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An evolutionary origin of collective decision making in humans and machines

arXiv:2610.05676v1 Announce Type: cross Abstract: Groups of individuals can solve collective problems more accurately than any single member, by aggregating their opinions. Recent theoretical work has

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An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection

DGX agent

arXiv:2610.05600v1 Announce Type: new Abstract: High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the `

safetyarxiv-cs-lg

6 Oct 2026

Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report

arXiv:2610.06837v1 Announce Type: new Abstract: Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on i

Articulatory Entrainment and Coordination Complexity in Spontaneous Autistic and Non-autistic Dialogue

arXiv:2610.04071v1 Announce Type: new Abstract: Articulatory entrainment, the adaptation of vocal tract coordination to facilitate interaction remains underexplored in spontaneous dialogue, particular

ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience

arXiv:2610.05303v1 Announce Type: new Abstract: A large language model (LLM) agent solves long-horizon tasks through many reasoning-action turns, with one verification signal at termination. Deployed

Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning

arXiv:2610.04671v1 Announce Type: cross Abstract: A federated objective is a weighted sum of client risks, and the weights are almost always fixed in advance. We treat them instead as the only instrum

At an Australian hearing, OpenAI Chief Strategy Officer Jason Kwon pledged faster safety incident disclosures; Anthropic representatives made similar pledges (ABC)

ABC: At an Australian hearing, OpenAI Chief Strategy Officer Jason Kwon pledged faster safety incident disclosures; Anthropic representatives made similar pledges — By national AI reporter Cam Wilson

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Backdoor Attacks on Discrete Graph Diffusion Models

arXiv:2503.06340v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated remarkable generative capabilities in continuous data domains such as images and videos. Recently, discrete

Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck

arXiv:2610.03924v1 Announce Type: cross Abstract: This paper addresses autonomous landing of unmanned aerial vehicle (UAV) rotorcrafts on a heaving ship deck using a recurrent policy trained via asymm

Base Models Can Reason By Taking a Cue From Training Data

arXiv:2610.06851v1 Announce Type: new Abstract: In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior tha

Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation

arXiv:2610.06102v1 Announce Type: new Abstract: We investigate CLIP for zero-shot gender estimation from full-face and periocular images. Three CLIP backbones are evaluated on 11,299 frontal images fr

Best-of-N Guidance for Test-time Diffusion Alignment

arXiv:2610.05108v1 Announce Type: new Abstract: Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model.

Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models

arXiv:2610.06439v1 Announce Type: new Abstract: What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisa

Beyond In-Distribution Preservation: Recovering Generalization in Quantized VLAs via Vulnerability-Oriented Tuning

arXiv:2610.05745v1 Announce Type: new Abstract: Post-training quantization has been shown to preserve VLA performance under standard evaluation conditions, but whether it preserves the full-precision

Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction

arXiv:2610.03822v1 Announce Type: new Abstract: Tokens have become a unified interface for multimodal foundation models, making visual-token communication a natural paradigm for efficient image delive

Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers

arXiv:2610.04220v1 Announce Type: new Abstract: The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is i

Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation

arXiv:2610.05765v1 Announce Type: new Abstract: Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse

Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning

arXiv:2610.06083v1 Announce Type: new Abstract: Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper s

BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables

arXiv:2610.03759v1 Announce Type: cross Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases

Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

Add a voice travel concierge to an airline app with Amazon Bedrock AgentCore, Amazon Nova Sonic for real-time speech, and Amazon Bedrock Knowledge Bases for policy answers. Travelers change seats, che

safetyaws-ml-blog

CACFG: Curvature-Aware Classifier-Free Guidance and Optimal Control

arXiv:2610.05845v1 Announce Type: new Abstract: Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for co

Causally Fair Generation with Large Language Models

arXiv:2610.04444v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape conseque

CEENs: Causality-enforced evolutional networks for solving time-dependent partial differential equations

arXiv:2610.04405v1 Announce Type: new Abstract: Despite the growing popularity of physics-informed neural networks (PINNs), their applicability in the long-time integration of partial differential equ

