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2025-02-26T00:56:27.275000 | K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs | 2 | {
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learned style and content. However, existing methods either fail to effectively
preserve both the original subject and style simultaneously or require
additional training. In this paper, we argue that the intrinsic properties of
LoRA can effecti... | 15 | 67bea0cf2d6011a72335f7aa | https://k-lora.github.io/K-LoRA.io/ | https://github.com/HVision-NKU/K-LoRA | |
2025-02-26T00:38:42.527000 | Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI | 2 | {
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of 1B and 4B parameters designed to address data efficiency challenges in
multimodal learning. While recent VLMs achieve strong performance through
extensive training data, Shakti models leverage architectural innovations to
attain competitive ... | 3 | 67bea8cd7e54112af6c37305 | null | null | |
2025-02-25T22:26:11.421000 | Scale-Distribution Decoupling: Enabling Stable and Effective Training of Large Language Models | 2 | {
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Large Language Models | Training stability is a persistent challenge in the pre-training of large
language models (LLMs), particularly for architectures such as Post-Norm
Transformers, which are prone to gradient explosion and dissipation. In this
paper, we propose Scale-Distribution Decoupling (SDD), a novel approach that
stabilizes training... | 13 | 67be86753ea16c7e9491ff49 | null | null | |
2025-02-25T22:20:16.916000 | WebGames: Challenging General-Purpose Web-Browsing AI Agents | 2 | {
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"fullname... | 2025-02-25T16:45:08 | WebGames: Challenging General-Purpose Web-Browsing AI Agents | We introduce WebGames, a comprehensive benchmark suite designed to evaluate
general-purpose web-browsing AI agents through a collection of 50+ interactive
challenges. These challenges are specifically crafted to be straightforward for
humans while systematically testing the limitations of current AI systems
across fund... | 10 | 67be8868823e790d21a2bbea | null | null | |
2025-02-25T22:20:08.416000 | AAD-LLM: Neural Attention-Driven Auditory Scene Understanding | 3 | {
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process all sound inputs equally, independent of listener perception. However,
human auditory perception is inherently selective: listeners focus on specific
speakers while ignoring others in complex auditory scenes. Existing models do
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2025-02-25T22:18:24.064000 | Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective | 2 | {
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"fullname... | 2025-02-24T15:44:57 | Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based
Perspective | The rapid advancements in computing dramatically increase the scale and cost
of training Large Language Models (LLMs). Accurately predicting downstream task
performance prior to model training is crucial for efficient resource
allocation, yet remains challenging due to two primary constraints: (1) the
"emergence phenom... | 18 | 67bd3872a917fc506d9f3d8f | null | null | |
2025-02-25T22:04:57.351000 | SpargeAttn: Accurate Sparse Attention Accelerating Any Model Inference | 2 | {
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its quadratic time complexity. Fortunately, attention commonly exhibits
sparsity, i.e., many values in the attention map are near zero, allowing for
the omission of corresponding computations. Many studies have utilized the
sparse pattern to acc... | 50 | 67be8447ed8e258c0f70075f | null | null | |
2025-02-25T22:03:08.515000 | SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution | 5 | {
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"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/632a1762... | 2025-02-25T18:45:04 | SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open
Software Evolution | The recent DeepSeek-R1 release has demonstrated the immense potential of
reinforcement learning (RL) in enhancing the general reasoning capabilities of
large language models (LLMs). While DeepSeek-R1 and other follow-up work
primarily focus on applying RL to competitive coding and math problems, this
paper introduces S... | 61 | 67be845b8a5a8054231457d6 | null | null | |
2025-02-25T22:01:56.532000 | OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference | 2 | {
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"fullnam... | 2025-02-25T18:05:14 | OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference | Recent advancements in open-source multi-modal large language models (MLLMs)
have primarily focused on enhancing foundational capabilities, leaving a
significant gap in human preference alignment. This paper introduces
OmniAlign-V, a comprehensive dataset of 200K high-quality training samples
featuring diverse images, ... | 67 | 67be834ce7b05f9e43b1730a | null | null | |
2025-02-25T21:50:19.941000 | ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation | 4 | {
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Image Generation | Multi-layer image generation is a fundamental task that enables users to
isolate, select, and edit specific image layers, thereby revolutionizing
interactions with generative models. In this paper, we introduce the Anonymous
Region Transformer (ART), which facilitates the direct generation of variable
multi-layer trans... | 32 | 67be81464084d82ee69ad576 | null | null | |
2025-02-25T21:36:19.851000 | KV-Edit: Training-Free Image Editing for Precise Background Preservation | 3 | {
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"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/noa... | 2025-02-24T17:40:09 | KV-Edit: Training-Free Image Editing for Precise Background Preservation | Background consistency remains a significant challenge in image editing
tasks. Despite extensive developments, existing works still face a trade-off
between maintaining similarity to the original image and generating content
that aligns with the target. Here, we propose KV-Edit, a training-free approach
that uses KV ca... | 32 | 67bd6d2dbf6d46017e619f99 | null | null | |
2025-02-25T19:35:42.726000 | MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use | 2 | {
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from a large code repository, how do we provide context from the repo to the
LLM? One approach is to add the entire repo to the LLM's context window.
