Search: “benchmark”

arXiv:2603.28554v2 Announce Type: replace Abstract: Visual document understanding typically requires separate retrieval and generation models, doubling memory and system complexity. We present Hydra,

hardwarearxiv-cs-cv

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation

DGX agent

arXiv:2604.14114v1 Announce Type: cross Abstract: Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract

model-releasesarxiv-cs-lg

16 Apr 2026

Indexing Multimodal Language Models for Large-scale Image Retrieval

arXiv:2604.13268v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong cross-modal reasoning capabilities, yet their potential for vision-only tasks remain

researcharxiv-cs-cl

Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization

arXiv:2604.13484v1 Announce Type: cross Abstract: Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been ch

Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention

arXiv:2604.13479v1 Announce Type: cross Abstract: Semantic segmentation of histopathology images under class imbalance is typically addressed through frequency-based loss reweighting, which implicitly

safetyarxiv-cs-cv

Applications

Lite Any Stereo: Efficient Zero-Shot Stereo Matching

arXiv:2511.16555v3 Announce Type: replace Abstract: Recent advances in stereo matching have focused on accuracy, often at the cost of significantly increased model size. Traditionally, the community h

applicationsarxiv-cs-cv

Applications

Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning

arXiv:2601.02902v2 Announce Type: replace-cross Abstract: Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs), providing reliable and verifiable decis

applicationsarxiv-cs-cl

LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding

arXiv:2602.20913v2 Announce Type: replace Abstract: This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, a

Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data

arXiv:2604.13066v1 Announce Type: new Abstract: In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs c

MDPs with a State Sensing Cost

arXiv:2505.03280v3 Announce Type: replace Abstract: In many practical sequential decision-making problems, tracking the state of the environment incurs a sensing/communication/computation cost. In the

Applications

Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents

arXiv:2604.14004v1 Announce Type: cross Abstract: Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization

applicationsarxiv-cs-cl

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

arXiv:2512.04844v2 Announce Type: replace Abstract: Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the relianc

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning

arXiv:2604.13579v1 Announce Type: new Abstract: Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass ret

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

arXiv:2604.13287v1 Announce Type: new Abstract: Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre-trai

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models

arXiv:2601.11340v2 Announce Type: replace Abstract: Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models gener

researcharxiv-cs-cl

Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning

arXiv:2506.08125v3 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong reasoning abilities but often produce unnecessarily long explanations that reduce efficiency. Althoug

safetyarxiv-cs-cl

Olfactory pursuit: catching a moving odor source in complex flows

model-releasesarxiv-cs-ro

arXiv:2604.13121v1 Announce Type: new Abstract: Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncerta

Local Ai

One Token per Highly Selective Frame: Towards Extreme Compression for Long Video Understanding

arXiv:2604.14149v1 Announce Type: new Abstract: Long video understanding is inherently challenging for vision-language models (VLMs) because of the extensive number of frames. With each video frame ty

local-aiarxiv-cs-cv

Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference

arXiv:2604.13252v1 Announce Type: new Abstract: Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are

Parameter-efficient Quantum Multi-task Learning

arXiv:2604.13560v1 Announce Type: new Abstract: Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely us

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning

arXiv:2604.14010v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate th

PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction

arXiv:2604.13153v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this access

Peer-Predictive Self-Training for Language Model Reasoning

arXiv:2604.13356v1 Announce Type: new Abstract: Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Trai

PersonaVLM: Long-Term Personalized Multimodal LLMs

arXiv:2604.13074v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual pr

Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs

arXiv:2604.13723v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet m

researcharxiv-cs-lg

Agents

POINTS-Seeker: Towards Training a Multimodal Agentic Search Model from Scratch

arXiv:2604.14029v1 Announce Type: new Abstract: While Large Multimodal Models (LMMs) demonstrate impressive visual perception, they remain epistemically constrained by their static parametric knowledg

agentsarxiv-cs-cv

Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions

arXiv:2601.03173v2 Announce Type: replace Abstract: Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored i

PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling

arXiv:2604.13986v1 Announce Type: new Abstract: Predicting the effects of perturbations in-silico on cell state can identify drivers of cell behavior at scale and accelerate drug discovery. However, m

researcharxiv-cs-lg

Rare Event Analysis via Stochastic Optimal Control

arXiv:2604.13213v1 Announce Type: cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical sys

Red Skills or Blue Skills? A Dive Into Skills Published on ClawHub

arXiv:2604.13064v1 Announce Type: new Abstract: Skill ecosystems have emerged as an increasingly important layer in Large Language Model (LLM) agent systems, enabling reusable task packaging, public d

Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning

arXiv:2601.03027v3 Announce Type: replace Abstract: Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce halluc

safetyarxiv-cs-cl

ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks

arXiv:2603.28942v3 Announce Type: replace Abstract: The pervasive deployment of deep learning models across critical domains has concurrently intensified privacy concerns due to their inherent propens

researcharxiv-cs-lg

Rethinking Uncertainty in Segmentation: From Estimation to Decision

arXiv:2604.13262v1 Announce Type: new Abstract: In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions. We study the missing step: how uncertainty m

safetyarxiv-cs-cv

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges

arXiv:2604.13602v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multi

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

arXiv:2508.00222v5 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs

safetyarxiv-cs-cl

ROBOGATE: Adaptive Failure Discovery for Safe Robot Policy Deployment via Two-Stage Boundary-Focused Sampling

model-releasesarxiv-cs-ro

arXiv:2603.22126v3 Announce Type: replace Abstract: Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high

Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularization

arXiv:2604.13525v1 Announce Type: cross Abstract: The robust low-rank tensor completion problem addresses the challenge of recovering corrupted high-dimensional tensor data with missing entries, outli

Robust Reward Modeling for Large Language Models via Causal Decomposition

arXiv:2604.13833v1 Announce Type: new Abstract: Reward models are central to aligning large language models, yet they often overfit to spurious cues such as response length and overly agreeable tone.

RPS: Information Elicitation with Reinforcement Prompt Selection

arXiv:2604.13817v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known bu

Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model

arXiv:2510.18165v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) are emerging as a powerful and promising alternative to the dominant autoregressive paradigm, offering inhere

researcharxiv-cs-cl

SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion

arXiv:2204.13635v2 Announce Type: replace Abstract: Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guid

SFT-GRPO Data Overlap as a Post-Training Hyperparameter for Autoformalization

arXiv:2604.13515v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO) is a common post-training recipe. We conduct a controlled ablation ov

safetyarxiv-cs-lg

Singularity Avoidance in Inverse Kinematics: A Unified Treatment of Classical and Learning-based Methods

arXiv:2604.13405v1 Announce Type: new Abstract: Singular configurations cause loss of task-space mobility, unbounded joint velocities, and solver divergence in inverse kinematics (IK) for serial manip

researcharxiv-cs-ro

SLQ: Bridging Modalities via Shared Latent Queries for Retrieval with Frozen MLLMs

arXiv:2604.13710v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) exhibit strong reasoning and world knowledge, yet adapting them for retrieval remains challenging. Existing app

SocialMirror: Reconstructing 3D Human Interaction Behaviors from Monocular Videos with Semantic and Geometric Guidance

arXiv:2604.13581v1 Announce Type: new Abstract: Accurately reconstructing human behavior in close-interaction scenarios is crucial for enabling realistic virtual interactions in augmented reality, pre

SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention

arXiv:2604.13847v1 Announce Type: new Abstract: While sparse attention mitigates the computational bottleneck of long-context LLM training, its distributed training process exhibits extreme heterogene

Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning

model-releasesarxiv-cs-ro

arXiv:2604.13192v1 Announce Type: cross Abstract: Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing

Synthesizing Instruction-Tuning Datasets with Contrastive Decoding

arXiv:2604.13538v1 Announce Type: new Abstract: Using responses generated by high-performing large language models (LLMs) for instruction tuning has become a widely adopted approach. However, the exis