safety

RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

arXiv:2608.09853v1 Announce Type: cross Abstract: General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from

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SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry

DGX agent

arXiv:2608.09303v1 Announce Type: cross Abstract: The deployment of autonomous robotic systems in chemistry laboratories is accelerating experimental workflows and providing the foundational data for

safetyarxiv-cs-lg

11 Aug 2026

Safety Cost of Steering Vectors Is Separable and Reducible

arXiv:2608.08383v1 Announce Type: new Abstract: Steering vectors are a lightweight tool for controlling LLM behavior. However, emerging evidence shows that steering vectors can unintentionally comprom

Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance

arXiv:2608.09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO)

SC-Diff: Semantically Calibrated Diffusion for Visible-to-Infrared Image Translation

arXiv:2608.08555v1 Announce Type: new Abstract: Visible-to-infrared image translation provides a practical way to expand infrared training data using abundant visible images. Diffusion models are prom

Scalable extensions to given-data Sobol' index estimators

arXiv:2509.09078v3 Announce Type: replace-cross Abstract: Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol' index computation for computa

ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB

arXiv:2608.07945v1 Announce Type: cross Abstract: Cloud-native serverless data warehouses achieve fine-grained elasticity by decoupling storage from compute, yet determining the optimal resource alloc

SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning

arXiv:2608.07959v1 Announce Type: new Abstract: Ultra-long egocentric video understanding requires reasoning over temporally sparse evidence distributed across hours or days, challenging current multi

Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards

arXiv:2608.07531v1 Announce Type: cross Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Existing ext

Self Supervised Learning from Automatically Generated Demonstrations for Visual Robotic Manipulation

arXiv:2608.07553v1 Announce Type: new Abstract: Robotic manipulation often requires object specific programming, manual data annotation, or calibrated perception pipelines, which limits rapid deployme

SIP: Site in Pieces- A Dataset of Disaggregated Construction-Phase 3D Scans for Semantic Segmentation and Scene Understanding

arXiv:2512.09062v2 Announce Type: replace Abstract: Accurate 3D scene interpretation in active construction sites is essential for progress monitoring, safety assessment, and digital twin development.

SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

arXiv:2602.17632v3 Announce Type: replace-cross Abstract: Modern offline Reinforcement Learning (RL) methods find performant actor-critics, however, fine-tuning these actor-critics online with value-b

Software Engineering for and with GUI Agent

arXiv:2608.09278v1 Announce Type: cross Abstract: GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity

Spatiotemporal Context-dependent Personalized Movement Compensation in Delayed Telemanipulation

arXiv:2608.08200v1 Announce Type: new Abstract: Communication delay remains a central challenge in telerobotics, where it disrupts visuomotor coordination and reduces task precision. Motion scaling is

SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning

arXiv:2608.09138v1 Announce Type: cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execu

SR-OPSD: Self-Referenced On-Policy Self-Distillation

arXiv:2608.09745v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) converts feedback into dense token-level supervision on trajectories generated by the policy to be optimized, provi

Stealing Reasoning Traces from Proprietary LLM APIs

arXiv:2608.09867v1 Announce Type: cross Abstract: Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and lim

StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning

arXiv:2608.08326v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning. However, most existing me

Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity

arXiv:2608.00143v2 Announce Type: replace-cross Abstract: Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand.

SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model

arXiv:2608.07948v1 Announce Type: new Abstract: VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling comp

The Anatomy of a Prompt Injection: A Component Model for Structured Analysis

arXiv:2608.07808v1 Announce Type: cross Abstract: Four years after prompt injection was first identified in 2022, attacks are still predominantly documented as verbatim strings rather than structured

The Sample Complexity of Policy Learning with Mu-Resets

arXiv:2608.07772v1 Announce Type: new Abstract: We study policy-based reinforcement learning under the mu-resets interaction protocol of Kakade and Langford [KL02]. This interaction protocol enables t

The Scaling Paradox in Human-AI Collaboration

arXiv:2608.00818v2 Announce Type: replace Abstract: The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, thei

The Theory of Strategic Evolution: Games with Endogenous Players and the Seven Laws of Strategic Replicators

arXiv:2512.07901v4 Announce Type: replace-cross Abstract: Von Neumann founded both game theory and the theory of self-reproducing automata, but the two programs never merged. Rational players do not c

