Search: “safety”

ECoMEM: Explicit Concept Memory for Memory-Dependent Robot Control

arXiv:2610.00801v1 Announce Type: new Abstract: A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has alr

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts

DGX agent

arXiv:2606.18967v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for large language models (LLMs), enabling strong reasoning and agent

safetyarxiv-cs-lg

2 Oct 2026

EgoForge: Goal-Directed Egocentric World Simulator

arXiv:2603.20169v2 Announce Type: replace Abstract: Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint chan

EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception

arXiv:2610.00319v1 Announce Type: new Abstract: Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating oc

ESCROW: Guarded and Dual-Objective Continual Maintenance for Agents in Policy-Governed Enterprise Workflows

arXiv:2608.01772v2 Announce Type: replace Abstract: LLM agents increasingly run policy-bound enterprise workflows, where they must apply rules consistently and stay auditable. Deploying such an agent

Even if AIs become conscious, that doesn't imply that humanity should hand over control to them. https://eigenism.org

Even if AIs become conscious, that doesn't imply that humanity should hand over control to them. https://eigenism.org NEW: According to a bombshell report in the New York Times, Anthropic co-founder C

safetydan-hendrycks--x

Exact Distinguishability in Non-Markovian Decision Processes

arXiv:2610.01527v1 Announce Type: cross Abstract: Non-Markovian environments are often modeled as Regular Decision Processes (RDPs), where dynamics depend on the interaction history through a finite a

Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch

arXiv:2610.01171v1 Announce Type: new Abstract: With the increasing use of robot-free demonstration interfaces that provide state trajectories without action labels, imitation from observation has bec

EyeTAG: Eye Trajectory-Aware Gaze Estimation

arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict

FAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training

arXiv:2610.00385v1 Announce Type: cross Abstract: Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and do

FairSSL: Fair Multimodal Self-Supervised Learning

arXiv:2508.16748v2 Announce Type: replace-cross Abstract: Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-rel

FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid

arXiv:2603.08961v2 Announce Type: replace Abstract: Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinema

FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification

arXiv:2610.00693v1 Announce Type: new Abstract: Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentra

FERPO: Forward Entropy-Regularized Policy Optimization

arXiv:2610.02198v1 Announce Type: cross Abstract: Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic.

Finetuning with Sampling: SFT Learns Better Than You Think

arXiv:2610.02140v1 Announce Type: cross Abstract: Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and re

Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies

arXiv:2610.01882v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment.

From Isolated Feature to Orbits: Discovering Music Concepts via Multi-SAE Alignment

arXiv:2610.01864v1 Announce Type: cross Abstract: How can we understand what a music foundation model has learned extit{internally}? Most interpretability approaches, such as probing and Sparse Autoen

Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation

arXiv:2610.00447v1 Announce Type: new Abstract: Can a text-to-3D leaderboard change when every generated scene stays fixed? We audit this question for rendered-image evaluation, where camera settings

FutureWorlds: Learning Robotic World Models from Alternative Futures

arXiv:2610.01019v1 Announce Type: new Abstract: Robotic world models predict action-conditioned future scenes, providing a foundation for understanding action outcomes. However, turning alternative pr

GeoLatent: Geometry-Guided Latent Structuring with Routed Optimization for 3D Reasoning

arXiv:2610.02091v1 Announce Type: cross Abstract: Despite progress in vision-language models, 3D spatial reasoning from 2D images remains challenging. Text-based methods describe intermediate geometry

Gestalt: Large Multimodal Interplay Model

arXiv:2610.00576v1 Announce Type: new Abstract: In this paper, we propose Gestalt, a new paradigm of large multimodal model built around multimodal interplay. Despite rapid advances, large multimodal

Getting Out and Getting Back: World and Behavior Grounding in Real2Sim2Real Co-Training

arXiv:2610.00821v1 Announce Type: new Abstract: Simulation can expand scarce real demonstrations for co-training, yet how world fidelity and similarity to human behavior affect policy performance rema

Gradient-Aligned Pair Selection for Personalized Preference Optimization

arXiv:2610.00061v1 Announce Type: new Abstract: Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Dir

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

arXiv:2610.01128v1 Announce Type: new Abstract: Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economi

Heavy-Tailed Memory Traces in Long-Horizon Language Agents

arXiv:2610.00010v1 Announce Type: new Abstract: Long-horizon language agents increasingly rely on external memory as a frozen world model, yet current memory systems are usually judged only by task su

HierGF: Hierarchical Gaussian Fields via Geometry-perception Message Passing for Sparse-view 3D Reconstruction

arXiv:2610.01056v1 Announce Type: new Abstract: Sparse view 3D reconstruction is an important and common scenario in multimedia applications, such as augmented reality/virtual reality (AR/VR) content

