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# Goodfire: a handful of forking words decide where a reasoning chain lands
- URL: https://globalfeed.ai/en/goodfire-a-handful-of-forking-words-decide-where-a-reasoning-chain-lands/
- Published: 2026-08-28T02:39:11.000Z
- Updated: 2026-08-28T13:41:48.000Z
- Description: The company resamples every step of a model's chain of thought to find the tokens that actually change the outcome. It has also made its research agent Silico generally available.
- Author: GlobalFeed Editor
- Tags: Goodfire, interpretability, ai, research, dil-en, elle

Goodfire, which works on interpretability, has published a method for examining the reasoning chains of language models: Forking Paths Analysis.

The logic is straightforward. As a model thinks through a question step by step, you re-run the chain many times from each point onward. You then collect the answers all those alternative paths arrive at and turn them into a distribution. That shows where along the chain the answer actually changes.

What comes out is this: not every part of a long chain of thought carries equal weight. A few **forking tokens** decide the outcome, and the model's answer often sits a single word away from a different one. The company argues this matters for oversight of reasoning, because watching a few critical points may be enough rather than the whole chain. Goodfire reports the method yields the same information at far lower compute than earlier approaches; that efficiency figure is the company's own measurement.

Goodfire has also made its research agent **Silico** generally available. By the company's account, the platform reproduced three studies from the interpretability literature — Interpretability in the Wild, ROME and Mixing Mechanisms — without access to the authors' code. Silico is offered as a paid service.

The company's work is on the [Goodfire research page](https://www.goodfire.com/research?ref=globalfeed.ai).