Search: “safety”

AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy

arXiv:2609.31431v1 Announce Type: new Abstract: Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive t

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design

DGX agent

arXiv:2609.31329v1 Announce Type: new Abstract: Morphology--control co-design jointly optimizes an agent's body structure and control policy as an integrated embodied system. However, existing methods

safetyarxiv-cs-lg

28 Sept 2026

Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks

arXiv:2609.30951v1 Announce Type: new Abstract: Differentiable simulation provides analytic gradients of robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, ob

Can a Robot Read Braille? - Learning to Adapt Contact via Imitation Learning for Tactile Braille Recognition

arXiv:2609.30676v1 Announce Type: new Abstract: For people who are blind, touch provides an essen-tial channel for accessing written information through Braille. Bringing a similar capability to robot

Common-Mode Collapse and Recovery in Direct Feedback Alignment

arXiv:2609.31589v1 Announce Type: new Abstract: Direct feedback alignment (DFA) trains hidden layers through fixed random projections of output error. With tanh hidden units and independent sigmoid ou

Conformal Prediction under Exponential-Tilt Joint Shift

arXiv:2609.30886v1 Announce Type: cross Abstract: Conformal prediction can lose coverage when the data distribution changes after deployment. We study adaptation using labeled source data and unlabele

Counterfactual Online Conformal Prediction Under Adaptive Logging

arXiv:2609.30811v1 Announce Type: new Abstract: Online conformal prediction can fail when predictions shape actions and actions determine which outcomes enter calibration. Standard adaptive methods ma

Demo: Generative AI helps Radiotherapy Planning with User Preference

arXiv:2512.08996v2 Announce Type: replace-cross Abstract: Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing d

Deterministic Regime Switching and Feasibility Inversion in Dynamic Tensor Rematerialization

arXiv:2609.31250v1 Announce Type: new Abstract: We report fine-grained, deterministic instability in Dynamic Tensor Rematerialization (DTR), an online eviction policy for memory-constrained DNN traini

DIAL: Position-Debiased LLM Judges with Adaptive Human Preference Calibration

arXiv:2609.31215v1 Announce Type: new Abstract: Large language models (LLMs) as a judge enable scalable evaluation, but their judgments can be sensitive to response order and, even after removing such

Do we need to answer that question? Salience and Answerability of Potential Questions in Naturalistic Dialogue

arXiv:2609.31130v1 Announce Type: new Abstract: We empirically investigate Question Under Discussion based modelling in naturalistic dialogue by studying whether the salience of generated potential qu

safetyarxiv-cs-cl

Dynamic Welfare-Maximizing Pooled Testing

arXiv:2601.22419v2 Announce Type: replace-cross Abstract: Pooled testing uses one test to certify several agents as healthy when the pooled result is negative. We study a budget-constrained welfare pr

Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)

arXiv:2609.30417v1 Announce Type: new Abstract: As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many

Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence

arXiv:2609.30535v1 Announce Type: new Abstract: Children in multilingual communities often code-switch, using multiple languages in a single utterance. Can we induce cross-lingual alignment in languag

safetyarxiv-cs-cl

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

arXiv:2512.14253v4 Announce Type: replace Abstract: In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting t

FLIP: Final Layer Inference-Time Probing for Vision-Language Models

arXiv:2609.30993v1 Announce Type: cross Abstract: We present FLIP, a final-layer inference-time probe for testing whether a logit-facing intervention site in an open-weight vision-language model (VLM)

FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation

arXiv:2609.30965v1 Announce Type: new Abstract: Vision-Language-Action models achieve strong performance in robot manipulation, but often require large numbers of parameters. In this work, we propose

From Shortcut Learning to Discrete Neural Insertion Sort

arXiv:2609.31114v1 Announce Type: cross Abstract: Neural algorithmic reasoning aims to train neural networks to follow known algorithms and generalize beyond the input sizes seen during training. Howe

From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learning

arXiv:2609.30391v1 Announce Type: new Abstract: Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task infere

G2MAF: Test-Time Gradient Guidance for Multi-Agent Flow Policies

arXiv:2609.31286v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) learns cooperative policies from fixed datasets without further environment interaction and a learned

Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators

arXiv:2609.31577v1 Announce Type: new Abstract: General purpose humanoids require locomotion controllers that are multi-skill, perceptive, dynamic, and robust enough to go anywhere humans can. In this

Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization

arXiv:2609.31383v1 Announce Type: cross Abstract: End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for extit{De Novo} Peptide Sequencing

arXiv:2609.30542v1 Announce Type: new Abstract: De novo peptide sequencing from tandem mass spectra is essential for identifying peptides without relying on reference databases. Despite advances in de

HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation

arXiv:2609.30594v1 Announce Type: new Abstract: For humanoids to be useful in everyday environments, they must perform a wide range of tasks that couple locomotion and manipulation. Existing approache

In-Context Binding Capacity in Language Models

arXiv:2609.30634v1 Announce Type: new Abstract: How many assignments can a language model recall before it loses track of which value belongs to which entity? We measure this limit using continuous re

Insurance Reserve Intelligence Platform

arXiv:2609.30765v1 Announce Type: new Abstract: Insurance reserve estimation is a fundamental actuarial task supporting premium pricing, solvency assessment, financial reporting, capital planning, and

