Search: “benchmark”

PromptNCE: Pointwise Mutual Information Predictions Using Only LLMs and Contrastive Estimation Prompts

arXiv:2605.21776v1 Announce Type: new Abstract: Estimating mutual information from text usually requires training a task-specific critic, which limits its use in low-data settings. We ask whether larg

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Reducing Political Manipulation with Consistency Training

DGX agent

arXiv:2605.22771v1 Announce Type: new Abstract: Large language models (LLMs) exhibit systematic political bias across a variety of sensitive contexts. We find that LLMs handle counterpart topics from

safetyarxiv-cs-cl

22 May 2026

Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning

arXiv:2602.17062v2 Announce Type: replace Abstract: Value decomposition is a core approach for cooperative multi-agent reinforcement learning (MARL). However, existing methods still rely on a single o

safetyarxiv-cs-ai

SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching

arXiv:2605.21788v1 Announce Type: new Abstract: Zero-shot 3D visual grounding requires localizing objects in unstructured environments from free-form natural language. Recent vision-language model (VL

Search-E1: Self-Distillation Drives Self-Evolution in Search-Augmented Reasoning

arXiv:2605.22511v1 Announce Type: cross Abstract: Post-training has become the dominant recipe for turning a language model into a competent search-augmented reasoning agent. A line of recent work pus

Local Ai

SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentation

arXiv:2605.22658v1 Announce Type: new Abstract: While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reasoni

local-aiarxiv-cs-cv

SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation

arXiv:2605.22572v1 Announce Type: new Abstract: Accurate segmentation of brain tumour sub-regions from multi-parametric MRI is critical for treatment planning yet remains challenging due to morphologi

Tutorials

Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking

arXiv:2605.22538v1 Announce Type: new Abstract: Traditional visual object tracking (VOT) methods typically rely on task-specific supervised training, limiting their generalization to unseen objects an

tutorialsarxiv-cs-cv

SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks

arXiv:2509.17086v2 Announce Type: replace Abstract: Detecting and localizing poultry is essential for advancing smart poultry farming. Despite the progress of detection-centric methods, challenges per

SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction

arXiv:2605.22031v1 Announce Type: new Abstract: Accelerated MRI reconstruction requires recovering missing details while preserving anatomically coherent structures across large spatial regions. State

SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving

arXiv:2512.10719v2 Announce Type: replace Abstract: End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual under

stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation

arXiv:2605.21800v1 Announce Type: cross Abstract: World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is cur

researcharxiv-cs-ro

Agents

Stdlib or Third-Party? Empirical Performance and Correctness of LLM-Assisted Zero-Dependency Python Libraries

arXiv:2605.21405v1 Announce Type: cross Abstract: Third-party Python libraries introduce dependency management overhead, supply chain risk, and deployment friction in constrained environments. A natur

agentsarxiv-cs-ai

Structural Anchor Pruning: Training-Free Multi-Vector Compression for Visual Document Retrieval

arXiv:2601.20107v2 Announce Type: replace-cross Abstract: Recent Vision-Language Models (e.g., ColPali) enable fine-grained Visual Document Retrieval (VDR) but incur prohibitive multi-vector index sto

Local Ai

Structured-Sparse Attention for Entity Tracking with Subquadratic Sequence Complexity

arXiv:2605.22476v1 Announce Type: cross Abstract: Entity tracking requires maintaining and updating latent states for entities and attributes over long sequences. Recent task-specific attention operat

local-aiarxiv-cs-cl

Swift Sampling: Selecting Temporal Surprises via Taylor Series

arXiv:2605.22678v1 Announce Type: new Abstract: While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features dev

Temporal Coding as a Substrate for Sensorimotor Object Inference: A Spiking Reinterpretation of Thousand Brains Architecture

model-releasesarxiv-cs-ro

arXiv:2605.22206v1 Announce Type: cross Abstract: The Thousand Brains Theory (TBT) and its open-source Monty framework model object recognition through sensorimotor inference -- identifying objects by

Thermo-VL: Extending Vision-Language Models to Thermal Infrared Perception

arXiv:2605.21882v1 Announce Type: new Abstract: Vision-language models (VLMs) often fail under low illumination because their visual grounding is learned predominantly from RGB imagery, whereas therma

Token-Level LLM Collaboration via FusionRoute

arXiv:2601.05106v4 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a singl

Towards Selection of Large Multimodal Models as Engines for Burned-in Protected Health Information Detection in Medical Images

arXiv:2511.02014v2 Announce Type: replace Abstract: The detection of Protected Health Information (PHI) in medical imaging is critical for safeguarding patient privacy and ensuring compliance with reg

Training-Trajectory-Aware Token Selection

arXiv:2601.10348v2 Announce Type: replace Abstract: Efficient distillation is a key pathway for converting expensive reasoning capability into deployable efficiency, yet in the frontier regime where t

Transcription and Recognition of Italian Parliamentary Speeches Using Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2603.28103v2 Announce Type: replace-cross Abstract: Parliamentary proceedings represent a rich yet challenging resource for computational analysis, particularly when preserved only as scanned hi

Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework

arXiv:2605.22620v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of LLMs, but often depends on external supervis

Ultra-High-Definition Image Quality Assessment via Graph Representation Learning

arXiv:2605.22192v1 Announce Type: new Abstract: Blind image quality assessment (BIQA) for ultrahighdefinition (UHD) images remains challenging because native-resolution inference is computationally ex

Understanding Data Temporality Impact on Large Language Models Pre-training

arXiv:2605.22769v1 Announce Type: new Abstract: Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal groun

