Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The traditional view of agent modelling is to infer the explicit parameters of another agent’s strategy (i.e., their probability of taking each action in each situation). Unfortunately, in complex domains with high dimensional strategy spaces, modelling every parameter often requires a prohibitive number of observations. Furthermore, given a model of such a strategy, computing a response strategy that is robust to modelling error may be impractical to compute online. Instead, we propose an implicit modelling framework where agents aim to estimate the utility of a fixed portfolio of pre-computed strategies. Using the domain of heads-up limit Texas hold’em poker, this work describes an end-to-end approach for building an implicit modelling agent. We compute robust response strategies, show how to select strategies for the portfolio, and apply existing variance reduction and online learning techniques to dynamically adapt the agent’s strategy to its opponent. We validate the approach by showing that our implicit modelling agent would have won the heads-up limit opponent exploitation event in the 2011 Annual
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it