Search: “context”

Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors

model-releasesarxiv-cs-lg

arXiv:2604.21893v1 Announce Type: cross Abstract: Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constrainin

24 Apr 2026

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SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding

DGX agent

arXiv:2510.26615v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but it must balance limited effective context, re

model-releasesarxiv-cs-cl

24 Apr 2026

Slot Machines: How LLMs Keep Track of Multiple Entities

arXiv:2604.21139v1 Announce Type: new Abstract: Language models must bind entities to the attributes they possess and maintain several such binding relationships within a context. We study how multipl

24 Apr 2026

SPIRE: Structure-Preserving Interpretable Retrieval of Evidence

arXiv:2604.20849v1 Announce Type: cross Abstract: Retrieval-augmented generation over semi-structured sources such as HTML is constrained by a mismatch between document structure and the flat, sequenc

24 Apr 2026

Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData

model-releasesarxiv-cs-cv

arXiv:2511.02849v2 Announce Type: replace-cross Abstract: Individualized therapy is driven forward by medical data analysis, which provides insight into the patient's context. In particular, for Type

Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

arXiv:2604.19921v1 Announce Type: new Abstract: Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural

Tutorials

Efficient Symbolic Computations for Identifying Causal Effects

arXiv:2604.20516v1 Announce Type: cross Abstract: Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear

tutorialsarxiv-cs-lg

Knowledge Capsules: Structured Nonparametric Memory Units for LLMs

arXiv:2604.20487v1 Announce Type: cross Abstract: Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining. Retrieval-augmented gene

Hardware

Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling

arXiv:2604.20819v1 Announce Type: new Abstract: The scalability of long-context large language models is fundamentally limited by the quadratic memory cost of exact self-attention, which often leads t

hardwarearxiv-cs-lg

A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding

arXiv:2604.19689v1 Announce Type: new Abstract: Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large

A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation

arXiv:2604.18988v1 Announce Type: new Abstract: Multimodal empathetic response generation (MERG) aims to generate emotionally engaging and empathetic responses based on users' multimodal contexts. Exi

agentsarxiv-cs-cv

Bootstrapping Code Translation with Weighted Multilanguage Exploration

arXiv:2601.03512v2 Announce Type: replace-cross Abstract: Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data pair

Cell-Based Representation of Relational Binding in Language Models

arXiv:2604.19052v1 Announce Type: new Abstract: Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relati

Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection

arXiv:2604.19005v1 Announce Type: new Abstract: Half-truths, claims that are factually correct yet misleading due to omitted context, remain a blind spot for fact verification systems focused on expli

agentsarxiv-cs-cl

Efficient Autoregressive Inference for Transformer Probabilistic Models

arXiv:2510.09477v2 Announce Type: replace-cross Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tab

researcharxiv-cs-lg

Safety

Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck

arXiv:2601.12499v2 Announce Type: replace Abstract: Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which caus

safetyarxiv-cs-ai

STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2601.03248v2 Announce Type: replace Abstract: Spatio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context. This capab

Tadabur: A Large-Scale Quran Audio Dataset

arXiv:2604.18932v1 Announce Type: cross Abstract: Despite growing interest in Quranic data research, existing Quran datasets remain limited in both scale and diversity. To address this gap, we present

VISTA: Verification In Sequential Turn-based Assessment

arXiv:2510.27052v5 Announce Type: replace Abstract: Hallucination--defined here as generating statements unsupported or contradicted by available evidence or conversational context--remains a major ob

Safety

Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation

arXiv:2604.17325v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated conte

safetyarxiv-cs-cl

ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation

arXiv:2604.18362v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) remains unreliable in long-form settings, where retrieved evidence is noisy or contradictory, making it difficult f

Safety

CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion

arXiv:2604.16411v1 Announce Type: new Abstract: We study asynchronous alignment, a first-class multimodal learning setting in which a dense primary stream must be fused with sporadic external context

safetyarxiv-cs-lg

Exploring Concreteness Through a Figurative Lens

arXiv:2604.18296v1 Announce Type: new Abstract: Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor

HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents

arXiv:2604.16839v1 Announce Type: new Abstract: Long-term memory is a critical challenge for Large Language Model agents, as fixed context windows cannot preserve coherence across extended interaction

agentsarxiv-cs-cl

Latent Abstraction for Retrieval-Augmented Generation

arXiv:2604.17866v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for enhancing large language models (LLMs) with external knowledge, mitigating hallu

Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection

model-releasesarxiv-cs-cl

arXiv:2506.00955v2 Announce Type: replace Abstract: Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, exi

Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models

arXiv:2604.18199v1 Announce Type: new Abstract: Transformer-based embedding models suffer from quadratic computational and linear memory complexity, limiting their utility for long sequences. We propo

MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression

arXiv:2604.17695v1 Announce Type: cross Abstract: KV cache memory is the dominant bottleneck for long-context LLM inference. Existing compression methods each act on a single axis of the four-dimensio

MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models

arXiv:2603.19044v2 Announce Type: replace Abstract: Scientific ideation aims to propose novel solutions within a given scientific context. Existing LLM-based agentic approaches emulate human research

agentsarxiv-cs-cl

MuSteerNet: Human Reaction Generation from Videos via Observation-Reaction Mutual Steering

arXiv:2603.20187v2 Announce Type: replace Abstract: Video-driven human reaction generation aims to synthesize 3D human motions that directly react to observed video sequences, which is crucial for bui

researcharxiv-cs-cv

ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization

arXiv:2604.17512v1 Announce Type: new Abstract: Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operation

Applications

Physics-Informed Neural Networks for Biological 2D{+}t Reaction-Diffusion Systems

arXiv:2604.18548v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-info

applicationsarxiv-cs-lg

Sparse Feature Coactivation Reveals Causal Semantic Modules in Large Language Models

arXiv:2506.18141v3 Announce Type: replace Abstract: We identify semantically coherent, context-consistent network components in large language models (LLMs) using coactivation of sparse autoencoder (S

What makes an entity salient in discourse?

arXiv:2508.16464v2 Announce Type: replace Abstract: Entities in discourse vary in salience: main participants, objects and locations stay prominent, while others are quickly forgotten, raising questio

CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning

arXiv:2601.05858v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). Some follow-up work

20 Apr 2026

Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research

arXiv:2412.04497v5 Announce Type: replace-cross Abstract: Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite the

20 Apr 2026

Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

arXiv:2602.11327v2 Announce Type: replace-cross Abstract: The rapid development of the AI agent communication protocols, including the Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent

agentsarxiv-cs-ai

20 Apr 2026

Adaptive Layer Selection for Layer-Wise Token Pruning in LLM Inference

arXiv:2601.07667v2 Announce Type: replace Abstract: Due to the prevalence of large language models (LLMs), key-value (KV) cache reduction for LLM inference has received remarkable attention. Among num

Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems

model-releasesarxiv-cs-cl

arXiv:2604.14228v1 Announce Type: cross Abstract: Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes

Leveraging graph neural networks and mobility data for COVID-19 forecasting

arXiv:2501.11711v2 Announce Type: replace Abstract: The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of co

researcharxiv-cs-lg

Quantum-inspired tensor networks in machine learning models

arXiv:2604.14287v1 Announce Type: new Abstract: Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations

researcharxiv-cs-lg

SafeHarness: Lifecycle-Integrated Security Architecture for LLM-based Agent Deployment

arXiv:2604.13630v1 Announce Type: cross Abstract: The performance of large language model (LLM) agents depends critically on the execution harness, the system layer that orchestrates tool use, context

Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations

arXiv:2604.14172v1 Announce Type: new Abstract: Large Language Models (LLMs) are essential for analyzing and addressing vulnerabilities in cybersecurity. However, among over 200,000 vulnerabilities we

A closer look at how large language models trust humans: patterns and biases

arXiv:2504.15801v2 Announce Type: replace Abstract: As large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts, understanding the trust dynamics

AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning

arXiv:2509.25699v2 Announce Type: replace Abstract: Interleaved-Modal Chain-of-Thought (I-MCoT) advances vision-language reasoning, such as Visual Question Answering (VQA). This paradigm integrates sp

researcharxiv-cs-cv

Tutorials

Kwame 2.0: Human-in-the-Loop Generative AI Teaching Assistant for Large Scale Online Coding Education in Africa

arXiv:2603.29159v2 Announce Type: replace Abstract: Providing timely and accurate learning support in large-scale online coding courses is challenging, particularly in resource-constrained contexts. W

tutorialsarxiv-cs-cl

YOCO++: Enhancing YOCO with KV Residual Connections for Efficient LLM Inference

arXiv:2604.13556v1 Announce Type: new Abstract: Cross-layer key-value (KV) compression has been found to be effective in efficient inference of large language models (LLMs). Although they reduce the m

Cooperative Memory Paging with Keyword Bookmarks for Long-Horizon LLM Conversations

arXiv:2604.12376v1 Announce Type: cross Abstract: When LLM conversations grow beyond the context window, old content must be evicted -- but how does the model recover it when needed? We propose cooper