Topic
AI Agents & Reasoning
Getting LLMs to plan, use tools, remember, and reason — what the agent papers actually demonstrate and how the patterns hold up in production.
Toolformer: What the Paper Actually Says
A 6.7B model beats GPT-3 175B on math by learning to use a calculator. Toolformer's self-supervised training pipeline is the interesting part — and it's more constrained than the demos imply.
Generative Agents: What the Paper Actually Says
The Generative Agents paper isn't just a simulation demo. It's a formal architecture for agent memory: stream, retrieval, reflection, and planning. Every agent framework you're using borrowed from it.
The AI agent observability stack — what to measure, what to use
Most teams reach for one observability tool and call it solved. Different tools measure different things — traces, hallucinations, cost, drift. Here's a map of the space and where to start.
Tree of Thoughts: What the Paper Actually Says About LLM Search
Chain-of-thought is one path through a reasoning problem. Tree of Thoughts is a search over many paths. The NeurIPS 2023 paper shows 74% vs 4% on certain benchmarks. The production story is harder.
ReAct: What the Reasoning + Acting Paper Actually Says
Every agent framework you've used is built on ReAct's thought/action/observation loop. The paper is more specific — and more fragile — than the abstractions suggest.