Search: “context”

MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents

arXiv:2605.03482v1 Announce Type: cross Abstract: Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We for

researcharxiv-cs-lg

6 May 2026

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SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

DGX agent

arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg

safetyarxiv-cs-cl

6 May 2026

Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers

arXiv:2605.03780v1 Announce Type: cross Abstract: Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to

6 May 2026

Towards Multi-Agent Autonomous Reasoning in Hydrodynamics

model-releasesarxiv-cs-ai

arXiv:2605.01102v1 Announce Type: new Abstract: Single-agent systems (SAS) have become the default pattern for LLM-driven scientific workflows, but routing planning, tool use, and synthesis through a

6 May 2026

Finite-Sample Analysis of Elimination in Active Hypothesis Testing

model-releasesarxiv-cs-lg

arXiv:2605.01039v1 Announce Type: new Abstract: A fixed-confidence, finite-sample problem of active hypothesis testing arises in many safety-critical applications. Situated in the context of sequentia

GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory

arXiv:2605.01688v1 Announce Type: new Abstract: Long-horizon conversational agents rely on memory systems with increasingly sophisticated retrieval mechanisms. However, retrieved fragments are typical

LinMU: Multimodal Understanding Made Linear

arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th

researcharxiv-cs-cv

On Stable Long-Form Generation: Benchmarking and Mitigating Length Volatility

arXiv:2605.01357v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at long-context understanding but exhibit significant limitations in long-form generation. Existing studies primarily

PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression

arXiv:2605.01320v1 Announce Type: new Abstract: LiDAR point cloud compression is vital for autonomous systems to handle massive data from high-resolution sensors. While learned entropy modeling built

agentsarxiv-cs-cv

The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance

arXiv:2605.01537v1 Announce Type: new Abstract: Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compre

applicationsarxiv-cs-cl

ClustViT: Clustering-based Token Merging for Semantic Segmentation

arXiv:2510.01948v2 Announce Type: replace Abstract: Vision Transformers can achieve high accuracy and strong generalization across various contexts, but their practical applicability on real-world rob

applicationsarxiv-cs-cv

Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues

arXiv:2605.00119v1 Announce Type: new Abstract: There is a significant gap in evaluating cultural reasoning in LLMs using conversational datasets that capture culturally rich and dialectal contexts. M

Safety

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory

arXiv:2605.00702v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving pre

safetyarxiv-cs-cl

ML-Bench&Guard: Policy-Grounded Multilingual Safety Benchmark and Guardrail for Large Language Models

arXiv:2605.00689v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety in diverse regulatory and cultural environments

State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning

arXiv:2605.00206v1 Announce Type: cross Abstract: Current transformers discard their rich latent residual stream between positions, reconstructing latent reasoning context at each new position and lea

VideoDetective: Clue Hunting via both Extrinsic Query and Intrinsic Relevance for Long Video Understanding

arXiv:2603.22285v2 Announce Type: replace Abstract: Long video understanding remains challenging for multimodal large language models (MLLMs) due to limited context windows, which necessitate identify

researcharxiv-cs-cv

Bayesian Hierarchical Models and the Maximum Entropy Principle

model-releasesarxiv-cs-lg

arXiv:2603.10252v2 Announce Type: replace-cross Abstract: Bayesian hierarchical models are frequently used in practical data analysis contexts. One interpretation of these models is that they provide

Beyond Accuracy: LLM Variability in Evidence Screening for Software Engineering SLRs

model-releasesarxiv-cs-ai

arXiv:2604.27006v1 Announce Type: cross Abstract: Context: Study screening in systematic literature reviews is costly, inconsistency-prone, and risk-asymmetric, since false negatives can compromise va

LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning

arXiv:2604.27960v1 Announce Type: new Abstract: Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical incons

M-DaQ: Retrieving Samples with Multilingual Diversity and Quality for Instruction Fine-Tuning Datasets

arXiv:2509.15549v2 Announce Type: replace Abstract: Multilingual instruction fine-tuning (IFT) empowers large language models to generalize across diverse linguistic and cultural contexts; however, hi

Safety

MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness

arXiv:2604.28030v1 Announce Type: cross Abstract: Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specif

safetyarxiv-cs-ai

Pragmos: A Process Agentic Modeling System

arXiv:2604.27311v1 Announce Type: cross Abstract: The advent of Large Language Models (LLMs) has significantly transformed tasks across Software Engineering. In the context of Business Process Managem

agentsarxiv-cs-ai

Syntactically-guided Information Maintenance in Sentence Comprehension

arXiv:2604.27468v1 Announce Type: new Abstract: Maintaining information in context is essential in successful real-time language comprehension, but maintenance is cognitively costly and can slow proce

TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents

arXiv:2601.02845v2 Announce Type: replace-cross Abstract: Long-horizon conversational agents have to manage ever-growing interaction histories that quickly exceed the finite context windows of large l

Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests

arXiv:2601.17617v3 Announce Type: replace-cross Abstract: LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understan

Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction

arXiv:2604.26209v1 Announce Type: cross Abstract: Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context.

Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent

arXiv:2604.26197v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential.

agentsarxiv-cs-lg

Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation

arXiv:2604.25980v1 Announce Type: cross Abstract: In the context of quantum secure scenarios, existing research on mobile edge devices and intelligent computing and edge (ICE) systems based on the Non

agentsarxiv-cs-ai

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

model-releasesarxiv-cs-ai

arXiv:2604.05190v2 Announce Type: replace-cross Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures.

Seamless Indoor-Outdoor Mapping for INGENIOUS First Responders

arXiv:2604.26368v1 Announce Type: new Abstract: In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Resp

researcharxiv-cs-cv

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion

arXiv:2508.16131v2 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context. It can boost developer productivity while providing

When Hidden States Drift: Can KV Caches Rescue Long-Range Speculative Decoding?

arXiv:2604.26412v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the specul

A systematic literature Review for Transformer-based Software Vulnerability detection

arXiv:2604.24822v1 Announce Type: cross Abstract: Context: Software vulnerabilities pose significant security threats to software systems, especially as software is increasingly used across many areas

applicationsarxiv-cs-lg

ADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models

arXiv:2604.24940v1 Announce Type: new Abstract: Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word with a single vector, creating representa

Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories

arXiv:2411.05174v2 Announce Type: replace Abstract: We consider the problem of estimating the transition dynamics T^* from near-optimal expert trajectories in the context of offline model-based reinfo

applicationsarxiv-cs-lg

Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models

model-releasesarxiv-cs-lg

arXiv:2509.17625v2 Announce Type: replace Abstract: In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-

Drivetrain simulation using variational autoencoders

arXiv:2501.17653v3 Announce Type: replace Abstract: This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk signals from torque demand in the context of limited real-world drive

applicationsarxiv-cs-lg

FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments

arXiv:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environme

Tutorials

Provable Accelerated Bayesian Optimization with Knowledge Transfer

arXiv:2511.03125v2 Announce Type: replace-cross Abstract: We study how to accelerate Bayesian optimization (BO) on a target task by transferring historical knowledge from related source tasks. Existin

tutorialsarxiv-cs-lg

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

model-releasesarxiv-cs-cv

arXiv:2604.25884v1 Announce Type: cross Abstract: Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representatio

The Structured Output Benchmark: A Multi-Source Benchmark for Evaluating Structured Output Quality in Large Language Models

arXiv:2604.25359v1 Announce Type: new Abstract: Large Language Models are increasingly being deployed to extract structured data from unstructured and semi-structured sources: parsing invoices, medica

Always Tell Me The Odds: Fine-grained Conditional Probability Estimation

arXiv:2505.01595v2 Announce Type: replace-cross Abstract: We present a state-of-the-art model for fine-grained probability estimation of propositions conditioned on context. Recent advances in large l

Local Ai

Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulation

arXiv:2604.24665v1 Announce Type: cross Abstract: This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensit

local-aiarxiv-cs-ai

ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering

arXiv:2510.13312v2 Announce Type: replace Abstract: We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an import

Consistency of Lloyd's Algorithm Under Perturbations

arXiv:2309.00578v2 Announce Type: replace Abstract: In the context of unsupervised learning, Lloyd's algorithm is one of the most widely used clustering algorithms. It has inspired a plethora of work

researcharxiv-cs-lg

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation

arXiv:2604.24361v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorl

Generalising maximum mean discrepancy: kernelised functional Bregman divergences

model-releasesarxiv-cs-cv

arXiv:2604.24047v1 Announce Type: cross Abstract: Bregman divergences play a pivotal role in statistics, machine learning and computational information geometry. Particularly in the context of machine

Integrative neurocybernetic modeling in the era of large-scale neuroscience

arXiv:2604.23903v1 Announce Type: cross Abstract: Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented

researcharxiv-cs-lg