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Multicalibration Boosting: Theory, Convergence, and Transferability

arXiv:2605.24364v1 Announce Type: cross Abstract: Multicalibration extends classical calibration by requiring predictions to be unbiased over a rich collection of functions, encompassing both predicti

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Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation

DGX agent

arXiv:2605.23961v1 Announce Type: cross Abstract: The design of RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Despite recent pro

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26 May 2026

Multimodal Functional Maximum Correlation for Emotion Recognition

arXiv:2512.23076v2 Announce Type: replace-cross Abstract: Emotional states manifest as coordinated yet heterogeneous physiological responses across central and autonomic systems, posing a fundamental

MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection

arXiv:2505.23803v2 Announce Type: replace-cross Abstract: Phishing email detection faces significant challenges due to evolving adversarial tactics and heterogeneous attack patterns. Traditional appro

Music Transcription with (Almost) No Supervision

arXiv:2605.24193v1 Announce Type: cross Abstract: Competitive music transcription models require large amounts of paired audio-score data, which is scarce due to collection costs, alignment difficulty

NeuralTouch: Neural Descriptors for Precise Sim-to-Real Tactile Robot Control

arXiv:2510.20390v2 Announce Type: replace Abstract: Grasping accuracy is a critical prerequisite for precise object manipulation, often requiring careful alignment between the robot hand and object. N

Neuro-Inspired Inverse Learning for Planning and Control

arXiv:2605.24152v1 Announce Type: new Abstract: We present a neuro-inspired framework for embodied planning and control. Building on three principles that enable fast and highly effective goal-directe

Not All Transitions Matter: Evidence from PPO

arXiv:2605.24071v1 Announce Type: cross Abstract: Training a reinforcement learning agent on-policy means collecting fresh experience at every update, and that experience comes with a hidden problem.

Not only where, But when: Temporal Scheduling for RLVR

arXiv:2605.25381v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post-training of Large Language Models (LLMs). While policy optimi

OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation

arXiv:2605.25829v1 Announce Type: cross Abstract: Recent vision-language-action (VLA) models and world action models (WAMs) advance robotic manipulation by enriching intermediate representations with

oh. my. god. could this word cloud diagram be … conscious?

Gary Marcus, a prominent AI researcher and critic, questions whether a word cloud diagram could possess consciousness, likely engaging in ironic commentary on overclaimed AI capabilities or consciousn

safetygary-marcus--x

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

arXiv:2602.10635v2 Announce Type: replace Abstract: Socially intelligent AI systems must entail reasoning across diverse human behavioral tasks, and generalization to new contexts. However, AI has yet

On Reliability of Efficient Membership Inference Vulnerability Evaluation

arXiv:2605.25819v1 Announce Type: new Abstract: Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through mode

On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits

arXiv:2605.25789v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem where an agent is granted a free exploration budget before regret accumulates, a setting not captured

On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks

arXiv:2605.24649v1 Announce Type: cross Abstract: Recurrent Neural Networks (RNNs) can learn to predict Signal Temporal Logic (STL) verdicts online from partial trajectories, but deploying them as run

One-Step Bellman Alignment Enables Provably Efficient Transfer in Online RL

arXiv:2601.21924v2 Announce Type: replace Abstract: We study online transfer reinforcement learning (RL) in episodic Markov decision processes, where experience from related source tasks is available

openai truly tanked a golden reputation, and squandered its lead, yet its board stands by its CEO, and looks for a band aid.

openai truly tanked a golden reputation, and squandered its lead, yet its board stands by its CEO, and looks for a band aid. OpenAI has a PR challenge, and although it's spoken to several candidates f

safetygary-marcus--x

Operationalizing Reconstructive Authority: Runtime Construction, Dependency Resolution, and Execution Gating in Autonomous Agent Systems

arXiv:2605.23935v1 Announce Type: new Abstract: Autonomous agent systems fail not only due to incorrect decisions, but due to executing decisions whose authority no longer holds at runtime. Prior work

Optimizing Token Choice for Code Watermarking: An RL Approach

arXiv:2508.11925v3 Announce Type: replace-cross Abstract: Protecting intellectual property on LLM-generated code necessitates effective watermarking systems that can operate within code's highly struc

PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models

arXiv:2506.09084v2 Announce Type: replace-cross Abstract: Whole-page optimization (WPO) decides how search and recommendation results are surfaced to users, and large language models (LLMs) open a new

ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation

arXiv:2605.25029v1 Announce Type: new Abstract: Autonomous parking demands precise low-speed maneuvering within narrow, cluttered, and highly constrained environments, where vehicles must navigate tig

“pass me the crack pipe” @edels0n on SpaceX’s ludicrous valuation:

Gary Marcus critiques SpaceX's valuation as unreasonably inflated, using hyperbolic language to suggest the company's market assessment lacks rational justification. The post likely discusses concerns

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PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs

arXiv:2601.20539v3 Announce Type: replace Abstract: Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks'

Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use

arXiv:2605.26037v1 Announce Type: new Abstract: We test the standard RLVR tool-use recipe -- GRPO on Qwen2.5-7B-Instruct -- on a deliberately minimal knowledge-graph tool API: four Freebase navigation

PID-Guided Partial Alignment for Multimodal Decentralized Federated Learning

arXiv:2601.10012v2 Announce Type: replace Abstract: Multimodal decentralized federated learning (DFL) must support collaboration among agents that hold different modality subsets and often different m

PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training

arXiv:2605.24570v1 Announce Type: cross Abstract: Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training be

PolyGnosis 2.0: Enhancing LLM Reasoning via Agentic Harness Engineering for Polymarket and OSINT Insight Extraction

arXiv:2605.25958v1 Announce Type: new Abstract: This paper introduces PolyGnosis 2.0, a pioneering multi-agent architecture designed to extract predictive intelligence by synthesizing Polymarket anoma

Polynomial Context-Truncation Sensitivity in Autoregressive Language Models: Sequential Wyner-Ziv Bounds for KV Cache Compression

arXiv:2605.25085v1 Announce Type: cross Abstract: We study the rate-distortion limits of online KV cache compression in autoregressive language models, formulating it as sequential Wyner-Ziv source co

Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System

arXiv:2605.25362v1 Announce Type: new Abstract: The strong dynamic coupling between the manipulator and the base poses a significant challenge to maintaining spacecraft attitude stability, potentially

PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets

arXiv:2605.24249v1 Announce Type: new Abstract: The growing availability of clinical data has increased the use of machine learning, yet centralized data aggregation is often infeasible for sensitive

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents

arXiv:2605.24900v1 Announce Type: new Abstract: Proactive task-oriented agents must autonomously anticipate user needs, identify actionable opportunities, and trigger software actions at appropriate m

QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity

arXiv:2605.25066v1 Announce Type: cross Abstract: Quantum machine learning (QML) is moving from research prototypes to deployed cloud services. As QML enters regulated industries, the integrity of the

Quantitative Evaluation of the Severity of Posttraumatic Stress Disorder through Transfer Learning from Specific Phobia Data

arXiv:2605.25933v1 Announce Type: cross Abstract: Posttraumatic stress disorder (PTSD) is a prevalent and debilitating mental health condition with significant personal and societal impacts. Current c

Quantum Frog: Emergent Cooperation and Difficulty Scaling in a Quantized-Time Cooperative Game

arXiv:2605.23930v1 Announce Type: new Abstract: We introduce Quantum Frog, a two-player cooperative game built on a novel quantized-time mechanic in which the environment advances only when a player a

Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs

arXiv:2603.09095v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided a

Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving

arXiv:2605.24004v1 Announce Type: new Abstract: Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic

Reasoning as an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs

arXiv:2605.24497v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in reasoning and generation tasks and are increasingly deployed in real-world ap

RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment

arXiv:2602.00682v2 Announce Type: replace-cross Abstract: Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains l

Referential Security as a New Paradigm for AI Evaluations

arXiv:2605.25673v1 Announce Type: cross Abstract: Security evaluations inherently depend on stable identifiers. Any finding, audit, or regulatory decision must remain attached to the specific artifact

Refined Analysis of Entropy-Regularized Actor-Critic

arXiv:2605.24357v1 Announce Type: new Abstract: In this paper, we study the role of the critic in actor--critic for entropy-regularized, finite, discounted environments. We establish that, when the cr

Reinforcement Learning from Denoising Feedback

arXiv:2605.25638v1 Announce Type: new Abstract: Policy loss estimation remains a fundamental and long-standing challenge in reinforcement learning (RL) for diffusion language models (dLLMs). We introd

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach

arXiv:2605.25429v1 Announce Type: new Abstract: Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches

Rewarding Structural Conformance of Reasoning using Process Mining

arXiv:2510.25065v3 Announce Type: replace Abstract: Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (RL)-based language model post-training. Howe

Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search

arXiv:2605.23944v1 Announce Type: new Abstract: We model the interaction between a user and an AI driven recommendation system. The user initiates the process by conveying preference information throu

RiskBridge: Turning CVEs into Business-Aligned Patch Priorities

arXiv:2601.06201v2 Announce Type: replace-cross Abstract: Enterprises are confronted with an unprecedented escalation in cybersecurity vulnerabilities, with thousands of new CVEs disclosed each month.

RouteScan: A Non-Intrusive Approach to Auditing MoE LLMs Safety via Expert Routing Telemetry

arXiv:2605.24817v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have become an increasingly important paradigm for scaling Large Language Models (LLMs). As MoE models are incr

Safety-Critical Whole-Body Control for Humanoid Robots via Input-to-State Safe Control Barrier Functions

arXiv:2605.25546v1 Announce Type: new Abstract: Safety-critical control is essential for humanoid robots operating in complex human-centered environments, where physical safety constraints such as joi

Safety-Oriented Routing Analysis of Mixtral MoE Under Benign and Harmful Prompts

arXiv:2605.24270v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) language models activate only a small subset of parameters for each token, making router behavior a central part of mode