However, most tasks involve only fraction of symbols from a repo, longer
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2025-02-25T17:06:48.440000 | Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation | 2 | {
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"u... | 2025-02-20T04:22:30 | Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and
Texture Generation | This report presents a comprehensive framework for generating high-quality 3D
shapes and textures from diverse input prompts, including single images,
multi-view images, and text descriptions. The framework consists of 3D shape
generation and texture generation. (1). The 3D shape generation pipeline
employs a Variation... | 5 | 67be3ec21c80786468704886 | null | null | |
2025-02-25T16:46:31.986000 | Mind the Gap! Static and Interactive Evaluations of Large Audio Models | 2 | {
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"statusLastChangedA... | 2025-02-21T20:29:02 | Mind the Gap! Static and Interactive Evaluations of Large Audio Models | As AI chatbots become ubiquitous, voice interaction presents a compelling way
to enable rapid, high-bandwidth communication for both semantic and social
signals. This has driven research into Large Audio Models (LAMs) to power
voice-native experiences. However, aligning LAM development with user goals
requires a clear ... | 3 | 67be3400e30b2f126c599503 | null | null | |
2025-02-25T12:50:27.642000 | Self-Taught Agentic Long Context Understanding | 2 | {
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"fullnam... | 2025-02-21T20:29:36 | Self-Taught Agentic Long Context Understanding | Answering complex, long-context questions remains a major challenge for large
language models (LLMs) as it requires effective question clarifications and
context retrieval. We propose Agentic Long-Context Understanding (AgenticLU), a
framework designed to enhance an LLM's understanding of such queries by
integrating ta... | 2 | 67bd1ae385a048f74fd1b9ba | null | null | |
2025-02-25T12:26:42.547000 | Grounded Persuasive Language Generation for Automated Marketing | 3 | {
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"statusLastChangedAt": ... | 2025-02-24T03:36:57 | Grounded Persuasive Language Generation for Automated Marketing | This paper develops an agentic framework that employs large language models
(LLMs) to automate the generation of persuasive and grounded marketing content,
using real estate listing descriptions as our focal application domain. Our
method is designed to align the generated content with user preferences while
highlighti... | 10 | 67bdfc15c45e6063fed00c7a | null | null | |
2025-02-25T11:58:10.154000 | InductionBench: LLMs Fail in the Simplest Complexity Class | 2 | {
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"statusLastChangedAt": "2... | 2025-02-20T03:48:00 | InductionBench: LLMs Fail in the Simplest Complexity Class | Large language models (LLMs) have shown remarkable improvements in reasoning
and many existing benchmarks have been addressed by models such as o1 and o3
either fully or partially. However, a majority of these benchmarks emphasize
deductive reasoning, including mathematical and coding tasks in which rules
such as mathe... | 6 | 67bdf5d24dc920400e28c2cb | null | null | |
2025-02-25T11:40:15.745000 | Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models | 2 | {
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Reliability of Large Language Models | Large Language Models (LLMs) have emerged as powerful tools for addressing
modern challenges and enabling practical applications. However, their
computational expense remains a significant barrier to widespread adoption.
