Search: “safety”

A Multi-Layer AI Framework for Information Landscape Analysis

arXiv:2606.26115v1 Announce Type: cross Abstract: This paper proposes a multi-layer AI framework for information landscape analysis in the context of information disorder. Rather than treating misinfo

A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO

DGX agent

arXiv:2606.27188v1 Announce Type: new Abstract: We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacin

safetyarxiv-cs-ai

26 Jun 2026

Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System

arXiv:2606.26267v1 Announce Type: new Abstract: Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess. However, they inherently suffer from response lag due to the

Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)

arXiv:2606.27005v1 Announce Type: new Abstract: Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocat

Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols

arXiv:2606.26203v1 Announce Type: new Abstract: As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an

AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

arXiv:2606.26859v1 Announce Type: new Abstract: Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remai

'AI Is Dumber Than Investors Think — ft. Gary Marcus' Ed Elson is joined by Gary Marcus to discuss why he’s concerned about the fact that we…

'AI Is Dumber Than Investors Think — ft. Gary Marcus' Ed Elson is joined by Gary Marcus to discuss why he’s concerned about the fact that we’re all-in on AI. They explore why he argues generative AI i

AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing

arXiv:2606.26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and m

Although the tech bros and some pharma think AI will cure cancer, Nobel Laureate Jennifer Doudna, inventor of the Crispr gene-editing techno…

Although the tech bros and some pharma think AI will cure cancer, Nobel Laureate Jennifer Doudna, inventor of the Crispr gene-editing technology says that innovation is still really in the domain of h

Always playing checkers and never chess.

Always playing checkers and never chess. President Trump on truth social: any country that imposes a Digital Services Tax on American companies will immediately be met with a 100% tariff on any and al

“America’s preoccupation with “winning” the AI race with China could well lead to unprecedented catastrophes, even catastrophes on a global …

“America’s preoccupation with “winning” the AI race with China could well lead to unprecedented catastrophes, even catastrophes on a global scale. Not all games are zero-sum, and if this fact doesn’t

“An AI future where only a few US companies, subject to the whims of the US government, have frontier AI runs a high risk of dystopian outco…

“An AI future where only a few US companies, subject to the whims of the US government, have frontier AI runs a high risk of dystopian outcomes.” An AI future where only a few US companies, subject to

asking about profit, not revenue. sorry this was not clear.

Gary Marcus clarifies a distinction between profit and revenue in a discussion, apologizing for previous lack of clarity on this economic/financial concept. The post likely addresses common confusion

Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach

arXiv:2606.26522v1 Announce Type: new Abstract: While corporate narrative disclosures provide crucial information to capital markets, comprehensively evaluating their qualitative changes over time rem

safetyarxiv-cs-cl

Automating Potential-based Reward Shaping with Vision Language Model Guidance

arXiv:2606.27180v1 Announce Type: cross Abstract: Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly a

bad at war, bad at AI.

Gary Marcus argues that poor performance in military applications correlates with or reflects fundamental weaknesses in AI capabilities and development. The post likely critiques limitations in curren

Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts

arXiv:2606.26603v1 Announce Type: new Abstract: Handheld data collection systems, such as the Universal Manipulation Interface (UMI), enable scalable data collection across diverse environments but on

safetyarxiv-cs-ro

Calibrated Harmonic Overlaid Implicit Neural Representations for Multi-Dimensional Data

arXiv:2606.26763v1 Announce Type: new Abstract: Implicit neural representation (INR) has emerged as a powerful prior for multi-dimensional data (e.g., multispectral images and videos). However, most I

Continual Robot Policy Learning via Variational Neural Dynamics

arXiv:2606.27353v1 Announce Type: new Abstract: Robots deployed in the real world rarely operate under a single fixed dynamics model: wind changes, payloads vary, batteries drain, contacts shift, and

safetyarxiv-cs-ro

Correction: contemplated delaying their IPO, per NYT report.