ClasSAE: Class-Aligned Sparse Autoencoders via Differentiable Feature-Class Affinity

arXiv:2610.04020v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingl

Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM

arXiv:2510.07971v2 Announce Type: replace Abstract: Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loop

Collaborative Personalized Preference Alignment for LLMs under Data Deficiency

arXiv:2610.05898v1 Announce Type: new Abstract: Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these respo

Conditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks

arXiv:2610.06104v1 Announce Type: new Abstract: Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles. We presen

ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution

arXiv:2610.04605v1 Announce Type: new Abstract: Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cues to semantically meaningful conce

Conformal Prediction with Paraphrase-Aware Scoring for LLM Uncertainty Quantification

arXiv:2610.04239v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) for large language models (LLMs) aims to provide reliable measures of predictive confidence, yet current methods are o

Considering Context: When World Models Need Context Encoders

arXiv:2610.06651v1 Announce Type: new Abstract: Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environm

Continual Humanoid Motion Learning

arXiv:2610.04231v1 Announce Type: new Abstract: Humanoid whole-body controllers can now track a diverse set of dynamic motions, but they are typically trained offline and then frozen, so teaching such

Contrastive Time Series Forecasting with Anomalies

arXiv:2512.11526v2 Announce Type: replace Abstract: Time series forecasting predicts future values from past data. In real-world settings, some anomalous events have lasting effects and influence the

Cross-Time Directional Selection in Diffusion Sampling

arXiv:2610.05199v1 Announce Type: new Abstract: How strongly do the remaining diffusion-sampling steps amplify a perturbation at a late latent state? Standard measurements answer this question with ne

CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery

arXiv:2610.04851v1 Announce Type: new Abstract: Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a froz

Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture

arXiv:2610.05678v1 Announce Type: new Abstract: Most humanoid loco-manipulation controllers require human motion data to learn whole-body coordination and posture, leaving policies reliant on external

Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation

arXiv:2610.04700v1 Announce Type: new Abstract: Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protoco

Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization

arXiv:2504.06470v2 Announce Type: replace-cross Abstract: Ensuring fairness is essential as machine learning increasingly informs consequential decisions. However, many fairness-aware methods focus on

DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management

arXiv:2603.19621v2 Announce Type: replace Abstract: Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. How

Demonstration-Calibrated Port-Hamiltonian Retuning for Manipulation Policies

arXiv:2610.05755v1 Announce Type: new Abstract: Diffusion and VLA policies for manipulation are often deployed through downstream impedance controllers. The stiffness and damping gains of these contro

DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection

arXiv:2511.04086v2 Announce Type: replace Abstract: With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGA

Dependable AI-Assisted Engineering: A Formal Framework for AI Participation and Assurance in Safety-Critical Workflows

arXiv:2610.04084v1 Announce Type: new Abstract: Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical wor

Direction-Conditioned Policies for Online Goal-Conditioned Reinforcement Learning

arXiv:2610.05087v1 Announce Type: new Abstract: Contrastive Reinforcement Learning (CRL) learns representations that estimate goal reachability, yet its policy remains conditioned on raw goals and the

Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning

arXiv:2503.23835v2 Announce Type: replace Abstract: Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the

Dissecting Quantization Error: A Concentration-Alignment Perspective

arXiv:2603.04359v2 Announce Type: replace Abstract: Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-

Distributionally Robust Deep Q-Learning

arXiv:2505.19058v2 Announce Type: replace Abstract: We propose a novel distributionally robust Q-learning algorithm for the non-tabular case accounting for continuous state spaces where the state tran

Do VLAs Understand and Adapt to the Objects They Handle, or Simply Replay Learned Behaviors?

arXiv:2610.06078v1 Announce Type: new Abstract: This paper asks whether VLA generalization is grounded in a global understanding of objects' physical properties that enables policies to adapt their mo

Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation

arXiv:2610.06327v1 Announce Type: cross Abstract: Vision-based autonomous navigation for low-cost robots remains a fundamental challenge, primarily due to the significant gap between simulated trainin

E^2-OPSD: Taming Entropy Overshoot in On-Policy Self-Distillation

arXiv:2610.05048v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solutio