Quantization has emerged as a promising technique to democratize access and
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2025-02-25T11:02:34.002000 | Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures | 2 | {
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Architectures | The COVID-19 pandemic strained healthcare resources and prompted discussion
about how machine learning can alleviate physician burdens and contribute to
diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few
studies predict the severity of a patient's condition from CXRs. In this study,
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2025-02-25T09:17:04.777000 | Early-Exit and Instant Confidence Translation Quality Estimation | 2 | {
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and generation. Unfortunately, quality estimation models are often opaque and
computationally expensive, making them impractical to be part of large-scale
pipelines. In this work, we tackle two connected challenges: (1) reducing the
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2025-02-25T09:00:19.900000 | MegaLoc: One Retrieval to Place Them All | 2 | {
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component of multiple computer vision tasks, like Visual Place Recognition,
Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However,
existing solutions are built to specifically work for one of these tasks, and
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2025-02-25T05:51:02.881000 | TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning | 2 | {
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Reinforcement Learning | Hierarchical organization is fundamental to biological systems and human
societies, yet artificial intelligence systems often rely on monolithic
architectures that limit adaptability and scalability. Current hierarchical
reinforcement learning (HRL) approaches typically restrict hierarchies to two
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2025-02-25T05:40:40.152000 | Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam | 2 | {
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4-bit training, revealing that low-bit precision amplifies sensitivity to
learning rates and often causes unstable gradient norms, leading to divergence
at higher learning rates. Among these, SPAM, a recent optimizer featuring
momentum reset ... | 16 | 67bd9b41478ef7c36240c724 | null | null | |
2025-02-25T04:11:18.915000 | Can Community Notes Replace Professional Fact-Checkers? | 2 | {
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social media are (i) fact-checking by professional organisations and (ii)
community moderation by platform users. Policy changes by Twitter/X and, more
recently, Meta, signal a shift away from partnerships with fact-checking
organisations and towa... | 5 | 67b8681bd00e69f10c1f3267 | null | null | |
2025-02-25T04:03:39.758000 | The snake in the Brownian sphere | 2 | {
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"u... | 2025-02-18T17:21:44 | The snake in the Brownian sphere | The Brownian sphere is a random metric space, homeomorphic to the
two-dimensional sphere, which arises as the universal scaling limit of many
types of random planar maps. The direct construction of the Brownian sphere is
via a continuous analogue of the Cori--Vauquelin--Schaeffer (CVS) bijection.
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2025-02-25T03:36:50.480000 | M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment | 2 | {
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Quality Assessment | The rapid advancement of AI-generated image (AGI) models has introduced
significant challenges in evaluating their quality, which requires considering
multiple dimensions such as perceptual quality, prompt correspondence, and
authenticity. To address these challenges, we propose M3-AGIQA, a comprehensive
framework for ... | 1 | 67bc7ea26f88ef9a2b828473 | null | null | |
2025-02-25T02:06:00.809000 | GCC: Generative Color Constancy via Diffusing a Color Checker | 2 | {
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sensors due to varying spectral sensitivities. We present GCC, which leverages
diffusion models to inpaint color checkers into images for illumination
estimation. Our key innovations include (1) a single-step deterministic
inference approach t... | 27 | 67bd6b4d8edd1ce8ad560401 | null | null | |
2025-02-25T01:02:05.395000 | Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation | 2 | {
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Robotic Manipulation | Solving complex long-horizon robotic manipulation problems requires
sophisticated high-level planning capabilities, the ability to reason about the
physical world, and reactively choose appropriate motor skills. Vision-language
models (VLMs) pretrained on Internet data could in principle offer a framework
for tackling ... | 11 | 67bd3bcf797e4d53ce0bc7ff | null | null | |
2025-02-25T00:37:53.138000 | MONSTER: Monash Scalable Time Series Evaluation Repository | 2 | {
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"fu... | 2025-02-21T00:54:40 | MONSTER: Monash Scalable Time Series Evaluation Repository | We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a
collection of large datasets for time series classification. The field of time
series classification has benefitted from common benchmarks set by the UCR and
UEA time series classification repositories. However, the datasets in these
benchmark... | 2 | 67bbd6d6ba0bb31293e11258 | null | null | |
2025-02-25T00:17:51.431000 | X-Dancer: Expressive Music to Human Dance Video Generation | 3 | {
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"user"... | 2025-02-24T18:47:54 | X-Dancer: Expressive Music to Human Dance Video Generation | We present X-Dancer, a novel zero-shot music-driven image animation pipeline
that creates diverse and long-range lifelike human dance videos from a single
static image. As its core, we introduce a unified transformer-diffusion
framework, featuring an autoregressive transformer model that synthesize
extended and music-s... | 11 | 67bd526101d5bfa0abfcc62c | null | null | |
2025-02-25T00:13:12.214000 | VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing | 4 | {
