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2025-03-04T12:05:25.041000 | Efficient Test-Time Scaling via Self-Calibration | 1 | {
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the quality of responses in Large Language Models (LLMs). While Best-of-N
sampling and Self-Consistency with majority voting are simple and effective,
they require a fixed number of sampling responses for each query, regardless of
its complexit... | 8 | 67c732c34aaf26f75cea0df7 | null | null | |
2025-03-04T10:47:26.717000 | Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis | 1 | {
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... | 2025-02-27T18:56:26 | Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security
Analysis | Recent advancements in Web AI agents have demonstrated remarkable
capabilities in addressing complex web navigation tasks. However, emerging
research shows that these agents exhibit greater vulnerability compared to
standalone Large Language Models (LLMs), despite both being built upon the same
safety-aligned models. T... | 1 | 67c284e96e9f0735ea1c43dd | https://vulnerable-ai-agents.github.io/ | null | |
2025-03-04T08:19:57.557000 | General Reasoning Requires Learning to Reason from the Get-go | 1 | {
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"fullname... | 2025-02-26T18:51:12 | General Reasoning Requires Learning to Reason from the Get-go | Large Language Models (LLMs) have demonstrated impressive real-world utility,
exemplifying artificial useful intelligence (AUI). However, their ability to
reason adaptively and robustly -- the hallmarks of artificial general
intelligence (AGI) -- remains fragile. While LLMs seemingly succeed in
commonsense reasoning, p... | 4 | 67c66a6521d722b4247e59c8 | null | null | |
2025-03-04T08:11:33.371000 | PodAgent: A Comprehensive Framework for Podcast Generation | 1 | {
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"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth... | 2025-03-01T11:35:17 | PodAgent: A Comprehensive Framework for Podcast Generation | Existing Existing automatic audio generation methods struggle to generate
podcast-like audio programs effectively. The key challenges lie in in-depth
content generation, appropriate and expressive voice production. This paper
proposed PodAgent, a comprehensive framework for creating audio programs.
PodAgent 1) generate... | 5 | 67c6facfd8af5b36fd4b5a45 | https://podcast-agent.github.io/demo/ | https://github.com/yujxx/PodAgent | |
2025-03-04T06:41:49.997000 | When an LLM is apprehensive about its answers -- and when its uncertainty is justified | 1 | {
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uncertainty is justified | Uncertainty estimation is crucial for evaluating Large Language Models
(LLMs), particularly in high-stakes domains where incorrect answers result in
significant consequences. Numerous approaches consider this problem, while
focusing on a specific type of uncertainty, ignoring others. We investigate
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2025-03-04T05:28:10.012000 | SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity | 1 | {
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Coordinating Data Quality and Diversity | Existing pretraining data mixing methods for large language models (LLMs)
typically follow a domain-wise methodology, a top-down process that first
determines domain weights and then performs uniform data sampling across each
domain. However, these approaches neglect significant inter-domain overlaps and
commonalities,... | 7 | 67c67d03c8d296910ca7494f | null | null | |
2025-03-04T05:13:44.578000 | Word Form Matters: LLMs' Semantic Reconstruction under Typoglycemia | 1 | {
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as Typoglycemia, primarily by relying on word form; if word form alone is
insufficient, they further utilize contextual cues for interpretation. While
advanced large language models (LLMs) exhibit similar abilities, the underlying
mechanisms r... | 5 | 67c6d22e983375492193ab13 | null | null | |
2025-03-04T05:12:10.849000 | Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator | 1 | {
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"fullnam... | 2025-03-03T02:06:22 | Direct Discriminative Optimization: Your Likelihood-Based Visual
Generative Model is Secretly a GAN Discriminator | While likelihood-based generative models, particularly diffusion and
autoregressive models, have achieved remarkable fidelity in visual generation,
the maximum likelihood estimation (MLE) objective inherently suffers from a
mode-covering tendency that limits the generation quality under limited model
capacity. In this ... | 2 | 67c6d1c65e896ed9153740e4 | https://research.nvidia.com/labs/dir/ddo/ | null | |
2025-03-04T04:56:33.061000 | From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation up to 100K Tokens | 1 | {
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Generation up to 100K Tokens | Generating ultra-long sequences with large language models (LLMs) has become
increasingly crucial but remains a highly time-intensive task, particularly for
sequences up to 100K tokens. While traditional speculative decoding methods
exist, simply extending their generation limits fails to accelerate the process
and can... | 7 | 67c6cbd7e52534aa6ada2e79 | null | https://github.com/bigai-nlco/TokenSwift | |
2025-03-04T04:54:04.054000 | DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion | 1 | {
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"us... | 2025-03-03T05:15:34 | DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End
Full-Length Song Generation with Latent Diffusion | Recent advancements in music generation have garnered significant attention,
yet existing approaches face critical limitations. Some current generative
models can only synthesize either the vocal track or the accompaniment track.
