Search: “safety”

Moral Competence Before Moral Content: Why LLM Agents Lack the Prerequisites for Coherent Alignment

arXiv:2609.05036v1 Announce Type: new Abstract: AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there is no correct target, but a shared prerequ

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Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue

DGX agent

arXiv:2609.04250v1 Announce Type: cross Abstract: An avatar that holds a conversation should decide what to say and to move while saying it, yet these abilities live in separate model families: spoken

safetyarxiv-cs-cv

7 Sept 2026

Multi-Modal Time Series Prediction via Mixture of Modulated Experts

arXiv:2601.21547v2 Announce Type: replace-cross Abstract: Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecastin

MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning

arXiv:2609.04574v1 Announce Type: cross Abstract: Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current arch

Nested Inductive Bias Framework for SPD Manifold Learning

arXiv:2609.04466v1 Announce Type: new Abstract: In Geometric Deep Learning, inductive biases serve two primary functions: enforcing manifold constraints and embedding relational priors. Currently, rep

On Epistemic Diversity in Large Language Models

arXiv:2609.04835v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In su

PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization

arXiv:2609.04837v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is

Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generation

arXiv:2609.04484v1 Announce Type: cross Abstract: This paper investigates structural priming in language model (LM) production, examining how preceding structural context influences sentence completio

Persistent Teacher Anchoring for Tool-Using Agents

arXiv:2609.04773v1 Announce Type: cross Abstract: Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-generated trajectories to prepare the student

ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding

arXiv:2609.05094v1 Announce Type: new Abstract: Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment bet

Reasoning Without Inference Cost: Latent Semantic Scaffolding for Robot VLA Policies

arXiv:2609.04893v1 Announce Type: new Abstract: Vision-language-action (VLA) models are trained by imitation and capture what action to take but not why; adding causal reasoning improves manipulation,

safetyarxiv-cs-ro

REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation

arXiv:2609.04415v1 Announce Type: cross Abstract: Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge graphs (TKGs) provide a natural foundation by

Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach

arXiv:2609.04880v1 Announce Type: new Abstract: Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing adoption gains against public expenditure

RL-VLA^3: A Flexible and Asynchronous Reinforcement Learning Framework for VLA Training

arXiv:2602.05765v3 Announce Type: replace Abstract: Reinforcement learning (RL) has emerged as a critical paradigm for post-training Vision-Language-Action (VLA) models, enabling embodied agents to ad

Scalable Context Orchestration for Serving LLMs Over Voice

arXiv:2609.04288v1 Announce Type: cross Abstract: Voice AI applications are gaining popularity as advances in large language models (LLMs) enable more natural and accessible spoken interactions. Servi

SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping

arXiv:2609.04751v1 Announce Type: new Abstract: 3D surface cutting and UV unwrapping are fundamental problems in computer graphics. Traditional geometric optimization methods mainly focus on reducing

Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics

arXiv:2602.21203v2 Announce Type: replace-cross Abstract: Visual reinforcement learning is appealing for robotics but expensive. Off-policy methods are sample-efficient yet slow while on-policy method

TNFlow: Amortized Posterior Inference for Trans-Neptunian Object Surface Composition

arXiv:2609.04305v1 Announce Type: cross Abstract: We present TNFlow, a transformer and normalizing flow architecture for inferring the surface composition of Trans-Neptunian Objects (TNOs) from their

Towards Neuro-Symbolic Procedural Reasoning for Long-Horizon Vision-Language-Action Manipulation

arXiv:2609.05369v1 Announce Type: cross Abstract: Vision-language-action (VLA) models can execute short manipulation skills, but remain brittle in long-horizon procedures requiring persistent task sta

Train What You Deploy:Token-Faithful Post-Training of a Production Coding

arXiv:2609.04678v1 Announce Type: new Abstract: Existing post-training pipelines for coding and terminal agents suffer severe token and control fidelity errors: simplified training environments mismat

Training-Free Speech-Centric Omni Understanding with Frozen VLMs

arXiv:2609.04242v1 Announce Type: cross Abstract: Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships.

