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Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander

arXiv:2607.01736v1 Announce Type: cross Abstract: We study how to predict the downstream closed-loop performance of a learned latent world model from validation-time diagnostics alone. Choosing the ri

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Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction

DGX agent

arXiv:2607.02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making. To ensure reliability with imperfect pred

safetyarxiv-cs-lg

3 Jul 2026

Purified OPSD: On-Policy Self-Distillation Without Losing How to Think

arXiv:2607.02234v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference

Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score

arXiv:2607.01527v1 Announce Type: cross Abstract: Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when

Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement

arXiv:2607.01731v1 Announce Type: cross Abstract: This study provides a theoretical expansion of the recent Data Relativistic Uncertainty (DRU) framework by formalizing a physics-to-AI paradigm for im

Rank-Then-Act: Reward-Free Control from Frame-Order Progress

arXiv:2607.01897v1 Announce Type: cross Abstract: We introduce Rank-Then-Act (RTA), a framework for learning control policies from expert video demonstrations without environment rewards. RTA trains a

RedCoder: Automated Multi-Turn Red Teaming for Code LLMs

arXiv:2507.22063v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software developme

Rethinking Post-Hoc Calibration in Semantic Segmentation

arXiv:2607.01902v1 Announce Type: cross Abstract: Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead do

Risk Architecture for AI-Native Engineering Teams: An Organizational Framework for Agentic System Governance

arXiv:2607.01421v1 Announce Type: cross Abstract: Engineering management research has produced mature frameworks for software risk: ownership by feature, escalation by severity, and assurance by test

SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs

arXiv:2607.01901v1 Announce Type: cross Abstract: Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, la

Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model

arXiv:2607.01595v1 Announce Type: new Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive reco

Safeguarding LLM Agents from Misalignment through Provenance Analysis

arXiv:2607.01236v1 Announce Type: cross Abstract: As LLM agents gain increasing access to powerful tools, ensuring that their actions are aligned with the user's intent becomes critical. When an agent

Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving

arXiv:2604.03497v2 Announce Type: replace-cross Abstract: Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-cra

SPLC: Social Preference Learning for Crowd Robot Navigation

arXiv:2607.01925v1 Announce Type: new Abstract: Offline reinforcement learning (RL) holds significant potential for crowd robot navigation in human-robot coexistence applications. However, the inheren

Structuring the Space of Sociotechnical Alignment

arXiv:2607.01250v1 Announce Type: cross Abstract: Sociotechnical alignment concerns the social desirability of AI behavior and is thus inherently normative, not merely technical. While NLP research in

The Rising Unsustainability of AI Graphics Cards Production

arXiv:2607.01258v1 Announce Type: cross Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven b

Tight Lower Bounds for the Multi-Secretary Problem via Bellman Certificates

arXiv:2607.02150v1 Announce Type: cross Abstract: This paper studies additive regret in the multi-secretary problem, defined as the gap between the expected offline prophet reward and the reward of th

Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games

arXiv:2607.01498v1 Announce Type: new Abstract: We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three cont

Transformer Geometry Observatory TGO-II: Representational Similarity Observatory

arXiv:2607.02386v1 Announce Type: cross Abstract: While Vision Transformers have achieved remarkable success across computer vision and language applications, the geometric evolution of their internal

Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models

arXiv:2512.01715v2 Announce Type: replace Abstract: Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given pre

VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment

arXiv:2607.01586v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remai

WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning

arXiv:2607.01281v1 Announce Type: new Abstract: Autonomous landing of unmanned aerial vehicles (UAVs) on wave-disturbed marine platforms remains challenging due to stochastic platform motion, time-var

WBMM: Windowed Batch Matrix Multiplication for Efficient Large Receptive Field Convolution

arXiv:2607.02097v1 Announce Type: cross Abstract: Large kernel depthwise convolutions achieve strong performance but suffer from significant degradation as kernel size grows due to irregular memory ac

What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates

arXiv:2607.02507v1 Announce Type: new Abstract: LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly t

When Sample Selection Bias Precipitates Model Collapse

arXiv:2606.13732v2 Announce Type: replace Abstract: The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distr

When Should Service Agents Reconsider? Difficulty-Routed Control in Customer-Service Operations

arXiv:2607.01426v1 Announce Type: new Abstract: Autonomous customer-service agents are shifting from conversational interfaces toward operational execution roles: they retrieve firm records, apply ser

Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence

arXiv:2607.01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's a

WorldSample: Closed-loop Real-robot RL with World Modelling

arXiv:2607.02431v1 Announce Type: cross Abstract: Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-

Wow, even I was surprised how high AI ranked! Very good to see!

