Search: “safety”

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

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

DGX agent

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)

safetyarxiv-cs-ai

28 Sept 2026

Florida gets it: we need an injunction NOW, otherwise we are playing Russian roulette, hoping that some new software package can save us bef…

Florida gets it: we need an injunction NOW, otherwise we are playing Russian roulette, hoping that some new software package can save us before some massive and possibly deadly AI-induced infrastructu

safetygary-marcus--x

Applications

Florida invokes extinction fears in legal bid to halt OpenAI development

Florida has sought a temporary injunction against OpenAI, arguing that large‑language model development without third‑party approved safety guardrails poses an existential threat and preys on vulnerab

applicationsars-technica

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

Jensen expressing hope, but what if we just don’t have an adequate engineering solution in view? What if LLMs just aren’t a stable enough fo…

Jensen expressing hope, but what if we just don’t have an adequate engineering solution in view? What if LLMs just aren’t a stable enough foundation? Then what? 🇺🇸 Jensen Huang's pessimism about rogue

safetygary-marcus--x

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

safetyarxiv-cs-cv

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

safetyarxiv-cs-cv

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

ok i think i finally figured out why alignment has gone basically nowhere after 15 years and several billion dollars. the problem is hard su…

ok i think i finally figured out why alignment has gone basically nowhere after 15 years and several billion dollars. the problem is hard sure. but have you met the people in charge of it 1) the leade

safetyyann-lecun--x

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

safetyarxiv-cs-cv

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

Population loss in shallow ReLU networks: Bias & families of critical points

arXiv:2609.30661v1 Announce Type: new Abstract: The main result presented is a formula for the population loss in the student-teacher kernel model that is applicable to shallow ReLU networks with bias

PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem

arXiv:2609.30948v1 Announce Type: cross Abstract: The Job Shop Scheduling Problem (JSSP) is a fundamental combinatorial optimization problem in industrial optimization. This work introduces Pretrained

Praxis: Distilling Physical Interaction Priors from Egocentric Videos for Generalizable Whole-Body Manipulation

arXiv:2609.30735v1 Announce Type: new Abstract: Mobile humanoid manipulation requires both reaching a usable workspace and preserving precise hand-object interactions as object poses and contact condi

Privacy-Preserving Prompted Policy Search for Robotic Control

arXiv:2609.30554v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated promising capabilities as in-context policy optimizers for Reinforcement Learning (RL), enabling

Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study

arXiv:2609.30553v1 Announce Type: new Abstract: Expensive evolutionary search does not always need an exact fitness estimate for every candidate. It often needs a reliable answer to a simpler question

Recursive Self-Improvement via On-Policy Distillation for Reasoning

arXiv:2609.30652v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student model by having it generate trajectories, then matching its next-token predictions with an external teache

safetyarxiv-cs-cl

Reinforcement Learning of Communication in a Mesh of Small Language Models

arXiv:2609.30578v1 Announce Type: new Abstract: Language models gain accuracy from more compute at test time, but majority voting over independent samples saturates: as samples grow, the vote converge

Rep. Ro Khanna will introduce the Human Control Over AI Act, with strict liability and a ban on recursive self-improving AI until government safeguards exist (Garrett Downs/CNBC)

Garrett Downs / CNBC: Rep. Ro Khanna will introduce the Human Control Over AI Act, with strict liability and a ban on recursive self-improving AI until government safeguards exist — Silicon Valley Dem

safetytechmeme

Resource-Optimized and Energy-Aware Agentic AI Framework Anchored on Blockchain for Secure Software Supply Chains

arXiv:2609.31282v1 Announce Type: cross Abstract: This paper proposes a blockchain-backed agentic security framework designed to safeguard the complete software development lifecycle (SDLC) while also

Robust Graph Clustering Network for Multiple Missing Data

arXiv:2609.31033v1 Announce Type: new Abstract: Clustering on graphs where both node attributes and structural links are partially missing remains a challenging task. Existing methods typically rely o