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You can build a measurably better agent from data you already have, without retraining a thing. The data is what your experienced humans do when they correct the AI. Capture... The post Building a sel

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CooT: Learning to Coordinate In-Context with Coordination Transformers

DGX agent

arXiv:2506.23549v3 Announce Type: replace Abstract: Effective coordination among unfamiliar partners remains a major challenge in multi-agent systems. Existing approaches, such as population-based met

model-releasesarxiv-cs-ai

19 May 2026

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

arXiv:2605.18419v1 Announce Type: cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology

Tutorials

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting

arXiv:2605.17730v1 Announce Type: cross Abstract: Mainstream methods for multivariate time-series forecasting largely follow the Direct-Mapping paradigm. They learn a unified mapping from history to t

tutorialsarxiv-cs-ai

LegalCheck: Retrieval- and Context-Augmented Generation for Drafting Municipal Legal Advice Letters

arXiv:2605.12012v2 Announce Type: replace Abstract: Public-sector legal departments in the Netherlands face acute staff shortages, increased case volumes, and increased pressure to meet regulatory com

safetyarxiv-cs-ai

Matrix-Decoupled Concentration for Autoregressive Sequences: Dimension-Free Guarantees for Sparse Long-Context Rewards

arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent token generation. Establishing tight concentratio

researcharxiv-cs-lg

Medical Context Distorts Decisions in Clinical Vision Language Models

arXiv:2605.17436v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require inte

Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs

model-releasesarxiv-cs-lg

arXiv:2605.15904v1 Announce Type: new Abstract: Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable soluti

Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation

arXiv:2605.15789v1 Announce Type: new Abstract: Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely

safetyarxiv-cs-lg

Agents

NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol

arXiv:2605.15227v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software r

agentsarxiv-cs-ai

Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Editing via In-Context Learning

arXiv:2605.15523v1 Announce Type: new Abstract: Scene text editing aims to modify text in a target region of an image while preserving surrounding background style and texture. Existing methods rely s

researcharxiv-cs-cv

SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference

model-releasesarxiv-cs-lg

arXiv:2605.15488v1 Announce Type: new Abstract: Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appr

ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data

arXiv:2510.02952v3 Announce Type: replace Abstract: Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tis

local-aiarxiv-cs-lg

15 May 2026

Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation

arXiv:2605.14380v1 Announce Type: new Abstract: Psychological defense mechanisms (PDMs) are unconscious cognitive processes that modulate how individuals perceive and respond to emotional distress. Au

researcharxiv-cs-cl

15 May 2026

Agents

Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG

arXiv:2605.15109v1 Announce Type: new Abstract: Retrieval-Augmented Generation can improve factuality by grounding answers in external evidence, but Agentic GraphRAG complicates what it means for cita

agentsarxiv-cs-ai

15 May 2026

IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning

arXiv:2605.12924v1 Announce Type: new Abstract: The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification

researcharxiv-cs-lg

14 May 2026

RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation

arXiv:2605.13542v1 Announce Type: new Abstract: Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states u

14 May 2026

Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction

arXiv:2305.12678v3 Announce Type: replace Abstract: Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-

In-Context Multi-Objective Optimization

arXiv:2512.11114v2 Announce Type: replace Abstract: Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a

safetyarxiv-cs-lg

Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment

arXiv:2605.11439v1 Announce Type: new Abstract: Rapid and accurate situational awareness is essential for effective response during natural disasters, where delays in analysis can significantly hinder

applicationsarxiv-cs-cv

Red-Teaming Text-to-Image Models via In-Context Experience Replay and Semantic-Preserving Prompt Rewriting

arXiv:2411.16769v3 Announce Type: replace-cross Abstract: Understanding the capabilities of text-to-image (T2I) models in harmful content generation is essential to safety and compliance. However, hum

LLMs with in-context learning for Algorithmic Theoretical Physics

arXiv:2605.08212v1 Announce Type: cross Abstract: There is an increasing number of algorithmic computations in theoretical physics. These, while conceptually simple, can nevertheless be time-consuming

12 May 2026

Tools

From observability to context: What’s next for Arize Phoenix

As agents start changing software, they need a way to verify their work that includes traces, evals, feedback, and APIs. This is where Phoenix goes next — not the next release, but what this product b

toolsarize-ai

11 May 2026

UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios

arXiv:2410.06355v3 Announce Type: replace-cross Abstract: This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multi

11 May 2026

BeeLlama.cpp: advanced DFlash & TurboQuant with support of reasoning and vision. Qwen 3.6 27B Q5 with 200k context on 3090, 2-3x faster than baseline (peak 135 tps!)

model-releasesr-localllama

BeeLlama.cpp is an optimized implementation featuring advanced DFlash and TurboQuant quantization techniques with support for reasoning and vision capabilities. The project demonstrates running Qwen 3