While some models can generate combined vocal and accompaniment, they typically
rely on me... | 18 | 67c6a16021d722b4248bda37 | https://aslp-lab.github.io/DiffRhythm.github.io/ | https://github.com/ASLP-lab/DiffRhythm | |
2025-03-04T04:17:23.806000 | Unposed Sparse Views Room Layout Reconstruction in the Age of Pretrain Model | 1 | {
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Model | Room layout estimation from multiple-perspective images is poorly
investigated due to the complexities that emerge from multi-view geometry,
which requires muti-step solutions such as camera intrinsic and extrinsic
estimation, image matching, and triangulation. However, in 3D reconstruction,
the advancement of recent 3... | 2 | 67c65c0be116e36157440751 | null | https://github.com/justacar/Plane-DUSt3R | |
2025-03-04T03:56:04.503000 | OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment | 1 | {
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"fullname":... | 2025-02-26T09:25:10 | OneRec: Unifying Retrieve and Rank with Generative Recommender and
Iterative Preference Alignment | Recently, generative retrieval-based recommendation systems have emerged as a
promising paradigm. However, most modern recommender systems adopt a
retrieve-and-rank strategy, where the generative model functions only as a
selector during the retrieval stage. In this paper, we propose OneRec, which
replaces the cascaded... | 18 | 67c6bfe396b9f5fa18c518e5 | null | null | |
2025-03-04T03:20:03.380000 | AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond Human Understanding | 1 | {
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"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/63136a8... | 2025-03-02T23:59:52 | AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond
Human Understanding | This paper investigates the potential for large language models (LLMs) to
develop private tonal languages for machine-to-machine (M2M) communication.
Inspired by cryptophasia in human twins (affecting up to 50% of twin births)
and natural tonal languages like Mandarin and Vietnamese, we implement a
precise character-to... | 1 | 67c6b72c7aad9a016ae607bb | null | null | |
2025-03-04T02:48:58.261000 | Liger: Linearizing Large Language Models to Gated Recurrent Structures | 1 | {
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"fullname":... | 2025-03-03T13:08:00 | Liger: Linearizing Large Language Models to Gated Recurrent Structures | Transformers with linear recurrent modeling offer linear-time training and
constant-memory inference. Despite their demonstrated efficiency and
performance, pretraining such non-standard architectures from scratch remains
costly and risky. The linearization of large language models (LLMs) transforms
pretrained standard... | 13 | 67c6b06035198d0f397adcc4 | null | null | |
2025-03-04T02:27:17.351000 | CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic Environments | 1 | {
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Environments | Large Language Models (LLMs) exhibit remarkable capabilities in the
hierarchical decomposition of complex tasks through semantic reasoning.
