LLM Evaluation and Belief Management
This page tracks evaluation methodology, model belief revision, reasoning, forgetting, and societies-of-thought style approaches.
Sources in this batch
- “When Should Models Change Their Minds?” studies contextual belief management.
- Wix’s 250 AI-agent evals test the skills-versus-docs question.
- AlphaEvolve is relevant as an agent evaluated through scientific/algorithmic outputs.
- “Neural Garbage Collection” asks whether models can learn to forget while learning to reason.
- Cameron Wolfe’s statistics-for-LLM-evals piece highlights eval methodology.
- “Reasoning Models Generate Societies of Thought” suggests internal or multi-perspective structures for reasoning.
Research interest
The most interesting thread is evaluation moving from static accuracy toward dynamic epistemic behavior: when should a model revise beliefs, forget stale context, or coordinate multiple lines of thought? This is central for agents because they operate over changing state and long contexts. The “societies of thought” framing is worth tracking if it produces measurable gains beyond prompting style.
Open questions:
- How should evals score belief revision under contradictory context?
- Can forgetting be learned without catastrophic loss of useful reasoning traces?
- Are multi-agent/multi-thought internal structures mechanistically different from ordinary sampling?