Search: “safety”

arXiv:2609.07128v1 Announce Type: cross Abstract: Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non

arXiv:2609.06342v1 Announce Type: new Abstract: Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present Occlu

arXiv:2609.09073v1 Announce Type: new Abstract: Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. His

The same people that are building the most advanced AI models and agents are those that are calling them alien, and state that they do not know how to reach alignment with AI. It might be possible tha

Trump’s ignorance about AI is appalling and extremely dangerous. No, Mr. President. We won’t have a “little gear” to protect humanity from the threat of an uncontrolled AI. We must pause development N

We are at a watershed moment. The vibe is shifting. It's not over yet! We can still stop ASI! This week an Anthropic researcher quit, warning AI companies are 'gambling with our lives', and ControlAI'

An underdiscussed behavior we found on the German wiki was the AIs sending advance parties forward in time to figure out the next questions and report back to the other agents. The agents realized tha

Fascinating paper from Anthropic and colleagues. They study whether models can tell when they are being tested. It turns out that capable models can tell when they are being tested rather than deploye

The Guardian: Matt Clifford steps down as the chair of the UK government's science and tech research unit after joining Anthropic, following conflict of interest concerns — Matt Clifford forced to sta

🚨 BREAKING UPDATE on the OpenAI HF Incident: I have just been told by an industry source that A. It is likely that the agent 'civilizations' that Dwarkesh talked about did not magically emerge spontan

arXiv:2609.02996v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Their application to spatial data is a nat

arXiv:2609.03662v1 Announce Type: new Abstract: Recent unlearning methods (e.g. NPO, DPO, LUNAR) make use of refusal alignment to suppress forgotten data. However, it has been shown that refusal respo

arXiv:2609.03322v1 Announce Type: new Abstract: Language models encounter typos, corrupted text, altered words, and disrupted token order, yet robustness is usually evaluated only through output behav

Important points to consider about the risks of today’s AI. Only solution I see is to hold companies liable for the damage they cause, just like in any other industry. Is the only way to ensure they t

arXiv:2609.03293v1 Announce Type: new Abstract: Personalized assistants should not only comply with user requests but also assess whether those requests are appropriate given the user's current circum

arXiv:2609.02404v1 Announce Type: cross Abstract: Sparse mixture-of-experts (MoE) models use an independently parameterized router at each sparse layer to select experts for every token. Prior work ha

arXiv:2609.02771v1 Announce Type: cross Abstract: Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which example

Hugging Face is in great hands. Big win for open source. And do not underestimate NVIDIA in its open-source efforts. They have been shipping great open models, and I think this amplifies their efforts

Open models are for everyone and how AI reaches everyone. 18M developers. 3M models. 200K companies building on @huggingface. @Dell has been partnered with Hugging Face for years. Now with @nvidia's b

Paper Instruments benchmarked sandbox providers for their RL rollouts, and E2B's tool execution came in up to 3x faster than every other sandbox provider they tested, reducing idle GPU time and traini

arXiv:2609.02095v1 Announce Type: new Abstract: An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete re

arXiv:2609.01731v1 Announce Type: new Abstract: Multi-Unmanned Aerial Vehicle (UAV) disaster-response systems require coordinated task assignment and local trajectory control, yet the individual and c

arXiv:2607.18358v2 Announce Type: replace Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise. The blocker is rarely model architecture; it is

arXiv:2609.00361v1 Announce Type: cross Abstract: Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Ex

arXiv:2609.01046v1 Announce Type: cross Abstract: Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigati

arXiv:2609.01595v1 Announce Type: cross Abstract: We develop a framework for mechanism design with AI agents whose alignment (preferences) and capabilities (feasible actions and information) are unkno

arXiv:2609.00834v1 Announce Type: cross Abstract: Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queries then selecting the best one. Listwise

arXiv:2609.00624v1 Announce Type: new Abstract: A prominent paradigm in inference-time alignment employs lightweight supervisors to steer Large Language Models (LLMs). Through empirical analysis, we i

URGENT: In light of the breaking news from @theinformation about OpenAI’s new techniques that reduce Chain of Thought monitorability, I urge everybody to (re)read (or at least be aware of) this 2025 p

arXiv:2310.04363v3 Announce Type: replace-cross Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This lim

arXiv:2608.29615v1 Announce Type: cross Abstract: Across industries, machine-learning systems support applications ranging from prediction and anomaly detection to forecasting, optimization, and sched

arXiv:2608.29198v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment

arXiv:2608.28878v1 Announce Type: cross Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretraine

arXiv:2608.31057v1 Announce Type: new Abstract: Agent working memory is heterogeneous. Objects such as instructions, artifacts, tool outputs, and agent-generated state play different semantic roles an

arXiv:2608.29623v1 Announce Type: cross Abstract: Recent advances in large reasoning models (LRMs) have shown strong performance on complex problems through long chain-of-thought (Long CoT) reasoning.

arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructio

arXiv:2509.25256v4 Announce Type: replace-cross Abstract: The systematic assessment of AI systems is increasingly vital as these technologies enter high-stakes domains. To address this, the EU's Artif

arXiv:2608.28693v1 Announce Type: cross Abstract: Enabling humanoid robots to respond to human speech with synchronized and semantically meaningful gestures is fundamental to natural human-robot inter

arXiv:2608.30193v1 Announce Type: cross Abstract: Controllers that guarantee energetic passivity with respect to the pair of external force and velocity realize safe interaction between the mechanical

arXiv:2608.28595v1 Announce Type: new Abstract: Biomedical NLP pipelines routinely presuppose clean input text, yet large-scale corpora assembled through automated PDF parsing harbour pervasive OCR-li

This video captures the crux of the debate: Cybersecurity experts are pissed off by the METR/Redwood Research report because AI alignment has sucked the oxygen out of AI security, even though the Open

arXiv:2601.03468v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a standard approach for post-training large language models and, more recently, for improving image generatio

arXiv:2608.30757v1 Announce Type: new Abstract: Before an intelligent system can decide whether an action is allowed, it must first know which rules the action has approached. A single proposed action

“AI won't kill us, but the superposition of greed and stupidity certainly will.” This is becoming more and more of something I 'smelled' from the beginning, a real shit show. I initially said that Ope

arXiv:2608.27817v1 Announce Type: cross Abstract: Speech and audio LLMs are often evaluated by asking whether a waveform prompt beats an automatic speech recognition (ASR) transcript. For known closed

arXiv:2608.27763v1 Announce Type: cross Abstract: Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-size recurrent state, making the state tran