safety

Gary's no way as bad as some people make him out to be.

Gary's no way as bad as some people make him out to be. some data i shared yesterday on anthropic revenue possibly slowing down aren’t as a compelling as i thought; i have deleted my posts and await b

Generalist Graph Anomaly Detection via Prototype-Based Distillation

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2605.26857v1 Announce Type: new Abstract: Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector tran

safetyarxiv-cs-lg

27 May 2026

GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation

arXiv:2602.16449v2 Announce Type: replace-cross Abstract: Generative model evaluation commonly relies on high-dimensional embedding spaces to compute distances between samples. We show that dataset re

Grounding Text Embeddings in Stakeholder Associations

arXiv:2605.27168v1 Announce Type: cross Abstract: Text embeddings are widely used to analyse large corpora of complex texts. However, it is unclear whether the embeddings capture the same semantic dis

Heterogeneous AAV Logistics Task Allocation: A Reinforcement Learning Enhanced Overlapping Coalition Formation Game Approach

arXiv:2605.26471v1 Announce Type: new Abstract: In dynamic urban logistics, the stochastic emergence of time-sensitive tasks poses a significant optimality challenge for heterogeneous AAVs logistics t

Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation

arXiv:2602.11799v2 Announce Type: replace Abstract: Multi-modal recommendation has gained traction as items possess rich attributes like text and images. Semantic ID-based approaches effectively discr

How Reliable are LLMs for Reasoning on the Re-ranking task?

arXiv:2508.18444v2 Announce Type: replace-cross Abstract: With the improving semantic understanding capability of Large Language Models (LLMs), they exhibit a greater awareness and alignment with huma

HyperSim: A Holistic Sim-To-Real Framework For Robust Robotic Manipulation

arXiv:2605.26638v1 Announce Type: new Abstract: Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to e

i so wish i could fast forward a few years to see how all this turned out.

Gary Marcus expresses curiosity about the long-term outcomes of current developments, likely related to artificial intelligence or technology trends given his expertise in AI and cognitive science. Th

Illinois Legislature passes SB 315, a bill requiring annual independent third-party safety audits of leading AI companies; the bill heads to the governor's desk (Jared Perlo/NBC News)

Jared Perlo / NBC News: Illinois Legislature passes SB 315, a bill requiring annual independent third-party safety audits of leading AI companies; the bill heads to the governor's desk — The measure,

safetytechmeme

Image Thresholding: Understanding Bias of Evaluation Metrics towards Specific Evaluation Functions

arXiv:2605.27132v1 Announce Type: new Abstract: Multilevel image thresholding is widely used for segmentation in applications ranging from medical imaging to remote sensing. Classical objective functi

safetyarxiv-cs-cv

Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures

arXiv:2502.06963v3 Announce Type: replace-cross Abstract: The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external

Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification

arXiv:2510.00902v2 Announce Type: replace Abstract: Transfer learning is crucial for medical imaging, yet the selection of source datasets often relies on researchers' intuition rather than systematic

safetyarxiv-cs-cv

It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty

arXiv:2605.27288v1 Announce Type: cross Abstract: Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behav

Jailbreak susceptibility prediction and mitigation via the behavioral geometry of models

arXiv:2605.26409v1 Announce Type: cross Abstract: Evaluating and mitigating a generative system's susceptibility to jailbreak attacks is critical to its safe deployment. Given the number of deployable

KARMA: Karma-Aligned Reward Model Adaptation

arXiv:2605.26738v1 Announce Type: new Abstract: Human communication depends on implicit social signals where effectiveness is shaped by tone, context, and conversational norms rather than semantic con

KZ-SafetyPrompts: A Kazakh Safety Evaluation Prompt Dataset for Large Language Models

arXiv:2605.26947v1 Announce Type: new Abstract: Kazakh is underrepresented in resources for evaluating the safety behavior of large language models. We present KZ-SafetyPrompts, a Kazakh prompt datase

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback

arXiv:2509.18384v2 Announce Type: replace Abstract: Large language models (LLMs) can translate natural language instructions into executable action plans for robotics, autonomous driving, and other do

LearnedCache: An eBPF-Integrated Perceptron-Based Eviction Policy for the Linux Page Cache

arXiv:2605.26168v1 Announce Type: cross Abstract: Linux is the foundation of the digital age, accounting for the majority of the cloud and mobile OS markets. Any device that runs Linux uses the Linux

Learning Dynamic Graph Representations through Timespan View Contrasts

arXiv:2605.27063v1 Announce Type: new Abstract: The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node f

Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy

arXiv:2605.27046v1 Announce Type: new Abstract: Motor thermal management is often overlooked in the context of electrically-actuated robots, particularly legged robots, but motor overheating is a key

Learning to Orchestrate Agents under Uncertainty

arXiv:2605.27073v1 Announce Type: new Abstract: Adaptive orchestration of heterogeneous agents requires making sequential delegation decisions under uncertain and evolving agent behaviour, e.g., coord