Gary Marcus posted a correction regarding a New York Times report about a company contemplating delays to their IPO plans. The post appears to address inaccuracies or clarifications needed in the orig

CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation

arXiv:2606.26423v1 Announce Type: cross Abstract: Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-bo

Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

arXiv:2606.27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting

arXiv:2510.20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerica

Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning

arXiv:2606.27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initia

Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis

arXiv:2409.01447v3 Announce Type: replace Abstract: We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-respons

Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation

arXiv:2606.26743v1 Announce Type: new Abstract: Point clouds are an important carrier of three-dimensional spatial information, and their quality directly affects the performance of downstream percept

Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search

arXiv:2606.27291v1 Announce Type: new Abstract: Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles. We present

Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning

arXiv:2606.26397v1 Announce Type: cross Abstract: Real-world decision-making often requires balancing multiple conflicting objectives, a challenge that standard Reinforcement Learning (RL) frequently

Diagnosing Task Insensitivity in Language Agents

arXiv:2606.26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key sourc

DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents

arXiv:2606.26122v1 Announce Type: new Abstract: Recent methods train search agents via reinforcement learning from (question, answer, evidence) tuples without requiring expert trajectories. The tuples

Don't Settle at the Mode! Mitigating Diversity Collapse in Pretrained Flow Models via Feature Self-Guidance

arXiv:2606.27371v1 Announce Type: new Abstract: State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multiple

Dream machine -- the next creative economy

arXiv:2606.26114v1 Announce Type: cross Abstract: We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning poli

E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation

arXiv:2606.27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reaso

Effective Covariance Dynamics in Solvable High-Dimensional GANs

arXiv:2606.27246v1 Announce Type: new Abstract: We study a solvable high-dimensional model of generative adversarial network (GAN) training in which a linear generator learns a low-dimensional subspac

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606.26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each cand

Fantastic interview: Ed Elson is talking with Gary Marcus about AI tech, overrelience on LLMs, the business of AI, etc. I totally enjoyed th…

Fantastic interview: Ed Elson is talking with Gary Marcus about AI tech, overrelience on LLMs, the business of AI, etc. I totally enjoyed this: https://youtu.be/_pSivPlRx5o?si=dzrJKpUGTHE0Y76I @edels0

Finding the Time to Think: Learning Planning Budgets in Real-Time RL

arXiv:2606.26463v1 Announce Type: new Abstract: Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits inde

Forecasting With LLMs: Improved Generalization Through Feature Steering

arXiv:2606.27199v1 Announce Type: new Abstract: Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply

safetyarxiv-cs-cl

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

arXiv:2606.26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnos

From query to action: Introducing SQL alerting in Cloud Monitoring Observability Analytics

Traditional alerting systems often force a compromise: you can either alert immediately on simple, noisy log events, or monitor rigid, pre-configured metrics that fail when faced with data with many u

safetygoogle-cloud-ai

from tokenmaxxing to this in less than three months. anyone who doesn’t see this as affecting Anthropic and OpenAI is not paying attention.

from tokenmaxxing to this in less than three months. anyone who doesn’t see this as affecting Anthropic and OpenAI is not paying attention. UBS says 60% of companies now watching AI budgets are moving

“Gary Marcus … says that investments into scaling AI is the “greatest capital misallocation in history” which leaves everyone “on the hook”.…

“Gary Marcus … says that investments into scaling AI is the “greatest capital misallocation in history” which leaves everyone “on the hook”. However, he also says that it is still early days for the A

@GaryMarcus, great interview and explanations of the challenges in AI. https://youtu.be/_pSivPlRx5o?is=dlFN6YBXsMA-tW4h

Gary Marcus discusses fundamental challenges and limitations in current AI systems during an interview, likely covering topics such as the gap between narrow and general intelligence, reasoning limita

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

arXiv:2606.26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items. In m

GEOALIGN: Geometric Rollout Curation for Robust LLM Reinforcement Learning

arXiv:2606.26917v1 Announce Type: cross Abstract: Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or mi

Geometric Fairness-Aware Routing for Federated Edge Networks

arXiv:2606.26125v1 Announce Type: cross Abstract: Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices. Existing f

Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems

arXiv:2606.26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This pape

Graph Neural Networks Applications Across Domains: All Insights You Need

arXiv:2606.27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The