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Practical estimation of the optimal classification error with soft labels and calibration

arXiv:2505.20761v3 Announce Type: replace Abstract: While the performance of machine learning systems has experienced significant improvement in recent years, relatively little attention has been paid

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

DGX agent

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

17 Apr 2026

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration

arXiv:2509.21823v2 Announce Type: replace Abstract: Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods strugg

PROXIMA: A Reliability Scoring Framework for Proxy Metrics in Online Controlled Experiments

arXiv:2604.14352v1 Announce Type: cross Abstract: Online A/B testing at scale relies on proxy metrics -- short-term, easily-measured signals used in place of slow-moving long-term outcomes. When the p

Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options

arXiv:2604.14634v1 Announce Type: new Abstract: Multiple choice evaluation is widely used for benchmarking large language models, yet near ceiling accuracy in low option settings can be sustained by s

QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment

arXiv:2604.14175v1 Announce Type: new Abstract: We present a unified system addressing both Subtask 3 (answer generation) and Subtask 4 (evidence sentence alignment) of the ArchEHR-QA Shared Task. For

R3D: Revisiting 3D Policy Learning

arXiv:2604.15281v1 Announce Type: new Abstract: 3D policy learning promises superior generalization and cross-embodiment transfer, but progress has been hindered by training instabilities and severe o

RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework

arXiv:2604.15308v1 Announce Type: new Abstract: High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interac

RaTA-Tool: Retrieval-based Tool Selection with Multimodal Large Language Models

arXiv:2604.14951v1 Announce Type: cross Abstract: Tool learning with foundation models aims to endow AI systems with the ability to invoke external resources -- such as APIs, computational utilities,

Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models

arXiv:2604.14888v1 Announce Type: new Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains

RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care

arXiv:2502.05740v2 Announce Type: replace-cross Abstract: Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group of cancers that account for more than 35% of cancer-related death

Reinforcement Learning via Value Gradient Flow

arXiv:2604.14265v1 Announce Type: new Abstract: We study behavior-regularized reinforcement learning (RL), where regularization toward a reference distribution (the dataset in offline RL or the base m

Reward-Aware Trajectory Shaping for Few-step Visual Generation

arXiv:2604.14910v1 Announce Type: new Abstract: Achieving high-fidelity generation in extremely few sampling steps has long been a central goal of generative modeling. Existing approaches largely rely

RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning

arXiv:2604.15201v1 Announce Type: new Abstract: As reinforcement learning (RL) deployments expand into safety-critical domains, existing evaluation methods fail to systematically identify hazards aris

RoSLAC: Robust Simultaneous Localization and Calibration of Multiple Magnetometers

arXiv:2604.14353v1 Announce Type: new Abstract: Localization of autonomous mobile robots (AMRs) in enclosed or semi-enclosed environments such as offices, hotels, hospitals, indoor parking facilities,

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

SatBLIP: Context Understanding and Feature Identification from Satellite Imagery with Vision-Language Learning

arXiv:2604.14373v1 Announce Type: new Abstract: Rural environmental risks are shaped by place-based conditions (e.g., housing quality, road access, land-surface patterns), yet standard vulnerability i

Scouting By Reward: VLM-TO-IRL-Driven Player Selection For Esports

arXiv:2604.14474v1 Announce Type: new Abstract: Traditional esports scouting workflows rely heavily on manual video review and aggregate performance metrics, which often fail to capture the nuanced de

SeaAlert: Critical Information Extraction From Maritime Distress Communications with Large Language Models

arXiv:2604.14163v1 Announce Type: new Abstract: Maritime distress communications transmitted over very high frequency (VHF) radio are safety-critical voice messages used to report emergencies at sea.

