Search: “safety”

Stein Variational Distributional Reinforcement Learning: Score-Guided Particles for Multivariate Return Distributions

arXiv:2610.17925v1 Announce Type: new Abstract: Decisions that weigh several reward signals against each other require the joint distribution of the return vector. Categorical critics scale exponentia

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference

DGX agent

arXiv:2610.14567v1 Announce Type: cross Abstract: This paper develops strong Gaussian approximations (KMT type) for constant learning rate stochastic gradient descent (SGD). We work with heavy tailed

safetyarxiv-cs-lg

14 Oct 2026

Structured Stateful Relational Scripts with a High-Quality Dataset for Multi-Shot Audio-Visual Video Generation

arXiv:2610.15512v1 Announce Type: new Abstract: Coherent multi-shot audio-visual video generation requires maintaining consistent identities while depicting evolving states and coordinating sounds acr

Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing

arXiv:2503.07775v3 Announce Type: replace Abstract: Resource-efficiently computing representations of probability distributions and the distances between them while only having access to the samples i

SuperAI: Merging Capabilities Across Heterogeneous Foundation Models

arXiv:2610.15322v1 Announce Type: cross Abstract: Enabling the reuse and composition of knowledge and capabilities distributed across foundation models is an important step toward building scalable ar

SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation

arXiv:2603.06280v2 Announce Type: replace Abstract: Long-horizon mobile manipulation needs whole-body demonstrations, and collecting them is the bottleneck. Keeping the robot in the loop ties the data

SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction

arXiv:2610.18110v1 Announce Type: cross Abstract: Multimodal medical data captures complementary aspects of disease. It provides a comprehensive basis for diagnosis and the identification of biologica

Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling

arXiv:2610.14440v1 Announce Type: new Abstract: Asynchronous reinforcement-learning post-training of large language models is computationally efficient but inherently off-policy: the learner updates o

Teach Me After You Learn It: Lookahead Self-Distillation for RLVR

arXiv:2610.18256v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a popular training paradigm for LLMs, providing dense, token-level supervision by aligning a model's o

Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints

arXiv:2610.18459v1 Announce Type: new Abstract: External tools can extend the capabilities of small language models (SLMs), enabling their deployment in resource constrained environments. However, the

The Gradient That Top-k Distillation Never Computes

arXiv:2610.15589v1 Announce Type: new Abstract: Many deployed distillation pipelines show the student a truncated view of the teacher: keep the top-k probabilities, renormalize, and minimize a diverge

The Human--VLA Communication Gap: What Humans Leave Implicit and What Policies Cannot Recover

arXiv:2610.17746v1 Announce Type: new Abstract: A robot may know how to perform a task yet fail when a human asks for it naturally. We study this human--VLA communication gap by separating an informat

The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment

arXiv:2511.08328v2 Announce Type: replace Abstract: Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can b

Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence

arXiv:2610.18447v1 Announce Type: cross Abstract: World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the modalities they model and the f

Throwing4: Phase-Aware Pose Modeling for Throw-Distance Prediction in Elite Athletics

arXiv:2610.16383v1 Announce Type: new Abstract: Elite athletics throwing provides a structured biomechanical action sequence together with an objective continuous outcome, namely throw distance, makin

Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration

arXiv:2610.16448v1 Announce Type: new Abstract: Diffusion models, particularly Diffusion Transformers, achieve strong image and video generation quality but remain expensive at inference time due to r

TPPO: Tracking-Value PPO For Language Model Reasoning

arXiv:2610.16794v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) is widely used in reinforcement learning with verifiable rewards for language-model reasoning. While value evolution

TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments

arXiv:2610.17999v1 Announce Type: new Abstract: Humanoid behavior foundation models (BFMs) enable diverse whole-body control, but adapting a pretrained controller to a new embodiment remains challengi

Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors

arXiv:2610.15038v1 Announce Type: new Abstract: Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations

Training Generalist Navigation Policies via Action Self-Distillation

arXiv:2610.17593v1 Announce Type: cross Abstract: Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning t

Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents

arXiv:2610.14683v1 Announce Type: new Abstract: Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing pri

TRIG: Toward Measuring Texture Realism in Image Generation

arXiv:2610.15164v1 Announce Type: new Abstract: This paper studies the backbone used by metrics for evaluating the quality of generated images. Although generative models have advanced rapidly, some o

Trust-Calibrated VLA Policy Post-Training with Evolving Imagination

arXiv:2610.16517v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are often pretrained via imitation learning, making reinforcement learning (RL) post-training important when deplo

TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour

arXiv:2602.02331v2 Announce Type: replace Abstract: Achieving highly dynamic parkour with humanoid robots on complex terrains unseen during the pre-training stage remains a challenge in robotics. Alth

Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation

arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has

UMM-RM: An Upcycle-and-Merge MoE Reward Model for Mitigating Reward Hacking

arXiv:2512.00724v3 Announce Type: replace Abstract: Reward models (RMs) are critical to reinforcement learning from human feedback (RLHF). However, policy models can exploit biases or spurious correla

Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection

arXiv:2610.17996v1 Announce Type: new Abstract: Sugarcane leaf diseases severely impact crop yield and quality, making accurate and timely detection critical for sustainable agriculture. Conventional

Understanding and Reducing Misalignment in Persuasion Optimization

arXiv:2610.16070v1 Announce Type: cross Abstract: Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training mo

Understanding Helpfulness and Harmless Tension in Reward Models

arXiv:2606.13209v2 Announce Type: replace Abstract: Reward models (RMs) are a key component of reinforcement learning from human feedback, enabling the alignment of large language models with human va

Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing

arXiv:2601.17673v2 Announce Type: replace Abstract: Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object loc

UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models

arXiv:2610.16945v1 Announce Type: new Abstract: Recent advances in multimodal foundation models, exemplified by GPT-6 Astra, raise the prospect of general-purpose embodied reasoning and planning. Usin

Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes

arXiv:2610.18356v1 Announce Type: new Abstract: City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outd

VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment

arXiv:2610.18189v1 Announce Type: new Abstract: Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic

Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design

arXiv:2610.14260v1 Announce Type: new Abstract: Augmentation-based self-supervised learning can recover the content shared across views without separating its underlying factors into individual coordi

Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards

arXiv:2610.15888v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a question a

VAttnPO: Visual Attention-Guided Policy Optimization

arXiv:2610.16735v1 Announce Type: new Abstract: Multimodal reinforcement learning with verifiable rewards commonly assigns one sequence advantage to an entire response. This outcome-level signal disti

VbotNav-0: Asynchronous Cloud-Edge Dual-Policy Navigation across Indoor and Outdoor Environments

arXiv:2610.14634v1 Announce Type: new Abstract: Continuous indoor-outdoor navigation connects destination-level reasoning with motion decisions that must respond to current observations. We present Vb

Verify, Act, Distill: Turning Test-Time Compute into Test-Time Adaptation for Vision-Language-Action Models

arXiv:2610.14885v1 Announce Type: new Abstract: Test-time compute (TTC), which scores multiple candidate actions at inference time, is a powerful way to improve the execution performance of vision-lan

VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

arXiv:2602.16681v2 Announce Type: replace Abstract: Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundat

VH-GPO: Verified Handoff for LLM Agent Reinforcement Learning

arXiv:2610.14000v1 Announce Type: new Abstract: Group-relative reinforcement learning methods such as GRPO train large language model (LLM) agents by comparing rollouts for the same task. When every t

Vision Models Capture Complementary Representations in Children That Converge in Adults

arXiv:2610.16860v1 Announce Type: new Abstract: How the relationship between image-text aligned and vision-only models changes across development remains unknown. We applied time-resolved representati

Visual Sevoing from Lines

arXiv:2610.18363v1 Announce Type: cross Abstract: In this work, we present a fundamentally new approach to visual servoing using lines. It is based on a theoretical and geometrical study of the main l

What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor

arXiv:2602.16842v2 Announce Type: replace Abstract: We study the offline data-driven newsvendor problem when historical demand is censored by past inventory levels. We introduce a class of policies fo

What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs

arXiv:2610.17376v1 Announce Type: cross Abstract: A shared latent space is often assumed to make representations from different schemas directly interchangeable, yet sharing parameters, dimensionality

When do explicit penalties improve flow matching?

arXiv:2610.15689v1 Announce Type: cross Abstract: Flow matching can learn structure directly from data, raising the question of when additional constraints improve generation. We study penalties that

When Left Is Right? Camera-Motion Understanding beyond Screen

arXiv:2610.17242v1 Announce Type: new Abstract: Can a vision-language model tell left from right? Modern VLMs solve visual puzzles and follow hour-long videos, yet when asked whether the camera moved

When More Memory Fails: Hidden Feasibility Holes in Dynamic Tensor Rematerialization

arXiv:2610.14369v1 Announce Type: new Abstract: Dynamic Tensor Rematerialization (DTR) trains neural networks under a memory budget by evicting tensors and recomputing them on demand. A natural expect

When Sparsity Breaks Connectivity: Functional Reachability and Guided Topology Dynamics in Extreme Sparse Training

arXiv:2610.17425v1 Announce Type: new Abstract: Dynamic Sparse Training (DST) periodically prunes and regrows connections, inducing a discrete dynamical mechanism over network topologies. At extreme s