Search: “benchmark”

COMPOSE: Composing Future Theorems from Citations and Formal Structure

arXiv:2605.30333v1 Announce Type: new Abstract: A plausible future mathematical claim must satisfy two constraints: it should follow the direction of prior work and respect the formal dependencies tha

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Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference

DGX agent

arXiv:2605.29467v1 Announce Type: cross Abstract: Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be pre

model-releasesarxiv-cs-ai

29 May 2026

CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems

arXiv:2605.29612v1 Announce Type: cross Abstract: Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over s

agentsarxiv-cs-cl

Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models

arXiv:2605.28866v1 Announce Type: cross Abstract: Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior s

Contrastive Representation Regularization for Vision-Language-Action Models

arXiv:2510.01711v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained V

Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation

arXiv:2605.30000v1 Announce Type: new Abstract: Front-end web code has become a core product surface for every frontier LLM release, yet evaluating these interactive applications at development speed

CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation

arXiv:2605.29886v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive question answering by incorporating external evidence. However, existing RAG methods

CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective

arXiv:2502.03805v2 Announce Type: replace Abstract: Large language models have revolutionized natural language processing but face significant challenges of high storage and runtime costs, due to the

Croissant Tasks: A Metadata Format for Reproducible Machine Learning Evaluations

arXiv:2605.29786v1 Announce Type: new Abstract: Reproducibility is fundamental to the scientific method, yet remains a critical challenge in machine learning. Contributing factors include underspecifi

Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design

arXiv:2602.20176v2 Announce Type: replace-cross Abstract: D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-condition

researcharxiv-cs-lg

Cycle Consistency in Video Object-Centric Learning

arXiv:2605.30211v1 Announce Type: new Abstract: Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Obje

DCFO: Density-Based Counterfactuals for Outliers -- Additional Material

arXiv:2512.10659v3 Announce Type: replace Abstract: Outlier detection identifies data points that significantly deviate from the majority of the data distribution. Explaining outliers is crucial for u

researcharxiv-cs-lg

DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning

arXiv:2605.29568v1 Announce Type: new Abstract: Tool-Integrated Reasoning (TIR) extends LLM capabilities by leveraging external environments. However, existing methods lack the deliberation during seq

Deja View: Looping Transformers for Multi-View 3D Reconstruction

arXiv:2605.30215v1 Announce Type: new Abstract: Recent feed-forward 3D reconstruction transformers have scaled to over a billion parameters, following the broader trend of increasing model capacity in

DenseSteer: Steering Small Language Models towards Dense Math Reasoning

arXiv:2605.29247v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong chain-of-thought (CoT) reasoning abilities, while smaller models (<= 3B parameters) significantly underp

Tutorials

Detecting Unknown Objects via Energy-based Separation for Open World Object Detection

arXiv:2603.29954v2 Announce Type: replace Abstract: In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to

tutorialsarxiv-cs-cv

Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking

arXiv:2605.30107v1 Announce Type: new Abstract: Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-para

Diffusion differentiable resampling

arXiv:2512.10401v3 Announce Type: replace-cross Abstract: This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). Drawing on reparam

Domain-Agnostic Feature Modulation for Semi-Supervised Domain Generalization

arXiv:2503.20897v2 Announce Type: replace Abstract: Semi-supervised domain generalization (SSDG) leverages a small fraction of labeled data alongside unlabeled data to enhance model generalization. Mo

researcharxiv-cs-cv

Tutorials

Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

arXiv:2605.30039v1 Announce Type: new Abstract: Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains throu

tutorialsarxiv-cs-ai

Applications

Dual Quaternion SE(3) Synchronization with Recovery Guarantees

arXiv:2602.00324v2 Announce Type: replace-cross Abstract: Synchronization over the special Euclidean group SE(3) aims to recover absolute poses from noisy pairwise relative transformations and is a co

applicationsarxiv-cs-cv

Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model

arXiv:2510.27607v3 Announce Type: replace Abstract: Augmenting vision-language-action models (VLAs) with world models is promising for robotic policy learning but faces challenges in jointly predictin

