Hallucinations, Watermarks, Removers, and a Squeezed Balloon

That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It

The best AI models still hallucinate. These hallucinations are sometimes funny, and sometimes cause actual damage.

Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent

Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.

Text Watermarking in Python: Catch Whoever Copies Your Writing

The article explains how AI companies embed watermarks into billions of words daily and shows how readers can apply three main watermarking techniques to their own writing. It details practical steps for implementing these methods in Python and discusses experimental results that test which watermarks endure copy‑paste, editing, and paraphrasing. The post provides actionable guidance for writers seeking to protect their content from unauthorized duplication.

Most RAG Hallucinations Are Retrieval Failures: Fix Retrieval, Not the Prompt

Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.

Is This Slop? Detecting AI-Generated Content Without a Model

Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why' The post Is This Slop? Detecting AI-Generated Content Without a Model appeared first on Towards Data Science .

Model Hypnosis: Strong control of AI via additive subliminal effects

arXiv:2608. 16834v1 Announce Type: cross Abstract: We demonstrate that AI models are broadly susceptible to a phenomenon we call model hypnosis, in which individually weak and seemingly irrelevant cues in the prompt can be systematically combined to strongly control model behavior.

AI Watermarking 101: Tools and Techniques

What AI Agents Should Never Do on Their Own

How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .

Concrete AI safety problems

We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.

AI Made Me 5x Faster. It Also Made Me 5x Worse at My Job.

One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code? The post AI Made Me 5x Faster. It Also Made Me 5x Wors...

Detecting Vulnerabilities in Agent Skills with SkillSpector: From Green Checkmark to Real Security Judgment

Static analysis nailed the malicious skill and over-flagged the useful one. The gap between those results is where human judgement actually earns its keep.

Watermarks Without Verification: AI Text Watermarking After the EU AI Act

The paper discusses the EU AI Act’s requirement for generative AI providers to embed detectable watermarks in their outputs, noting that Anthropic’s Claude models and Google’s Gemini use SynthID‑Text by default. It critiques the lack of verifiability of claims about watermark quality, privacy, and robustness, and evaluates the open‑source SynthID‑Text implementation on two open‑weight models, finding minimal impact on prose and modest correctness loss on code. The authors argue that the real governance issue is the inability to verify these assertions and outline necessary steps—such as output release, configuration disclosure, accredited audits, shared evaluation protocols, and interoperable detection—to address the gaps.