However, their application in embodied systems faces challenges in ensuring
reliable execution of subtask sequences and achieving one-shot success in
long-term task completion. To ... | 2 | 67c6ab42c0b62d612c54df71 | https://sp4595.github.io/CLEA/ | https://github.com/SP4595/CLEA-Closed-Loop-Embodied-Agent | |
2025-03-04T02:21:00.460000 | Speculative Ad-hoc Querying | 1 | {
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"fullname":... | 2025-03-02T03:44:31 | Speculative Ad-hoc Querying | Analyzing large datasets requires responsive query execution, but executing
SQL queries on massive datasets can be slow. This paper explores whether query
execution can begin even before the user has finished typing, allowing results
to appear almost instantly. We propose SpeQL, a system that leverages Large
Language M... | 8 | 67c6a804025b72f14ccb0994 | https://github.com/lihy0529/SpeQL | https://github.com/lihy0529/SpeQL | |
2025-03-04T02:16:25.633000 | CodeArena: A Collective Evaluation Platform for LLM Code Generation | 1 | {
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their exceptional comprehension of natural language and programming syntax,
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2025-03-04T01:56:03.632000 | Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions | 1 | {
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multimodal content, improve user experiences by integrating visual and textual
information into results (or items). The challenge of improving user
experiences in complex systems with search and recommendation (S\&R) services
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2025-03-04T01:19:45.715000 | Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation | 1 | {
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However, the quality and generalizability of 3D content generation remain
limited. State-of-the-art methods often require large-scale 3D assets for
training, which are challenging to collect. In this work, we introduce
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2025-03-04T00:52:22.204000 | Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models | 1 | {
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reconstruction and novel-view synthesis task. However, achieving photorealistic
rendering from extreme novel viewpoints remains challenging, as artifacts
persist across representations. In this work, we introduce Difix3D+, a novel
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2025-03-04T00:29:56.570000 | VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation | 1 | {
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Generation | Text-to-video generative models convert textual prompts into dynamic visual
content, offering wide-ranging applications in film production, gaming, and
education. However, their real-world performance often falls short of user
expectations. One key reason is that these models have not been trained on
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2025-03-04T00:09:04.418000 | Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs | 1 | {
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Habits of Highly Effective STaRs | Test-time inference has emerged as a powerful paradigm for enabling language
models to ``think'' longer and more carefully about complex challenges, much
like skilled human experts. While reinforcement learning (RL) can drive
self-improvement in language models on verifiable tasks, some models exhibit
substantial gains... | 13 | 67c68add0457c9f809c22e31 | null | null | |
2025-03-03T23:44:06.105000 | Large-Scale Data Selection for Instruction Tuning | 1 | {
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when instruction-tuning language models, as carefully curated datasets often
produce models that outperform those trained on much larger, noisier datasets.
Automated data selection approaches for instruction-tuning are typically tested
by selecti... | 5 | 67c67ff9dec55d10cb10fcef | null | null | |
2025-03-03T23:29:27.952000 | Visual-RFT: Visual Reinforcement Fine-Tuning | 1 | {
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learns from feedback on its answers, which is especially useful in applications
when fine-tuning data is scarce. Recent open-source work like DeepSeek-R1
demonstrates that reinforcement learning with verifiable reward is one key
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2025-03-03T23:15:05.187000 | Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs | 3 | {
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Models via Mixture-of-LoRAs | We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable
language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language
model trained on high-quality web and synthetic data, significantly
outperforming recent open-source models of similar size and matching the
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2025-03-03T22:35:45.299000 | DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with Dynamic Multi-Sequence Drafting | 1 | {
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Dynamic Multi-Sequence Drafting | Large language models (LLMs) exhibit exceptional performance across a wide
range of tasks; however, their token-by-token autoregressive generation process
significantly hinders inference speed. Speculative decoding presents a
promising draft-then-verify framework that reduces generation latency while
maintaining output... | 8 | 67c673bdf47209364f0cecb7 | null | https://github.com/KaiLv69/DuoDecoding | |
2025-03-03T21:22:16.512000 | Predictive Data Selection: The Data That Predicts Is the Data That Teaches | 1 | {
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Teaches | Language model pretraining involves training on extensive corpora, where data
quality plays a pivotal role. In this work, we aim to directly estimate the
contribution of data during pretraining and select pretraining data in an
efficient manner. Specifically, we draw inspiration from recent findings
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2025-03-03T11:25:57.425000 | Multi-Turn Code Generation Through Single-Step Rewards | 2 | {
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Existing methods either generate code without feedback or use complex,
hierarchical reinforcement learning to optimize multi-turn rewards. We propose
a simple yet scalable approach, muCode, that solves multi-turn code
