Search: “context”

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling

arXiv:2603.10174v2 Announce Type: replace Abstract: Autonomous underwater vehicles (AUVs) are increasingly used to survey coral reefs, yet efficiently locating specific coral species of interest remai

agentsarxiv-cs-ro

7 Jul 2026

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Criterion-Conditional In-Context Learning: Evaluating Criterion-Shift Adaptation in Vision-Language Models

DGX agent

arXiv:2607.02575v1 Announce Type: cross Abstract: Vision-language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the suppor

model-releasesarxiv-cs-ai

7 Jul 2026

Tools

7/ Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking Paper: https://arxiv.org/abs/2602.21196

Untied Ulysses is a memory-efficient technique for context parallelism that processes attention heads in chunks rather than sequences, reducing memory overhead during transformer inference and trainin

toolstogether-ai--x

1 Jul 2026

Probing Memorization of Tabular In-Context Learning

arXiv:2606.31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.e., tabular foundation models leveraging in-context learning (ICL), achieve state-of-the-art performance on tabular tasks

applicationsarxiv-cs-lg

1 Jul 2026

Context engineering has its own track at the @aiDotEngineer World's Fair this year. 🎉 I've respected what @swyx and the @latentspacepod te…

Context engineering has its own track at the @aiDotEngineer World's Fair this year. 🎉 I've respected what @swyx and the @latentspacepod team have been building for years — and I'm pumped to be a part

agentsswyx--x

FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks

arXiv:2606.30336v1 Announce Type: new Abstract: We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a

applicationsarxiv-cs-lg

Memory-Managed Long-Context Attention: A Preliminary Study of Editable Request-Local Memory

model-releasesarxiv-cs-cl

arXiv:2606.28876v1 Announce Type: new Abstract: Long-context language models often conflate two different goals: compressing history into an efficient state, and maintaining reliable long-term memory.

SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing

arXiv:2606.29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies,

Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation

arXiv:2606.27732v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. Howev

Can LLMs Judge Better Than They Generate? Evaluating Task Asymmetry, Mechanistic Interpretability and Transferability for In-Context QA

arXiv:2606.28050v1 Announce Type: cross Abstract: LLM-as-a-Judge and self-evaluation pipelines implicitly assume that evaluation is easier than generation. We test this in a controlled in-context QA s

Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits

arXiv:2603.26629v2 Announce Type: replace Abstract: Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typicall

Toward Robust In-Context Segmentation via Concept Guidance

arXiv:2606.28149v1 Announce Type: cross Abstract: In-context segmentation (ICS) requires a model to segment target regions in a query image using only a few reference images and their corresponding ma

In-Context World Modeling for Robotic Control

model-releasesarxiv-cs-cv

arXiv:2606.26025v1 Announce Type: cross Abstract: Modern Vision-Language-Action (VLA) models often fail to generalize to novel setups, such as altered camera viewpoints or robot morphologies, because

25 Jun 2026

Tutorials

Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention

arXiv:2606.25342v1 Announce Type: new Abstract: Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context

tutorialsarxiv-cs-lg

25 Jun 2026

CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference

arXiv:2606.24467v1 Announce Type: new Abstract: Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiti

24 Jun 2026

The African Language Tax: Quantifying the Cost, Latency, and Context Penalty of Tokenizing African Languages in Frontier LLMs

arXiv:2606.24460v1 Announce Type: cross Abstract: Commercial large language models bill, scale latency, and budget context per token. Yet tokenizers assign more subword tokens to the same meaning in s

24 Jun 2026

In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts

arXiv:2603.25857v2 Announce Type: replace Abstract: The capabilities of large language models (LLMs) have expanded beyond natural language processing to scientific prediction tasks, including molecula

23 Jun 2026

Measuring Epistemic Resilience of LLMs Under Misleading Medical Context

arXiv:2606.12291v1 Announce Type: new Abstract: Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical ju

agentsarxiv-cs-cl

11 Jun 2026

Efficient Scaling of LLM Training with Flexible Context Parallelism

arXiv:2602.21788v2 Announce Type: replace-cross Abstract: Scaling long-context capabilities is crucial for Large Language Models (LLMs). However, real-world data contain a large number of sequences wi

applicationsarxiv-cs-lg

9 Jun 2026

Finite Certificates for In-Context Determinacy and a Threshold Theory of Emergence in Language Models

arXiv:2606.07623v1 Announce Type: new Abstract: This paper develops a model-theoretic framework for verifying context-conditioned language-model behavior by replacing benchmark labels with finite sema

9 Jun 2026

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

Model Context Protocols in Adaptive Transport Systems: A Survey

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

8 Jun 2026

Safety

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

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

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

researcharxiv-cs-cl

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

A full tour through RAG, document context, and AI agents - from 2023 to 2026 🌎🤖 @hexapode gave a comprehensive 90-min workshop at @aiDotEn…

A full tour through RAG, document context, and AI agents - from 2023 to 2026 🌎🤖 @hexapode gave a comprehensive 90-min workshop at @aiDotEngineer Singapore last week that comprehensively traces through

agentsjerry-liu--x

25 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

Safety

LLM agents & memory systems operate in continuously updated environments (Git repos, evolving docs). They must process long contexts, recove…

LLM agents & memory systems operate in continuously updated environments (Git repos, evolving docs). They must process long contexts, recover earlier information, and reason over many updates that cre

safetyjeremy-howard--x

20 May 2026

Safety

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

Many AI agents in finance rely on extremely high quality context engineering from documents 📑 They can be roughly divided into two categori…

Many AI agents in finance rely on extremely high quality context engineering from documents 📑 They can be roughly divided into two categories: 1️⃣ Repetitive, operational work common in back-office us

agentsjerry-liu--x

17 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