Search: “context”

Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation

arXiv:2603.13683v2 Announce Type: replace Abstract: Although debiased large language models (LLMs) excel at handling known or low-bias prompts, they often fail on unfamiliar and high-bias prompts. We

safetyarxiv-cs-cl

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Psychological Steering of Large Language Models

DGX agent

arXiv:2604.14463v1 Announce Type: new Abstract: Large language models (LLMs) emulate a consistent human-like behavior that can be shaped through activation-level interventions. This paradigm is conver

researcharxiv-cs-cl

17 Apr 2026

Quantitative Approximation Rates for Group Equivariant Learning

arXiv:2602.20370v2 Announce Type: replace Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approx

Safe Reinforcement Learning using Action Projection: Safeguard the Policy or the Environment?

arXiv:2509.12833v2 Announce Type: replace Abstract: Projection-based safety filters, which modify unsafe actions by mapping them to the closest safe alternative, are widely used to enforce safety cons

SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications

arXiv:2604.13180v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliab

agentsarxiv-cs-ai

Segment-Level Coherence for Robust Harmful Intent Probing in LLMs

arXiv:2604.14865v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and Nucl

researcharxiv-cs-cl

Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?

arXiv:2604.13826v1 Announce Type: cross Abstract: Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limita

researcharxiv-cs-ai

SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing

arXiv:2604.14399v1 Announce Type: new Abstract: Autonomous on-orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi-phase

agentsarxiv-cs-ro

Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects

arXiv:2510.07890v3 Announce Type: replace Abstract: Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. However, dialects are pri

applicationsarxiv-cs-cl

The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure

arXiv:2604.14197v1 Announce Type: new Abstract: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently. Our purp

safetyarxiv-cs-cl

The Specification Trap: Why Static Value Alignment Alone Is Insufficient for Robust Alignment

arXiv:2512.03048v4 Announce Type: replace-cross Abstract: Static content-based AI value alignment is insufficient for robust alignment under capability scaling, distributional shift, and increasing au

Theory of Mind in Action: The Instruction Inference Task in Dynamic Human-Agent Collaboration

arXiv:2507.02935v2 Announce Type: replace Abstract: Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction

Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback

arXiv:2507.15066v5 Announce Type: replace Abstract: Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanat

Towards AI-assisted Neutrino Flavor Theory Design

arXiv:2506.08080v2 Announce Type: replace-cross Abstract: Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A

agentsarxiv-cs-lg

Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity

arXiv:2507.23121v2 Announce Type: replace Abstract: In this work, we study a critical research problem regarding the trustworthiness of large language models (LLMs): how LLMs behave when encountering

VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs

arXiv:2604.14550v1 Announce Type: cross Abstract: Generating synthesizable Verilog for large, hierarchical hardware designs remains a significant challenge for large language models (LLMs), which stru

VoxSafeBench: Not Just What Is Said, but Who, How, and Where

arXiv:2604.14548v1 Announce Type: cross Abstract: As speech language models (SLMs) transition from personal devices into shared, multi-user environments, their responses must account for far more than

Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization

arXiv:2604.14765v1 Announce Type: new Abstract: We present a geometric framework for Reinforcement Learning (RL) that views policies as maps into the Wasserstein space of action probabilities. First,

When Does Content-Based Routing Work? Representation Requirements for Selective Attention in Hybrid Sequence Models

arXiv:2603.20997v2 Announce Type: replace Abstract: We identify a routing paradox in hybrid sequence models: content-based routing - deciding which tokens deserve expensive attention - requires pairwi

Young people's perceptions and recommendations for conversational generative artificial intelligence in youth mental health

arXiv:2604.13381v1 Announce Type: cross Abstract: Conversational generative artificial intelligence agents (or genAI chatbots) could benefit youth mental health, yet young people's perspectives remain

agentsarxiv-cs-ai

Activation-Guided Local Editing for Jailbreaking Attacks

arXiv:2508.00555v2 Announce Type: replace-cross Abstract: Jailbreaking is an essential adversarial technique for red-teaming these models to uncover and patch security flaws. However, existing jailbre

safetyarxiv-cs-cl

An Empirical Investigation of Practical LLM-as-a-Judge Improvement Techniques on RewardBench 2

arXiv:2604.13717v1 Announce Type: new Abstract: LLM-as-a-judge, using a language model to score or rank candidate responses, is widely used as a scalable alternative to human evaluation in RLHF pipeli

Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data

arXiv:2310.02540v2 Announce Type: replace Abstract: Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data dist

Coherence in the brain unfolds across separable temporal regimes

arXiv:2512.20481v4 Announce Type: replace-cross Abstract: To maintain coherence in language, the brain must satisfy key competing temporal demands: the gradual accumulation of meaning across extended

Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET

arXiv:2406.12632v3 Announce Type: replace-cross Abstract: Positron emission tomography (PET) provides molecular biomarkers for Alzheimer's disease and related dementias (ADRD) and is increasingly used

Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering

arXiv:2604.13307v1 Announce Type: new Abstract: An unsupervised framework for hyperspectral image (HSI) clustering is proposed that incorporates masked deep representation learning with diffusion-base

Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection

arXiv:2604.13882v1 Announce Type: new Abstract: The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread avail

F-Actor: Controllable Conversational Behaviour in Full-Duplex Models

arXiv:2601.11329v3 Announce Type: replace Abstract: Spoken conversational systems require more than accurate speech generation to have human-like conversations: to feel natural and engaging, they must

Local Ai

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

arXiv:2508.08791v3 Announce Type: replace Abstract: Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of eff

local-aiarxiv-cs-cl

From Order to Distribution: A Spectral Characterization of Forgetting in Continual Learning

arXiv:2604.13460v1 Announce Type: new Abstract: A central challenge in continual learning is forgetting, the loss of performance on previously learned tasks induced by sequential adaptation to new one

GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses

arXiv:2604.13248v1 Announce Type: new Abstract: Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments.

agentsarxiv-cs-ro

Getting the Numbers Rightnicode{x2014}Modelling Multi-Class Object Counting in Dense and Varied Scenes

arXiv:2510.02213v2 Announce Type: replace Abstract: Density map estimation enables accurate object counting in heavily occluded, and densely packed scenes where detection-based counting fails. In mult

Giving Voice to the Constitution: Low-Resource Text-to-Speech for Quechua and Spanish Using a Bilingual Legal Corpus

arXiv:2604.13288v1 Announce Type: new Abstract: We present a unified pipeline for synthesizing high-quality Quechua and Spanish speech for the Peruvian Constitution using three state-of-the-art text-t

applicationsarxiv-cs-cl

Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection

arXiv:2604.13942v1 Announce Type: new Abstract: Recent vision-language-action (VLA) systems have demonstrated strong capabilities in embodied manipulation. However, most existing VLA policies rely on

agentsarxiv-cs-ro

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs

arXiv:2604.13258v1 Announce Type: new Abstract: Attribution methods seek to explain language model predictions by quantifying the contribution of input tokens to generated outputs. However, most exist

HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System

arXiv:2604.14125v1 Announce Type: new Abstract: While end-to-end Vision-Language-Action (VLA) models offer a promising paradigm for robotic manipulation, fine-tuning them on narrow control data often

applicationsarxiv-cs-cv

Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment

arXiv:2604.13462v1 Announce Type: cross Abstract: Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as fina

applicationsarxiv-cs-lg

Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions

arXiv:2604.13128v1 Announce Type: cross Abstract: Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Underst

Learning the Cue or Learning the Word? Analyzing Generalization in Metaphor Detection for Verbs

arXiv:2604.13713v1 Announce Type: new Abstract: Metaphor detection models achieve strong benchmark performance, yet it remains unclear whether this reflects transferable generalization or lexical memo

Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs

arXiv:2604.13979v1 Announce Type: new Abstract: Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a c

LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding

model-releasesarxiv-cs-cv

arXiv:2602.20913v2 Announce Type: replace Abstract: This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, a

Local Ai

MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Images

arXiv:2604.13970v1 Announce Type: new Abstract: In diagnostic reports, experts encode complex imaging data into clinically actionable information. They describe subtle pathological findings that are m

local-aiarxiv-cs-cv

Tutorials

Nonparametric Sparse Online Learning of the Koopman Operator

arXiv:2405.07432v4 Announce Type: replace-cross Abstract: The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing dat

tutorialsarxiv-cs-lg

Physically-Guided Optical Inversion Enable Non-Contact Side-Channel Attack on Isolated Screens

arXiv:2604.13419v1 Announce Type: new Abstract: Noncontact exfiltration of electronic screen content poses a security challenge, with side-channel incursions as the principal vector. We introduce an o

POINTS-Seeker: Towards Training a Multimodal Agentic Search Model from Scratch

arXiv:2604.14029v1 Announce Type: new Abstract: While Large Multimodal Models (LMMs) demonstrate impressive visual perception, they remain epistemically constrained by their static parametric knowledg

agentsarxiv-cs-cv

Power Transform Revisited: Numerically Stable, and Federated

arXiv:2510.04995v3 Announce Type: replace Abstract: Power transforms are popular parametric methods for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analys

applicationsarxiv-cs-lg

Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions

arXiv:2601.03173v2 Announce Type: replace Abstract: Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored i

Quantifying and Understanding Uncertainty in Large Reasoning Models

arXiv:2604.13395v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LR