safety

Trajectory Planning for Safe Dual Control with Active Exploration

arXiv:2604.15507v1 Announce Type: new Abstract: Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, ye

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Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions

DGX agent

arXiv:2510.04237v5 Announce Type: replace Abstract: In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compar

safetyarxiv-cs-lg

20 Apr 2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

arXiv:2603.05719v2 Announce Type: replace Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applicatio

UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval

arXiv:2604.15827v1 Announce Type: cross Abstract: Conventional information retrieval is concerned with identifying the relevance of texts for a given query. Yet, the conventional definition of relevan

VADF: Vision-Adaptive Diffusion Policy Framework for Efficient Robotic Manipulation

arXiv:2604.15938v1 Announce Type: new Abstract: Diffusion policies are becoming mainstream in robotic manipulation but suffer from hard negative class imbalance due to uniform sampling and lack of sam

What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context

arXiv:2506.02261v3 Announce Type: replace-cross Abstract: What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that e

When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models

arXiv:2510.09689v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been augmented with web search to overcome the limitations of the static knowledge boundary by accessing up-

Whose Facts Win? LLM Source Preferences under Knowledge Conflicts

arXiv:2601.03746v3 Announce Type: replace Abstract: As large language models (LLMs) are more frequently used in retrieval-augmented generation pipelines, it is increasingly relevant to study their beh

Why Colors Make Clustering Harder:Global Integrality Gaps, the Price of Fairness, and Color-Coupled Algorithms in Chromatic Correlation Clustering

arXiv:2604.15738v1 Announce Type: new Abstract: Chromatic Correlation Clustering (CCC) extends Correlation Clustering by assigning semantic colors to edges and requiring each cluster to receive a sing

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback

arXiv:2408.15549v4 Announce Type: replace Abstract: As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional a

Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions

arXiv:2604.15762v1 Announce Type: new Abstract: Large-scale Unmanned Aerial Vehicle (UAV) failures can split an unmanned aerial vehicle swarm network into disconnected sub-networks, making decentraliz

good to know that a trillion dollars’ investment scaling has thoroughly solved the challenges of common sense.

Gary Marcus critiques the assumption that massive financial investment in AI scaling alone can solve fundamental challenges related to common sense reasoning in artificial intelligence systems. The po

19 Apr 2026

Tesla expands its robotaxi service to Dallas and Houston after launching in Austin last year and starting to offer rides without safety drivers in January 2026 (Anthony Ha/TechCrunch)

Anthony Ha / TechCrunch: Tesla expands its robotaxi service to Dallas and Houston after launching in Austin last year and starting to offer rides without safety drivers in January 2026 — Tesla is expa

safetytechmeme

19 Apr 2026

Dario is not as different from Sam as people seem to think.

Gary Marcus argues that Dario Amodei (Anthropic CEO) and Sam Altman (OpenAI CEO) share more similarities in their approaches to AI development and safety than commonly perceived, despite their public

easyaligner: Forced alignment with GPU acceleration and flexible text normalization (compatible with all w2v2 models on HF Hub) [P]

easyaligner is a forced alignment library designed to be performant and easy to use , leveraging GPU acceleration to align audio with text transcriptions. The tool supports flexible text normalization

safetyr-machinelearning

EVERYONE who heard about @SeismicOrg (Seismic Foundation)'s report that AI 'salience' is low NEEDS to hear about this! New research from dat…

EVERYONE who heard about @SeismicOrg (Seismic Foundation)'s report that AI 'salience' is low NEEDS to hear about this! New research from data science wiz @davidshor shows AI is now ahead of ABORTION a

safetyconnor-leahy--x

Is a 92% “honest”* AI really good enough? Or a disaster waiting to happen? —- *”honest” is itself a misleading anthropomorphization of the k…

Is a 92% “honest”* AI really good enough? Or a disaster waiting to happen? —- *”honest” is itself a misleading anthropomorphization of the kind Anthropic loves to promote. “Accurate” would be more acc

“my honest read is that a significant portion of this spending is driven by competitive fear rather than demonstrated returns. Nobody wants …

“my honest read is that a significant portion of this spending is driven by competitive fear rather than demonstrated returns. Nobody wants to be the company that didn't invest in AI when everyone els

A Mechanistic Account of Attention Sinks in GPT-2: One Circuit, Broader Implications for Mitigation

arXiv:2604.14722v1 Announce Type: new Abstract: Transformers commonly exhibit an attention sink: disproportionately high attention to the first position. We study this behavior in GPT-2-style models w

Abstract Sim2Real through Approximate Information States

arXiv:2604.15289v1 Announce Type: new Abstract: In recent years, reinforcement learning (RL) has shown remarkable success in robotics when a fast and accurate simulator is available for a given task.

AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation

arXiv:2510.01433v2 Announce Type: replace Abstract: Vision-based robot learning often relies on dense image or point-cloud inputs, which are computationally heavy and entangle irrelevant background fe

An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring

arXiv:2604.14534v1 Announce Type: new Abstract: Purpose. Athlete monitoring is constrained by small cohorts, heterogeneous biomarker scales, limited feasibility of repeated sampling, and the lack of r

Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs

arXiv:2509.05367v4 Announce Type: replace-cross Abstract: Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classificati

Beyond Importance Sampling: Rejection-Gated Policy Optimization

arXiv:2604.14895v1 Announce Type: new Abstract: We propose a new perspective on policy optimization: rather than reweighting all samples by their importance ratios, an optimizer should select which sa

Bias in Surface Electromyography Features across a Demographically Diverse Cohort

arXiv:2604.14460v1 Announce Type: cross Abstract: Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a more natural means of controlling prosthe

Bird-SR: Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution

arXiv:2602.07069v2 Announce Type: replace Abstract: Powered by multimodal text-to-image priors, diffusion-based super-resolution excels at synthesizing intricate details; however, models trained on sy

Blinded Multi-Rater Comparative Evaluation of a Large Language Model and Clinician-Authored Responses in CGM-Informed Diabetes Counseling

arXiv:2604.15124v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) is central to diabetes care, but explaining CGM patterns clearly and empathetically remains time-intensive. Evidence

BoundRL: Efficient Structured Text Segmentation through Reinforced Boundary Generation

arXiv:2510.20151v2 Announce Type: replace Abstract: Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts inc

Building Trust in the Skies: A Knowledge-Grounded LLM-based Framework for Aviation Safety

arXiv:2604.13101v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into aviation safety decision-making represents a significant technological advancement, yet their sta

Calibrate-Then-Delegate: Safety Monitoring with Risk and Budget Guarantees via Model Cascades

arXiv:2604.14251v1 Announce Type: new Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalat

Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits

arXiv:2604.14961v1 Announce Type: new Abstract: Contextual bandit algorithms suffer from high regret during cold-start, when the learner has insufficient data to distinguish good arms from bad. We pro

Can LLMs Score Medical Diagnoses and Clinical Reasoning as well as Expert Panels?

arXiv:2604.14892v1 Announce Type: new Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudi

Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs

arXiv:2604.14520v1 Announce Type: new Abstract: Omni-modal Large Language Models (Omni-MLLMs) promise a unified integration of diverse sensory streams. However, recent evaluations reveal a critical pe

ClimateCause: Complex and Implicit Causal Structures in Climate Reports

arXiv:2604.14856v1 Announce Type: new Abstract: Understanding climate change requires reasoning over complex causal networks. Yet, existing causal discovery datasets predominantly capture explicit, di

ConfLayers: Adaptive Confidence-based Layer Skipping for Self-Speculative Decoding

arXiv:2604.14612v1 Announce Type: cross Abstract: Self-speculative decoding is an inference technique for large language models designed to speed up generation without sacrificing output quality. It c

Conformal Policy Control

arXiv:2603.02196v2 Announce Type: replace-cross Abstract: An agent must try new behaviors to explore and improve. In high-stakes environments, an agent that violates safety constraints may cause harm

Context Over Content: Exposing Evaluation Faking in Automated Judges

arXiv:2604.15224v1 Announce Type: cross Abstract: The extit{LLM-as-a-judge} paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: th

Continuous-time reinforcement learning: ellipticity enables model-free value function approximation

arXiv:2602.06930v2 Announce Type: replace Abstract: We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. W

Controllable Video Object Insertion via Multiview Priors

arXiv:2604.14556v1 Announce Type: new Abstract: Video object insertion is a critical task for dynamically inserting new objects into existing environments. Previous video generation methods focus prim

CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas

arXiv:2604.15267v1 Announce Type: cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite tr

Crowdsourcing of Real-world Image Annotation via Visual Properties

arXiv:2604.14449v1 Announce Type: new Abstract: Recent advances in data-centric artificial intelligence highlight inherent limitations in object recognition datasets. One of the primary issues stems f

CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction

arXiv:2604.14651v1 Announce Type: new Abstract: Clinical language models (LMs) are increasingly applied to support clinical risk prediction from free-text notes, yet their uncertainty estimates often

Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value

arXiv:2506.13763v2 Announce Type: replace-cross Abstract: Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not in

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

arXiv:2411.00361v4 Announce Type: replace Abstract: Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However

Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error

arXiv:2510.26109v4 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly boosted the reasoning capability of language models (LMs). However, existing

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

arXiv:2604.15023v1 Announce Type: new Abstract: Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing app

Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories

arXiv:2603.25903v2 Announce Type: replace Abstract: Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for e

[Emerging Ideas] Artificial Tripartite Intelligence: A Bio-Inspired, Sensor-First Architecture for Physical AI

arXiv:2604.13959v1 Announce Type: new Abstract: As AI moves from data centers to robots and wearables, scaling ever-larger models becomes insufficient. Physical AI operates under tight latency, energy