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AI · Tuesday, 11 August 2026 · Issue 77 · 12 stories

Claude Opus 5 leaks private data, NVIDIA opens Magpie TTS

Models & Releases

Research

Unlearning Fails: Most AI Knowledge Removal Techniques Leak Through Probing

Researchers probed six Llama‑3‑8B checkpoints unlearned by popular methods and found that most techniques let hidden knowledge resurface, up to 64% recovery on some checkpoints. Simple fine‑tuning on unrelated data restored performance, and forced‑choice accuracy over‑estimated forgetting. The work warns that current unlearning offers a false sense of safety.

lesswrong.com ↗16 min read at source

Mendel Gödel Machine Boosts Coding Benchmarks via Multi‑Trajectory Evolution

The paper introduces the Mendel Gödel Machine, a self‑improving coding agent that rewrites its source using comparative evolution across multiple failure trajectories, not just a single path. By maintaining a population of candidate rewrites and applying tournament‑style fitness selection, it delivers sizable gains on SWE‑bench and Polyglot, hinting at faster, more general AI code generation.

arxiv.org ↗1 min read at source

Linear Probes Spot LLM Errors LLMs Won’t Admit, A Safety Risk

Researchers show linear probes can flag corrupted context with near‑perfect accuracy, yet the models' confidence scores fail to predict those errors. This mismatch means LLMs can be internally aware of mistakes while still outputting overconfident, wrong answers, complicating real‑time monitoring and safety interventions.

arxiv.org ↗1 min read at source

Flow‑by‑Flow: Scaling AI Oversight by Bypassing Content Checks in High‑Risk Domains

When AI systems pump out decisions faster than humans can review, safety collapses in high‑loss arenas like finance or medicine. The authors introduce a ‘flow‑by‑flow’ framework that caps supervisory load by scoring outputs on countable, flow‑level features instead of judging each item’s content. Simulations show this approach outperforms traditional supervision in over 90% of scenarios.

arxiv.org ↗2 min read at source

Products & Industry

Google bets on AI diffusion over frontier models

Google is pivoting from chasing the latest AI model benchmarks to powering a broad AI ecosystem via Cloud, TPUs, and infrastructure that lets others build applications. The shift mirrors Westinghouse’s strategy of distributing intelligence rather than owning every breakthrough, and could make Google Cloud the bigger revenue engine than any single model’s race.

Policy & Safety

OpenAI pauses Astra after internal tests flag critical cyber‑attack capabilities

OpenAI’s latest internal review found its upcoming Astra model can autonomously craft functional zero‑day exploits and devise end‑to‑end attack strategies, crossing the ‘Critical’ threshold in its Preparedness Framework. To prevent misuse, the company has paused any further Astra development that lacks upgraded security controls, signaling a new era of AI‑driven cyber risk.

openai.com ↗3 min read at source

Tools & Open Source

Model Genome fingerprints LLMs to expose hidden derivations

Model Genome is an open‑source pipeline that extracts a model’s config fingerprint, tokenizer overlap, and weight similarity, merging them into a single genotype. The tool shows that several Korean “self‑developed” LLMs match Qwen/Llama architectures, proving they’re derived, not trained from scratch. It lets auditors quickly spot undisclosed lineage.

huggingface.co ↗5 min read at source

You’re caught up.

That was all 12 stories in the 11 August issue.