UniMate: One Unified Model to Animate Diverse Skeletons

arXiv:2609.05415v1 Announce Type: new Abstract: Recent advances in automatic rigging now deliver animation-ready 3D assets at scale, yet generating the motion to drive them remains a bottleneck. Exist

VICAL: Vicinal Consistency Alignment for Long-Tailed Visual Recognition

arXiv:2609.04948v1 Announce Type: cross Abstract: Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their presumed ability to benefit from expert di

VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models

arXiv:2609.04355v1 Announce Type: cross Abstract: Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability. Applyin

What Matters in On-Policy Distillation? A Perspective on Data Efficiency and Data Selection

arXiv:2609.05198v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has emerged as a widely adopted post-training paradigm for enhancing large language models in reasoning domains. However, t

What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies

arXiv:2609.05376v1 Announce Type: cross Abstract: Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacl

A comparative study on the accuracy & repeatability of mobile robotic platforms for the delivery of precision NDE measurement

arXiv:2609.03794v1 Announce Type: new Abstract: Mobile robotic platforms offer a flexible alternative to fixed manipulators for non-destructive evaluation (NDE) of large aerospace structures, but thei

safetyarxiv-cs-ro

A Computationally Feasible Framework for Causal Probabilistic Explanation

arXiv:2609.04177v1 Announce Type: new Abstract: Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis.

A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle

arXiv:2609.04147v1 Announce Type: cross Abstract: This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platfo

A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms

arXiv:2511.02192v3 Announce Type: replace Abstract: This paper presents a quantitative comparison between centralised and distributed multi-agent reinforcement learning (MARL) architectures for contro

safetyarxiv-cs-ro

Accountable AI with Grounded, Faithful, Consistent, Actionable Rationales: A Case Study in Clinical Trial Matching with VERDICT

arXiv:2609.03366v1 Announce Type: new Abstract: Accountability means a decision can be examined, justified, and contested. LLMs make this hard: fluent output may be ungrounded, incomplete, or unfaithf

Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

arXiv:2505.12037v2 Announce Type: replace Abstract: Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-mak

Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis

arXiv:2609.04096v1 Announce Type: cross Abstract: This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generalizable grasp synthesis across different rob

An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data

arXiv:2609.03258v1 Announce Type: new Abstract: Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learning approaches rely on large amounts of anno

Artificial Intelligence for Energy Optimization in Data Centers

arXiv:2609.03716v1 Announce Type: new Abstract: Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded by it. The literature treats these as two

Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++

arXiv:2609.03615v1 Announce Type: new Abstract: While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samp

Model Releases

Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation

model-releasesarxiv-cs-ai

arXiv:2609.03407v1 Announce Type: new Abstract: People increasingly turn to large language models (LLMs) for everyday advice, making ethically charged interpersonal problems a practical moral-advisory

Computing stable configurations of confined smectic liquid crystals with a deep variational framework

arXiv:2609.03389v1 Announce Type: cross Abstract: Smectic liquid crystals are layered liquid-crystalline phases characterized by orientational order and periodic density modulation. Although their str

CROCODIL: Cross-Model Code Editing with LLMs

arXiv:2609.03894v1 Announce Type: new Abstract: Large language models (LLMs) have become ubiquitous tools for code generation and editing. However, development teams often use multiple LLM assistants.

DE-Venus: A Data-Efficient RLVR Framework for Large Language Models

arXiv:2609.03324v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-

Decoupled Analysis-Judging: An Automated Creativity Evaluator Using LLMs in Complex Multi-step Creativity Tasks

arXiv:2609.03432v1 Announce Type: new Abstract: Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to biases such as verbosity bias and leniency bia

Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

arXiv:2609.04011v1 Announce Type: cross Abstract: Reliable transport models are essential when modelling and optimising many chemical engineering processes, yet, most models assume hand-picked constit

Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data

arXiv:2609.03878v1 Announce Type: cross Abstract: Reconstruction-based anomaly detectors are accurate but opaque: a deep autoencoder flags a sample without telling a practitioner which feature ranges

DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training

arXiv:2609.04094v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work

EF1-Constrained Nash Social Welfare with Identical Additive Valuations: Complexity, Guarantees, and Experiments

arXiv:2609.03846v1 Announce Type: cross Abstract: We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy-freeness up to one good (EF1) and Nash

Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Data

arXiv:2609.03391v1 Announce Type: cross Abstract: Contrastive language-image learning (CLIP) has become a key paradigm for remote sensing vision-language understanding. However, existing remote sensin

FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow

arXiv:2609.03563v1 Announce Type: new Abstract: We present FlashRender, a few-step generative rendering framework that retakes a source video along a target camera trajectory in seconds. We identify s

FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

arXiv:2609.03241v1 Announce Type: cross Abstract: A reasoning model can improve from its own on-policy experience, but this inner loop is fragile: terminal verifiers provide reliable yet sparse superv