Search: “context”

TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning

arXiv:2605.11380v1 Announce Type: new Abstract: Learning transferable representations for electroencephalography (EEG) remains challenging because EEG signals are inherently multi-channel and non-stat

researcharxiv-cs-lg

13 May 2026

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Safety

Trajectory First: A Curriculum for Discovering Diverse Policies

DGX agent

arXiv:2506.01568v3 Announce Type: replace Abstract: Being able to solve a task in diverse ways makes agents more robust to task variations and less prone to local optima. In this context, constrained

safetyarxiv-cs-lg

13 May 2026

What Does It Mean for a Medical AI System to Be Right?

model-releasesarxiv-cs-cv

arXiv:2605.11963v1 Announce Type: new Abstract: This paper examines what it means for a medical AI system to be right by grounding the question in a specific clinical context: the automatic classifica

13 May 2026

7 lessons from the first wave of agentic AI deployment: theCUBE + NYSE Wired’s AI Agent Conference insights

The enterprise artificial intelligence stack is getting smarter — but is it getting the context it needs to make agentic AI deployment actually work? The defining problem of the agentic era might neve

agentssiliconangle

A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization

arXiv:2605.06226v2 Announce Type: replace Abstract: Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workfl

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models

arXiv:2603.10126v2 Announce Type: replace-cross Abstract: We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refresh

Block-Wise Differentiable Sinkhorn Attention: Tail-Refinement Gradients with a Gap-Aware Dustbin Bridge

arXiv:2605.08123v1 Announce Type: cross Abstract: We study long-context balanced entropic optimal transport (OT) attention on TPU hardware through a stopped-base, fixed-depth tail-refinement surrogate

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing

arXiv:2504.21228v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are susceptible to indirect prompt injection attacks, where the model inadvertently responds to instructions inje

Decomposing and Steering Functional Metacognition in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.08942v1 Announce Type: new Abstract: Large language models (LLMs) increasingly exhibit behaviors suggesting awareness of their evaluation context, often adapting their reasoning strategies

Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment

arXiv:2605.08462v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge in Large Language Models (LLMs), particularly in context-grounded settings such as RAG and agentic AI sys

EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding

model-releasesarxiv-cs-cl

arXiv:2605.08847v1 Announce Type: new Abstract: In the context of today's high-pressure, aging society, the demand for large-scale emotional models capable of providing empathetic support is more crit

Local Ai

FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts

arXiv:2605.08648v1 Announce Type: new Abstract: Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal

local-aiarxiv-cs-lg

Tutorials

Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study

arXiv:2604.07558v2 Announce Type: replace-cross Abstract: Digital mental health (DMH) tools have extensively explored personalization of interventions to users' needs and contexts. However, this perso

tutorialsarxiv-cs-ai

GRC: Unifying Reasoning-Driven Generation, Retrieval and Compression

arXiv:2605.09100v1 Announce Type: new Abstract: Text embedding and generative tasks are usually trained separately based on large language models (LLMs) nowadays. This causes a large amount of trainin

agentsarxiv-cs-cl

HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds

arXiv:2605.10536v1 Announce Type: cross Abstract: Rare semantic innovations in high-dimensional, mission-critical domains are often obscured by dense background contexts, a challenge we define as exti

How Should LLMs Listen While Speaking? A Study of User-Stream Routing in Full-Duplex Spoken Dialogue

arXiv:2605.10199v1 Announce Type: new Abstract: Full-duplex spoken dialogue requires a model to keep listening while generating its own spoken response. This is challenging for large language models (

researcharxiv-cs-cl

Local Ai

I built ForgePilot: a Codex-style desktop workspace for Ollama with tools, MCP, web research, and document support

ForgePilot is a desktop workspace application designed for Ollama that combines local language model capabilities with development tools, including support for Model Context Protocol (MCP), web resear

local-air-ollama

Instruction Anchor: Dissecting the Mechanistic Dynamics of Modality Arbitration

arXiv:2602.03677v2 Announce Type: replace Abstract: Modality following is the ability to selectively leverage multimodal contexts based on user instructions. It is fundamental to the safety and reliab

ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs

arXiv:2603.02676v2 Announce Type: replace-cross Abstract: Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that

Applications

LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification

arXiv:2605.08448v1 Announce Type: new Abstract: Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In t

applicationsarxiv-cs-ai

Manifold scores 7,700 MCP servers in Manifest expansion aimed at agent security teams

Artificial intelligence detection and response platform startup Manifold Security Inc. today announced an expansion of its Manifest supply chain intelligence tool to cover Model Context Protocol serve

agentssiliconangle

Masked Generative Transformer Is What You Need for Image Editing

arXiv:2605.10859v1 Announce Type: new Abstract: Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications t

researcharxiv-cs-cv

MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments

arXiv:2605.09131v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has unified the interface between Large Language Models (LLMs) and external tools, yet a fundamental gap remains in how

Mela: Test-Time Memory Consolidation based on Transformation Hypothesis

arXiv:2605.10537v1 Announce Type: new Abstract: Memory consolidation, the process by which transient experiences are transformed into stable, structured representations, is a foundational organizing p

researcharxiv-cs-cl

Mem-W: Latent Memory-Native GUI Agents

arXiv:2605.09317v1 Announce Type: new Abstract: GUI agents are beginning to operate the web, mobile, and desktop as interactive worlds, where successful control depends on carrying forward visual, pro

Industry

Meta won’t let you block its AI account on Threads

Meta announced on Tuesday that it's testing a Threads feature that lets users tag a Meta AI account to get answers to questions or context about a conversation on the platform. If you've spent any tim

industrythe-verge-ai

Optimal Regret for Single Index Bandits

arXiv:2605.09454v1 Announce Type: cross Abstract: We study the extit{single-index bandit} problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an

researcharxiv-cs-lg

PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools

arXiv:2604.01532v2 Announce Type: replace Abstract: LLM agents are beginning to invoke industrial asset-management tools through the Model Context Protocol (MCP), yet whether they can act reliably on

safetyarxiv-cs-ai

Positional Encoding via Token-Aware Phase Attention

arXiv:2509.12635v3 Announce Type: replace-cross Abstract: We prove under practical assumptions that Rotary Positional Embedding (RoPE) introduces an intrinsic distance-dependent bias in attention scor

safetyarxiv-cs-ai

Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases

arXiv:2605.09472v1 Announce Type: new Abstract: Positional encoding in transformers is commonly implemented through positional embeddings, attention masks, or bias terms, but formal connections betwee

safetyarxiv-cs-lg

PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines

arXiv:2605.10614v1 Announce Type: new Abstract: Multi-agent LLM systems introduce a security risk in which sensitive information accessed by one agent can propagate through shared context and reappear

RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology

model-releasesarxiv-cs-cv

arXiv:2605.10761v1 Announce Type: new Abstract: Cancer screening is a reasoning task. A radiologist observes findings, compares them to prior scans, integrates clinical context, and reaches a diagnost

REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?

arXiv:2505.10872v4 Announce Type: replace-cross Abstract: Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. A

SAP SAPPHIRE 2026: Google Cloud unveils unified agentic vision and massive compute scaling

model-releasesgoogle-cloud-ai

In today's hyper-connected market, an enterprise's most valuable asset — mission-critical data — often remains trapped in legacy silos. For years, leadership teams have navigated a data pipeline dilem

SAR-RAG: ATR Visual Question Answering by Semantic Search, Retrieval, and MLLM Generation

arXiv:2602.04712v2 Announce Type: replace-cross Abstract: We present a visual-context image-retrieval-augmented generation (ImageRAG)- assisted AI agent for automatic target recognition (ATR) of synth

Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks

arXiv:2605.10395v1 Announce Type: cross Abstract: We study the information-theoretic limits of learning a one-hidden-layer teacher network with hierarchical features from noisy queries, in the context

researcharxiv-cs-lg

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization

arXiv:2605.08809v1 Announce Type: cross Abstract: Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddin

SSA: Improving Performance With a Better Scoring Function

arXiv:2508.14685v4 Announce Type: replace Abstract: While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We anal

researcharxiv-cs-cl

Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering

arXiv:2605.10052v1 Announce Type: cross Abstract: As artificial intelligence engineering paradigms shift from single-agent Prompt and Context Engineering toward multi-agent extbf{Coordination Engineer

The Trap of Trajectory: Towards Understanding and Mitigating Spurious Correlations in Agentic Memory

arXiv:2605.09330v1 Announce Type: cross Abstract: Agentic memory enables LLMs to persist information beyond a single context window and reuse it in later decisions, but it also introduces a new vulner

Tight Generalization Bounds for Noiseless Inverse Optimization

model-releasesarxiv-cs-lg

arXiv:2605.08866v1 Announce Type: cross Abstract: Inverse optimization (IO) seeks to infer the parameters of a decision-maker's objective from observed context--action data. We study noiseless IO, whe

Hardware

Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference

arXiv:2603.29002v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse atte

hardwarearxiv-cs-ai

Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA

arXiv:2605.10850v1 Announce Type: new Abstract: Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a defa

safetyarxiv-cs-cv

When Does Value-Aware KV Eviction Help? A Fixed-Contract Diagnostic for Non-Monotone Cache Compression

arXiv:2605.08234v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by the memory and bandwidth cost of reading large KV caches during decoding. KV compression reduces this co

Accurate and Efficient Statistical Testing for Word Semantic Breadth

arXiv:2605.08048v1 Announce Type: new Abstract: Measuring the breadth of a word's meaning, or its spread across contexts, has become feasible with contextualized token embeddings. A word type can be r

Belief Memory: Agent Memory Under Partial Observability

arXiv:2605.05583v2 Announce Type: replace Abstract: LLM agents that operate over long context depend on external memory to accumulate knowledge over time. However, existing methods typically store eac

Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility

model-releasesarxiv-cs-cl

arXiv:2605.06856v1 Announce Type: cross Abstract: Generative AI systems achieve impressive performance on standard benchmarks yet fail to deliver real-world utility, a disconnect we identify across 28

EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding

arXiv:2605.07859v1 Announce Type: new Abstract: Driver cognitive distraction is a major cause of road collisions and remains difficult to detect. Unlike manual or visual distraction, cognitive distrac

researcharxiv-cs-cv