Understanding Multimodal Failure in Action-Chunking Behavioral Cloning

arXiv:2605.22493v1 Announce Type: cross Abstract: Behavioral cloning becomes difficult when the same observation admits several valid actions. We study this problem for action-chunking policies and sh

researcharxiv-cs-ro

Unifying Masked Diffusion Models with Various Generation Orders and Beyond

arXiv:2602.02112v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) are a potential alternative to autoregressive models (ARMs) for language generation, but generation quality dep

UniSD: Towards a Unified Self-Distillation Framework for Large Language Models

arXiv:2605.06597v2 Announce Type: replace Abstract: Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD

Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining

arXiv:2605.21906v1 Announce Type: new Abstract: Computed tomography (CT) is a central to three-dimensional medical imaging, yet CT-based artificial intelligence remains fragmented across task-specific

safetyarxiv-cs-cv

UniVL: Unified Vision-Language Embedding for Spatially Grounded Contextual Image Generation

arXiv:2605.21611v1 Announce Type: new Abstract: We introduce spatially grounded contextual image generation, a controllable image generation task that reframes the conditioning paradigm. Instead of su

Vendi Novelty Scores for Out-of-Distribution Detection

arXiv:2602.10062v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems. Existing post-hoc detectors typically rel

Video as Natural Augmentation: Towards Unified AI-Generated Image and Video Detection

arXiv:2605.21977v1 Announce Type: new Abstract: AI-generated content (AIGC) is rapidly improving, creating an urgent need for detectors that generalize across data sources, deployment pipelines, and v

Virtual 3D H&E Staining from Phase-contrast Back-illumination Interference Tomography

arXiv:2605.22000v1 Announce Type: new Abstract: Three-dimensional (3D) histopathology of unprocessed tissues has the potential to transform disease management by enabling volumetric characterization o

Agents

What Does Vision Tool-Use Reinforcement Learning Really Learn? Disentangling Tool-Induced and Intrinsic Effects for Crop-and-Zoom

arXiv:2602.01334v2 Announce Type: replace Abstract: Vision tool-use reinforcement learning (RL) can equip vision language models with visual operators such as crop-and-zoom and achieves strong perform

agentsarxiv-cs-cv

When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging

arXiv:2602.05536v2 Announce Type: replace-cross Abstract: Model merging combines multiple fine-tuned models into a single model by adding their weight updates, providing a lightweight alternative to r

researcharxiv-cs-cl

Why Semantic Entropy Fails: Geometry-Aware and Calibrated Uncertainty for Policy Optimization

arXiv:2605.21801v1 Announce Type: cross Abstract: Post-training has become central to improving reasoning and alignment in large language models, where critic-free models enable scalable learning from

A strongly annotated passive acoustic dataset for tropical bird monitoring

arXiv:2605.20578v1 Announce Type: cross Abstract: Passive acoustic monitoring enables continuous, non-invasive biodiversity assessment across diverse ecosystems. The scale of these datasets has driven

A Unified Framework for Uncertainty-Aware Explainable Artificial Intelligence: A Case Study in Power Quality Disturbance Classification

arXiv:2605.21114v1 Announce Type: new Abstract: Post-hoc explainable AI (XAI) methods typically produce deterministic attribution maps, whereas Bayesian neural networks (BNNs) induce a distribution ov

ACL-Verbatim: hallucination-free question answering for research

arXiv:2605.21102v1 Announce Type: new Abstract: Academic researchers need efficient and reliable methods for collecting high-quality information from trusted sources, but modern tools for AI-assisted

Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation

arXiv:2605.21125v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO), a prominent algorithm within the Reinforcement Learning from Verifiable Rewards (RLVR) framework, has achieve

safetyarxiv-cs-lg

Adversarial Robustness in One-Stage Learning-to-Defer

arXiv:2510.10988v2 Announce Type: replace-cross Abstract: Learning-to-Defer (L2D) enables hybrid decision-making by routing inputs either to a predictor or to external experts. While promising, L2D is

Agentic Physical AI toward a Domain-Specific Foundation Model for Nuclear Reactor Control

arXiv:2512.23292v3 Announce Type: replace-cross Abstract: The prevailing paradigm in AI for physical systems (scaling general-purpose foundation models toward universal multimodal reasoning) confronts

AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback

arXiv:2605.20722v1 Announce Type: new Abstract: Reinforcement learning improves LLM reasoning, but PPO/GRPO typically use fixed clipping and decoding temperature, which makes training brittle and tuni

AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction

arXiv:2305.09620v4 Announce Type: replace Abstract: Nationally representative surveys track public opinion, yet they ask only a limited set of questions each year, limiting its potential to capture hi

researcharxiv-cs-cl

Local Ai

AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing

arXiv:2605.21421v1 Announce Type: new Abstract: Motion capture is the gold standard for measuring human movement, but clinical use remains limited by cost, technical complexity, and privacy concerns.

local-aiarxiv-cs-cv

AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI

arXiv:2605.20649v1 Announce Type: cross Abstract: Wi-Fi-based human activity recognition (HAR) has emerged as a promising approach for contactless sensing, leveraging channel state information (CSI) c

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees

arXiv:2605.20521v1 Announce Type: new Abstract: Fine-tuning adapts a pretrained machine learning model to a small, sensitive dataset, but this process risks memorizing individual new data points, maki

APEX: Autonomous Policy Exploration for Self-Evolving LLM Agents

arXiv:2605.21240v1 Announce Type: new Abstract: LLM agents have shown strong performance across a wide range of complex tasks, including interactive environments that require long-horizon decision mak