
equally important yet underexplored question is whether LLMs can serve as
general-purpose surrogate code executors, to predict the output and behavior of
a program wi... | 10 | 67b4221ebc387d2eda6f8717 | null | null | |
2025-02-18T00:58:24.094000 | ReLearn: Unlearning via Learning for Large Language Models | 2 | {
"_id": "620b3bbb0668e435407c8d0a",
"avatarUrl": "/avatars/e0fccbb2577d76088e09f054c35cffbc.svg",
"followerCount": 19,
"fullname": "Ningyu Zhang",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Ningyu",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/620b3bbb0668e435407c8d0a/A4YB7t6hDVty6QrvLN0a7.png"
] | 2502.11190 | [
{
"_id": "67b420dfb2528c023491f455",
"hidden": false,
"name": "Haoming Xu",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b420dfb2528c023491f456",
"hidden": true,
"name": "Ningyuan Zhao",
"status": "admin_assigned",
"statusLastChangedAt": "2... | 2025-02-16T16:31:00 | ReLearn: Unlearning via Learning for Large Language Models | Current unlearning methods for large language models usually rely on reverse
optimization to reduce target token probabilities. However, this paradigm
disrupts the subsequent tokens prediction, degrading model performance and
linguistic coherence. Moreover, existing evaluation metrics overemphasize
contextual forgettin... | 29 | 67b420e2b2528c023491f506 | null | null | |
2025-02-18T00:49:53.124000 | Learning Getting-Up Policies for Real-World Humanoid Robots | 3 | {
"_id": "6201fc5d91d53938a6432fbf",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6201fc5d91d53938a6432fbf/VLs8ZYaZrop4KBpZn53fH.jpeg",
"followerCount": 3,
"fullname": "Runpei Dong",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "RunpeiDong",
"type": "user"
} | true | [
"https://cdn-uploads.huggingface.co/production/uploads/6201fc5d91d53938a6432fbf/x35BuXOhc6ubukxLfiVzt.mp4"
] | 2502.12152 | [
{
"_id": "67b41ed52867282b4eb37ce4",
"hidden": false,
"name": "Xialin He",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b41ed52867282b4eb37ce5",
"hidden": false,
"name": "Runpei Dong",
"status": "claimed_verified",
"statusLastChangedAt": "2... | 2025-02-17T18:59:06 | Learning Getting-Up Policies for Real-World Humanoid Robots | Automatic fall recovery is a crucial prerequisite before humanoid robots can
be reliably deployed. Hand-designing controllers for getting up is difficult
because of the varied configurations a humanoid can end up in after a fall and
the challenging terrains humanoid robots are expected to operate on. This paper
develop... | 36 | 67b41edb2867282b4eb37ddf | null | null | |
2025-02-18T00:28:31.293000 | SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? | 5 | {
"_id": "60f1abe7544c2adfd699860c",
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg",
"followerCount": 6280,
"fullname": "AK",
"isHf": true,
"isMod": false,
"isPro": false,
"name": "akhaliq",
"type": "user"
} | false | null | 2502.12115 | [
{
"_id": "67b41a72a38d04cc6148d80e",
"hidden": false,
"name": "Samuel Miserendino",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67b41a72a38d04cc6148d80f",
"hidden": false,
"name": "Michele Wang",
"status": null,
"statusLastChangedAt": null,
... | 2025-02-17T18:41:16 | SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance
Software Engineering? | We introduce SWE-Lancer, a benchmark of over 1,400 freelance software
engineering tasks from Upwork, valued at \1 million USD total in real-world
payouts. SWE-Lancer encompasses both independent engineering tasks--ranging
from 50 bug fixes to \$32,000 feature implementations--and managerial tasks,
where models choose b... | 42 | 67b41a74a38d04cc6148d84b | null | null |