Search: “context”

GALAR-TemporalNet v2: Anatomy-Guided Dual-Branch Temporal Classification with Bidirectional Mamba and Dual-Graph GCN for Video Capsule Endoscopy -- after competition results

arXiv:2605.22209v1 Announce Type: new Abstract: Video Capsule Endoscopy (VCE) poses a challenging multi-label temporal classification problem, requiring simultaneous localization of 8 anatomical regio

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General Agentic Planning Through Simulative Reasoning with World Models

arXiv:2507.23773v3 Announce Type: replace-cross Abstract: What does it mean to plan? Current agentic systems, whether scaffolded workflows or end-to-end policies, rely on reactive decision-making: sel

agentsarxiv-cs-cl

Highlights: 👉 Long-horizon autonomy: maintained coherent execution across a 35-hour autonomous kernel optimization run 👉 Agentic coding: l…

Highlights: 👉 Long-horizon autonomy: maintained coherent execution across a 35-hour autonomous kernel optimization run 👉 Agentic coding: leading Terminal-Bench 2.0-Terminus performance for terminal-ba

agentstogether-ai--x

Hypergraph as Language

arXiv:2605.21858v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential in modeling relational structures. However, existing approaches remain fundamentally g

local-aiarxiv-cs-cl

ImProver: Agent-Based Automated Proof Optimization

arXiv:2410.04753v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been used to generate formal proofs of mathematical theorems in proofs assistants such as Lean. However, we

agentsarxiv-cs-cl

InnerQ: Hardware-Aware Tuning-Free Quantization of KV Cache for Large Language Models

arXiv:2602.23200v2 Announce Type: replace-cross Abstract: When transformer-based language models are deployed for text generation, most of the inference time is spent in the decoding stage, where outp

Internal narratives parameterise affective states

arXiv:2502.09487v3 Announce Type: replace Abstract: Characterising how we verbalise our feelings is central to psychological assessment and intervention, yet the mapping between narrative and affectiv

researcharxiv-cs-cl

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance

arXiv:2605.22567v1 Announce Type: new Abstract: Reinforcement learning has proven effective for enhancing multi-step reasoning in large language models (LLMs), yet its benefits have not fully translat

safetyarxiv-cs-cl

M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis

model-releasesarxiv-cs-ai

arXiv:2507.01053v4 Announce Type: replace-cross Abstract: Large-scale clinical databases offer opportunities for medical research, but their complexity creates barriers to effective use. The Medical I

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models

arXiv:2510.23090v2 Announce Type: replace Abstract: Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with L

MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues

arXiv:2605.21954v1 Announce Type: new Abstract: Video temporal grounding (VTG), which localizes the start and end times of a queried event in an untrimmed video, is a key test of whether multimodal la

Applications

Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance

arXiv:2605.22423v1 Announce Type: new Abstract: Motion degradation, manifested as blur in global shutter (GS) images or rolling shutter (RS) distortion in RS counterparts, remains a fundamental challe

applicationsarxiv-cs-cv

MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding

arXiv:2605.22550v1 Announce Type: new Abstract: Two-wheelers account for a disproportionately high share of road fatalities in the Global South. Research on two-wheeler rider behavior, however, lags f

Probabilistic Attribution For Large Language Models

arXiv:2605.21726v1 Announce Type: new Abstract: The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the

researcharxiv-cs-cl

Quantifying Full-Body Immersion

arXiv:2605.22521v1 Announce Type: new Abstract: Humanity is at the forefront of yet another digital revolution, where the lines between real and virtual worlds are dissolving, reshaping how we perceiv

researcharxiv-cs-ro

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

safetyarxiv-cs-cl

Security Document Classification with a Fine-Tuned Local Large Language Model: Benchmark Data and an Open-Source System

model-releasesarxiv-cs-ai

arXiv:2605.20368v1 Announce Type: cross Abstract: Organizations that scan documents for sensitive information face a practical problem. Cloud services require data to be sent to external infrastructur

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

SPIRAL: Self-Evolving Action-Conditioned Video Generation via Reflective Planning Agents

arXiv:2603.08403v3 Announce Type: replace Abstract: Long-horizon action-conditioned video generation aims to synthesize temporally coherent videos that follow complex action instructions over extended

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. F…

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, co

agentsdair-ai--x

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

Tokenization with Split Trees

arXiv:2605.22705v1 Announce Type: new Abstract: We introduce Tokenization with Split Trees (ToaST), a subword tokenization method that directly optimizes compression under a new recursive inference pr

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

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

safetyarxiv-cs-cl

VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents

arXiv:2602.00122v2 Announce Type: replace Abstract: In recent years, image editing models have made significant progress, enabling users to manipulate visual content in a flexible and interactive mann