generation using only si... | 24 | 67c34e3ceae05d8f94f8010e | https://portal-cornell.github.io/muCode/ | https://github.com/portal-cornell/muCode | |
2025-03-03T10:56:33.810000 | Preference Learning Unlocks LLMs' Psycho-Counseling Skills | 2 | {
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emerging and meaningful approach, driven by the significant gap between patient
needs and the availability of mental health support. However, current LLMs
struggle to consistently provide effective responses to client speeches,
largely due to th... | 6 | 67c36b36e12b50f698e7db51 | null | null | |
2025-03-03T10:26:31.746000 | EgoNormia: Benchmarking Physical Social Norm Understanding | 2 | {
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world, humans not only follow norms, but also consider the trade-off between
different norms However, machines are often trained without explicit
supervision on norm understanding and reasoning, especially when the norms are
grounded in a physica... | 4 | 67c5c857e7c5cfb1d2b52994 | https://egonormia.org | https://github.com/open-social-world/egonormia | |
2025-03-03T09:49:10.381000 | How far can we go with ImageNet for Text-to-Image generation? | 2 | {
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2025-03-03T09:44:46.734000 | DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping | 2 | {
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A general-purpose robot must be capable of grasping diverse objects in
arbitrary scenarios. However, existing research typically relies on specific
assumptions, such as single-object settings or limited environments, leading to
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2025-03-03T09:33:49.658000 | TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval | 2 | {
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with external data sources to enhance factual correctness and domain coverage.
Modern RAG pipelines rely on large datastores, leading to system challenges in
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2025-03-03T08:13:06.912000 | MIGE: A Unified Framework for Multimodal Instruction-Based Image Generation and Editing | 2 | {
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2025-03-03T07:33:14.717000 | LettuceDetect: A Hallucination Detection Framework for RAG Applications | 2 | {
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2025-03-03T07:04:47.515000 | Optimal Brain Apoptosis | 2 | {
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2025-03-03T04:21:42.563000 | Tell me why: Visual foundation models as self-explainable classifiers | 2 | {
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2025-03-03T02:35:09.967000 | Chain of Draft: Thinking Faster by Writing Less | 4 | {
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2025-03-02T22:22:01.895000 | ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents | 2 | {
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2025-03-02T22:08:44.891000 | Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids | 2 | {
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2025-03-02T22:04:15.087000 | HAIC: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models | 2 | {
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2025-03-02T22:00:31.796000 | SoS1: O1 and R1-Like Reasoning LLMs are Sum-of-Square Solvers | 2 | {
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2025-03-02T21:48:46.577000 | LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation | 2 | {
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2025-03-02T21:35:24.437000 | DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking | 4 | {
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2025-02-28T16:51:51.551000 | PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving | 3 | {
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complex planning problems due to limitations in verifying generated plans or
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2025-02-28T13:21:13.227000 | Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation | 2 | {
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2025-02-28T08:54:03.125000 | On Relation-Specific Neurons in Large Language Models | 2 | {
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2025-02-28T08:46:19.110000 | Guardians of the Agentic System: Preventing Many Shots Jailbreak with Agentic System | 2 | {
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2025-02-28T07:55:48.923000 | Training Consistency Models with Variational Noise Coupling | 2 | {
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2025-02-28T07:25:35.166000 | Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via Sparse Time-Variant Attribute Modeling | 2 | {
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2025-02-28T04:47:08.197000 | Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting | 2 | {
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modeling, particularly for complex multi-part articulated objects. We introduce
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2025-02-28T04:36:05.045000 | MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning | 3 | {
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2025-02-28T04:02:19.534000 | Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think | 3 | {
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2025-02-28T03:27:32.294000 | NeoBERT: A Next-Generation BERT | 6 | {
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2025-02-28T01:55:41.427000 | Lean and Mean: Decoupled Value Policy Optimization with Global Value Guidance | 2 | {
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2025-02-28T01:14:11.268000 | FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving | 2 | {
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2025-02-28T00:14:01.841000 | Mobius: Text to Seamless Looping Video Generation via Latent Shift | 2 | {
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2025-02-28T00:10:30.864000 | FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute | 2 | {
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2025-02-28T00:03:34.893000 | R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning | 2 | {
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Learning | Despite recent breakthroughs in reasoning-enhanced large language models
(LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine
translation (MT), where human translators naturally employ structured,
multi-layered reasoning chain-of-thoughts (CoTs), is yet underexplored.