The Transparency Trap: How AI Disclaimers Create Overconfidence in High-Stakes Decisions

arXiv:2608.07493v1 Announce Type: cross Abstract: Current AI disclaimers often fail to function as intended due to warning habituation and a transparency paradox. As AI-generated information becomes p

The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints

arXiv:2608.07980v1 Announce Type: cross Abstract: In recent years, the term voiceprint has regained attention, particularly in technological applications and policy-making contexts, often carrying the

There are no lossless transformations of natural-language text

There are no lossless transformations of natural-language text Sophie Alpert shares her 'internal policy on acceptable use of AI writing by engineers'. It's a short read (supporting its own recommenda

safetysimon-willison

Three Necessary Principles for Self-Supervised Visual Representation Learning

arXiv:2608.08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: sema

ToolUniverse: An open platform for democratizing AI scientists

arXiv:2509.23426v3 Announce Type: replace Abstract: AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because

Topographic Constraints Shape Brain-Like Component Structure in Auditory Models

arXiv:2509.24039v2 Announce Type: replace-cross Abstract: If topography is a fundamental feature of the brain, it should influence both how neurons are arranged in space (i.e. explain brain maps) and

Towards Expressive and Faithful Audio-to-Image Generation: A Unified Multimodal Dataset and Synthesis Framework

arXiv:2608.09529v1 Announce Type: new Abstract: As an important subfield of cross-modal generation, synthesizing static visual content in the form of images from audio, namely audio-to-image (A2I) gen

Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM

arXiv:2511.18721v4 Announce Type: replace-cross Abstract: The SmoothLLM defense provides a certification guarantee against jailbreaking attacks, but it relies on a strict 'k-unstable' assumption that

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation

arXiv:2608.09052v1 Announce Type: cross Abstract: Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. H

Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents

arXiv:2608.08601v1 Announce Type: new Abstract: To anticipate socio-technical risks from AI agents, organizations need taxonomies to classify them. However, existing AI risk taxonomies focus on broad

Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, ex

Understanding Reasoning from Pretraining to Post-Training

arXiv:2607.16097v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is l

Unimodality-Promoting Regularized Learning for Ordinal Regression

arXiv:2608.08359v1 Announce Type: new Abstract: Ordinal regression, also called ordinal classification, is classification of ordinal data, in which the underlying target variable is categorical and co

UnsDrive: Towards Robust End-to-End Autonomous Driving in Unstructured Scenes

arXiv:2608.09098v1 Announce Type: new Abstract: End-to-end planning has shown strong promise for autonomous driving, but most existing methods are designed for structured urban roads and generalize po

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models

arXiv:2608.08622v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated strong performance in open-ended video understanding, yet they remain prone to fluent responses u

VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction

arXiv:2608.09448v1 Announce Type: cross Abstract: Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains d

Vid2WAM: Distilling Video Diffusion Priors into World Action Models

arXiv:2608.08558v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling future visual dynamics and actions. However, their scalability and generali

VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference

arXiv:2608.08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks. Despite this progres

WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training

arXiv:2608.09447v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch o

What Keeps Agent Skills from Being Reusable? Evidence from 138K SKILL.md Files

arXiv:2608.08453v1 Announce Type: new Abstract: Under the current standard, Agent Skills are SKILL.md files that combine instructions with supporting resources, enabling Large Language Model (LLM) age

When Does Trace-Driven Evaluation Mislead MoE Expert Caching? Replay Semantics, Workload Contamination, and Operating Regimes

arXiv:2608.07911v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have outgrown accelerator memory, and offloading expert weights to host memory is now standard. This makes expert cache

Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space

arXiv:2608.07786v1 Announce Type: new Abstract: Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate lineage relationships t

World Tokens: Enhancing Embodied Policies with Training-Time World Modeling

arXiv:2608.09730v1 Announce Type: new Abstract: Vision-language-action (VLA) models are a widely adopted paradigm for embodied policies. They excel at efficient closed-loop control but do not explicit

Yesterday's Shield, Today's Spear: A Self-Evolving Safety Guardrail in Production

arXiv:2608.08471v1 Announce Type: new Abstract: Deployed LLM safety guardrails are predominantly static: trained once and frozen at release, while new jailbreak techniques and previously un-addressed