VISTA: Validation-Guided Integration of Spatial and Temporal Foundation Models with Anatomical Decoding for Rare-Pathology VCE Event Detection -- after competition results

arXiv:2605.22096v1 Announce Type: new Abstract: Capsule endoscopy event detection is challenging because clinically relevant findings are sparse, visually heterogeneous, and evaluated at the event lev

When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering

arXiv:2605.21807v1 Announce Type: new Abstract: Across medical specialties, clinical practice is anchored in evidence-based guidelines that codify best studied diagnostic and treatment pathways. These

When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey

arXiv:2602.06995v2 Announce Type: replace-cross Abstract: This paper surveys the state-of-the-art in the nexus of SLAM and Wireless Communications, attributing the bidirectional impact of each with a

agentsarxiv-cs-cv

Zero-Shot Temporal Action Localization Through Textual Guidance

arXiv:2605.22201v1 Announce Type: new Abstract: Zero-shot temporal action localization (ZS-TAL) consists of classifying and localizing actions in untrimmed videos, where action classes are unseen at t

A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models

arXiv:2605.20495v1 Announce Type: new Abstract: Deep-learning pipelines for microscopy image classification often require expensive, labor- and time-intensive expert annotation to produce high-quality

A Sharper Picture of Generalization in Transformers

arXiv:2605.20988v1 Announce Type: new Abstract: We study transformers' generalization behavior on boolean domains from the perspective of the Fourier Spectra of their target functions. In contrast to

researcharxiv-cs-lg

AgentAtlas: Beyond Outcome Leaderboards for LLM Agents

arXiv:2605.20530v1 Announce Type: cross Abstract: Large language model agents now act on codebases, browsers, operating systems, calendars, files, and tool ecosystems, but the benchmarks used to evalu

AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation

arXiv:2605.21083v1 Announce Type: cross Abstract: Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological respo

safetyarxiv-cs-lg

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists

arXiv:2605.21481v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs, placing increasi

agentsarxiv-cs-cl

Always read the fine print: Anthropic is projecting its first (slightly) profitable quarter ever, which is amazing—assuming it actually happ…

Always read the fine print: Anthropic is projecting its first (slightly) profitable quarter ever, which is amazing—assuming it actually happens —but if it does it will be in no small part because they

safetygary-marcus--x

Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

arXiv:2602.19320v2 Announce Type: replace Abstract: Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and pe

Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization

arXiv:2605.20249v1 Announce Type: new Abstract: Gaussian Process (GP) kernels are central to Bayesian optimization (BO), yet designing effective kernels for high-dimensional problems still relies on e

researcharxiv-cs-lg

b9270

local-aillama-cpp-releases

Release b9270 adds support for the HybridDNATokenizer used by the Carbon-3B model family, implementing a new BPE pre-type for tokenizing DNA sequences. The tokenizer handles DNA k-mers with fixed 6-me

Applications

Beyond Semantic Similarity: A Two-Phase Non-Parametric Retrieval Workflow for Corporate Credit Underwriting

arXiv:2605.20684v1 Announce Type: new Abstract: Corporate credit underwriting requires analysts to extract actionable evidence from long, heterogeneous financial documents spanning hundreds of pages a

applicationsarxiv-cs-cl

Causal Path Alignment: Anchoring the Optimization Trajectory for Controllable In-Parameter Knowledge Editing

arXiv:2506.04042v2 Announce Type: replace Abstract: Knowledge editing is pivotal for efficiently updating the parametric memory of Large Language Models (LLMs), enabling them to function as evolving a

CMC-Opt: Constraint Manifold with Corners for Inequality-Constrained Optimization

arXiv:2605.20796v1 Announce Type: new Abstract: We introduce a manifold-based framework for addressing optimization problems with equality and inequality constraints found in robotics. Our approach tr

researcharxiv-cs-ro

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models

arXiv:2602.02304v2 Announce Type: replace-cross Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning w

safetyarxiv-cs-lg

DASH: Fast Differentiable Architecture Search for Hybrid Attention in Minutes on a Single GPU

arXiv:2605.20936v1 Announce Type: cross Abstract: Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality,

Deformba: Vision State Space Model with Adaptive State Fusion

arXiv:2605.21308v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional seq

DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation

model-releasesarxiv-cs-lg

arXiv:2605.20856v1 Announce Type: cross Abstract: Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entangle

Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation

arXiv:2605.20405v1 Announce Type: cross Abstract: Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare cl

DNACHUNKER: Learnable Tokenization for DNA Language Models

arXiv:2601.03019v4 Announce Type: replace-cross Abstract: DNA language models are increasingly used to represent genomic sequence, yet their effectiveness depends critically on how raw nucleotides are

local-aiarxiv-cs-cl

Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting

arXiv:2605.20678v1 Announce Type: new Abstract: Non-stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture-of-Experts (M

researcharxiv-cs-lg