Topics — The twenty-eight kinds of claim this anthology files against — the primary axis of both the practice registry and the reading list, and the same twenty-eight for each (ADR-026). The filing rule is the point: take a domain topic when the claim is ABOUT the domain; a claim merely discovered in one still takes its kind. The axis organizes the record, it does not bound it: content the list cannot place is evidence the list is short (ADR-059).
Adaptation and tuning — taking a trained model somewhere new — fine-tuning and transfer, preference training and safety alignment, parameter-efficient adaptation, context extension.
2 of 129 THEORY documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| THEORY-082 | Causal tracing shows where a language model stores a fact, and that is where the fact should be edited | Meng et al. (2022), LIT-578 — tracing puts factual recall in mid-layer MLPs at the last subject token, and ROME and MEMIT edit there on that basis. Hase et al. (2023), LIT-574, test the link per fact on GPT-J. Tracing peaks are spread across layers, while edits succeed at layer 6 for almost everything. The edit layer explains 94.7% of the variance in success and tracing 0.1% more. The editors work. The reason given for where they edit does not hold. | Rejected — per-fact causal-tracing localization explains 0.1% of the variance in edit success once the edit layer is accounted for, and edits succeed at layers tracing does not point to |
| THEORY-122 | A diffusion or flow sampler can be trained by a likelihood-ratio policy gradient only when each step is stochastic with a tractable density, and a same-marginal SDE built from the trained velocity supplies that without changing the marginals | Black et al. (2023), LIT-776, and Liu et al. (2025), LIT-779. A policy gradient needs the log-density of each action. Treated as one action, a diffusion model’s sample has only a variational bound. Treated as T actions, each DDPM step is an isotropic Gaussian with an exact density. A flow’s ODE step is deterministic, so it has no per-step density and explores nothing past the initial noise. The stochastic- interpolant identity (LIT-645) gives, for any noise level, an SDE with the ODE’s marginals, built from the trained velocity alone. Each of its steps is Gaussian, so the ratio and the KL to the reference are closed form. All of this is derivation. What is not shown is that it survives the ten-step discretization used for rollouts. | Active |