Search: “context”

Drawing on Memory: Dual-Trace Encoding Improves Cross-Session Recall in LLM Agents

arXiv:2604.12948v1 Announce Type: new Abstract: LLM agents with persistent memory store information as flat factual records, providing little context for temporal reasoning, change tracking, or cross-

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Man and machine: artificial intelligence and judicial decision making

DGX agent

arXiv:2603.19042v4 Announce Type: replace Abstract: The integration of artificial intelligence (AI) technologies into judicial decision-making, particularly in pretrial, sentencing, and parole context

safetyarxiv-cs-ai

15 Apr 2026

Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation

arXiv:2511.17097v2 Announce Type: replace Abstract: Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they

safetyarxiv-cs-ro

ROSE: An Intent-Centered Evaluation Metric for NL2SQL

arXiv:2604.12988v1 Announce Type: cross Abstract: Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasing

SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models

arXiv:2604.12377v1 Announce Type: cross Abstract: Korean is a morphologically rich language with a featural writing system in which each character is systematically composed of subcharacter units know

Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems

arXiv:2603.01045v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agen

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs

arXiv:2604.12610v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. Howe

Applications

Domain-Specific Data Generation Framework for RAG Adaptation

arXiv:2510.11217v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external ret

applicationsarxiv-cs-ai

Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders

arXiv:2604.10424v1 Announce Type: new Abstract: Foundation-style ECG encoders pretrained with self-supervised learning are increasingly reused across tasks, institutions, and deployment contexts, ofte

researcharxiv-cs-lg

Applications

Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE

arXiv:2602.17314v2 Announce Type: replace-cross Abstract: Open datasets play a crucial role in three research domains that intersect data science and education: learning analytics, educational data mi

applicationsarxiv-cs-lg

Should We be Pedantic About Reasoning Errors in Machine Translation?

arXiv:2604.09890v1 Announce Type: cross Abstract: Across multiple language pairings (English o {Spanish, French, German, Mandarin, Japanese, Urdu, Cantonese}), we find reasoning errors in translation.

Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets

arXiv:2604.02460v2 Announce Type: replace Abstract: Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation.

VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG

arXiv:2604.05418v2 Announce Type: replace-cross Abstract: Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generatio

safetyarxiv-cs-ai

Agents

WaterAdmin: Orchestrating Community Water Distribution Optimization via AI Agents

arXiv:2604.10343v1 Announce Type: new Abstract: We study the operation of community water systems, where pumps and valves must be scheduled to reliably meet water demands while minimizing energy consu

agentsarxiv-cs-lg

Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography

arXiv:2604.09066v1 Announce Type: new Abstract: Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to

Benchmarking CNN- and Transformer-Based Models for Surgical Instrument Segmentation in Robotic-Assisted Surgery

model-releasesarxiv-cs-cv

arXiv:2604.09151v1 Announce Type: new Abstract: Accurate segmentation of surgical instruments in robotic-assisted surgery is critical for enabling context-aware computer-assisted interventions, such a

BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

arXiv:2604.09497v1 Announce Type: cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.

Distributionally Robust Token Optimization in RLHF

arXiv:2604.08577v1 Announce Type: cross Abstract: Large Language Models (LLMs) tend to respond correctly to prompts that align to the data they were trained and fine-tuned on. Yet, small shifts in wor

Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise

arXiv:2604.09532v1 Announce Type: cross Abstract: Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual conten

Current LLMs still cannot 'talk much' about grammar modules: Evidence from syntax

arXiv:2603.20114v4 Announce Type: replace Abstract: We aim to examine the extent to which Large Language Models (LLMs) can 'talk much' about grammar modules, providing evidence from syntax core proper

researcharxiv-cs-cl

Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models

arXiv:2604.06871v1 Announce Type: cross Abstract: Large Speech Language Models (LSLMs) typically operate at high token rates (tokens/s) to ensure acoustic fidelity, yet this results in sequence length

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism

arXiv:2601.05524v2 Announce Type: replace Abstract: Parallel Speculative Decoding (PSD) accelerates traditional Speculative Decoding (SD) by overlapping draft generation with verification. However, it

researcharxiv-cs-cl

Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction

arXiv:2604.07659v1 Announce Type: new Abstract: Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hal

Tutorials

EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems

arXiv:2503.23078v3 Announce Type: replace Abstract: Large language models have improved dialogue systems, but often process conversational turns in isolation, overlooking the event structures that gui

tutorialsarxiv-cs-cl

Tutorials

ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits

arXiv:2604.06559v1 Announce Type: cross Abstract: Understanding and explaining the structure of generated test inputs is essential for effective software testing and debugging. Existing approaches--in

tutorialsarxiv-cs-lg

GS-Surrogate: Deformable Gaussian Splatting for Parameter Space Exploration of Ensemble Simulations

arXiv:2604.06358v1 Announce Type: cross Abstract: Exploring ensemble simulations is increasingly important across many scientific domains. However, supporting flexible post-hoc exploration remains cha