Learning to Reason Efficiently with Discounted Reinforcement Learning

arXiv:2510.23486v2 Announce Type: replace Abstract: Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. More broadly, in goal reaching sequential de

Less is More: Early Stopping Rollout for On-Policy Distillation

arXiv:2605.27028v1 Announce Type: cross Abstract: On-policy distillation has recently emerged as a promising alternative to standard sequence-level imitation, training a student by scoring its own rol

Linear and Neural Dueling Bandits with Delayed Feedback

arXiv:2605.26554v1 Announce Type: cross Abstract: Contextual dueling bandits form a cornerstone of preference-based decision-making, with critical applications in recommender systems and large languag

Look Further: Socially-Compliant Navigation System in Residential Buildings

arXiv:2605.26710v1 Announce Type: new Abstract: The distance at which a mobile robot reacts to a person strongly impacts various qualities of the human-robot interaction. In this paper, we focus on th

MAIGO: Mitigating Lost-in-Conversation with History-Cleaned On-Policy Self-Distillation

arXiv:2605.27186v1 Announce Type: new Abstract: Large language models often solve tasks from a fully specified prompt but degrade when the same requirements unfold over multiple turns, known as the lo

MATCHA: Matching Text via Contrastive Semantic Alignment

arXiv:2605.27345v1 Announce Type: new Abstract: Reliable evaluation is essential for understanding large language model (LLM) performance, yet today's go-to metrics, namely token-overlap scores (e.g.,

Measuring Prediction Uncertainty in Neural Cellular Automata

arXiv:2605.26726v1 Announce Type: cross Abstract: Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability

arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recover

MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning

arXiv:2605.26154v1 Announce Type: cross Abstract: LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents

🚨 MICHAEL BURRY WARNS THREE UPCOMING IPOs COULD COMPLETELY CRASH THE STOCK MARKET. Michael Burry reported that the upcoming public listings…

🚨 MICHAEL BURRY WARNS THREE UPCOMING IPOs COULD COMPLETELY CRASH THE STOCK MARKET. Michael Burry reported that the upcoming public listings for SpaceX, OpenAI, and Anthropic are going to pull more cap

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias

arXiv:2605.27097v1 Announce Type: new Abstract: The successful training of neural networks hinges on the use of first order optimization methods, yet the theoretical characterization of these methods

Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation

arXiv:2603.25415v2 Announce Type: replace Abstract: Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond purely geometric representations. In Org

Monte Carlo Permutation Search

arXiv:2510.06381v2 Announce Type: replace-cross Abstract: We propose Monte Carlo Permutation Search (MCPS), a general-purpose Monte Carlo Tree Search (MCTS) algorithm that improves upon the GRAVE algo

More CEOs saying the obvious. AI has been hyped so much over the last few years that every other technology, market trend, and idea is overl…

More CEOs saying the obvious. AI has been hyped so much over the last few years that every other technology, market trend, and idea is overlooked. When will be get past the mania about imminent AGI, c

Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation

arXiv:2605.26878v1 Announce Type: new Abstract: Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility ag

MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation

arXiv:2602.09878v2 Announce Type: replace Abstract: World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely im

safetyarxiv-cs-cv

Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

arXiv:2605.26942v1 Announce Type: new Abstract: LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce una

Olaf-World: Orienting Latent Actions for Video World Modeling

arXiv:2602.10104v2 Announce Type: replace-cross Abstract: Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control

On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach

arXiv:2605.26162v1 Announce Type: cross Abstract: Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale

On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series

arXiv:2605.26194v1 Announce Type: new Abstract: Clinical time-series learning is routinely constrained by small, heterogeneous cohorts and protocol drift, while its downstream use spans both classific

Only thing completely clear about the future of work is that Altman and Amodei will say whatever they think will drive up their IPOs.

Only thing completely clear about the future of work is that Altman and Amodei will say whatever they think will drive up their IPOs. Altman and Amodei have said for years that AI was so powerful that

Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks

arXiv:2605.26526v1 Announce Type: new Abstract: Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an ass

OpenAI announces partnerships to combat election misinformation, offering cybersecurity products to state officials and backing legislation to curb deepfakes (Maria Curi/Axios)

Maria Curi / Axios: OpenAI announces partnerships to combat election misinformation, offering cybersecurity products to state officials and backing legislation to curb deepfakes — OpenAI is announcing

safetytechmeme

Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization

arXiv:2602.00827v2 Announce Type: replace Abstract: Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dyn

Pair-In, Pair-Out: Latent Multi-Token Prediction for Efficient LLMs

arXiv:2605.27255v1 Announce Type: cross Abstract: Long chain-of-thought reasoning has made autoregressive decoding the dominant inference cost of modern large language models. Existing methods target

Palantir CEO Alex Karp goes after AI slop. The fight over AI “slop” is really a fight over whether software is performing or merely pretendi…

Palantir CEO Alex Karp goes after AI slop. The fight over AI “slop” is really a fight over whether software is performing or merely pretending. 'The appearance of software working is not software work