Search: “benchmark”

Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation

arXiv:2310.15676v2 Announce Type: replace-cross Abstract: Multi-modal 3D Intelligence has gained considerable attention due to its wide applications in autonomous driving and world simulation, etc. Co

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Agents

Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems

DGX agent

arXiv:2606.01351v1 Announce Type: new Abstract: The transition from single-turn models to Multi-Agent Systems (MAS) promises enhanced problem-solving capabilities, yet the centralized orchestration to

agentsarxiv-cs-ai

2 Jun 2026

Reconsidering Positional Supervision in Masked Diffusion Language Model Training

arXiv:2601.22947v2 Announce Type: replace Abstract: Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive l

safetyarxiv-cs-cl

RedDebate: Safer Responses Through Multi-Agent Red Teaming Debates

arXiv:2506.11083v3 Announce Type: replace Abstract: We introduce RedDebate, a novel multi-agent debate framework that provides the foundation for Large Language Models (LLMs) to identify and mitigate

safetyarxiv-cs-cl

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models

arXiv:2505.18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pr

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief

arXiv:2606.00680v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets. A bottleneck of this paradigm is managing epistemic uncertain

Relative Energy Learning for LiDAR Out-of-Distribution Detection

arXiv:2511.06720v3 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles an

Tutorials

Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions

arXiv:2606.02326v1 Announce Type: new Abstract: Hard constraints are usually treated as terminal vetoes: once a candidate violates a requirement, the learned rule rejects it and any repair is handled

tutorialsarxiv-cs-ai

Residual Decoder Adapter: ID-Preserving Tokenizer Adaption for Autoregressive Text Rendering

arXiv:2606.01911v1 Announce Type: new Abstract: Visual Autoregressive (AR) models generate images by predicting discrete tokens that are decoded by a visual tokenizer. Despite demonstrating strong ove

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation

arXiv:2602.07083v2 Announce Type: replace-cross Abstract: Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specifica

Rethinking the Role of Temperature in Large Language Model Distillation

arXiv:2606.00306v1 Announce Type: cross Abstract: Reverse Kullback-Leibler (RKL) divergence is widely favored over forward KL (FKL) in large language models (LLM) distillation, yet this preference is

researcharxiv-cs-ai

Tutorials

Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction

arXiv:2606.00559v1 Announce Type: cross Abstract: Neural algorithmic reasoning has emerged as a popular research direction. It aims to train neural networks to mimic the step-by-step behavior of class

tutorialsarxiv-cs-ai

RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models

model-releasesarxiv-cs-ro

arXiv:2606.02277v1 Announce Type: new Abstract: Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should gu

Robust Learning of a Group DRO Neuron

arXiv:2601.18115v2 Announce Type: replace Abstract: We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional

Tutorials

Robust Reasoning via Dynamic Token Selection for Distribution-Aligned Self-Distillation

arXiv:2606.00628v1 Announce Type: new Abstract: Self-distillation improves learning efficiency by rewriting reference answers as training data that better matches the model's own distribution. However

tutorialsarxiv-cs-cl

ROGUE: Misaligned Agent Behavior Arising from Ordinary Computer Use

arXiv:2606.00341v1 Announce Type: cross Abstract: As AI agents are increasingly deployed in real personal and corporate settings (email accounts, development workflows, company databases, etc.), safet

RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection

arXiv:2606.01689v1 Announce Type: cross Abstract: The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime

RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction

arXiv:2602.20807v2 Announce Type: replace Abstract: Combining 3D Gaussian splatting with Simultaneous Localization and Mapping (SLAM) has gained popularity as it enables continuous 3D environment reco

researcharxiv-cs-cv

RynnVLA-002: A Unified Vision-Language-Action and World Model

model-releasesarxiv-cs-ro

arXiv:2511.17502v3 Announce Type: replace Abstract: We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict futu

Applications

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer acces

applicationsarxiv-cs-lg

Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients

arXiv:2512.02342v3 Announce Type: replace-cross Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive perfor

researcharxiv-cs-lg

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning

arXiv:2503.11832v5 Announce Type: replace Abstract: Recent vision language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images. Howev

