Provably Total Functions in the Polynomial Hierarchy
Bibliographic record
Abstract
TFNP studies the complexity of total, verifiable search problems, and represents the first layer of the total function polynomial hierarchy (TFPH). Recently, problems in higher levels of the TFPH have gained significant attention, partly due to their close connection to circuit lower bounds. However, very little is known about the relationships between problems in levels of the hierarchy beyond TFNP. Connections to proof complexity have had an outsized impact on our understanding of the relationships between subclasses of TFNP in the black-box model. Subclasses are characterized by provability in certain proof systems, which has allowed for tools from proof complexity to be applied in order to separate TFNP problems. In this work we begin a systematic study of the relationship between subclasses of total search problems in the polynomial hierarchy and proof systems. We show that, akin to TFNP, reductions to a problem in TFΣ_d are equivalent to proofs of the formulas expressing the totality of the problems in some Σ_d-proof system. Having established this general correspondence, we examine important subclasses of TFPH. We show that reductions to the StrongAvoid problem are equivalent to proofs in a Σ₂-variant of the (unary) Sherali-Adams proof system. As well, we explore the TFPH classes which result from well-studied proof systems, introducing a number of new TFΣ₂ classes which characterize variants of DNF resolution, as well as TFΣ_d classes capturing levels of Σ_d-bounded-depth Frege.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| 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".