DVSM: Decoder-only View Synthesis Model Done Right

arXiv:2605.29891v1 Announce Type: new Abstract: Recent Large View Synthesis Models (LVSMs) advocate an encoder-decoder architecture that separates reconstruction and rendering into distinct networks.

researcharxiv-cs-cv

Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning

arXiv:2506.05985v3 Announce Type: replace Abstract: A generalist agent must continuously learn and adapt throughout its lifetime, achieving efficient forward transfer while minimizing catastrophic for

EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance

arXiv:2509.23730v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced in reasoning when optimized with reinforcement learning (RL) under verifiable rewards. Existing

Enhancing Multi-Agent Communication through Attention Steering with Context Relevance

arXiv:2605.30136v1 Announce Type: new Abstract: LLM-based multi-agent systems have demonstrated remarkable performance on complex tasks through collaborative reasoning. However, these systems tend to

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models

arXiv:2605.29303v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) followed by reinforcement learning (RL) has become a standard post-training paradigm for large language models. This paradi

Hardware

ESAM++: Efficient Online 3D Perception on the Edge

arXiv:2605.29505v1 Announce Type: new Abstract: Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computa

hardwarearxiv-cs-cv

Estimating the Empowerment of Language Model Agents

arXiv:2509.22504v3 Announce Type: replace Abstract: As language model (LM) agents become increasingly capable and adopted in real-world applications, there is a growing need for scalable evaluation fr

Evaluating Cross-lingual Knowledge Consistency in Code-Mixed vis-a-vis Indian Languages using IndicKLAR

arXiv:2605.29637v1 Announce Type: new Abstract: Large language models recall knowledge reliably in English but often fail on the same query posed in a lower-resourced language -- a crosslingual consis

EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation

model-releasesarxiv-cs-cv

arXiv:2605.29977v1 Announce Type: new Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by ex

Evolutionary Rule Extraction from Corporate Default Prediction Models

arXiv:2605.29478v1 Announce Type: cross Abstract: Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerabil

Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems

arXiv:2605.29790v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) have emerged as an effective paradigm for complex and long-horizon tasks. However, in real-world tasks, MAS often

Fairness-Aware Federated Learning with Trajectory Shapley Value

arXiv:2605.30336v1 Announce Type: new Abstract: Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple

Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging

model-releasesarxiv-cs-cv

arXiv:2605.29827v1 Announce Type: new Abstract: Medical image analysis models can exhibit performance disparities across patient subgroups, threatening clinical safety and fairness. Existing methods t

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection

arXiv:2605.30062v1 Announce Type: new Abstract: The development of generative artificial intelligence technologies has propelled the visual realism of synthetic images to an unprecedented level. Altho

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning

arXiv:2605.29002v1 Announce Type: new Abstract: Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories

FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions

arXiv:2605.29427v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulator

Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control

arXiv:2605.29937v1 Announce Type: cross Abstract: Diffusion models are effective for waypoint prediction in visual navigation, but standard sampling and test time guidance can produce unreliable or in

safetyarxiv-cs-lg

FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration

arXiv:2605.29704v1 Announce Type: new Abstract: Traditional large-scale formation planning either oversimplify the formation representation which leads to poor performance, or they employ complete col

agentsarxiv-cs-ro

Applications

Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs

arXiv:2605.29453v1 Announce Type: cross Abstract: Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typi

applicationsarxiv-cs-ai

From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning

arXiv:2601.21909v2 Announce Type: replace Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcem

From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges

arXiv:2601.08654v2 Announce Type: replace-cross Abstract: Rubric-based text evaluation increasingly uses large language models (LLMs) as scalable judges, but aligning frozen black-box models with huma

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

arXiv:2605.28965v1 Announce Type: new Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration

GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering

arXiv:2605.29584v1 Announce Type: new Abstract: Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe

safetyarxiv-cs-cl

GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation

arXiv:2602.17200v2 Announce Type: replace Abstract: Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. In th

Tutorials

Gated Graph Attention Networks with Learnable Temperature

arXiv:2605.29803v1 Announce Type: new Abstract: Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable featur

tutorialsarxiv-cs-lg

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models

arXiv:2605.29398v1 Announce Type: cross Abstract: Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intra