Search: “context”

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

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Model Releases

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

DGX agent

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

model-releasesarxiv-cs-ai

12 May 2026

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

safetyarxiv-cs-cl

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

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

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

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

researcharxiv-cs-ai

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

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

researcharxiv-cs-ai

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

safetyarxiv-cs-cl

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

Local Ai

GESR: Graph-Based Edge Semantic Reconstruction for Stealthy Communication Detection with Benign-Only Training

arXiv:2605.07536v1 Announce Type: cross Abstract: Detecting stealthy malicious communications from flow logs under benign-only training remains a critical challenge in network security. Malicious comm

local-aiarxiv-cs-lg

How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment

arXiv:2605.06850v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encom

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation

arXiv:2605.07038v1 Announce Type: new Abstract: Risk-aware navigation should be selective: a policy should expose evasive degrees of freedom only when the local scene admits a lower-risk feasible mane

Tutorials

LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

arXiv:2605.06676v1 Announce Type: cross Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compress

tutorialsarxiv-cs-cl

Applications

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

arXiv:2605.07723v1 Announce Type: cross Abstract: Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and c

applicationsarxiv-cs-ai

MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents

arXiv:2605.07594v1 Announce Type: new Abstract: Existing memory systems for embodied agents typically inject retrieved memory as static context at episode start, a paradigm we term Ahead-of-time Monol

agentsarxiv-cs-ro

NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

model-releasesarxiv-cs-cl

arXiv:2603.08256v2 Announce Type: replace Abstract: Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1-5 scale in the context of short nar

RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion

model-releasesarxiv-cs-cv

arXiv:2503.06223v5 Announce Type: replace Abstract: Large Vision-Language Models (VLMs) are increasingly deployed in open-ended environments, where ensuring reliable safety under multimodal inputs is

Retrieval Heads are Dynamic

arXiv:2602.11162v2 Announce Type: replace Abstract: Recent studies have identified 'retrieval heads' in Large Language Models (LLMs) responsible for extracting information from input contexts. However

researcharxiv-cs-cl

Hardware

SpikingBrain: Spiking Brain-inspired Large Models

arXiv:2509.05276v4 Announce Type: replace-cross Abstract: Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence

hardwarearxiv-cs-ai

Applications

Switching-time bioprocess control with pulse-width-modulated optogenetics

arXiv:2511.22893v2 Announce Type: replace-cross Abstract: Biotechnology can benefit from dynamic control to improve production efficiency. In this context, optogenetics enables modulation of gene expr

applicationsarxiv-cs-ai

The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents

arXiv:2605.08060v1 Announce Type: cross Abstract: Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social

ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models

arXiv:2510.01290v2 Announce Type: replace Abstract: The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (

Visual Text Compression as Measure Transport

arXiv:2605.06708v1 Announce Type: cross Abstract: Visual text compression (VTC) promises efficient long-context processing by rendering text into an image and re-encoding it with a vision-language mod

Local Ai

A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments

arXiv:2605.03743v1 Announce Type: cross Abstract: Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is imp

local-aiarxiv-cs-ai

7 May 2026

Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa

model-releasesarxiv-cs-cl

arXiv:2605.04177v1 Announce Type: new Abstract: As LLMs enter conflict monitoring, understanding systematic distortions in their outputs is critical for humanitarian accountability. We evaluate four v

7 May 2026

Practical validation of synthetic pre-crash scenarios

arXiv:2605.04564v1 Announce Type: new Abstract: The representativeness of synthetic pre-crash scenarios is crucial for assessing the safety impact of Driving Automation Systems through virtual simulat

safetyarxiv-cs-ro

7 May 2026

Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study

arXiv:2603.01486v2 Announce Type: replace Abstract: Accurately mapping user queries to business categories is a fundamental Information Retrieval challenge for multi-category marketplaces, where conte

eOptShrinkQ: Near-Lossless KV Cache Compression Through Optimal Spectral Denoising and Quantization

arXiv:2605.02905v1 Announce Type: new Abstract: We show that the key-value (KV) cache in transformer attention heads admits a natural decomposition into a low-rank shared context component and a full-

From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection

arXiv:2602.14012v2 Announce Type: replace-cross Abstract: The integration of LLMs into vulnerability detection (VD) has shifted the field toward more interpretable and context-aware analysis. While po

'I Don't Know' -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation

arXiv:2605.00957v1 Announce Type: cross Abstract: Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LL

Local Ai

MASRA: MLLM-Assisted Semantic-Relational Consistent Alignment for Video Temporal Grounding

arXiv:2605.03398v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) faces a cross-modal semantic gap that often leads to background features being incorrectly aligned with the query, while

local-aiarxiv-cs-cv

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers

arXiv:2602.00933v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is rapidly becoming the standard interface for Large Language Models (LLMs) to discover and invoke external t