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2025-02-27T23:34:45.416000 | UniTok: A Unified Tokenizer for Visual Generation and Understanding | 2 | {
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2025-02-27T23:04:14.619000 | CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale | 2 | {
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Evolution at Scale | Large Language Models (LLMs) have exhibited exceptional performance in
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library APIs. This limitation, stemming from static pre-training datasets,
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2025-02-27T22:38:04.562000 | SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning | 2 | {
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fully leverage open-source development resources. We propose Subtask-oriented
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2025-02-27T22:27:24.486000 | R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts | 5 | {
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(e.g., visual representations) is usually not on par with the large language
models (LLMs)' powerful reasoning capabilities, deterring LMMs' performance on
challenging downstream tasks. This weakness has been recently mitigated by
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2025-02-27T22:22:53.713000 | LongRoPE2: Near-Lossless LLM Context Window Scaling | 2 | {
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pre-trained large language models (LLMs) to the target length, while preserving
the performance on the original shorter context window. This is achieved by
three contributions: (1) a hypothesis that insufficient training in higher RoPE
dimension... | 29 | 67c12b6e25c74ee5b6e2ceb5 | null | null | |
2025-02-27T22:15:54.222000 | Self-rewarding correction for mathematical reasoning | 6 | {
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simultaneously generate step-by-step reasoning and evaluate the correctness of
their outputs during the inference time-without external feedback. This
integrated approach allows a single model to independently guide its reasoning
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2025-02-27T21:19:58.170000 | Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications | 2 | {
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Communications | Effective communication in Air Traffic Control (ATC) is critical to
maintaining aviation safety, yet the challenges posed by accented English
remain largely unaddressed in Automatic Speech Recognition (ASR) systems.
Existing models struggle with transcription accuracy for Southeast
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2025-02-27T21:02:33.864000 | MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge | 2 | {
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allowing them to correct outdated or inaccurate information without retraining
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2025-02-27T14:03:36.365000 | Towards Optimal Multi-draft Speculative Decoding | 2 | {
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language processing tasks. However, autoregressive sampling has become an
efficiency bottleneck. Multi-Draft Speculative Decoding (MDSD) is a recent
approach where, when generating each token, a small draft model generates
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2025-02-27T11:09:15.703000 | FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users | 2 | {
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LLMs Elicits Effective Personalization to Real Users | Effective personalization of LLMs is critical for a broad range of
user-interfacing applications such as virtual assistants and content curation.
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2025-02-27T10:12:03.128000 | Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization | 3 | {
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Re-initialization | The Mixture of Experts (MoE) architecture reduces the training and inference
cost significantly compared to a dense model of equivalent capacity. Upcycling
is an approach that initializes and trains an MoE model using a pre-trained
dense model. While upcycling leads to initial performance gains, the training
progresses... | 6 | 67c07172af68756abc571b53 | null | null | |
2025-02-27T09:41:49.469000 | Rank1: Test-Time Compute for Reranking in Information Retrieval | 2 | {
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test-time compute. Rank1 demonstrates the applicability within retrieval of
using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for
distillation in order to rapidly improve the performance of a smaller model. We
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2025-02-27T07:31:45.499000 | DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps | 2 | {
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Class Activation Maps | Weakly supervised semantic segmentation (WSSS) typically utilizes limited
semantic annotations to obtain initial Class Activation Maps (CAMs). However,
due to the inadequate coupling between class activation responses and semantic
information in high-dimensional space, the CAM is prone to object co-occurrence
or under-... | 2 | 67c05af3a2a76d8a27d33faf | null | null | |
2025-02-27T04:18:26.724000 | Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs | 2 | {
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Burdens via LLMs | Paywalls, licenses and copyright rules often restrict the broad dissemination
