safety

Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)

arXiv:2606.27163v1 Announce Type: cross Abstract: I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the on

Safety

Linguistics and Human Brain: A Perspective of Computational Neuroscience

DGX agent

arXiv:2602.08275v3 Announce Type: replace-cross Abstract: Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistic

safetyarxiv-cs-cl

26 Jun 2026

LISA: Likelihood Score Alignment for Visual-condition Controllable Generation

arXiv:2606.27192v1 Announce Type: new Abstract: The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pre

Listening Like a Judge: A Music-Aware Framework for Automatic Singing Performance Evaluation

arXiv:2606.26451v1 Announce Type: cross Abstract: Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations. However,

LLM-based Models for Detecting Emerging Topics in Service Feedback

arXiv:2606.26595v1 Announce Type: new Abstract: Enhancing the analysis of service feedback is essential for public sector organizations, particularly tax administrations, where trust and compliance de

Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models

arXiv:2601.03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large lang

MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment

arXiv:2606.27019v1 Announce Type: new Abstract: The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and co

NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing

arXiv:2603.19864v2 Announce Type: replace Abstract: Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inher

NaviCache: Test-Time Self-Calibration Caching for Video Generation

arXiv:2606.26795v1 Announce Type: cross Abstract: Video Diffusion Models (VDMs) is constrained by immense computational costs. While offline calibration-based acceleration suffers from calibration dat

Neural Speaker Diarization via Multilingual Training: Evaluation on Low-Resource Nepali-Hindi Speech

arXiv:2606.26144v1 Announce Type: cross Abstract: Speaker diarization, the task of determining 'who spoke when' in a multi-speaker recording, is a critical component in applications such as meeting tr

Normalizing Flows are Capable Models for Continuous Control

arXiv:2505.23527v4 Announce Type: replace Abstract: Modern reinforcement learning (RL) algorithms have found success by using powerful probabilistic models, such as transformers, energy-based models,

OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation

arXiv:2606.26201v1 Announce Type: new Abstract: Learning long-horizon humanoid loco-manipulation poses a dual challenge: it requires not only the robust execution of meta-skills but also their seamles

OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning

arXiv:2606.26790v1 Announce Type: new Abstract: Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little

Ordinal Neural Collapse as a Representation Prior for Visual Navigation

arXiv:2606.26839v1 Announce Type: cross Abstract: Learning robust navigation policies directly from visual observations remains a fundamental challenge in vision-based robotic navigation. In end-to-en

orwellian bullshit

This post likely discusses how Orwellian tactics—such as propaganda, doublespeak, and manipulation of language—are being employed in contemporary contexts, possibly relating to AI, technology, or poli

Overcoming State Inertia: Minimally Invasive Temporal Alignment for Evolving Contexts

arXiv:2512.03704v3 Announce Type: replace Abstract: Long-context dialogue systems suffer from state inertia, where models over-attend to history and fail to adapt to evolving intents. We demonstrate t

PAMAE: Phase-Aware-MoE Action Experts Towards Reliable Flow-Matching Vision-Language-Action Policies

arXiv:2606.27144v1 Announce Type: new Abstract: Reliable action generation for multi-stage robotic manipulation remains challenging for Vision-Language-Action (VLA) models. While existing flow-matchin

PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology

arXiv:2512.17621v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity

Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes

arXiv:2606.27210v1 Announce Type: new Abstract: We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS

Paying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models

arXiv:2606.27373v1 Announce Type: new Abstract: Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However,

People want jobs, not subsidies:

People want jobs, not subsidies: Taxing corporations to fund programs is usually controversial and partisan - but when it comes to AI job displacement voters are shockingly left wing. It's genuinely e

people with advanced degrees who can’t distinguish between pure LLMs (which is what I critiqued in 2022) and LLMs enhanced with neurosymboli…

people with advanced degrees who can’t distinguish between pure LLMs (which is what I critiqued in 2022) and LLMs enhanced with neurosymbolic techniques (which is what I championed in 2022) disappoint

PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models

arXiv:2606.26694v1 Announce Type: new Abstract: Recent game world models can synthesize visually plausible, action-conditioned rollouts. However, their interaction behaviors often remain limited to ex

PlanRL: A Trajectory Planning Architecture for Reinforcement Learning-based Driving Experts

arXiv:2606.26858v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a prominent framework for developing driving experts in autonomous vehicles. However, most existing RL-based expe

