Inferring the Host: A Bayesian Look at the Monty Hall Problem

Solving a Murder Mystery Using Bayesian Inference

How Knives Out teaches Bayesian thinking (without you realizing it) The post Solving a Murder Mystery Using Bayesian Inference appeared first on Towards Data Science .

Skill or MCP Server? Ask What Stops the LLM From Disobeying

The article titled "Skill or MCP Server? Ask What Stops the LLM From Disobeying" discusses the concept of spec-driven test automation, specifically in the context of part 4 of a series. It explores how to prevent large language models from acting against intended instructions. The piece was originally published on Towards Data Science.

Water Cooler Small Talk, Ep. 12: Byzantine Fault Tolerance

How do you make decisions when you can't trust anyone in the room? The post Water Cooler Small Talk, Ep.

Analogy as Nonparametric Bayesian Inference over Relational Systems

arXiv:2006. 04156v2 Announce Type: replace Abstract: Our inferences in the real world are rarely na\"ive - we acquire experiences through our lifetime that can help us more quickly understand the structure of something new.

Solving the 3Blue1Brown String Probability Problem (Without AI)

Let's practice data science thinking through a probability problem The post Solving the 3Blue1Brown String Probability Problem (Without AI) appeared first on Towards Data Science .

Welcome to Inference Providers on the Hub 🔥

My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax

what it costs, what it gains and the three mistakes that I make The post My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax appeared first on Towards Data Science .

Causal Preference Elicitation

arXiv:2602. 01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs).

BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation

arXiv:2606. 30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment.

I Told an AI Agent That Its Wrong Answers Were Right. One Model Believed Me.

The article discusses a research paper suggesting that reward labels have minimal impact on how AI agents learn. The author tested this claim on three different models, finding that one model behaved exactly as the incorrect reward labels instructed. The piece highlights how a single model can be misled by flawed feedback.

A Google Team Measured Half of My Argument, and Left the Other Half Open

The article titled "A Google Team Measured Half of My Argument, and Left the Other Half Open" is part of a series on spec‑driven test automation (Part 3). It discusses how a Google team evaluated only half of the author’s argument, leaving the remaining portion unresolved. The post was originally published on the Towards Data Science platform.

Your AI Bill Is a Toll Booth. Stop Paying Twice.

The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.