Wow, even I was surprised how high AI ranked! Very good to see! Interesting poll of Hill staffers from @PunchbowlNews. 250 years is a long time! But interesting to see that 'losing control of AI' is t

safetyconnor-leahy--x

YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models

arXiv:2601.15588v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained fil

safetyarxiv-cs-cl

A Category Theory Account of AI Identity

arXiv:2607.00220v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments. These transformatio

A Filtered Mixture-of-Generators for Fully Synthetic Survival Training

arXiv:2607.00127v1 Announce Type: new Abstract: Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohort

A Mechanism-Driven Theory of Phase Transitions in Active Learning

arXiv:2607.00144v1 Announce Type: cross Abstract: Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label counts that fail to generalize

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

arXiv:2607.00460v1 Announce Type: cross Abstract: Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural netwo

A small tax on every token produced could be transformative, without putting the government into bed with a specific company. And because ev…

A small tax on every token produced could be transformative, without putting the government into bed with a specific company. And because every token draws on uncompensated contributions from multiple

safetygary-marcus--x

Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization

arXiv:2607.00531v1 Announce Type: cross Abstract: Scientific reasoning is an increasingly important capability of large language models, yet improving the robustness and efficiency of training such re

Active Spatial Guidance: Eliminating Injected Positional Mechanisms in Vision Transformers

arXiv:2607.00580v1 Announce Type: new Abstract: Vision Transformers (ViTs) commonly rely on injected positional mechanisms to address self-attention's permutation invariance. Motivated by the spatial

AI Native Games: A Survey and Roadmap

arXiv:2607.00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime. Yet generation alone does not make a game AI-nat

Aligning Sentence Embeddings to Human Concepts via Sparse Autoencoders

arXiv:2607.00023v1 Announce Type: cross Abstract: Dense sentence embeddings are fundamental to modern Retrieval-Augmented Generation (RAG) systems but suffer from a lack of interpretability due to fea

Altman’s AI safety proposal: bail me out as we badly missed our revenue runway, or i will not be a multi-billionaire

Altman’s AI safety proposal: bail me out as we badly missed our revenue runway, or i will not be a multi-billionaire Altman’s AI safety proposal: let us win, or everybody loses https://ft.trib.al/UrDI

safetygary-marcus--x

ASPIRE: Agentic /Skills Discovery for Robotics

arXiv:2607.00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling divers

Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval

arXiv:2607.00379v1 Announce Type: cross Abstract: Unsupervised cross-modal hashing enables efficient retrieval of semantically related instances across different modalities without requiring manual se

AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation

arXiv:2607.01051v1 Announce Type: new Abstract: Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons.

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces

arXiv:2607.00481v1 Announce Type: cross Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs). While prior work has primarily studied attacks and

Bounded Morality: Defining the Space of Moral Computation

arXiv:2607.00002v1 Announce Type: new Abstract: Moral cognition has traditionally been modeled as adherence to fixed ethical theories--deontology, consequentialism, virtue ethics--implemented as stati

BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure

arXiv:2607.00573v1 Announce Type: new Abstract: Diffusion MRI probes brain microstructure with particular sensitivity to early cerebrovascular and neurodegenerative changes. Neurite Orientation Disper

Caption Bottleneck Models

arXiv:2607.00578v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide interpretability by routing predictions through a layer of human-understandable concepts. However, defining an

ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction

arXiv:2607.00798v1 Announce Type: new Abstract: Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment