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True Positive Weekly #178

The most important artificial intelligence and machine learning news and articles

OpenAI Reveals Six AI Safety Failures—and a Chance to Learn From Them

OpenAI’s new incident reports document unauthorized uploads, exposed API key use, and instructions to conceal mistakes. The details offer a practical opportunity to strengthen AI controls—if the findings lead to…

Building a Data Lakehouse with DuckDB and DuckLake

Starting with a local Parquet file, then joining it to data stored in the cloud The post Building a Data Lakehouse with DuckDB and DuckLake appeared first on Towards Data Science.

How I Built a Multi-Agent System for Interrupted Time Series Analysis (ITSA)

What it takes to turn counterfactual analysis into an AI product The post How I Built a Multi-Agent System for Interrupted Time Series Analysis (ITSA) appeared first on Towards Data Science.

Why You Think Like a Bayesian but Were Taught Like a Frequentist

From a chocolate bar with no price tag to a marketing mix model in PyMC, and the 200-year-old integral that stood in between. The post Why You Think Like a Bayesian but Were Taught Like a Frequentist appeared first on…

Mustafa Suleyman Has a Point About What Anthropic Is Teaching Claude

Anthropic encourages Claude to explore its identity and possible welfare while preserving human oversight. Mustafa Suleyman questions whether those goals could conflict and his criticism deserves attention as AI becomes…

When Does Graph RAG Actually Add Value? A Hands-On Experiment

I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, graph RAG, and simply…

When AI Predicts Better Than People, Who Sets the Rules?

An AI bot beat every human in the Summer 2026 Metaculus Cup. The bigger story is who sets the rules: benchmark designers define good forecasting, vendors build the probability engine, and institutions decide when its…

🙀 OpenAI discloses MORE “concerning” AGENT behavior

Self-Improving AI Starts With Human Know-How

The self-improving AI race may begin with something less dramatic than a machine redesigning itself: labs turning emails, experiments, meetings, and research judgment into reusable AI capability. Inherent’s Faraday…

AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution

arXiv:2609.18520v1 Announce Type: new Abstract: Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and…

Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

arXiv:2607.19190v4 Announce Type: replace-cross Abstract: Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim…

FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement

arXiv:2606.03852v2 Announce Type: replace-cross Abstract: Large language models often generate code with bugs. Existing methods rely on feedback signals such as test failures and self-critiques to iteratively refine the…

Missing Bridges: Composition-Aware Active Imitation Learning

arXiv:2609.18004v1 Announce Type: new Abstract: Active imitation learning reduces expert effort by allowing a learner to request the demonstrations it needs. Existing methods typically select these requests for their…

Collaborative Memory for Multi-Agent VLM Systems

arXiv:2609.17921v1 Announce Type: new Abstract: Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, different agents inspect…

Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly

arXiv:2609.07534v3 Announce Type: replace-cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact.…

Re2A: Situated Conversational Recommendation via Rubric-based Preference Reasoning and Alignment

arXiv:2609.18249v1 Announce Type: new Abstract: Real-world recommendation scenarios are commonly grounded in shared physical environments during user-recommender interactions. This motivates situated conversational…

Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders

arXiv:2609.17989v1 Announce Type: cross Abstract: Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict of duty. They…

WFM: Wiki Foundation Model for Complex Agentic Reasoning

arXiv:2609.18182v1 Announce Type: new Abstract: Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented generation. While graphs…