Do Chat LLMs Replicate Human Reasoning? What the Psychic's Con Study Shows

Do Chat LLMs Replicate Human Reasoning? What the Psychic's Con Study Shows

No. A chat LLM does not replicate human reasoning — on cognitive-bias tests, GPT-4 scored 96% where humans scored 38%. What LLMs do replicate is the mechanism of a psychic’s con: statistically generic statements, a subjective-validation loop, and an RLHF training signal that rewards agreement over truth. That distinction is the whole article. The phrase “chat LLM replicate human reasoning study” fuses two claims that the evidence treats very differently, and most coverage collapses them. The first claim — that models reason like people, with our shortcuts and our biases — is contested and, on the standard instruments, largely contradicted. The second claim — that models reproduce the social apparatus that makes reasoning seem present, whether or not it is — is well measured, replicated, and quantified in numbers strong enough to cite in a product review. ...

September 29, 2026 · 16 min · baeseokjae
EdotEnv (YC S26) Review: Quant Trading RL Environments for LLM Research in 2026

EdotEnv (YC S26) Review: Quant Trading RL Environments for LLM Research in 2026

EdotEnv (YC S26) is a startup founded by former quants Rui and Michael that builds self-improving reinforcement learning environments from quantitative trading workflows, designed specifically to evaluate and train LLM agents. Unlike static benchmarks that saturate as models improve, EdotEnv uses live market dynamics where alpha decays 30-50% per year, creating a continuously evolving difficulty curve that keeps evaluation meaningful even as frontier models advance. What Is EdotEnv and Why Does It Matter for LLM Research? EdotEnv launched on Hacker News in August 2026, receiving 39 points and 34 comments from the AI and quant finance communities. The company’s tagline — “Environments for intelligence that adapts” — captures its core thesis: the most useful benchmarks for evaluating LLM agents are those that get harder as the models get better. Traditional NLP benchmarks like MMLU, GSM8K, and HumanEval have all experienced significant saturation, with frontier models now scoring above 90% on many of them. EdotEnv proposes a radical alternative: use real financial markets as the evaluation environment, where the difficulty level is set by the collective intelligence of all market participants and naturally increases over time. ...

August 5, 2026 · 10 min · baeseokjae