Optimal Decay Spectra for Linear Recurrences

arXiv:2604.07658v1 Announce Type: cross Abstract: Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum:

researcharxiv-cs-cl

Prompt reinforcing for long-term planning of large language models

arXiv:2510.05921v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable success in a wide range of natural language processing tasks and can be adapted through prompt

Sensitivity-Positional Co-Localization in GQA Transformers

arXiv:2604.07766v1 Announce Type: new Abstract: We investigate a fundamental structural question in Grouped Query Attention (GQA) transformers: do the layers most sensitive to task correctness coincid

Small Vision-Language Models are Smart Compressors for Long Video Understanding

arXiv:2604.08120v1 Announce Type: cross Abstract: Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets

T-Gated Adapter: A Lightweight Temporal Adapter for Vision-Language Medical Segmentation

model-releasesarxiv-cs-cv

arXiv:2604.08167v1 Announce Type: new Abstract: Medical image segmentation traditionally relies on fully supervised 3D architectures that demand a large amount of dense, voxel-level annotations from c

Applications

exttt{SEM-CTRL}: Semantically Controlled Decoding

arXiv:2503.01804v4 Announce Type: replace Abstract: Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for r

applicationsarxiv-cs-cl

Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs

model-releasesarxiv-cs-cv

arXiv:2509.08016v2 Announce Type: replace Abstract: Video Large Language Models (VideoLLMs) face a critical bottleneck: increasing the number of input frames to capture fine-grained temporal detail le

Agents

CodeHealth Predicts the Token Cost of Agentic Code Review: A Pre-Registered Study of 465 Agent-Authored Pull Requests

arXiv:2610.21914v1 Announce Type: cross Abstract: Context: Organizations increasingly delegate code review to multi-agent large language model (LLM) pipelines billed per token. Low code quality raises

agentsarxiv-cs-ai

Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches

arXiv:2610.21126v1 Announce Type: new Abstract: Sparse KV compression reduces long-context inference costs by retaining selected historical states, yet these states typically keep their coordinates fr

Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits

model-releasesarxiv-cs-lg

arXiv:2610.22252v1 Announce Type: new Abstract: We study how to evaluate K target policies simultaneously in stochastic contextual bandits with linear reward structure. Given a known d-dimensional fea

Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing

arXiv:2610.22196v1 Announce Type: new Abstract: External-memory systems extend language-model applications beyond a context window, but their quality depends critically on the model executing the writ

KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents

arXiv:2610.22331v1 Announce Type: new Abstract: Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions

safetyarxiv-cs-ai

Memory as Weights: Internalizing Long-Term History for Streaming Videos

arXiv:2610.22070v1 Announce Type: cross Abstract: Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and u

Agents

Mosaic: Multi-Session Agentic Reinforcement Learning

arXiv:2610.22174v1 Announce Type: cross Abstract: Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulate

agentsarxiv-cs-ai

OmicsBench: a long-horizon multi-turn benchmark of AI agents on research-study-scale bioinformatics

arXiv:2610.21074v1 Announce Type: new Abstract: Computational biologists increasingly delegate substantial analyses to AI agents in long-horizon, multi-turn sessions, yet most benchmarks of agents' bi

PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning

arXiv:2610.21308v1 Announce Type: cross Abstract: Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text ra

Rethinking Classifier-Free Guidance for TI2I Flow Matching

arXiv:2610.21550v1 Announce Type: new Abstract: We study Text-and-Image-to-Image (TI2I) flow matching methods for controllable image editing. In a broader generative modeling context, Classifier-Free

safetyarxiv-cs-cv

Tool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes

arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to fre

Towards Unbiased On-Policy Distillation for Block Diffusion Language Models

arXiv:2610.05373v2 Announce Type: replace Abstract: On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffu

safetyarxiv-cs-cl

AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes

arXiv:2610.14487v1 Announce Type: new Abstract: Generative recommendation models based on autoregressive decoding have been adopted as alternatives to classical ranking-based systems. While their mode

safetyarxiv-cs-ai

15 Oct 2026

Combining scEEG and PPG for reliable sleep staging

arXiv:2602.15042v2 Announce Type: replace-cross Abstract: Reliable sleep staging remains challenging for lightweight wearable devices such as single-channel electroencephalography (scEEG) or photoplet

15 Oct 2026

CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis