Theory-of-Mind

TL;DR A new study probes whether you can teach perspective‑taking to ReAct‑style LLM agents by feeding them structured examples distilled from a symbolic planner: optimal goal paths (G‑type), information‑seeking paths (E‑type), and local contrastive decisions (L‑type). The punchline: agents became decent at common‑ground filtering (what the other party can see) but remained brittle at imagining occluded space and pricing the cost of asking vs. exploring. In business terms, they’re good at “don’t recommend what the customer can’t see,” but still bad at “should I go find out more before I act—and is it worth it?” ...

It has become fashionable for AI researchers to pepper their papers with references to psychology: System 1 and 2 thinking, Theory of Mind, memory systems, even empathy. But according to a recent meta-analysis titled “The Incomplete Bridge: How AI Research (Mis)Engages with Psychology”, these references are often little more than conceptual garnish. The authors analyze 88 AI papers from NeurIPS and ACL (2022-2023) that cite psychological concepts. Their verdict is sobering: while 78% use psychology as inspiration, only 6% attempt to empirically validate or challenge psychological theories. Most papers cite psychology in passing — using it as window dressing to make AI behaviors sound more human-like. ...

As large language models (LLMs) evolve from mere tools into interactive agents, they are increasingly expected to operate in multi-agent environments—collaborating, competing, and communicating not just with humans but with each other. But can they understand the beliefs, intentions, and misunderstandings of others? Welcome to the world of Theory of Mind (ToM)—and the cleverest AI benchmark you haven’t heard of: Decrypto. Cracking the Code: What is Decrypto? Inspired by the award-winning board game of the same name, Decrypto is a three-player game of secret codes and subtle hints, reimagined as a benchmark to test LLMs’ ability to coordinate and deceive. Each game features: ...