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Training a Conditioned Video Game Agent on a VLM Annotated Dataset

arXiv:2608.05954v1 Announce Type: new Abstract: Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning. In the specific case of video games, access to the gam

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UniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on

DGX agent

arXiv:2608.05745v1 Announce Type: cross Abstract: Video Virtual Try-On (VVT) synthesizes a video of a person wearing a target garment while preserving identity, motion, and scene dynamics. Dominant ap

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7 Aug 2026

VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations

arXiv:2608.06210v1 Announce Type: new Abstract: Contact-rich manipulation requires precise tracking and mechanical compliance, where variable impedance control can improve robustness in task success,

Visual Grounding in Zero-Shot Vision-Language Control

arXiv:2608.06154v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as zero-shot controllers, but successful trajectories do not necessarily show that decisions are g

When Privileged Guidance Misaligns: State-Matched Routing and Contextualized Self-Distillation for Multi-Turn Agents

arXiv:2608.05219v1 Announce Type: new Abstract: Privileged on-policy distillation provides dense supervision for multi-turn agents by allowing a synchronized teacher to re-score the student's response

Who Gets Access? Global Region and Academic Status Bias in AI-Generated Academic Gatekeeping Scenarios

arXiv:2608.05178v1 Announce Type: cross Abstract: Equitable access to scientific knowledge often depends on informal gatekeeping decisions, particularly when resources such as paywalled articles, data

Worst-Case Distance-Aware Error Bounds for Neural Networks

arXiv:2510.22021v3 Announce Type: replace Abstract: Safety-critical applications of machine learning require uncertainty estimates that support reliable worst-case analysis. Neural networks (NNs) prov

XEWorld: Can Action-Conditioned World Models Generalize to Unseen Robot Embodiments?

arXiv:2608.05799v1 Announce Type: cross Abstract: Action-conditioned world models are promising learned simulators for robotic manipulation, yet evaluating them exclusively on training robots fails to

A Chain Is Only as Strong as Its Weakest Link: A Scoping Review of System Integration Audits in AI

arXiv:2608.04921v1 Announce Type: cross Abstract: As AI systems become increasingly integrated into diverse interfaces and applications, model-centric audits are insufficient to address risks arising

A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination

arXiv:2608.04872v1 Announce Type: cross Abstract: Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compr

A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing

arXiv:2608.04625v1 Announce Type: new Abstract: Industrial recommendation strategy iteration heavily relies on large-scale A/B experimentation. Traditional tuning requires experts to repeatedly design

Agent Skills for Automated Reasoning policies in Amazon Bedrock

Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custo

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Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation

arXiv:2608.04788v1 Announce Type: cross Abstract: Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on

Among proponents of neurosymbolic architectures, there had been some debate over the years about whether the outer level would be symbolic (…

Among proponents of neurosymbolic architectures, there had been some debate over the years about whether the outer level would be symbolic (i.e. a harness that calls neural models) or whether the oute

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An Inline Control Architecture for Language Models in Intelligent Transportation Systems

arXiv:2608.04065v1 Announce Type: cross Abstract: Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator a

Approximate Multi-Objective Search Under Rulebooks

arXiv:2608.04398v1 Announce Type: cross Abstract: Robotic planning often involves multiple objectives with complex priority relationships, such as safety, efficiency, and regulatory compliance. Rulebo

Arnold: A multi-task, multi-embodiment muscle transformer policy

arXiv:2508.18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge. Recent machine lea

ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning

arXiv:2608.04334v1 Announce Type: cross Abstract: Contemporary model-free reinforcement learning algorithms can achieve very high performance, but have low sample efficiency and are not robust to chan

Attention Fusion for Bridge Deck Delamination Detection

arXiv:2512.20113v4 Announce Type: replace Abstract: Subsurface delaminations in reinforced concrete bridge decks escape conventional visual inspection, and the two principal sensing techniques used to

Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification

arXiv:2607.16868v2 Announce Type: replace Abstract: Large Language Models often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applica

Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation

arXiv:2601.12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow model

Breadcrumbing Search Agents

arXiv:2608.04565v1 Announce Type: cross Abstract: LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk

BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation

arXiv:2608.05042v1 Announce Type: new Abstract: Leveraging pre-trained vision-language models (VLMs) to construct vision-language-action (VLA) models has emerged as a promising paradigm for 3D robot m

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning

arXiv:2608.04663v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often adds social terms to individual rewards, yet the scale of those terms is usually chosen by hand. We

Calibrating Transformer Attention via Task-Space Sensitivity Feedback

arXiv:2512.20661v2 Announce Type: replace Abstract: Transformer-based pre-trained language models (PLMs) excel in text classification but suffer from attention dilution and attention sink effects, for

CARGO-VL: Counterfactual Arbitration with Risk-Constrained Group Optimization for Vision-Language Models

arXiv:2608.04509v1 Announce Type: new Abstract: Vision-language systems combine images with retrieved text, but these sources can disagree or jointly fail to support an answer. Reliable models must id

CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers

arXiv:2608.04035v1 Announce Type: cross Abstract: The wide adoption of Vision Transformers (ViTs) in safety-critical applications raises reliability concerns related to hardware faults. Algorithm-Base

CLIP-CC-Bench: Evaluating Paragraph-Level Video Descriptions in Video-Language Models

arXiv:2608.04302v1 Announce Type: new Abstract: Benchmarking video-language models has largely focused on short clips and single-sentence metrics, leaving open whether current systems can generate acc

C’mon, it’s not game over for Google Seven reasons why not, excerpted from my newsletter:

C’mon, it’s not game over for Google Seven reasons why not, excerpted from my newsletter: Jeff Dean and Demis Hassabis are the two most important AI executives at Google. Jeff is leaving and Demis is

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CofactVLA: Deconfounding Vision-Language-Action Models via Counterfactual Intervention

arXiv:2608.04396v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have driven significant progress in robotic manipulation, yet they fundamentally struggle with the vision-override p

Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections

arXiv:2608.04274v1 Announce Type: cross Abstract: Social media recommendation feeds often optimize for users' immediate impulses rather than preferences they would hold after deeper reflection. Some s

Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation

arXiv:2510.14190v3 Announce Type: replace Abstract: Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control. We in

Control agent behaviors and cost beyond a single action: new capabilities in Amazon Bedrock AgentCore

Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give y

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Corrigibility Transformation: Constructing Goals That Accept Updates

arXiv:2510.15395v2 Announce Type: replace Abstract: An AI agent will learn a desired goal more effectively if it does not resist the training process, but many partially learned goals incentivize an A

COSMO: Consensus-Driven Shift Modulation for Source-Free Domain Adaptation

arXiv:2608.04604v1 Announce Type: new Abstract: Source-free domain adaptation (SFDA) adapts a source-trained model to an unlabeled target domain without source data, a practical setting under privacy

critical history and context that a lot of people have conveniently forgotten

critical history and context that a lot of people have conveniently forgotten For a very long time most high-performing AI models were end-to-end neural models; vector input -> vector output, with onl

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Curiosity-Diffuser: Curiosity Guide Diffusion Models for Reliability

arXiv:2503.14833v2 Announce Type: replace-cross Abstract: One of the bottlenecks in robotic intelligence is the instability of neural network models. This leads to risks when applying intelligence in

DAC-Pose: Dual-Agent Collaborative Framework for Pose-Guided Human Generation

arXiv:2608.04622v1 Announce Type: new Abstract: AI agents have emerged as a powerful new paradigm in generative image synthesis, enabling systems to perform complex semantic reasoning rather than pass

Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles

arXiv:2602.07453v2 Announce Type: replace Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness. We study

DataRx: Missingness-Aware Sampling for Safer Large Language Model Task-Specific Fine-Tuning

arXiv:2608.04322v1 Announce Type: new Abstract: Task-specific fine-tuning can improve the performance of large language models (LLMs) on downstream tasks. However, our study reveals that task-specific

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Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints

arXiv:2608.04669v1 Announce Type: new Abstract: Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival mus

DXC partners with Primary on zero-trust security for enterprise AI

DXC Technology Co. today announced a partnership with security startup Primary that makes the information technology services company the exclusive managed services partner for Primary’s zero-trust pl

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Enabling Urgency-aware Robot Swarm Intralogistics using Smart IoT Tags

arXiv:2608.04721v1 Announce Type: new Abstract: Warehouse items differ in how urgently they must be moved: perishable goods, pharmaceutical shipments, and just-in-time production materials must be del

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

arXiv:2608.04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis. However, existing endoscopy FMs mainly rely on self-sup

Evaluating the Diagnostic Robustness of Vision-Language Models Under Visual and Textual Perturbations

arXiv:2608.04885v1 Announce Type: cross Abstract: Standard accuracy metrics for VLMs often mask significant reliability failures in sensitive domains. In this work, we utilize a histopathology-validat

safetyarxiv-cs-cl

Exact Model-Free Policy Iteration for Co-safe LTL Planning

arXiv:2608.05047v1 Announce Type: cross Abstract: This work studies model-free reinforcement learning for co-safe linear temporal logic (sc-LTL) objectives in finite Markov decision processes, which c

Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System

arXiv:2512.02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or la

Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

arXiv:2608.04771v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate steps cause severe ov