MétaCan
Menu
Back to cohort
Record W2160909640 · doi:10.1145/1806689.1806703

Hardness amplification in proof complexity

2010· article· en· W2160909640 on OpenAlexaff
Paul Beame, Trinh Huynh, Toniann Pitassi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComplexity and Algorithms in Graphs
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematical proofDegree (music)Proof complexityDiscrete mathematicsRank (graph theory)MathematicsResolution (logic)Tree (set theory)Structural proof theoryProof of conceptCombinatorial proofCombinatoricsProof theoryComputer sciencePhysicsProgramming language

Abstract

fetched live from OpenAlex

We present a general method for converting any family of unsatisfiable CNF formulas that is hard for one of the simplest proof systems -- tree resolution -- into formulas that require large rank in very strong proof systems, including any proof system that manipulates polynomials of degree at most k (known as Th(k) proofs). These include high degree versions of Lovasz-Schrijver and Cutting Planes proofs.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.048
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0020.007
Scholarly communication0.0060.020
Open science0.0050.014
Research integrity0.0020.013
Insufficient payload (model declined to judge)0.0290.004

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.

Opus teacher head0.040
GPT teacher head0.274
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicComplexity and Algorithms in GraphsFrench-language works237,207