Search: “context”

KVFetch: Temporal Prefetching for the Missing Half of KV Cache Compression

arXiv:2610.08811v1 Announce Type: new Abstract: As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference. Existing met

8 Oct 2026

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PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs

DGX agent

arXiv:2610.10455v1 Announce Type: new Abstract: Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post

model-releasesarxiv-cs-cl

8 Oct 2026

The Dichotomy Between Pattern Recognition and Step-by-Step Reasoning

arXiv:2610.09186v1 Announce Type: new Abstract: We argue that pattern recognition and step-by-step reasoning are two ends of a spectrum. A large language model (LLM) learns to reason step-by-step when

tutorialsarxiv-cs-lg

8 Oct 2026

Local Ai

Capacity, Responsiveness and Alignment: What Makes a Latent Structure Actionable

arXiv:2610.06897v1 Announce Type: new Abstract: Localizing latent structures in the activation space of language models (LMs) is central to understanding and controlling their behavior. Yet, localized

local-aiarxiv-cs-cl

Defense-in-Depth for LLMs: Evaluating Memory Gates Against Activation-Induced and Memory-Induced Sycophancy

arXiv:2610.07403v1 Announce Type: new Abstract: Long-term memory allows Large Language Models (LLMs) to maintain personalized context across interactions, but retrieved user history can induce memory-

Leveraging a four-quadrant approach for evaluating Redpine Science

arXiv:2610.07937v1 Announce Type: new Abstract: Redpine Science gives models and agents a single access point to a wide range of peer-reviewed literature, queried directly through the Model Context Pr

Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell

arXiv:2610.07782v1 Announce Type: new Abstract: Decomposing long-context inference across cooperating agents bounds the active KV cache per call rather than total evidence, which matters when KV-cache

agentsarxiv-cs-ai

Valid for Free: Homophily-Gated Conformal Prediction for Training-Free Node Classification with Tabular Foundation Models

arXiv:2610.08564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) can classify the nodes of a graph without training on it, by reading node and neighborhood features as table rows next

When Old Facts Return: Re-Reads, Reverts, and the Limits of Temporal Memory

arXiv:2610.07715v1 Announce Type: cross Abstract: A memory system can retire an obsolete value and later restore it merely because the same old statement appears again. A re-read of an old source and

researcharxiv-cs-cl

Modeling Deletion Requests in Machine Unlearning

arXiv:2610.04310v1 Announce Type: new Abstract: Machine unlearning is seen as a promising approach to enable users to exercise the 'right to erasure' in the context of AI models. We ask how users migh

Multi-Task Partially Supervised Learning for Super-Resolution and Semantic Segmentation on Earth Observation data

arXiv:2610.06389v1 Announce Type: new Abstract: Super-resolution and semantic segmentation are known to benefit one another, especially in the Earth observation context. However, learning both tasks i

researcharxiv-cs-cv

Pythia: Toward Foundation World Models for Multimodal Time Series

arXiv:2610.05240v1 Announce Type: new Abstract: Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide c

tutorialsarxiv-cs-lg

ReMAP: Restoring the Perceptual Cycle with Reasoning-Time Latent Visual Memory

model-releasesarxiv-cs-cv

arXiv:2610.05097v1 Announce Type: new Abstract: As multimodal large language models (MLLMs) reason for longer, attention to the initial visual input diminishes, weakening visual grounding. Visual memo

Time-series Foundation Models for Predictive Control: The Role of Excitation

arXiv:2610.06447v1 Announce Type: new Abstract: Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation models (

Towards Looped Models Done Right, Part II: Rethinking at Fixed Points

arXiv:2610.06833v1 Announce Type: new Abstract: Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning (RL). The closer recurrent states get t

tutorialsarxiv-cs-lg

BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration

arXiv:2610.02800v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, e

CONTRA: Discovering and Qualifying Behavior-Changing Questions for Selective Clarification in LLM Code Generation

arXiv:2610.01769v1 Announce Type: cross Abstract: Coding agents can generate code that appears correct but implements behavior the user never intended. This mismatch can arise when an agent silently r

CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache

arXiv:2610.02235v1 Announce Type: cross Abstract: Long-context decoding is increasingly constrained by key--value (KV) cache memory and bandwidth. Existing fixed-budget compression methods typically s

Event-guided Neural Video Compression

arXiv:2610.02265v1 Announce Type: cross Abstract: Neural video codecs derive motion and temporal contexts mainly from RGB frames, leaving room for cross-modal guidance from complementary temporal obse

researcharxiv-cs-cv

Learning a Fact Is Not Learning How to Retrieve It

arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on 'The capital of X is Y' may produce 'Y' after 'The capital of X is' but fail after 'The capital of X:'. We call these different ways

tutorialsarxiv-cs-ai

Local Ai

LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing

arXiv:2610.02497v1 Announce Type: new Abstract: Electromyography (EMG) signals vary substantially across individuals, body regions, recording sessions, and sensing hardware, limiting the generalizatio

local-aiarxiv-cs-lg

Sentry: Learning to Recover from LLM Agent Failures at Test Time

arXiv:2610.02994v1 Announce Type: cross Abstract: LLM agents often fail mid-task due to invalid tool calls, repeated actions, or poorly grounded reasoning, and learning from these failures is a path t

Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models

arXiv:2610.03077v1 Announce Type: new Abstract: Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. Ho

Local Ai

Counterfactual Auditing of Bias in Open-Source Large Language Models for Clinical Triage

arXiv:2610.01963v1 Announce Type: new Abstract: Emergency department (ED) triage is a high-stakes prioritization task in which demographic, socioeconomic, and system-context information may improperly

local-aiarxiv-cs-ai

Do Your Own Research: Learning to Forecast by Learning to Search

model-releasesarxiv-cs-lg

arXiv:2610.01955v1 Announce Type: new Abstract: Outcome-based reinforcement learning can train language models to forecast real-world events, but prior forecasting work either freezes research context

DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents

arXiv:2610.00316v1 Announce Type: cross Abstract: Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We

OpenMTB-Audit: Exposing Over-Refusal and Clinical Expert Perspectives in LLM-Based Molecular Tumor Board Safety Evaluation

arXiv:2610.01497v1 Announce Type: new Abstract: Molecular tumor boards integrate genomic findings, clinical context, and therapeutic evidence to support precision oncology. As AI enters this workflow,

Argus: Academic Integrity in the Era of Generative AI

arXiv:2609.36073v1 Announce Type: cross Abstract: The rapid proliferation of large language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic i

safetyarxiv-cs-ai

Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis

arXiv:2609.39955v1 Announce Type: new Abstract: Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among

tutorialsarxiv-cs-ai

Index-Translate: A Multilingual Translation Model Family -- Text, Speech, Controlled Dubbing, and Long-Document Translation

arXiv:2609.40181v1 Announce Type: new Abstract: We introduce Index-Translate, a multilingual translation model family that combines a shared multilingual foundation with specialized training for gener

Over-Personalization Is a Decision Failure: Generation-Induced Apply Bias in LLMs

arXiv:2609.34284v2 Announce Type: replace Abstract: Personalized LLMs must decide, for each stored preference, whether the current context calls for applying or suppressing it, which we call its appli

safetyarxiv-cs-cl

Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

arXiv:2609.39975v1 Announce Type: cross Abstract: This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluati

Semantic Chunking and the Entropy of Natural Language

arXiv:2602.13194v3 Announce Type: replace-cross Abstract: Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong re

SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling

arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation. H

agentsarxiv-cs-ai

Block Sparse Flash Attention

arXiv:2512.07011v2 Announce Type: replace-cross Abstract: Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity c

Evaluating Alignment of Behavioral Dispositions in LLMs

arXiv:2602.11328v2 Announce Type: replace Abstract: As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral disp

safetyarxiv-cs-cl

Fractional State Space Transition for Long Sequence Modeling

arXiv:2609.36314v1 Announce Type: cross Abstract: State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for

Illusory Truth or Mere Exposure? Model-Dependent Repetition Effects in LLM-Based Social Media Simulations

arXiv:2609.36278v1 Announce Type: new Abstract: Generative agent-based models (GABMs) are increasingly used to simulate social media dynamics, including misinformation spread. For such social simulati

Latent Inference-Time Guidance of Time Series Foundation Models

arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on i

LoopICL: Looping a single transformer block to solve tabular tasks

arXiv:2609.36108v1 Announce Type: cross Abstract: Tabular foundation models using in-context learning have recently surpassed gradient-boosted trees on predictive tabular tasks. However, recent mechan

Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression

arXiv:2609.36322v1 Announce Type: cross Abstract: Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer

REALHOP: Rethinking Multi-Hop Reasoning Evaluation via Behavioral Auditing

arXiv:2609.36984v1 Announce Type: new Abstract: Complex questions often require multi-hop reasoning that connects facts distributed across sources or distant regions of a long context through intermed

SIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation

arXiv:2609.36742v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for improving large language models (LLMs) on various tasks, yet it

safetyarxiv-cs-ai

Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning

arXiv:2609.37035v1 Announce Type: new Abstract: Streaming video assistance requires models to answer asynchronous questions from an observed prefix under a fixed context budget. Existing approaches mo

Agentic Video Understanding: A Survey

arXiv:2609.31713v1 Announce Type: new Abstract: As large language models (LLMs) become capable of processing increasingly diverse modalities and longer temporal contexts, an emerging line of work is m

agentsarxiv-cs-cv

Counterfactual Self-Evolving Agents for Evidence-Grounded Reasoning

arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task

Decision-Sufficient State Representations: Measuring and Reducing Write-Time Regret

arXiv:2609.32805v1 Announce Type: cross Abstract: Long tasks produce more history than an LLM agent can hold in its context, and more than it uses reliably even when the history fits. A growing line o

agentsarxiv-cs-lg

Decoupling Token Roles in Autoregressive Pretraining

arXiv:2609.33405v1 Announce Type: new Abstract: Autoregressive pretraining increasingly draws on heterogeneous data, making it important to understand how a model learns from an individual token. The