SALSA: Speech Aware LLM Adaptation via Learned Steering Activation Vectors

arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering

researcharxiv-cs-cl

Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

arXiv:2503.24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile

safetyarxiv-cs-lg

Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery

arXiv:2606.01316v1 Announce Type: new Abstract: Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces. Today, top scientific capabilities remain siloed--on

Score-Control for Hallucination Reduction in Diffusion Models

arXiv:2606.00377v1 Announce Type: new Abstract: Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their

Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs

arXiv:2510.08825v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with knowledge graphs (KGs) offer a promising approach for knowledge-intensive reasoning. Central to this app

researcharxiv-cs-cl

Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

arXiv:2503.06520v3 Announce Type: replace Abstract: Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-d

Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems

arXiv:2606.01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory,

Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation

model-releasesarxiv-cs-ro

arXiv:2601.22965v2 Announce Type: replace Abstract: Diffusion policies (DP) have demonstrated significant potential in visual navigation by capturing diverse multi-modal trajectory distributions. Howe

Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors

arXiv:2606.01525v1 Announce Type: new Abstract: Semi-supervised hierarchical clustering aims to learn a tree structure consistent with data patterns and user-provided supervision. Supervision is usual

Semimage: HSV-Based Semantic Image Encoding for Disentangled Text Representation

arXiv:2512.00088v2 Announce Type: replace Abstract: We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural net

researcharxiv-cs-cv

SENSE: Semantic Embedding Navigation with Soft-gated Evaluation for Retrieval-based Speculative Decoding

arXiv:2606.00021v1 Announce Type: cross Abstract: Speculative Decoding (SD) accelerates Large Language Model (LLM) inference by employing a lightweight draft model to propose candidate tokens, which a

SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition

arXiv:2606.00732v1 Announce Type: new Abstract: Learning long-range non-stationary temporal patterns remains a core challenge for modern sequence models, particularly in strict streaming settings. In

Applications

Shortcut to Nowhere: Demystifying Deep Spurious Regression

arXiv:2606.01723v1 Announce Type: cross Abstract: Real-world regression often exhibits shortcuts: attributes that are spuriously correlated with continuous targets in training, yet unreliable under de

applicationsarxiv-cs-ai

Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems

arXiv:2606.00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential a

Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning

arXiv:2603.11653v2 Announce Type: replace Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that c

SimSD: Simple Speculative Decoding in Diffusion Language Models

arXiv:2606.02544v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference throug

researcharxiv-cs-ai

Hardware

Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills

arXiv:2503.05641v4 Announce Type: replace-cross Abstract: Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coa

hardwarearxiv-cs-ai

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

arXiv:2606.00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to e

Agents

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

arXiv:2606.01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving

agentsarxiv-cs-ai

Agents

SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems

arXiv:2606.01314v1 Announce Type: new Abstract: Recent self-evolving agents have shown that skills can be discovered, refined, and accumulated through execution. However, existing skill-evolution fram

agentsarxiv-cs-ai

SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning

model-releasesarxiv-cs-cl

arXiv:2603.08000v2 Announce Type: replace Abstract: Large reasoning models (LRMs) like OpenAI o1 and DeepSeek-R1 achieve high accuracy on complex tasks by adopting long chain-of-thought (CoT) reasonin

Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context

arXiv:2606.01101v1 Announce Type: cross Abstract: The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts. The Naive Bayes Cogni

researcharxiv-cs-ai

Agents

SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL

arXiv:2512.04069v2 Announce Type: replace Abstract: Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required f

agentsarxiv-cs-cv

Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction

arXiv:2606.00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation. However, no

safetyarxiv-cs-lg

Local Ai

Spectral Audit of In-Context Operator Networks

arXiv:2606.02427v1 Announce Type: cross Abstract: Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not

local-aiarxiv-cs-lg

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

arXiv:2512.00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to coll