Separation is Optimal for LQR under Intermittent Feedback

arXiv:2603.27833v2 Announce Type: cross Abstract: In this work, we first prove that the separation principle holds for communication-constrained LQR problems under i.i.d. zero-mean disturbances with a

SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models

arXiv:2604.14672v1 Announce Type: new Abstract: Large language models (LLMs) are being increasingly used in urban planning, but since gendered space theory highlights how gender hierarchies are embedd

Step-level Denoising-time Diffusion Alignment with Multiple Objectives

arXiv:2604.14379v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a powerful tool for aligning diffusion models with human preferences, typically by optimizing a single rewa

StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation

arXiv:2604.14631v1 Announce Type: new Abstract: Effective code generation requires both model capability and a problem representation that carefully structures how models reason and plan. Existing app

Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models

arXiv:2604.14629v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown remarkable capabilities in joint vision-language understanding, but their large scale poses significant challen

Switch: Learning Agile Skills Switching for Humanoid Robots

arXiv:2604.14834v1 Announce Type: new Abstract: Recent advancements in whole-body control through deep reinforcement learning have enabled humanoid robots to achieve remarkable progress in real-world

The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spurious

arXiv:2604.14414v1 Announce Type: new Abstract: Turn-level metrics are widely used to evaluate properties of multi-turn human-LLM conversations, from safety and sycophancy to dialogue quality. However

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

arXiv:2604.14176v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly

The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows

arXiv:2604.14807v1 Announce Type: cross Abstract: The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing,

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

“The sharpest drop came from people who used the model for direct answers, not from those who used it more like a hint system, which suggest…

“The sharpest drop came from people who used the model for direct answers, not from those who used it more like a hint system, which suggests the real issue is not AI exposure itself but replacing eff

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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

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

arXiv:2603.18373v2 Announce Type: replace Abstract: When VLMs answer correctly, do they genuinely rely on visual information or exploit language shortcuts? We introduce the Tri-Layer Diagnostic Framew

TOPCELL: Topology Optimization of Standard Cell via LLMs

arXiv:2604.14237v1 Announce Type: new Abstract: Transistor topology optimization is a critical step in standard cell design, directly dictating diffusion sharing efficiency and downstream routability.

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms

arXiv:2506.09457v3 Announce Type: replace Abstract: Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficie

Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion

arXiv:2511.14178v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies st

Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt

arXiv:2604.13715v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, the

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration

arXiv:2604.13488v1 Announce Type: new Abstract: Autonomous Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) enable digital automation on end-user devices. Whil

Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources

arXiv:2604.14787v1 Announce Type: cross Abstract: Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows fro

Towards Verified and Targeted Explanations through Formal Methods

arXiv:2604.14209v1 Announce Type: new Abstract: As deep neural networks are deployed in safety-critical domains such as autonomous driving and medical diagnosis, stakeholders need explanations that ar

Trajectory Planning for a Multi-UAV Rigid-Payload Cascaded Transportation System Based on Enhanced Tube-RRT*

arXiv:2604.15074v1 Announce Type: new Abstract: This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning c

TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research

arXiv:2511.07412v2 Announce Type: replace Abstract: Developing embodied AI for intelligent surgical systems requires safe, controllable environments for continual learning and evaluation. However, saf

UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards

arXiv:2604.14967v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems t

Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization

arXiv:2604.15196v1 Announce Type: new Abstract: We propose a novel hierarchical spatiotemporal vector quantization framework for unsupervised skeleton-based temporal action segmentation. We first intr

Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees

arXiv:2604.15221v1 Announce Type: cross Abstract: We propose a framework for vision-based human pose estimation and motion prediction that gives conformal prediction guarantees for certifiably safe hu

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,

What some leaders think of using universal income to mitigate AI-fueled layoffs: Musk calls it the 'best way', OpenAI's policy doc mentions a Public Wealth Fund (Siladitya Ray/Forbes)

Siladitya Ray / Forbes: What some leaders think of using universal income to mitigate AI-fueled layoffs: Musk calls it the “best way”, OpenAI's policy doc mentions a Public Wealth Fund — Topline — Elo

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When Fairness Metrics Disagree: Evaluating the Reliability of Demographic Fairness Assessment in Machine Learning

arXiv:2604.15038v1 Announce Type: cross Abstract: The evaluation of fairness in machine learning systems has become a central concern in high-stakes applications, including biometric recognition, heal