Search: “context”

From Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLMs

arXiv:2606.09508v1 Announce Type: new Abstract: Existing sparse attention and KV cache compression methods for long-context LLM inference typically apply fixed sparsity patterns or uniform budgets acr

9 Jun 2026

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Agents

Model Context Protocols in Adaptive Transport Systems: A Survey

DGX agent

arXiv:2508.19239v2 Announce Type: replace Abstract: The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport system

agentsarxiv-cs-ai

8 Jun 2026

From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents

arXiv:2606.06223v1 Announce Type: new Abstract: Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal mode

safetyarxiv-cs-ai

6 Jun 2026

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606.06337v1 Announce Type: new Abstract: Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions a

6 Jun 2026

Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

model-releasesarxiv-cs-cl

arXiv:2606.06197v1 Announce Type: new Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs). However, they still face challenges in a

5 Jun 2026

Applications

LANTERN: Layered Archival and Temporal Episodic Retrieval Network for Long-Context LLM Conversations

arXiv:2606.05182v1 Announce Type: new Abstract: Large language models discard critical details when conversation history is compacted to fit within finite context windows. We present LANTERN (Layered

applicationsarxiv-cs-cl

5 Jun 2026

EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management

arXiv:2606.03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain funda

3 Jun 2026

Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context

arXiv:2606.01101v1 Announce Type: cross Abstract: The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts. The Naive Bayes Cogni

2 Jun 2026

A Context-Aware Middleware for Medical Image Based Reports: An approach based on image feature extraction and association rules

arXiv:2605.30699v1 Announce Type: cross Abstract: This work proposes a context-aware middleware for medical workflow organization and efficiency improvement. In hospitals, laboratories and teleradiolo

researcharxiv-cs-cv

1 Jun 2026

FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization

arXiv:2605.31145v1 Announce Type: cross Abstract: In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly wi

1 Jun 2026

LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards

arXiv:2605.31584v1 Announce Type: cross Abstract: Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive di

1 Jun 2026

Tutorials

CamC2V: Context-aware Controllable Video Generation

arXiv:2504.06022v3 Announce Type: replace Abstract: Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditi

tutorialsarxiv-cs-cv

Context Distillation as Latent Memory Management

arXiv:2605.28889v1 Announce Type: cross Abstract: Context distillation compresses contextual information into model parameters, yet existing methods often ignore how multiple distilled latent memories

From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration

arXiv:2605.29675v1 Announce Type: cross Abstract: Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was bein

ParaTool: Shifting Tool Representations from Context to Parameters

arXiv:2605.29561v1 Announce Type: new Abstract: Tool calling extends large language models (LLMs) by enabling grounded interaction with external executable interfaces, thereby supporting environment-c

Safe In-Context Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

28 May 2026

When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR

arXiv:2605.28211v1 Announce Type: new Abstract: SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard practice: users supply context in prompts with sens

28 May 2026

Tutorials

Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers

arXiv:2601.20796v2 Announce Type: replace Abstract: Transformer-based multimodal large language models often exhibit in-context learning (ICL) abilities. Motivated by this phenomenon, we ask: how do t

tutorialsarxiv-cs-cl

27 May 2026

ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation

arXiv:2605.26525v1 Announce Type: cross Abstract: Minute-scale cinematic video generation is a central challenge for generative video models. Existing paradigms address only fragments of this challeng

27 May 2026

Tool-Schema Compression Enables Agentic RAG Under Constrained Context Budgets

arXiv:2605.26165v1 Announce Type: cross Abstract: Agentic RAG systems that equip language models with dozens to hundreds of tool definitions face a critical resource conflict: tool schemas consume the

27 May 2026

IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference

arXiv:2605.25475v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly expected to operate over long contexts, yet standard softmax attention incurs a KV cache that grows line

26 May 2026

Soft Bayesian Context Tree Models for Real-Valued Time Series

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

researcharxiv-cs-lg

23 May 2026

How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning

arXiv:2605.16591v1 Announce Type: cross Abstract: In-context learning (ICL) excels at new tasks from minimal examples, yet we still lack a mechanistic explanation of how few-shot prompts shape a model

safetyarxiv-cs-ai

19 May 2026

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods

arXiv:2506.10959v3 Announce Type: replace-cross Abstract: While in-context learning (ICL) has achieved remarkable success in natural language and vision domains, its theoretical understanding-particul

19 May 2026

Context Training with Active Information Seeking

arXiv:2605.13050v1 Announce Type: cross Abstract: Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or nich

