safety

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

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

DGX agent

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

safetyarxiv-cs-cv

26 Jun 2026

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

Soft Token Alignment for Cross-Lingual Reasoning

arXiv:2606.26466v1 Announce Type: new Abstract: Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior wo

Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs

arXiv:2508.03247v2 Announce Type: replace Abstract: Prior clinical psychology research shows that Western individuals with depression tend to report psychological symptoms, while Eastern individuals r

source: “How much compute does the world really need? Scale cannot solve AI’s fundamental problem with accuracy” https://www.ft.com/content/…

This article argues that increasing computational scale alone cannot address fundamental accuracy and reliability problems in AI systems, suggesting that AI development requires solutions beyond raw c

Sources: Meta lobbyists are urging California lawmakers to exempt social media platforms from legislation that would increase penalties in child-harm cases (Tyler Katzenberger/Politico)

Tyler Katzenberger / Politico: Sources: Meta lobbyists are urging California lawmakers to exempt social media platforms from legislation that would increase penalties in child-harm cases — Meta's plea

safetytechmeme

SpatialFlow-GRPO: Where Spatial Credit Drives Image Editing

arXiv:2606.26872v1 Announce Type: new Abstract: Recent online reinforcement learning has substantially improved image editing quality. However, existing Flow-GRPO-style methods usually rely on a singl

SPCX - SPACEX BOND SELLOFF DEEPENS SpaceX's 25 billion bond sale is suffering unusually steep losses, with paper losses exceeding $300 mil…

SPCX - SPACEX BOND SELLOFF DEEPENS SpaceX's 25 billion bond sale is suffering unusually steep losses, with paper losses exceeding $300 million. Traders say fast-money investors may be exiting, while c

$SPCX falls below 150. If you bought it once it the market you lost money. If you bought at 225 you lost a third in less than two weeks. Non…

Gary Marcus comments on significant losses in $SPCX stock, noting that investors who purchased at 225 experienced approximately one-third losses within two weeks as the stock fell below 150. The post

State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading

arXiv:2606.27032v1 Announce Type: cross Abstract: Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints. This ma

Statistical and Structural Approaches to Algorithmic Fairness

arXiv:2606.26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures tha

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

arXiv:2606.26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. De

STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation

arXiv:2601.20381v3 Announce Type: replace Abstract: Visual foundation models provide strong perceptual features for robotics, but their dense representations lack explicit object-level structure, limi

SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

arXiv:2606.12808v3 Announce Type: replace-cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next exper

Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention

arXiv:2606.26663v1 Announce Type: new Abstract: World Action Models (WAMs) generate actions together with predicted futures, offering a powerful interface for robot decision making. In contact-rich ma

The Verification Horizon: No Silver Bullet for Coding Agent Rewards

arXiv:2606.26300v1 Announce Type: new Abstract: A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foun

Things came undone this week: Starmer resigned, SpaceX tanked, the Iran deal stumbled, and a housing bill that passed the Senate is dying on…

Things came undone this week: Starmer resigned, SpaceX tanked, the Iran deal stumbled, and a housing bill that passed the Senate is dying on Trump’s desk. Today’s conversation with @GaryMarcus is a mu

This is what’s causing Anthropic to aggressively beg for govt protection (see below). Customers are finding cheaper alternatives. Keeping em…

This is what’s causing Anthropic to aggressively beg for govt protection (see below). Customers are finding cheaper alternatives. Keeping employees requires continuing ultra-rich secondaries ($$) that