LandscapeSHAP: Which Persistent Homology Class Gets the Credit?

arXiv:2609.31469v1 Announce Type: cross Abstract: Shapley values, a solution concept from cooperative game theory, have recently become a standard tool for feature credit allocation in machine learnin

LAVOIR: Teaching a Single-Pass Decision Encoder When and What to Ask with Amortized Value of Information

arXiv:2609.30706v1 Announce Type: new Abstract: 'System One' decision models such as TypeSafe's Jev and its open counterpart Laya answer typed questions about a text in a single forward pass with cali

Learning Robot Policies from Sparse Success Signals via STL-Guided Stein Variational Policy Gradient

arXiv:2609.31606v1 Announce Type: new Abstract: Learning robot policies for tasks with sparse success signals is challenging when completion depends on coordinated actions, precise contact outcomes, o

Learning to Bias: Machine Learning-Enhanced Particle Filters

arXiv:2609.30498v1 Announce Type: new Abstract: Sequential inference estimates latent states from noisy and incomplete observations. Particle Filters (PFs), a class of Monte Carlo methods based on imp

Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion

arXiv:2609.31439v1 Announce Type: new Abstract: Policy learning and compliant control offer a promising route to reliable autonomous assembly under pose errors and contact uncertainty. However, combin

Learning Vision-Based Agile Gap Traversal: Differentiable Simulation with a Warm-Started Critic

arXiv:2609.30696v1 Announce Type: new Abstract: Traversing narrow gaps is challenging for autonomous quadrotors, especially when control commands come directly from high-dimensional visual observation

MA-WAM: Multi-Agent World-Action Model for Test-Time Planning

arXiv:2609.31281v1 Announce Type: new Abstract: Multi-agent cooperative tasks require different agents to execute a joint action simultaneously, and each agent's action affects both the observations a

MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics

arXiv:2601.23177v4 Announce Type: replace Abstract: We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric

MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

arXiv:2609.30698v1 Announce Type: new Abstract: Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented metho

MOCHA: Multi-Objective Co-Design using Hypernetwork Architectures

arXiv:2609.30570v1 Announce Type: new Abstract: In this work, we present MOCHA, the first, to our knowledge, reinforcement learning based approach to computing a family of Pareto-optimal policies acro

MOPD-Router: Rethinking Teacher Routing in Multi-Teacher On-Policy Distillation

arXiv:2609.30837v1 Announce Type: cross Abstract: Multi-teacher on-policy distillation (MOPD) integrates specialized capabilities into a single student, but existing practice typically hard-routes eac

Multi-agent Scaling Across Disjunctive and Compensatory Tasks

arXiv:2609.31563v1 Announce Type: new Abstract: Multi-agent LLM systems are often expected to improve as team size increases, yet the scaling behavior may depend on task structure. Our central contrib

MVAgent: Multi-Agent Video Generation via Consistent Condition Construction and Shot-Level Policy Optimization

arXiv:2609.30609v1 Announce Type: new Abstract: Multi-shot agentic video generation requires consistent character appearance, stable spatial layout across camera angles, and continuous character state

Nice Fold or Hero Call: Learning Budget-Efficient Thinking under Policy-Dependent Solvability

arXiv:2605.11625v2 Announce Type: replace Abstract: Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute. Existing efficiency metho

Offline Policy Evaluation as a decision support tool for designing Adaptive Experiments

arXiv:2609.30273v1 Announce Type: new Abstract: We investigate how historical data from fixed randomized experiments (A/B tests) can be used to inform the deployment of adaptive experiments based on c

OmouAI: Argumentative Human-AI Policy Deliberation with Simulated Personas

arXiv:2609.31078v1 Announce Type: new Abstract: Debates amongst agents driven by large language models (LLMs) have demonstrated vast potential in various applications, but when these interactions incl

OpenHail: An Event-Driven Gymnasium Environment for Electric Ride-Hailing Fleet Control

arXiv:2609.30628v1 Announce Type: new Abstract: Machine-learning policies have attracted increasing interest for ride-hailing fleet control in recent years. Reinforcement learning, in particular, requ

OSPO: Object-Centric Self-Improving Preference Optimization for Text-to-Image Generation

arXiv:2506.02015v4 Announce Type: replace Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled unified multimodal understanding and generation. However, they still strugg

Persistent Negatives for Adversarial Black-Box On-Policy Distillation

arXiv:2609.30864v1 Announce Type: cross Abstract: Black-box On-Policy Distillation (OPD) seeks to improve a student from its own generations when the teacher provides sampled responses but not token p

PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning

arXiv:2609.30889v1 Announce Type: new Abstract: Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While retrieval-augmented imitation lea

Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning

arXiv:2609.30462v1 Announce Type: new Abstract: Policies trained with imitation learning can accumulate errors over time, causing the robot to drift outside the training distribution. Existing methods

PolicyAttention: Softmax Attention Implements Policy Mirror Descent for Closed-Loop Control

arXiv:2609.30500v1 Announce Type: cross Abstract: Can causal softmax attention implement policy mirror descent as a repeated controller rather than a one-step algebraic identity? Negative-entropy poli