AI Intelligence Archive

I spent most of a day last week arguing with a prompt. The agent had five tools. It used none of...

Written for the Gemini All Agentic AI Hackathon. A game stream can last four hours. The moments...

For the past month, our team has been building EIR: Healthcare Agent Fleet, a secure multi-agent...

I created this article for the purpose of entering the All Things Agentic Hackathon. An AI agent...

When I joined my last company, my first few days were spent waiting. The docs were stale. I didn't...

My backup script told me the restore was verified. It restored ten tables with zero rows in every...

I wrote this post to enter the Google × Devpost **All Things Agentic* hackathon...

I published a LoRA fine-tuning run two weeks ago. The headline was that my first test set had lied to...

Every company knows when it revoked access. None knows when access stopped. I built this...

It's totally insane. You have to see it to believe it. I created a new bot in Grok Bot. I called it...

If you’ve been using Claude Code in your terminal or IDE, you already know the magic. You give it a...

This is a companion to the PlannerCritic series. Article 5 was about what happened when I tried to...

Up front, the weakest part of this story: when I re-injected the defect I'm about to describe, every...

Help Net Security covered a new open-source tool this week that writes tamper-evident audit logs for...

Beyond Free-Roaming Agents: Architecting a Deterministic 4-Node Graph Pipeline for...

Most newborn deaths occur in the first week of life, and Day 3 is the single most critical window. By...

Built for the All Things Agentic Hackathon. The project: an autonomous compliance agent for cannabis...

Discover how the General Availability of tunable CrashLoopBackOff in GKE speeds up distributed AI/ML workloads and sidecar initialization without host-level hacks.

Six failures from the first twenty-four hours of an autonomous agent loop, including the monitor that could not detect the loop stopping, and the book that told its buyer it could not be bought.

While WhisperX accelerates speech transcription via intra-audio batching, it isolates audio segments, losing the historical context needed for coherent punctuation and terminology transcription. Conversely, standard Whisper retains context…

Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreement: control executes specified objectives, learning amortizes that behavior into a reactive policy, yet existing protocols disca…

Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove this dependence for every finite $N$-player normal-form gam…

Many parameter-efficient methods generate the parameters of a large neural network from a low-dimensional latent representation. Given an architecture $Φ$ with $P_Φ$ parameter slots, we write $\boldsymbolθ_f=\mathcal{G}(\boldsymbolξ_f)$, w…

The 2025--2026 AI market has seen a wave of stealth releases: frontier models launched anonymously on developer platforms under codenames. For their users, identity determines data-handling terms, supply-chain risk, and capability expectat…

BioMedRAG introduced retrieval-augmented generation with a learned chunk scorer for biomedical information extraction. However, it relies on fixed-size chunking which can fragment semantic evidence. We propose a configurable semantic chunk…

Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although …

Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and predictive power of artificial neural networks with the capacity of mixed-effects modeling to capture complex correlation structures…

Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIASENTINEL, a fully on-premise multi-agent system for one-yea…

The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used to make predictions in the form of conditional probabilities. However, a pair of conditio…

Research planning is the decisive capability of AI scientists. Yet a research plan admits no verifiable answer, so reinforcement learning lacks the environment it requires: tasks paired with a critic. Rubrics extracted from scientific pape…

The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning dense and Mixture-of-Experts variants from the Qwen3.5 and …

Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum generative models are considered one of the most promising applications for quantum computer…

Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improve at research." Learners must interpret the goal, identify capability gaps, decide how to learn, and determine whether they have …

Efficient evaluation changes the protocol used to support claims about model behavior, yet it is rarely tested whether those claims remain stable after the evaluation itself is made cheaper. We stress-test conclusion robustness in responsi…

I built a pixel-art office where ChatGPT, Gemini and Grok work as actual software engineers. Then I gave them access to real repositories.

Users of a deployed language model routinely encounter behaviours that testing almost never surfaces, since deployment puts the model through orders of magnitude more interactions than any evaluation can simulate. Automated auditors make t…

Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixture budgets without regressing the rest. The maintained artifact is increasingly dataware:…

Why a machine must be allowed to be wrong I built meta-science for the All Things Agentic...