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).
Deployment and society — what a system does once it is running among people, and how anyone outside it could find out — audits of deployed platforms and recommenders, information ecosystems, moderation and governance, and the access problems and funding pressures of studying any of it from the outside.
7 of 449 SOTA documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| SOTA-290 | To measure what a deployed recommender does, use sock-puppets that never interact, follow sets curated to your question, and exposure weighted by rank | Proposed | |
| SOTA-291 v2 | Evaluate a specific intervention on a specific affordance rather than the net effect of the system, and never read an individual-level null as the absence of a collective effect | Active | |
| SOTA-292 | Check industry ties yourself when reading a literature about deployed platforms — disclosure is unreliable and the ties are mechanically detectable | Proposed | |
| SOTA-298 | Measure a writing assistant by the outcome it was deployed to improve, not by the text features that used to predict it | Corpus et al. (2025), LIT-487 — Change.org’s AI drafting tool made petitions 49% longer, more lexically varied and better rated by human judges on quality and persuasiveness, moving them toward the profile of successful pre-AI petitions. The share reaching 10 signatures fell 5.33 points. The features had been chosen because they predicted success; afterwards they predicted it weakly or negatively. | Proposed |
| SOTA-303 | Name the phase a time-saving tool accelerates before predicting what it will do to the quality of what people produce with it | Duede, Gross, Crockett and Bergstrom (2026), LIT-492 — in a marginal-value-theorem model, accelerating the phase before value is known makes producers more selective, accelerating the minimum cost of shipping makes them less selective, and both make each item less thoroughly developed — while accelerating discretionary improvement makes each item more thoroughly developed. Opposite signs from the same tool depending on where it acts, so “it saves time” does not determine the direction. | Proposed |
| SOTA-309 | Score a control interface by the mutual information between the operator’s command and the state change it induces, and choose the horizon of that state change deliberately | Reddy, Levine and Dragan (2022), LIT-503 — whatever the operator is trying to do, a better interface yields less noisy commands, so I(x_t, (s_t, s_{t+Δ})) scores it with no labels, no reward and no task knowledge. Spearman ρ = 0.43 against true task completion across 540K examples in 4 of 5 domains. In the fifth the correlation was strongly negative at Δ = 1 and positive at episode length — so the horizon is not a detail. | Proposed |
| SOTA-321 | Verify a model’s lineage from the singular spectra of its attention products, which survive permutation and rescaling | Wang et al. (2025), LIT-524 — compare the per-layer singular spectra of W_q W_k^T and W_v W_o. Those products absorb the permutation and rescaling that change weights without changing the function, so the spectra identify a model through fine-tuning, pruning, merging and expansion. F1 0.9867 over 55 model pairs, using no data and no queries. | Proposed |