High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

arXiv:2604.02578v2 Announce Type: replace-cross Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question

iADD: Improving Alignment and Diversity in Diffusion Policy Optimization

arXiv:2610.01789v1 Announce Type: cross Abstract: Reinforcement learning based post training of diffusion models, such as Denoising Diffusion Policy Optimization (DDPO), optimizes a reverse diffusion

💯💯💯: “If AI is going to produce trustworthy and genuinely novel insights in science, medicine and beyond, we need systems whose conclusio…

💯💯💯: “If AI is going to produce trustworthy and genuinely novel insights in science, medicine and beyond, we need systems whose conclusions arise from an auditable process of evidence, inference and b

safetygary-marcus--x

Improving Math Reasoning through Value-guided Informative Search

arXiv:2610.01080v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the mathematical reasoning capabilities of large language models. Recen

Integrating Fairness and Explainability in a Multiple Instance Reinforcement Learning System

arXiv:2610.00035v1 Announce Type: new Abstract: Predicting student performance from educational interaction data requires models that are both accurate and sufficiently transparent to support meaningf

InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation

arXiv:2602.20294v2 Announce Type: replace-cross Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approac

Iterative Policy Refinement through Semantic Rollout Analysis

arXiv:2610.01652v1 Announce Type: cross Abstract: Structured policies improve efficiency, robustness, and interpretability in imitation learning by introducing task-specific inductive bias, but existi

Just Align m{x}: Aligning Predictions, Not Representations

arXiv:2610.00600v1 Announce Type: new Abstract: Representation alignment has become an effective way to accelerate diffusion training, but its benefits do not transfer reliably to pixel-space clean-im

Kudos to Pope Leo. I am disturbed by how some of my fellow AI researchers keep trying to elevate the moral standing of checkpoints and harne…

Kudos to Pope Leo. I am disturbed by how some of my fellow AI researchers keep trying to elevate the moral standing of checkpoints and harnesses. I hope the rest of us on Team Meatbag reject this move

safetygary-marcus--x

Large Reward Models: Generalizable Online Robot Reward Generation with Vision-Language Models

arXiv:2603.16065v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has shown strong potential for improving robotic manipulation policies, yet its practical use remains bottlenecked

Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning

arXiv:2610.00849v1 Announce Type: new Abstract: Existing approaches to offline goal-conditioned reinforcement learning (GCRL) struggle with long-horizon tasks. Discounting shrinks value differences be

LEGO-OPD: Factorized Teacher Composition for Multimodal On-Policy Distillation

arXiv:2610.00333v1 Announce Type: cross Abstract: Multimodal on-policy distillation (OPD) aims to improve visual grounding while preserving the strong reasoning capabilities of language models. Recent

Linear Programming Representations and Strongly Polynomial Algorithms for Robust Markov Decision Processes

arXiv:2610.02131v1 Announce Type: new Abstract: We study linear programming (LP) representations and strongly polynomial algorithms for robust Markov decision processes (RMDPs) with rational polyhedra

Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL

arXiv:2610.00574v1 Announce Type: new Abstract: Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as

Manifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment

arXiv:2610.00365v1 Announce Type: cross Abstract: Recent advances in distillation and flow-map models have enabled deterministic one- or few-step generation for high-quality data, facilitating a new b

MASkillBlender: Decentralized Whole-Body Coordination for Multi-Humanoid Loco-Manipulation via Skill Blending

arXiv:2610.01102v1 Announce Type: cross Abstract: Coordinated multi-humanoid loco-manipulation is promising yet challenging due to high-dimensional whole-body control, decentralized decision making, a

Measuring Asset and Scene Reconstruction Effects in Real-to-Sim Robot Evaluation

arXiv:2610.00731v1 Announce Type: new Abstract: Simulated evaluation is increasingly used alongside real-world evaluation of robot policies because it is cheaper and easier to repeat; however, its val

Memory-Guided B-Roll Generation from User Video Collections

arXiv:2610.01884v1 Announce Type: new Abstract: We introduce an approach for collection-grounded B-roll sequence generation. Given a user's video collection, a directive given in natural language, and

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

arXiv:2610.00705v1 Announce Type: new Abstract: This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent s

MiLoop: Selective Memory Propagation for Neural Combinatorial Optimization

arXiv:2610.01685v1 Announce Type: new Abstract: Constructive neural combinatorial optimization (NCO) has emerged as a promising paradigm that learns to construct solutions to combinatorial optimizatio

Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

arXiv:2610.01284v1 Announce Type: cross Abstract: Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stabl

Modeling The Object Representations Underlying Human Physical Reasoning

arXiv:2602.12486v2 Announce Type: replace-cross Abstract: Humans appear to represent objects when reasoning about physics with coarse, volumetric 'bodies' that smooth concavities, trading fine visual