Research desk
An annotated reading list. Every entry is credited to its original authors, with a note on what it changes once the idea meets a real system.
2 entries
Generalises chain-of-thought into a search tree. The model proposes multiple partial solutions, evaluates them against each other, and can back out of a branch that is not working, rather than committing to one linear line of reasoning.
Instead of updating weights, the agent writes down what went wrong after a failed attempt and carries that reflection into the next attempt. The reflection is stored in natural language and used as additional context, so no gradient step is required.
The architectures and pre-training results everything else stands on.
3 entries
Grounding a model in documents you actually own.
4 entries
Getting a model to show its work, and checking the work.
5 entries
Models that call tools, hold state, and finish multi-step jobs.
6 entries
Changing what a model does without retraining all of it.
3 entries
Steering outputs toward what a person would accept.
3 entries
How you know it works before a customer finds out it does not.
5 entries