and reuse of scientific knowledge. We take the position that it is both legally
and technically feasible to extract the scientific knowledge in scholarly
texts. Current methods, like text embeddings, fail to reliably preserve factual
content,... | 19 | 67c02d6ba15ac71dcf1c7596 | null | null | |
2025-02-27T04:15:43.126000 | GHOST 2.0: generative high-fidelity one shot transfer of heads | 2 | {
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community, a related problem of head swapping remains largely unexplored. In
addition to skin color transfer, head swap poses extra challenges, such as the
need to preserve structural information of the whole head during synthesis and
inpaint... | 61 | 67c02b31b14cf3cbc800c34b | null | null | |
2025-02-27T02:43:05.341000 | BIG-Bench Extra Hard | 2 | {
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applications, demanding robust general reasoning capabilities and diverse
reasoning skillset. However, current LLM reasoning benchmarks predominantly
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reasoning proficiencies... | 6 | 67c01748e8c7d56a8e0cbe0b | null | null | |
2025-02-27T02:36:29.037000 | Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation | 2 | {
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Counterexample Creation | There is growing excitement about the potential of Language Models (LMs) to
accelerate scientific discovery. Falsifying hypotheses is key to scientific
progress, as it allows claims to be iteratively refined over time. This process
requires significant researcher effort, reasoning, and ingenuity. Yet current
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2025-02-27T00:47:02.948000 | CritiQ: Mining Data Quality Criteria from Human Preferences | 2 | {
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Existing approaches rely on manually designed heuristics, the perplexity of
existing models, training classifiers, or careful prompt engineering, which
require significant expert experience and human annotation effort while
introduce biases. W... | 7 | 67bffacc3f838c1e33e075a2 | null | null | |
2025-02-27T00:37:24.965000 | PosterSum: A Multimodal Benchmark for Scientific Poster Summarization | 2 | {
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is challenging, especially when dealing with visually complex content like
scientific posters. We introduce PosterSum, a novel benchmark to advance the
development of vision-language models that can understand and summarize
scientific posters i... | 2 | 67bff96d8d761fc6a75e27a0 | null | null | |
2025-02-27T00:17:58.262000 | Language Models' Factuality Depends on the Language of Inquiry | 2 | {
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consistently across languages, yet they often fail to transfer knowledge
between languages even when they possess the correct information in one of the
languages. For example, we find that an LM may correctly identify Rashed Al
Shashai as being... | 29 | 67bff528ca6e3c22b6e89ddd | null | null | |
2025-02-27T00:08:09.082000 | Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance | 2 | {
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(LLMs) remain underexplored for Greek financial context due to the linguistic
complexity of Greek and the scarcity of domain-specific datasets. Previous
efforts in multilingual financial natural language processing (NLP) have
exposed considerabl... | 30 | 67bfc298ca6e3c22b6d99caa | null | null | |
2025-02-26T23:05:13.440000 | Kanana: Compute-efficient Bilingual Language Models | 2 | {
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"statusLastChangedAt": ... | 2025-02-26T08:36:20 | Kanana: Compute-efficient Bilingual Language Models | We introduce Kanana, a series of bilingual language models that demonstrate
exceeding performance in Korean and competitive performance in English. The
computational cost of Kanana is significantly lower than that of
state-of-the-art models of similar size. The report details the techniques
employed during pre-training... | 58 | 67bfe1c04426925c82fe59a1 | null | null | |
2025-02-26T23:04:47.406000 | Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? | 2 | {
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"user... | 2025-02-26T17:59:27 | Can Large Language Models Detect Errors in Long Chain-of-Thought
Reasoning? | Recently, o1-like models have drawn significant attention, where these models
produce the long Chain-of-Thought (CoT) reasoning steps to improve the
reasoning abilities of existing Large Language Models (LLMs). In this paper, to
understand the qualities of these long CoTs and measure the critique abilities
of existing ... | 24 | 67bfe438ca6e3c22b6e2948e | null | null | |
2025-02-26T22:29:40.056000 | MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra | 2 | {
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Energy Spectra | Establishing the relationship between 3D structures and the energy states of
molecular systems has proven to be a promising approach for learning 3D
molecular representations. However, existing methods are limited to modeling
the molecular energy states from classical mechanics. This limitation results
in a significant... | 5 | 67bfdbd1302c06f220658ece | null | null | |
2025-02-26T22:18:06.494000 | Towards an AI co-scientist | 2 | {
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... | 2025-02-26T06:17:13 | Towards an AI co-scientist | Scientific discovery relies on scientists generating novel hypotheses that
undergo rigorous experimental validation. To augment this process, we introduce
