Search: “safety”

Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters

arXiv:2501.02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominan

Safety

Imagine if i had a business that gave you 20 bills for 10 bills. As word spread, my customer base would grow. My revenue (how many $10 bil…

DGX agent

Imagine if i had a business that gave you 20 bills for 10 bills. As word spread, my customer base would grow. My revenue (how many $10 bills I collected) would grow. And I would continue to lose money

safetygary-marcus--x

25 Jun 2026

In the end, will Elon be best known for his efforts to mainstream electric cars, for his rockets, …. or for his efforts to mainstream AI-gen…

In the end, will Elon be best known for his efforts to mainstream electric cars, for his rockets, …. or for his efforts to mainstream AI-generated porn? xAI is betting big on AI video and image genera

It is amazing to me how little the X-verse cares about the rise of authoritarianism in the United States. Is that because most of the “peopl…

It is amazing to me how little the X-verse cares about the rise of authoritarianism in the United States. Is that because most of the “people” here are actually Russian bots? 🤔 More Soviet-style decep

Learning Action Priors for Cross-embodiment Robot Manipulation

arXiv:2606.26095v1 Announce Type: cross Abstract: Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy

Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots

arXiv:2606.25706v1 Announce Type: new Abstract: High-level humanoid planners often output sparse task-space, low-rate trajectories, whereas whole-body controllers run at high frequency. This creates t

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose conse

Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations

arXiv:2606.26047v1 Announce Type: new Abstract: Robot crowd navigation requires the ability to infer human intentions while accounting for the structural constraints of the environment. Currently, dee

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

arXiv:2606.25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target da

Learning with a Single Rollout via Monte Carlo Pass@k Critic

arXiv:2606.25451v1 Announce Type: new Abstract: Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collecti

Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction

arXiv:2606.25401v1 Announce Type: cross Abstract: Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the

LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag

Looks like OpenAI’s IPO will be delayed until 2027. • might be a sign that their finances don’t look compelling yet. • gives Anthropic time …

Looks like OpenAI’s IPO will be delayed until 2027. • might be a sign that their finances don’t look compelling yet. • gives Anthropic time to go first If they can bump up the valuation by $999.3 bill

love that one of my readers made her own, explicit set of public predictions. 🙏

love that one of my readers made her own, explicit set of public predictions. 🙏 I want to post my predictions for 2ish years horizon. AI stock bubble collapses due to fundamentals not working out with

Low-Complexity Policy Tessellations in Structured Markov Decision Processes

arXiv:2606.25593v1 Announce Type: new Abstract: We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically app

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning

arXiv:2606.25526v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region

MAPL: Multi-Objective Preference Learning for Robot Locomotion

arXiv:2606.25398v1 Announce Type: new Abstract: Reward design remains a major bottleneck in reinforcement learning for robot locomotion, where successful policies often depend on carefully tuned, task

Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control

arXiv:2606.25136v1 Announce Type: new Abstract: General-purpose robots operating in partially observable environments, such as homes, require memory to support autonomy. They must recall diverse infor

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606.25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor gamma, finite state space m

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources

arXiv:2606.25832v1 Announce Type: new Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging probl

More Soviet-style deceptions that I never expected to see in the United States 😢

Gary Marcus expresses concern about deceptive practices in the United States that he compares to Soviet-style tactics, suggesting he has observed propaganda, misinformation, or manipulative government

Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

arXiv:2606.26080v1 Announce Type: new Abstract: Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-h

Neural Machine Translation for Low-Resource Tangkhul--English

arXiv:2606.25365v1 Announce Type: new Abstract: We present a study on low-resource machine translation for the Tangkhul-English (nmf-en) language pair. Tangkhul is a severely under-resourced Tibeto-Bu

noah smith, ignoring that Anthropic’s rumored profitable quarter is a. during peak tokenmaxxing b, before recent Chinese advances c. in a qu…

noah smith, ignoring that Anthropic’s rumored profitable quarter is a. during peak tokenmaxxing b, before recent Chinese advances c. in a quarter in which Elon gave them a big one time subsidy. that’s

Offline Multi-agent Continual Cooperation via Skill Partition and Reuse

arXiv:2606.25389v1 Announce Type: new Abstract: Extracting skills from multi-agent offline dataset improves learning efficiency via sharing task-invariant coordination skills among tasks. In settings

