Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Bibliographic record
Abstract
People are able to reason flexibly across a vast range of domains and contexts, from navigating new environments and social situations, to playing new games and even betting on the outcomes of new sports. How do we draw on our knowledge and past experiences to tractably make sense of any particular situation? Here, we explore the hypothesis that people use a combination of distributed and structured, symbolic knowledge to construct bespoke mental models tailored to novel situations. We propose a computational implementation of this idea -- a Model Synthesis Architecture' (MSA') -- using language models as a stand-in for distributional knowledge and a probabilistic programming language to express bespoke probabilistic symbolic models. We evaluate our model with respect to human judgments on a novel reasoning dataset. Our “Model Olympics” domain comprises a series of “sports commentary” vignettes, and is designed to test open-ended reasoning by requiring (i) reasoning about arbitrary causal structures described in language; (ii) drawing in relevant latent considerations from background knowledge; and (iii) flexibly adapting to an ‘open world’ setting with novel observations sourced from other human participants. We compare our MSA to hand-coded probabilistic programs and LM-only baselines. We find that our approach captures key hallmarks of rational inference from human judgments that the LM-only baselines do not, especially for very novel scenarios. See https://sites.google.com/view/openworld-msa?usp=sharing for additional details and preprint.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".