Search: “context”

Pooling Helps, Learned Weighting Hurts In-Context: Decomposing Group Attention

arXiv:2610.01831v1 Announce Type: new Abstract: Group attention, introduced by the time series forecasting model Chronos-2, attends over the variates of a group at a fixed patch index and serves both

researcharxiv-cs-lg

2 Oct 2026

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Safety

Learning to Reason with Compressed Context: Ground-Truth-Free Adaptation of OmniLLMs via Self-Distillation

DGX agent

arXiv:2609.39953v1 Announce Type: new Abstract: Omni-modal large language models (OmniLLMs) enable unified audio-video understanding, but their long multimodal token sequences make deployment computat

safetyarxiv-cs-cv

1 Oct 2026

Targeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting

arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mecha

1 Oct 2026

Exploring In-Context Learning for Handwritten Text Recognition

model-releasesarxiv-cs-cv

arXiv:2609.37195v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents. Not only do they cut down tim

Agents

In-Context Learning for Robots: Methods and Applications

arXiv:2609.36012v1 Announce Type: new Abstract: General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL)

agentsarxiv-cs-ro

Nonpreemptive Scheduling While Learning Context-Dependent Service Rates

model-releasesarxiv-cs-lg

arXiv:2609.37660v1 Announce Type: new Abstract: We study nonpreemptive contextual queueing bandits in a single-server system. Each job is represented by a d-dimensional context vector; in each round,

Applications

TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks

arXiv:2609.36968v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods r

applicationsarxiv-cs-lg

Distance-KV: Exploiting Relative Distance for Efficient Long-Context Inference

model-releasesarxiv-cs-lg

arXiv:2609.32663v1 Announce Type: new Abstract: The memory usage and decoding latency of LLM inference grow rapidly with context length. To reduce these costs, key-value (KV) cache compression methods

29 Sept 2026

Applications

Completed Pairs Hide Capped Failures: A ReVerPi Case Study of Selective Context Projection

arXiv:2609.31381v1 Announce Type: new Abstract: Context projection replaces older tool observations with compact, addressable excerpts, reducing repeated input while potentially adding evidence-retrie

applicationsarxiv-cs-ai

28 Sept 2026

Applications

JevOut: Natural Context Can Flip Decision Models

arXiv:2609.30243v1 Announce Type: new Abstract: Dedicated decision models such as Jev map unstructured language to probability distributions over finite choices, allowing their outputs to directly rou

applicationsarxiv-cs-cl

25 Sept 2026

Local Ai

MILO: Efficient Many-shot In-Context Learning with Block-wise Low-rank Compression

arXiv:2609.29913v1 Announce Type: new Abstract: Many-shot in-context learning (ICL) enables large language models (LLMs) to adapt to complex tasks by conditioning on thousands of demonstration example

local-aiarxiv-cs-cl

25 Sept 2026

MWE-ECL: Recoverable Long-Range Context Does Not Always Override Local Lexical Priors

arXiv:2609.27590v1 Announce Type: new Abstract: Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test th

24 Sept 2026

Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference

arXiv:2609.25537v1 Announce Type: new Abstract: Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latenc

23 Sept 2026

Local Ai

PatchKV: Efficient KV Cache Recovery for Dynamically Edited LLM Contexts

arXiv:2609.26219v1 Announce Type: cross Abstract: Long-running LLM agent workflows often revise interior context spans while retaining long suffixes. Although suffix tokens remain unchanged, altered c

local-aiarxiv-cs-lg

23 Sept 2026

Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models

arXiv:2609.22452v1 Announce Type: new Abstract: Long-context understanding remains a fundamental challenge for large language models, as excessively long inputs often lead models to forget salient inf

22 Sept 2026

Where Should a Document Live: Context, Representations, or Parameters?

arXiv:2609.17346v1 Announce Type: cross Abstract: To answer questions outside of their pre-training data, large language models (LLMs) need access to new information, which can be presented in the con

16 Sept 2026

MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup

arXiv:2609.15126v1 Announce Type: cross Abstract: Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory:

15 Sept 2026

Self-Indexing Attention for Compression-Compatible Sparse Long-Context LLM Inference

arXiv:2609.13205v1 Announce Type: cross Abstract: Sparse long-context inference requires efficient token retrieval in both prefill and decode. Existing methods often use different retrieval strategies

15 Sept 2026

Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting

arXiv:2609.11607v1 Announce Type: new Abstract: When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, we

12 Sept 2026

Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction

arXiv:2609.10810v1 Announce Type: new Abstract: Minimal-edit Grammatical Error Correction (GEC) is a challenging task for zero- and few-shot prompted Large Language Models (LLMs), which systematically

11 Sept 2026

AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learning

arXiv:2609.07334v1 Announce Type: new Abstract: The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations o

Do New Attention Mechanisms Actually Fix Attention Sinks at Million-Token Context?

arXiv:2609.08574v1 Announce Type: cross Abstract: Long context language models now advertise windows of one million tokens, but two habits limit how much of that window is used. Attention heads with n