Position: Align AI to Our Aspirations, Not Our Flaws

arXiv:2606.13755v2 Announce Type: replace-cross Abstract: We argue that aligning AI to aggregated human preferences is the wrong target. With current technology, one can train AIs to share the values

PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation

arXiv:2606.26741v1 Announce Type: cross Abstract: Humanoid motion imitation requires not only accurate perception of human kinematics but also faithful reproduction of physical interactions with the e

Prompt Injection in Automated Resume Screening with Large Language Models: Single and Multi-Injection Settings

arXiv:2606.27287v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate alg

Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation

arXiv:2606.27123v1 Announce Type: cross Abstract: Closed-loop traffic simulation remains challenging because it must generate interactive multi-agent behaviors that are scene-consistent and controllab

Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control

arXiv:2606.26313v1 Announce Type: new Abstract: This paper presents a hierarchical control framework using model predictive control (MPC) and reinforcement learning (RL) for active roll control to man

Radical AI Interpretability

arXiv:2606.26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanis

RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage

arXiv:2606.27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety. The growing volume of FDA recall records presents challenges i

Reconstruction Alignment Improves Unified Multimodal Models

arXiv:2509.07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture. However, conventional training relies

Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints

arXiv:2606.26106v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress. Whil

remember my botched bicycle examples? here’s an actual REI ad, h/t Oren Etzioni:

Gary Marcus shares an actual REI advertisement as a follow-up to previous discussion about flawed bicycle examples, crediting Oren Etzioni for the reference. The post appears to contrast a real-world

Residual RL-MPC for Robust Microrobotic Cell Pushing Under Time-Varying Flow

arXiv:2603.05448v2 Announce Type: replace-cross Abstract: Contact-rich micromanipulation in microfluidic flow is challenging because small disturbances can break pushing contact and induce large later

Retrieval-Warmed Energy-Based Reasoning: A Five-Arm Ablation Methodology for Diffusion-as-Inference on Structured Reasoning Tasks

arXiv:2606.26476v1 Announce Type: cross Abstract: Warm-started diffusion samplers accelerate iterative inference, but it is rarely clear which part of the pipeline carries the gain. We study extbf{ret

Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling

arXiv:2606.26922v1 Announce Type: cross Abstract: Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states. Large

RMTL: Reinforced Micro-task Learning for Long-Horizon Manipulation with VLM Rewards

arXiv:2606.26175v1 Announce Type: new Abstract: Reinforcement learning (RL) for robotic manipulation often requires manually designing a dense reward function, which is difficult to tune and often fra

RobOralScan: Learning Active Intraoral Scanning for Robotic Dental Reconstruction

arXiv:2606.26955v1 Announce Type: new Abstract: Intraoral scanning is widely used for digital optical impressions in prosthodontic, implant, and orthodontic treatment, but full-arch and long-span scan

RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning

arXiv:2606.26997v1 Announce Type: cross Abstract: Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models lear

Rotary Position Encodings for Graphs

arXiv:2509.22259v4 Announce Type: replace-cross Abstract: We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large langu

RouterVLA: Turning Smoke Tests into Supervision for Heterogeneous VLA Selection

arXiv:2606.27355v1 Announce Type: new Abstract: We study whether pre-deployment evaluation rollouts can be reused to supervise policy selection. Robot teams routinely smoke test candidate vision-langu

Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks

arXiv:2606.27147v1 Announce Type: cross Abstract: Unlike diffusion-based models that operate in continuous latent spaces, autoregressive unified multimodal models produce images by sequentially predic

Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

arXiv:2606.26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient auton

Scale Robot Policy Evaluation with Ray

This article discusses using Ray and Anyscale's distributed computing platform to scale the evaluation of robot control policies across multiple simulations in parallel. It likely covers techniques fo

safetyanyscale-ray

Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking

arXiv:2606.26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents. In modern information retrieval or recommendation systems, they are oft

Semantic Early-Stopping for Iterative LLM Agent Loops

arXiv:2606.27009v1 Announce Type: new Abstract: Multi-agent large language model (LLM) loops, for example a Writer that drafts and a Critic that revises, are almost always terminated by a fixed iterat

Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling

arXiv:2606.26617v1 Announce Type: new Abstract: Scaling laws describe how learning performance varies with model size, data size, and compute. While recent theoretical work has established scaling law