14 May 2026

Detecting Data Contamination in LLMs via In-Context Learning

model-releasesarxiv-cs-cl

arXiv:2510.27055v2 Announce Type: replace Abstract: We present Contamination Detection via Context (CoDeC), a practical and accurate method to detect and quantify training data contamination in large

Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer

model-releasesarxiv-cs-lg

arXiv:2605.11414v1 Announce Type: new Abstract: While traditional time-series classifiers assume full sequences at inference, practical constraints (latency and cost) often limit inputs to partial pre

Interpreting Context-Aware Human Preferences for Multi-Objective Robot Navigation

arXiv:2603.17510v2 Announce Type: replace Abstract: Robots operating in human-shared environments must not only achieve task-level navigation objectives such as safety and efficiency, but also adapt t

safetyarxiv-cs-ro

On Problems of Implicit Context Compression for Software Engineering Agents

arXiv:2605.11051v1 Announce Type: cross Abstract: LLM-based Software Engineering agents face a critical bottleneck: context length limitations cause failures on complex, long-horizon tasks. One promis

agentsarxiv-cs-cl

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space

arXiv:2605.12412v1 Announce Type: new Abstract: Large Language Models (LLMs) update their behavior in context, which can be viewed as a form of Bayesian inference. However, the structure of the latent

Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven

arXiv:2605.09463v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks. However, their deployment in long-context scenarios faces h

safetyarxiv-cs-cl

Context Learning for Multi-Agent Discussion

arXiv:2602.02350v2 Announce Type: replace Abstract: Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via struct

Local Ai

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem

arXiv:2509.15519v2 Announce Type: replace Abstract: This paper studies fully decentralized cooperative multi-agent reinforcement learning, where each agent solely observes the states, its local action

local-aiarxiv-cs-lg

MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading

arXiv:2605.10268v1 Announce Type: cross Abstract: To tackle long-context reasoning tasks without the quadratic complexity of standard attention mechanisms, approaches based on agent memory have emerge

Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression

arXiv:2605.08475v1 Announce Type: cross Abstract: Mechanistic accounts of in-context learning (ICL) have identified iterative algorithms for linear regression and related linear prediction tasks, ofte

Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought

model-releasesarxiv-cs-lg

arXiv:2605.07123v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) refers to the ability of RL agents to adapt to new tasks at inference time without parameter updates by conditi

11 May 2026

GRaSp: Automatic Example Optimization for In-Context Learning in Low-Data Tasks

arXiv:2605.07454v1 Announce Type: new Abstract: In-context learning enables large language models to adapt to new tasks, but their performance is highly sensitive to the selected examples. Finding eff

11 May 2026

AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse

arXiv:2605.03644v1 Announce Type: new Abstract: Many-Shot In-Context Learning (ICL) has emerged as a promising paradigm, leveraging extensive examples to unlock the reasoning potential of Large Langua

7 May 2026

Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training

arXiv:2511.07328v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and

researcharxiv-cs-lg

5 May 2026

Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

arXiv:2605.01752v1 Announce Type: new Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adve

researcharxiv-cs-lg

5 May 2026

Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection

arXiv:2602.03216v2 Announce Type: replace Abstract: The quadratic complexity of attention remains the central bottleneck in long-context inference for large language models. Prior acceleration methods

4 May 2026

PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning

arXiv:2601.11908v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execut

1 May 2026

Quantum Gatekeeper: Multi-Factor Context-Bound Image Steganography with VQC Based Key Derivation on Quantum Hardware

arXiv:2604.26413v1 Announce Type: cross Abstract: This paper presents Quantum Gatekeeper, a context-bound image steganography framework where successful payload recovery depends on both cryptographic

30 Apr 2026

What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective

arXiv:2604.25132v1 Announce Type: new Abstract: Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an

29 Apr 2026

CUB: Benchmarking Context Utilisation Techniques for Language Models

arXiv:2505.16518v3 Announce Type: replace-cross Abstract: Incorporating external knowledge is crucial for knowledge-intensive tasks, such as question answering and fact checking. However, language mod

28 Apr 2026

Tutorials

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective

arXiv:2604.23267v1 Announce Type: new Abstract: Large language models (LLMs) operate in two fundamental learning modes - fine-tuning (FT) and in-context learning (ICL) - raising key questions about wh

tutorialsarxiv-cs-cl

28 Apr 2026

Bimanual Robot Manipulation via Multi-Agent In-Context Learning

arXiv:2604.20348v1 Announce Type: cross Abstract: Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf

23 Apr 2026

Context Attribution with Multi-Armed Bandit Optimization

arXiv:2506.19977v2 Announce Type: replace Abstract: Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable a