Search: “context”

Quantum Knowledge Graph: Modeling Context-Dependent Triplet Validity

arXiv:2604.23972v1 Announce Type: cross Abstract: Knowledge graphs (KGs) are increasingly used to support large lan guage model (LLM) reasoning, but standard triplet-based KGs treat each relation as g

28 Apr 2026

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Hardware

RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference

DGX agent

arXiv:2505.02922v3 Announce Type: replace Abstract: Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and band

hardwarearxiv-cs-lg

28 Apr 2026

Agents

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

arXiv:2601.16746v3 Announce Type: replace-cross Abstract: LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts,

agentsarxiv-cs-cl

28 Apr 2026

Hardware

SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference

arXiv:2604.22575v1 Announce Type: new Abstract: Scaling context length is reshaping large-model development, yet full-attention Transformers suffer from prohibitive computation and inference bottlenec

hardwarearxiv-cs-lg

27 Apr 2026

Back to the Future: The Role of Past and Future Context Predictability in Incremental Language Production

arXiv:2511.07752v3 Announce Type: replace Abstract: Contextual predictability shapes how we choose and encode words in production. The effects of a word's predictability given preceding or past contex

applicationsarxiv-cs-cl

24 Apr 2026

Hardware

Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token (TTFT)

arXiv:2604.16395v2 Announce Type: replace-cross Abstract: Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting fo

hardwarearxiv-cs-ai

24 Apr 2026

TTKV: Temporal-Tiered KV Cache for Long-Context LLM Inference

arXiv:2604.19769v1 Announce Type: cross Abstract: Key-value (KV) caching is critical for efficient inference in large language models (LLMs), yet its memory footprint scales linearly with context leng

23 Apr 2026

Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation

arXiv:2604.18758v1 Announce Type: new Abstract: Low-resource machine translation requires methods that differ from those used for high-resource languages. This paper proposes a novel in-context learni

22 Apr 2026

AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment

arXiv:2604.18398v1 Announce Type: new Abstract: Creativity has become a core competence in the era of LLMs and human-AI collaboration, underpinning innovation in real-world problem solving. Crucially,

applicationsarxiv-cs-cl

Context Matters: Peer-Aware Student Behavioral Engagement Measurement via VLM Action Parsing and LLM Sequence Classification

arXiv:2601.06394v3 Announce Type: replace Abstract: Understanding student behavior in the classroom is essential to improve both pedagogical quality and student engagement. Existing methods for predic

researcharxiv-cs-cv

Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning

arXiv:2601.10306v2 Announce Type: replace-cross Abstract: While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards

safetyarxiv-cs-cl

How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models

arXiv:2510.02370v3 Announce Type: replace Abstract: Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially,

applicationsarxiv-cs-cl

KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs

arXiv:2604.13226v1 Announce Type: new Abstract: Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: re

16 Apr 2026

Latent-Condensed Transformer for Efficient Long Context Modeling

arXiv:2604.12452v1 Announce Type: new Abstract: Large language models (LLMs) face significant challenges in processing long contexts due to the linear growth of the key-value (KV) cache and quadratic

15 Apr 2026

UCS: Estimating Unseen Coverage for Improved In-Context Learning

arXiv:2604.12015v1 Announce Type: cross Abstract: In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heur

15 Apr 2026

SinkTrack: Attention Sink based Context Anchoring for Large Language Models

arXiv:2604.10027v1 Announce Type: new Abstract: Large language models (LLMs) suffer from hallucination and context forgetting. Prior studies suggest that attention drift is a primary cause of these pr

researcharxiv-cs-cv

14 Apr 2026

Tutorials

Transformers Learn Latent Mixture Models In-Context via Mirror Descent

arXiv:2604.10848v1 Announce Type: new Abstract: Sequence modelling requires determining which past tokens are causally relevant from the context and their importance: a process inherent to the attenti

tutorialsarxiv-cs-lg

14 Apr 2026

STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System

arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle

13 Apr 2026

Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset

arXiv:2604.05429v2 Announce Type: replace-cross Abstract: Addressing the critical need for intelligent, context-aware energy management in renewable systems, we introduce the OpenCEM Simulator and Dat

applicationsarxiv-cs-ai

10 Apr 2026

In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads

arXiv:2604.06356v1 Announce Type: cross Abstract: In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, we inv

10 Apr 2026

Transformer-based In-Context Score Operator Learning for Diffusion Models

arXiv:2610.13690v1 Announce Type: new Abstract: Diffusion models typically learn the generative dynamics of a single target distribution or a family of distributions indexed by conditioning variables