an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI
co-scientist is intended to help uncover new, original knowledge and to
formulate demonstrably n... | 37 | 67bfd958c2a9b64ab3f97afa | null | null | |
2025-02-26T22:16:03.582000 | AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement | 2 | {
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"user"... | 2025-02-24T02:11:52 | AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and
Improvement | As AI models are increasingly deployed across diverse real-world scenarios,
ensuring their safety remains a critical yet underexplored challenge. While
substantial efforts have been made to evaluate and enhance AI safety, the lack
of a standardized framework and comprehensive toolkit poses significant
obstacles to syst... | 5 | 67bfd8d646083445aacb464f | null | null | |
2025-02-26T22:10:20.646000 | Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator | 4 | {
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"use... | 2025-02-26T15:10:05 | Distill Any Depth: Distillation Creates a Stronger Monocular Depth
Estimator | Monocular depth estimation (MDE) aims to predict scene depth from a single
RGB image and plays a crucial role in 3D scene understanding. Recent advances
in zero-shot MDE leverage normalized depth representations and
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2025-02-26T22:07:49.438000 | TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding | 2 | {
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2025-02-26T22:05:16.150000 | Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems | 2 | {
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focus on human preferences, neglecting verifiable correctness signals which
have shown strong potential in training LLMs. In this paper, we propose agentic
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2025-02-26T22:02:50.690000 | VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model | 2 | {
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Environment Model | Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI)
agents via Reinforcement Learning (RL) faces critical challenges:
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methods struggle with distribution shift and reward generalization. We propose
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2025-02-26T18:40:15.965000 | Scaling LLM Pre-training with Vocabulary Curriculum | 2 | {
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2025-02-26T16:56:34.818000 | LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven Language Representation | 2 | {
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Language Representation | In this paper, we introduce LDGen, a novel method for integrating large
language models (LLMs) into existing text-to-image diffusion models while
minimizing computational demands. Traditional text encoders, such as CLIP and
T5, exhibit limitations in multilingual processing, hindering image generation
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2025-02-26T14:46:51.721000 | MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs | 2 | {
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Details with Multimodal LLMs | Multimodal Large Language Models (MLLMs) have experienced rapid progress in
visual recognition tasks in recent years. Given their potential integration
into many critical applications, it is important to understand the limitations
of their visual perception. In this work, we study whether MLLMs can perceive
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2025-02-26T12:51:05.089000 | Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents | 5 | {
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2025-02-26T12:34:59.916000 | An Overview of Large Language Models for Statisticians | 2 | {
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2025-02-26T10:53:44.153000 | WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging | 2 | {
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Challenging | We introduce WiCkeD, a simple method to increase the complexity of existing
multiple-choice benchmarks by randomly replacing a choice with "None of the
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2025-02-26T10:43:07.864000 | Prompt-to-Leaderboard | 3 | {
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like accuracy or human preference, averaging across users and prompts. This
averaging obscures user- and prompt-specific variations in model performance.
To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces
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2025-02-26T09:40:23.169000 | Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization | 2 | {
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Preference Optimization | Iterative data generation and model retraining are widely used to align large
language models (LLMs). It typically involves a policy model to generate
on-policy responses and a reward model to guide training data selection. Direct
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2025-02-26T07:28:05.618000 | LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models | 2 | {
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2025-02-26T02:37:36.287000 | Introducing Visual Perception Token into Multimodal Large Language Model | 2 | {
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2025-02-26T01:04:23.776000 | The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve? | 2 | {
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