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity

arXiv:2606.26091v1 Announce Type: new Abstract: On-policy self-distillation achieves strong pass@1 accuracy by using a single model as both teacher and student, with the teacher conditioned on a corre

One Body, Two Minds: Variable Autonomy Approach for a Co-embodied Robotic Hand

arXiv:2606.25575v1 Announce Type: new Abstract: Assistive robotic systems face a fundamental trade-off: fully autonomous systems lack user agency, while fully user-controlled systems demand continuous

OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning

arXiv:2606.25757v1 Announce Type: new Abstract: Reinforcement Learning (RL) has enabled LLMs to excel in objective reasoning tasks such as mathematics and code generation. However, applying RL to open

Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse

arXiv:2601.13317v2 Announce Type: replace Abstract: Climate discourse online shapes public understanding of climate change and informs political and policy debate, yet it unfolds across structurally d

People are not getting how significant this news – The White House delaying GPT 5.6 – is. Here’s the context and a suggestion. Both @pmarca …

People are not getting how significant this news – The White House delaying GPT 5.6 – is. Here’s the context and a suggestion. Both @pmarca and @DavidSacks did everything in their power to keep the Wh

PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data

arXiv:2507.20068v3 Announce Type: replace Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown t

Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection

arXiv:2606.25740v1 Announce Type: new Abstract: 3D anomaly detection in point clouds is critical for high-precision industrial manufacturing. Reconstruction-based methods have laid a strong foundation

Position Spaces and Graphs

arXiv:2606.25719v1 Announce Type: new Abstract: In this paper, we introduce position graphs, a graph-based reasoning framework based on the formalization of position spaces. This framework utilizes tw

Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

arXiv:2606.25680v1 Announce Type: new Abstract: Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing l

Reflective VLA: In-Context Action Consequences Make VLAs Generalize

arXiv:2606.25215v1 Announce Type: new Abstract: Most vision-language-action (VLA) models are reactive: they predict the next action from the current instruction and observation, implicitly assuming th

RGB: RL Guided Whole-Body MPPI for Humanoid Control

arXiv:2606.25123v1 Announce Type: new Abstract: Humanoid robots require whole-body controllers that are both robust and precise in contact-rich environments. While deep reinforcement learning (RL) ach

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning

arXiv:2601.23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and r

ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models

arXiv:2606.25800v1 Announce Type: new Abstract: Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional

SAC^2-Net: Semantic Anchoring and Complementary-Consensus Fusion for Multimodal Micro-Expression Recognition

arXiv:2606.25542v1 Announce Type: new Abstract: Micro-expression recognition (MER) is challenging due to subtle facial movements, limited data, and the ambiguous relationship between Action Units (AUs

SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation

arXiv:2606.25497v1 Announce Type: new Abstract: Object-Goal Navigation (ObjNav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations. Existing mo

ScalingAR: Scaling Confidence for Autoregressive Image Generation

arXiv:2509.26376v3 Announce Type: replace Abstract: Test-time strategies have shown remarkable success in improving large language models, but their application to next-token prediction (NTP) autoregr

Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents

arXiv:2606.25852v1 Announce Type: new Abstract: Group-based reinforcement learning effectively post-trains LLM agents for long-horizon, sparse-reward tasks by deriving step-level credit from trajector

Solving Markov Decision Processes with Future Information via MPC

arXiv:2606.24991v1 Announce Type: cross Abstract: Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finit

Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries

arXiv:2606.20615v2 Announce Type: replace Abstract: AI agents now participate as first-class team members across the software development lifecycle, yet no specification language exists for expressing

StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots

arXiv:2606.25765v1 Announce Type: new Abstract: Climbing hollow stairs remains a challenging problem for quadruped robots due to the high risk of leg trapping, severe depth sparsity, and high-frequenc

STOCKSTAY Another Day: The Latest Addition to Turla’s Intelligence Gathering Apparatus

Written by: Jordan Jones Introduction Google Threat Intelligence Group (GTIG) has conducted an in-depth analysis of a .NET backdoor, tracked as STOCKSTAY, that has been continually developed and deplo

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Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606.24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challe

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

arXiv:2606.25989v1 Announce Type: new Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Exis