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"statusLastChangedAt":... | 2025-02-24T15:39:14 | VideoGrain: Modulating Space-Time Attention for Multi-grained Video
Editing | Recent advancements in diffusion models have significantly improved video
generation and editing capabilities. However, multi-grained video editing,
which encompasses class-level, instance-level, and part-level modifications,
remains a formidable challenge. The major difficulties in multi-grained editing
include semant... | 71 | 67bd51620417e7f92283d4e9 | null | null | |
2025-02-25T00:09:04.483000 | RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers | 3 | {
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Transformers | Recent advancements in video generation have enabled models to synthesize
high-quality, minute-long videos. However, generating even longer videos with
temporal coherence remains a major challenge, and existing length extrapolation
methods lead to temporal repetition or motion deceleration. In this work, we
systematica... | 19 | 67bd3bd66faf9f04b21710d1 | null | null | |
2025-02-24T23:37:53.138000 | Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning | 2 | {
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Reasoning | Scaling pre-training compute has proven effective for achieving
mulitlinguality, but does the same hold for test-time scaling? In this work, we
introduce MCLM, a multilingual math benchmark featuring competition-level
problems in 55 languages. We test three test-time scaling methods-Outcome
Reward Modeling (ORM), Proce... | 24 | 67bd48d5becb766415a5d1e9 | null | null | |
2025-02-24T23:30:36.556000 | Forecasting Open-Weight AI Model Growth on Hugging Face | 3 | {
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... | 2025-02-21T22:52:19 | Forecasting Open-Weight AI Model Growth on Hugging Face | As the open-weight AI landscape continues to proliferate-with model
development, significant investment, and user interest-it becomes increasingly
important to predict which models will ultimately drive innovation and shape AI
ecosystems. Building on parallels with citation dynamics in scientific
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2025-02-24T23:14:20.487000 | Audio-FLAN: A Preliminary Release | 2 | {
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2025-02-24T23:14:12.363000 | Slamming: Training a Speech Language Model on One GPU in a Day | 2 | {
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2025-02-24T22:48:30.357000 | Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties | 4 | {
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2025-02-24T22:39:29.837000 | DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks | 3 | {
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2025-02-24T22:35:41.042000 | Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment | 4 | {
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2025-02-24T22:31:17.771000 | Mobile-Agent-V: Learning Mobile Device Operation Through Video-Guided Multi-Agent Collaboration | 2 | {
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2025-02-24T22:27:11.566000 | Thus Spake Long-Context Large Language Model | 6 | {
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2025-02-24T22:17:28.937000 | CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models | 3 | {
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2025-02-24T21:59:50.456000 | Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models | 2 | {
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whether they can handle inconsistencies in real-world, layout-rich content. To
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2025-02-24T21:59:15.571000 | Beyond Release: Access Considerations for Generative AI Systems | 2 | {
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2025-02-24T21:23:29.485000 | PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference | 2 | {
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2025-02-24T21:06:14.906000 | Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge | 2 | {
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2025-02-24T17:06:56.210000 | Learning to Discover Regulatory Elements for Gene Expression Prediction | 2 | {
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2025-02-24T15:59:07.128000 | Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson's Disease | 2 | {
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2025-02-24T12:53:11.851000 | Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis | 2 | {
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2025-02-24T12:18:28.662000 | Benchmarking LLMs for Political Science: A United Nations Perspective | 2 | {
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2025-02-24T11:28:12.754000 | FantasyID: Face Knowledge Enhanced ID-Preserving Video Generation | 2 | {
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2025-02-24T10:54:53.456000 | MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models | 2 | {
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2025-02-24T10:33:37.569000 | mStyleDistance: Multilingual Style Embeddings and their Evaluation | 2 | {
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2025-02-24T08:37:35.940000 | EgoSpeak: Learning When to Speak for Egocentric Conversational Agents in the Wild | 2 | {
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framework for real-time speech initiation prediction in egocentric streaming
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2025-02-24T07:41:08.352000 | Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models | 2 | {
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2025-02-24T07:37:26.684000 | WHAC: World-grounded Humans and Cameras | 2 | {
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2025-02-24T06:58:46.440000 | Evaluating Multimodal Generative AI with Korean Educational Standards | 3 | {
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2025-02-24T06:44:41.263000 | Beyond No: Quantifying AI Over-Refusal and Emotional Attachment Boundaries | 3 | {
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2025-02-24T05:43:47.767000 | KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding | 2 | {