9 May 2026

Programmatic Context Augmentation for LLM-based Symbolic Regression

arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scient

researcharxiv-cs-ai

7 May 2026

Single-Position Intervention Fails: Distributed Output Templates Drive In-Context Learning

arXiv:2605.04061v1 Announce Type: cross Abstract: Understanding how large language models encode task identity from few-shot demonstrations is a central open problem in mechanistic interpretability. P

7 May 2026

ARMOR 2025: A Military-Aligned Benchmark for Evaluating Large Language Model Safety Beyond Civilian Contexts

arXiv:2605.00245v1 Announce Type: new Abstract: Large language models (LLMs) are now being explored for defense applications that require reliable and legally compliant decision support. They also hol

Tutorials

Degradation-Aware Adaptive Context Gating for Unified Image Restoration

arXiv:2605.01236v1 Announce Type: new Abstract: Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradati

tutorialsarxiv-cs-cv

Quantum-inspired Techniques in Tensor Networks for Industrial Contexts

arXiv:2404.11277v2 Announce Type: replace-cross Abstract: In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for ind

researcharxiv-cs-lg

TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning

arXiv:2601.05300v2 Announce Type: replace-cross Abstract: Reasoning-oriented language models typically expose explicit reasoning as a long, front-loaded chain of 'thinking' tokens before the main outp

Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation

arXiv:2602.00665v3 Announce Type: replace Abstract: Customer-service question answering (QA) systems increasingly rely on conversational language understanding. While Large Language Models (LLMs) achi

4 May 2026

GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer

arXiv:2605.00799v1 Announce Type: new Abstract: Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges

researcharxiv-cs-cv

4 May 2026

Tutorials

Structured In-context Environment Scaling for Large Language Model Reasoning

arXiv:2509.23330v3 Announce Type: replace Abstract: Large language models (LLMs) have achieved significant advancements in reasoning capabilities through reinforcement learning (RL) via environmental

tutorialsarxiv-cs-cl

4 May 2026

In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models

arXiv:2503.01611v3 Announce Type: replace Abstract: Instruction following is a critical ability for Large Language Models to perform downstream tasks. The standard approach to instruction tuning has r

1 May 2026

Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection

arXiv:2509.11058v2 Announce Type: replace Abstract: Zero-Shot Video Anomaly Detection (ZS-VAD) requires temporally localizing anomalies without target domain training data, which is a crucial task due

local-aiarxiv-cs-cv

30 Apr 2026

Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners

arXiv:2604.26488v1 Announce Type: new Abstract: One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack r

researcharxiv-cs-cv

30 Apr 2026

SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization

arXiv:2604.26394v1 Announce Type: cross Abstract: Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMat

local-aiarxiv-cs-ai

30 Apr 2026

Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation

model-releasesarxiv-cs-cv

arXiv:2604.25188v1 Announce Type: new Abstract: Image classification remains a fundamental yet challenging task in computer vision, particularly when fine-grained feature extraction and background noi

Modeling Human-Like Color Naming Behavior in Context

arXiv:2604.25674v1 Announce Type: new Abstract: Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both le

researcharxiv-cs-cl

Qwen3.6 27B on dual RTX 5060 Ti 16GB with vLLM: ~60 tok/s, 204k context working

Qwen3.6 27B is a 27-billion parameter language model that can achieve approximately 60 tokens per second throughput when running on dual RTX 5060 Ti GPUs with 16GB memory each, using the vLLM inferenc

local-air-localllama

SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining

arXiv:2602.10718v3 Announce Type: replace-cross Abstract: While FP8 attention has shown substantial promise in innovations like FlashAttention-3, its integration into the decoding phase of the DeepSee

Using context graphs: build a data moat like Google’s using your enterprise data

Enterprise software is on the verge of its first compounding data loop, the same kind of self-reinforcing mechanism that built the most valuable consumer businesses of the last twenty years.... The po

applicationsarize-ai

Beyond Context: Large Language Models' Failure to Grasp Users' Intent

arXiv:2512.21110v3 Announce Type: replace Abstract: Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability

28 Apr 2026

See No Evil: Semantic Context-Aware Privacy Risk Detection for AR

arXiv:2604.22805v1 Announce Type: cross Abstract: Augmented reality (AR) systems pose unique privacy risks due to their continuous capture of visual data. Existing AR privacy frameworks lack semantic

applicationsarxiv-cs-ai

28 Apr 2026

A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration

arXiv:2604.21363v1 Announce Type: new Abstract: Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoni

applicationsarxiv-cs-ro

24 Apr 2026

Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations

arXiv:2604.21018v1 Announce Type: new Abstract: While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute allocation or sample from

researcharxiv-cs-ai

24 Apr 2026

Omission Constraints Decay While Commission Constraints Persist in Long-Context LLM Agents

arXiv:2604.20911v1 Announce Type: cross Abstract: LLM agents deployed in production operate under operator-defined behavioral policies (system-prompt instructions such as prohibitions on credential di