Hardware

Interface-Aware KV Cache Quantization for Dense On-Chip NVM in Long-Context LLM Decoding

arXiv:2609.05764v1 Announce Type: cross Abstract: The key-value (KV) cache is the dominant memory bottleneck in long-context large language model (LLM) decoding: every step reads it entirely, so decod

hardwarearxiv-cs-ai

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

arXiv:2609.10395v1 Announce Type: new Abstract: This paper describes the Rosetta system for Subtask 1 (Context-Aware English-to-Dialectal Arabic Dialogue Translation) of the AlexandriaX shared task, p

CAROL: Context-Aware Online Learning for Fuzzer Scheduling

arXiv:2609.06772v1 Announce Type: cross Abstract: Ensemble fuzzing runs multiple fuzzers on a target while a scheduler allocates CPU time among them. Existing schedulers base these decisions on compac

researcharxiv-cs-lg

CONTHER: Context-Aware Reinforcement Learning for Robotic Manipulation with Sparse Rewards

arXiv:2503.15895v2 Announce Type: replace Abstract: This paper investigates whether sequential context improves goal-conditioned Reinforcement Learning in sparse-reward manipulation tasks. While Hinds

safetyarxiv-cs-ro

Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning

arXiv:2609.08080v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires anticipating changes in risk before they lead to unsafe behavior. Existing appr

safetyarxiv-cs-lg

Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling

model-releasesarxiv-cs-lg

arXiv:2609.08981v1 Announce Type: new Abstract: A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing in

Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG

arXiv:2609.05152v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retrieved context inflates the input and degrades

7 Sept 2026

Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts

arXiv:2606.12807v2 Announce Type: replace Abstract: Summaries of real-world events can become outdated as contexts evolve and new information arrives. A common response is to generate a new summary fr

7 Sept 2026

MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models

arXiv:2609.01772v1 Announce Type: new Abstract: Meme understanding goes beyond recognizing visual content or literal text; it requires implicit cultural knowledge and pragmatic inference that most vis

3 Sept 2026

Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning

arXiv:2602.18493v2 Announce Type: replace-cross Abstract: Long-context LLMs and Retrieval-Augmented Generation defer state tracking and evidence consolidation to query time, which is brittle when fact

2 Sept 2026

CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation

arXiv:2608.30689v1 Announce Type: new Abstract: Autoregressive large models have recently advanced Text-to-SVG generation from simple icons to complex, long-context graphics, yet standard autoregressi

researcharxiv-cs-cv

Context Staircase: Signature-Aligned Dynamics of Token Embeddings under Small Initialization

arXiv:2608.30315v1 Announce Type: cross Abstract: Token embeddings are the basic representational units that connect discrete tokens with continuous computation in language models. Although modern lan

safetyarxiv-cs-cl

ContextBias: Controlled Evaluation of Bias Persistence Under Context Shift in Text-to-Image Models

arXiv:2608.29847v1 Announce Type: cross Abstract: Text-to-image models learn associations between concepts - in the case of this paper, people's professions, which we refer to as roles - and visual at

FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents

arXiv:2510.03204v2 Announce Type: replace Abstract: Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens

Hardware

Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference

arXiv:2512.16391v2 Announce Type: replace-cross Abstract: Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG.

hardwarearxiv-cs-ai

SALT: Salience-Aware Lexical Trie for Long-Context Compression

arXiv:2607.17486v2 Announce Type: replace-cross Abstract: As large language models (LLMs) process increasingly longer prompts, computation and KV-cache memory costs have emerged as major bottlenecks i

CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection

model-releasesarxiv-cs-cv

arXiv:2608.28070v1 Announce Type: new Abstract: Automated detection of surface micro-defects on industrial components, such as copper tubes, is critically important for quality assurance but remains c

31 Aug 2026

Reasoning about In-Context Samples for Machine-Translation

arXiv:2608.27036v1 Announce Type: new Abstract: Large Language Models (LLMs) can be trained to perform chain-of-thoughts reasoning in order to improve the reliability of their responses. In this work,

28 Aug 2026

Narcissus: Program Synthesis Using Context-Aware LLM Approximations

arXiv:2608.25657v1 Announce Type: cross Abstract: Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training dat

researcharxiv-cs-lg

Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context

arXiv:2608.25655v1 Announce Type: new Abstract: Conversations with chat assistants increasingly span many topics in a single long-running thread, challenging memory systems. Existing long-context and

Local Ai

Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping

arXiv:2608.24956v1 Announce Type: new Abstract: Data-driven methods are widely used in landslide susceptibility mapping (LSM) because they can effectively model the complex relationships between lands

local-aiarxiv-cs-cv

Hardware

Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures

arXiv:2608.25096v1 Announce Type: new Abstract: The growing adoption of large language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference.

hardwarearxiv-cs-lg

In-Context Inpainting for Time Series Forecasting

arXiv:2608.23855v1 Announce Type: new Abstract: We propose ICI-Time, a novel framework that reframes time series forecasting as a visual inpainting task, leveraging the generalisation power of large v

26 Aug 2026

IntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning

model-releasesarxiv-cs-cv

arXiv:2608.23330v1 Announce Type: new Abstract: Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human actions

25 Aug 2026

Affective Context Amplifies Sycophancy in LLM Responses

arXiv:2608.21242v1 Announce Type: new Abstract: As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates L

24 Aug 2026

Anchoring Instruction Outside Mask: Exact Reference Caching for Efficient In-Context Diffusion Transformers