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2025-02-24T05:35:44.667000 | ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation | 3 | {
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2025-02-24T04:52:30.963000 | MoBA: Mixture of Block Attention for Long-Context LLMs | 2 | {
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2025-02-24T04:29:42.452000 | JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework | 2 | {
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2025-02-24T02:07:41.624000 | LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers | 3 | {
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store contextual information, revealing that tokens often seen as minor (e.g.,
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2025-02-24T01:16:03.517000 | MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction | 2 | {
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autonomous driving models with strong generalization. The prevailing driving
world model mainly build on video prediction model. Although these models can
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2025-02-24T01:13:24.911000 | CrossOver: 3D Scene Cross-Modal Alignment | 3 | {
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2025-02-24T00:36:34.341000 | VLM$^2$-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues | 2 | {
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2025-02-24T00:07:05.804000 | LightThinker: Thinking Step-by-Step Compression | 5 | {
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2025-02-24T00:02:52.495000 | Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path? | 2 | {
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2025-02-23T23:43:43.529000 | StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following | 2 | {
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domain-specific capability assessment, yet overlook the crucial structural
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2025-02-23T23:17:33.152000 | UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning | 2 | {
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2025-02-23T22:55:04.409000 | PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data | 6 | {
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onto photographs. Photo doodling is challenging because the inserted elements
must appear seamlessly integrated with the background, requiring realistic
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2025-02-23T22:24:55.500000 | One-step Diffusion Models with $f$-Divergence Distribution Matching | 2 | {
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their practical deployment, especially for interactive applications. To
accelerate generation speed, recent approaches distill a multi-step diffusion
model into a single-step student generator via variational score distillation,
which matches... | 6 | 67bbe6837727595ca5979e8c | null | null | |
2025-02-23T22:17:18.309000 | SIFT: Grounding LLM Reasoning in Contexts via Stickers | 3 | {
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significant issue during the reasoning process of large language models,
spanning from smaller models like Llama3.2-3B-Instruct to cutting-edge ones
like DeepSeek-R1. For example, in the phrase "10 dollars per kilo," LLMs might
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2025-02-23T22:11:17.789000 | Think Inside the JSON: Reinforcement Strategy for Strict LLM Schema Adherence | 2 | {
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Adherence | In this paper, we address the challenge of enforcing strict schema adherence
in large language model (LLM) generation by leveraging LLM reasoning
capabilities. Building on the DeepSeek R1 reinforcement learning framework, our
approach trains structured reasoning skills of a 1.5B parameter model through a
novel pipeline... | 9 | 67bbe0530aabd5d571a72437 | null | null | |
2025-02-23T21:52:51.059000 | Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model | 2 | {
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advances in drug discovery, requiring interdisciplinary knowledge across
chemistry and biology. Although large molecular language models have achieved
notable success in interpreting molecular structures, their instruction
datasets are limited to the... | 42 | 67b7ceae3e8a45f770b2609f | null | null | |
2025-02-23T21:44:33.443000 | InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback | 2 | {
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interactive intelligence with human users which is vital for developing
general-purpose AI assistants. We design InterFeedback, an interactive
framework, which can be applied to any LMM and dataset to assess this ability
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2025-02-23T21:40:17.216000 | The Relationship Between Reasoning and Performance in Large Language Models -- o3 (mini) Thinks Harder, Not Longer | 2 | {
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Models -- o3 (mini) Thinks Harder, Not Longer | Large language models have demonstrated remarkable progress in mathematical
reasoning, leveraging chain-of-thought and test-time compute scaling. However,
many open questions remain regarding the interplay between reasoning token
usage and accuracy gains. In particular, when comparing models across
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2025-02-23T21:39:54.375000 | SurveyX: Academic Survey Automation via Large Language Models | 5 | {
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capabilities and a vast knowledge base, suggesting that LLMs can serve as
efficient tools for automated survey generation. However, recent research
related to automated survey generation remains constrained by some critical
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2025-02-21T21:19:35.358000 | Generating Skyline Datasets for Data Science Models | 2 | {
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machine learning models has become a cornerstone task in data-driven analysis.
Conventional data discovery methods typically integrate datasets towards a
single pre-defined quality measure that may lead to bias for downstream tasks.
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2025-02-21T13:42:50.546000 | Symmetrical Visual Contrastive Optimization: Aligning Vision-Language Models with Minimal Contrastive Images | 2 | {
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Models with Minimal Contrastive Images | Recent studies have shown that Large Vision-Language Models (VLMs) tend to
neglect image content and over-rely on language-model priors, resulting in
errors in visually grounded tasks and hallucinations. We hypothesize that this
issue arises because existing VLMs are not explicitly trained to generate texts
that are ac... | 3 | 67b7cdb8904136d47c396910 | null | null | |
2025-02-21T13:05:36.173000 | Generating $π$-Functional Molecules Using STGG+ with Active Learning | 2 | {
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challenge in molecular discovery. While supervised learning methods generate
high-quality molecules similar to those in a dataset, they struggle to
generalize to out-of-distribution properties. Reinforcement learning can
explore new chemical spac... | 4 | 67b8c05f109d4be55d85d249 | null | null | |
2025-02-21T11:36:30.717000 | How to Get Your LLM to Generate Challenging Problems for Evaluation | 2 | {
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approaches for rigorous and comprehensive evaluation. Traditional human
annotation is increasingly impracticable due to the complexities and costs
involved in generating high-quality, challenging problems. In this work, we
introduce CHASE, a unified... | 16 | 67b8862a8512a3eca0668c00 | null | null | |
2025-02-21T11:34:53.838000 | Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision Language Models | 2 | {
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Language Models | Reward models play an essential role in training vision-language models
(VLMs) by assessing output quality to enable aligning with human preferences.
Despite their importance, the research community lacks comprehensive open
benchmarks for evaluating multimodal reward models in VLMs. To address this
gap, we introduce Mu... | 7 | 67b8aaa6ef55d96f2cbd7edf | null | null | |
2025-02-21T09:39:36.504000 | LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention | 2 | {
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Attention | Large language models (LLMs) have shown remarkable potential in processing
long sequences, yet efficiently serving these long-context models remains
challenging due to the quadratic computational complexity of attention in the
prefilling stage and the large memory footprint of the KV cache in the decoding
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2025-02-21T08:26:31.100000 | Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data | 3 | {
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with Self-Synthesized Data | Large multimodal models (LMMs) have shown impressive capabilities in a wide
range of visual tasks. However, they often struggle with fine-grained visual
reasoning, failing to identify domain-specific objectives and provide
justifiable explanations for their predictions. To address this, we propose a
novel visual reject... | 7 | 67b87e3d346553e4006bf416 | null | null | |
2025-02-21T08:18:34.557000 | NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization | 2 | {
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Image Geo-localization | Image geo-localization is the task of predicting the specific location of an
image and requires complex reasoning across visual, geographical, and cultural
contexts. While prior Vision Language Models (VLMs) have the best accuracy at
this task, there is a dearth of high-quality datasets and models for analytical
reason... | 11 | 67b8760ef8311235c642d89d | null | null | |
2025-02-21T08:00:41.165000 | From RAG to Memory: Non-Parametric Continual Learning for Large Language Models | 2 | {
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key feature of human intelligence that AI systems must approximate to unlock
their full potential. Given the challenges in continual learning with large
language models (LLMs), retrieval-augmented generation (RAG) has become the
dominant way to ... | 11 | 67b878bcf17ca6989fd21f7a | null | null | |
2025-02-21T07:52:55.537000 | CLIPPER: Compression enables long-context synthetic data generation | 2 | {
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high-quality data for complex long-context reasoning tasks remains challenging.
We introduce CLIPPER, a compression-based approach for generating synthetic
data tailored to narrative claim verification - a task that requires reasoning
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2025-02-21T07:16:00.307000 | LLM-based User Profile Management for Recommender System | 2 | {
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opportunities in recommender systems by enabling zero-shot recommendation
without conventional training. Despite their potential, most existing works
rely solely on users' purchase histories, leaving significant room for
improvement by incorporating u... | 5 | 67b8439599159e6fc9399735 | null | null | |
2025-02-21T05:29:18.157000 | How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM? | 8 | {
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limited by the knowledge learned during pre-training and stored in the model's
parameters. Low-rank adaptation (LoRA) is a popular and efficient training
technique for updating or domain-specific adaptation of LLMs. In this study, we
investigate h... | 82 | 67b83fd79fb3eedaf6d0af16 | null | null | |
2025-02-21T05:28:42.882000 | How Much Do LLMs Hallucinate across Languages? On Multilingual Estimation of LLM Hallucination in the Wild | 2 | {
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Estimation of LLM Hallucination in the Wild | In the age of misinformation, hallucination -- the tendency of Large Language
Models (LLMs) to generate non-factual or unfaithful responses -- represents the
main risk for their global utility. Despite LLMs becoming increasingly
multilingual, the vast majority of research on detecting and quantifying LLM
hallucination ... | 3 | 67b597156e53744c2a39e36f | null | null | |
2025-02-21T05:00:18.645000 | S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning | 2 | {
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Learning | Recent studies have demonstrated the effectiveness of LLM test-time scaling.
However, existing approaches to incentivize LLMs' deep thinking abilities
generally require large-scale data or significant training efforts. Meanwhile,
it remains unclear how to improve the thinking abilities of less powerful base
models. In ... | 28 | 67b69b6817ccb022c6a95b6e | null | null | |
2025-02-21T03:33:40.641000 | Unstructured Evidence Attribution for Long Context Query Focused Summarization | 2 | {
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Summarization | Large language models (LLMs) are capable of generating coherent summaries
from very long contexts given a user query. Extracting and properly citing
evidence spans could help improve the transparency and reliability of these
summaries. At the same time, LLMs suffer from positional biases in terms of
which information t... | 3 | 67b83a21a9fa331061e84f36 | null | null | |
2025-02-21T03:33:28.852000 | Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework | 2 | {
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"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/65407ba... | 2025-02-19T14:21:25 | Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
Human-Like Reasoning Framework | Geolocation, the task of identifying an image's location, requires complex
reasoning and is crucial for navigation, monitoring, and cultural preservation.
However, current methods often produce coarse, imprecise, and non-interpretable
localization. A major challenge lies in the quality and scale of existing
geolocation... | 4 | 67b83a2226e7d5f7cb0b7d66 | null | null | |
2025-02-21T01:11:34.971000 | Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning | 4 | {
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with reinforcement learning | The realization of scalable fault-tolerant quantum computing is expected to
hinge on quantum error-correcting codes. In the quest for more efficient
quantum fault tolerance, a critical code parameter is the weight of
measurements that extract information about errors to enable error correction:
as higher measurement we... | 36 | 67b81873cc6b0136b3d8430a | null | null | |
2025-02-20T23:02:42.672000 | Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information | 2 | {
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Time-specific Information | While the ability of language models to elicit facts has been widely
investigated, how they handle temporally changing facts remains underexplored.
We discover Temporal Heads, specific attention heads primarily responsible for
processing temporal knowledge through circuit analysis. We confirm that these
heads are prese... | 25 | 67b7fa9ac3f48f8b3fc63452 | null | null | |
2025-02-20T22:41:47.210000 | Dynamic Concepts Personalization from Single Videos | 2 | {
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but extending this personalization to text-to-video models presents unique
challenges. Unlike static concepts, personalizing text-to-video models has the
potential to capture dynamic concepts, i.e., entities defined not only by their
appearance... | 15 | 67b7f5f18b3dff28b749bf45 | null | null | |
2025-02-20T22:39:48.180000 | PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC | 3 | {
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Task Automation on PC | In the field of MLLM-based GUI agents, compared to smartphones, the PC
scenario not only features a more complex interactive environment, but also
involves more intricate intra- and inter-app workflows. To address these
issues, we propose a hierarchical agent framework named PC-Agent. Specifically,
from the perception ... | 18 | 67b7f55b7f4d732dc46927c1 | null | https://github.com/X-PLUG/MobileAgent/tree/main | |
2025-02-20T22:39:21.551000 | LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models | 2 | {
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"fullname"... | 2025-02-20T18:47:36 | LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in
Vision-Language Models | Existing Large Vision-Language Models (LVLMs) can process inputs with context
lengths up to 128k visual and text tokens, yet they struggle to generate
coherent outputs beyond 1,000 words. We find that the primary limitation is the
absence of long output examples during supervised fine-tuning (SFT). To tackle
this issue... | 24 | 67b7f3c7d00e69f10cff2258 | null | null | |
2025-02-20T22:38:36.406000 | Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation | 2 | {
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Multimodal Data Generation | Reasoning about images with rich text, such as charts and documents, is a
critical application of vision-language models (VLMs). However, VLMs often
struggle in these domains due to the scarcity of diverse text-rich
vision-language data. To address this challenge, we present CoSyn, a framework
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