Search: “safety”

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

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

DGX agent

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

safetyarxiv-cs-ai

27 May 2026

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

safetyarxiv-cs-ro

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

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

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

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

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 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

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.,

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

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

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

per comments from @GergelyOrosz below i don’t think these data are compelling after all, and am deleting the OP

Gary Marcus deleted an original post after Gergely Orosz provided comments questioning the compelling nature of the data presented. The post appears to have been withdrawn due to critical feedback tha

PICACO: Pluralistic In-Context Value Alignment of LLMs via Total Correlation Optimization

arXiv:2507.16679v3 Announce Type: replace-cross Abstract: In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and

Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives

arXiv:2602.04990v3 Announce Type: replace Abstract: The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly tra

PyCAT4: A Hierarchical Vision Transformer-based Framework for 3D Human Pose Estimation

arXiv:2508.02806v3 Announce Type: replace Abstract: Recently, a significant improvement in the accuracy of 3D human pose estimation has been achieved by combining convolutional neural networks (CNNs)

Quantized Keys Steal Attention: Bias Correction for KV-Cache Compression in Video Diffusion

arXiv:2605.26266v1 Announce Type: cross Abstract: Chunk-wise autoregressive video diffusion models rely on a KV cache of previously generated chunks to avoid redundant computation, but this cache quic

Real Images, Worse Judgments: Evaluating Vision-Language Models on Concreteness and Imagery

arXiv:2605.27315v1 Announce Type: new Abstract: Visual inputs are often assumed to improve language understanding in multimodal models. We examine this assumption by asking whether vision-language mod

Rethinking the Trust Region in LLM Reinforcement Learning

arXiv:2602.04879v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) ser

Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective

arXiv:2605.26441v1 Announce Type: cross Abstract: This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposa

RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents

arXiv:2605.26352v1 Announce Type: new Abstract: Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate qu

Sample Complexity of Policy Gradient for Log-Growth Control

arXiv:2605.26640v1 Announce Type: cross Abstract: We study the sample complexity of policy gradient for log-growth control -- the problem of learning, from observed state transitions, a feedback gain

Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization

arXiv:2605.26282v1 Announce Type: new Abstract: Model-based reinforcement learning (RL) can be effectively supported at scale through the use of world models. However, in practice, scaling such approa

SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception

arXiv:2605.27009v1 Announce Type: new Abstract: Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at