12 Oct 2026

Tutorials

Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations

arXiv:2610.11907v1 Announce Type: cross Abstract: Hallucination remains a significant challenge in Large Vision-Language Models (LVLMs). Existing training-free methods generally mitigate hallucination

tutorialsarxiv-cs-ai

9 Oct 2026

SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning

arXiv:2610.11132v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general

9 Oct 2026

Spatial Induction Heads: In-Context Learning of Multidimensional Cellular Automata

arXiv:2610.09124v1 Announce Type: new Abstract: Induction heads provide a mechanistic account of in-context learning in sequential data, but existing theory largely assumes that the context relevant t

safetyarxiv-cs-lg

8 Oct 2026

Internalizing Agent Experience into Diffusion Model Weights via On-Policy Context Distillation

arXiv:2610.07250v1 Announce Type: new Abstract: Wrapping an image generation model in an agentic harness can effectively boost Text-to-Image task performance: the harness can leverage memory, skills,

safetyarxiv-cs-ai

7 Oct 2026

Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents

arXiv:2610.05195v1 Announce Type: new Abstract: The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single ta

safetyarxiv-cs-cv

pi^2: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models

arXiv:2604.05114v2 Announce Type: replace-cross Abstract: We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, pi^2, constructs h

researcharxiv-cs-lg

RepICL: Reusable In-Context Prediction Across Heterogeneous Representation Spaces

arXiv:2610.05852v1 Announce Type: new Abstract: Frozen representations are widely reused for downstream classification, yet each new task typically requires fitting a new predictor. We ask whether the

TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

arXiv:2610.06156v1 Announce Type: new Abstract: A representation family is a distinct way of extracting features from time series. Ensemble algorithms that combine several representation families rema

Dual-Context Analog Retrieval for Time Series Forecasting

arXiv:2610.03491v1 Announce Type: new Abstract: Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does

applicationsarxiv-cs-lg

5 Oct 2026

Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning

arXiv:2610.00562v1 Announce Type: cross Abstract: Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient

2 Oct 2026

Shifting Mechanisms: How Positional Encoding Choice Shapes In-Context Retrieval

arXiv:2609.38530v1 Announce Type: new Abstract: Language models increasingly use architectures that vary attention span and positional encoding across layers, such as applying RoPE with sliding-window

1 Oct 2026

When Context Changes: Understanding Update Failures in LLMs

arXiv:2609.38866v1 Announce Type: new Abstract: As preferences, goals, and facts change, LLM agents must use the current state while earlier versions remain in context. Yet they can answer with an old

1 Oct 2026

Adapting Linear-Time Architectures for Tabular In-Context Learning

arXiv:2609.36337v1 Announce Type: new Abstract: Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large da

researcharxiv-cs-lg

Are In-Context Images Worth 10 Dimensions?

arXiv:2609.37659v1 Announce Type: new Abstract: There has been significant work on understanding the In-Context Learning capabilities of Large Language Models, especially on the induction circuit. For

researcharxiv-cs-cv

Agents

CADOC: Cache-Aware Dynamic Object Context for Long-Horizon Agents

arXiv:2609.37012v1 Announce Type: new Abstract: For a long-horizon agent, context is the bottleneck: the history is resent with every request, the window caps task length, and reasoning degrades as th

agentsarxiv-cs-ai

ContextRender: From Execution Dependencies to Agent Context

arXiv:2609.37743v1 Announce Type: new Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even

Local Ai

KV-Kaizen: Learning Context-Adaptive Cache Compression Choices

arXiv:2609.37988v1 Announce Type: new Abstract: As the context size of text processed with an LLM grows, the size of KV caches can outstrip the memory allocated for the original model weights. This im

local-aiarxiv-cs-ai

ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context

model-releasesarxiv-cs-cl

arXiv:2609.36684v1 Announce Type: new Abstract: Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the wa

Salt++: Context-Aligned Post-Training for Few-Step Streaming Multimodal Generation

arXiv:2609.36995v1 Announce Type: cross Abstract: Few-step streaming audio--video generation requires both causal modeling and step distillation, yet standard training recipes face two context-related

safetyarxiv-cs-ai

Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings

arXiv:2609.37782v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential as training-free text encoders for long-context embeddings. Existing approaches primarily impro

Tutorials

CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

arXiv:2512.11352v2 Announce Type: replace Abstract: Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (G

tutorialsarxiv-cs-lg

Local Ai

Instance-Adaptive Prompts as Context for Time-Series Foundation Models

arXiv:2609.34786v1 Announce Type: new Abstract: Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual

local-aiarxiv-cs-lg

Instruct, Not Answer: Using Instruction Privileges in On-Policy Context Distillation

arXiv:2609.32201v1 Announce Type: cross Abstract: On-Policy Context Distillation (OPCD) has recently emerged as a powerful technique for transferring context to student models and for self-improvement

safetyarxiv-cs-lg

Hardware

KVCMAS: Efficient KV cache Correction for Shared Context in Multi-Agent Systems

arXiv:2609.34060v1 Announce Type: new Abstract: Prompt-specialized multi-agent systems enable multiple agents to share a model while performing complementary roles to solve complex tasks. However, age

hardwarearxiv-cs-lg

Agents

Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation

arXiv:2609.30337v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates knowledge obsolescence and factual hallucination in large language models by introducing external context

agentsarxiv-cs-lg

28 Sept 2026

UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification

arXiv:2605.06221v2 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer conte

28 Sept 2026

Context-Continuous Preference Learning for Exoskeleton Personalization