Auto-Walksat
Algorithms AIMech:AutoWalksat DRAFT
A preprocessing-and-run algorithm that estimates a fixed noise setting for a stochastic SAT solver by probing a terminal-score mean-to-standard-deviation ratio, searching the proxy with bracketing and parabolic interpolation, and using an empirical post-minimum adjustment.
Identity Notes & Concept Scope
Auto-Walksat denotes the Patterson–Kautz 2001 fixed-noise SAT preprocessing controller, distinct from its supplied Walksat-SKC solver and later online-adaptation variants. Fresh collection on 15 October 2026 at22:42:01–22:42:36 UTC followed actual merged base4f3b4047856ef4391d42fe9de4d1e191e9d1aa86. All785 native and442 pinned AIO record identities were compared (4,873 field occurrences,3,721 normalized values), with zero candidate collisions and17 context-only fields in ten neighboring records. Four complete ignored/hidden-inclusive searches retained37,418 occurrences in1,707 distinct contents. The attributed assessment accounts for every group; three supporting partitions read complete matched contents0–1499, while root read full small contents and identity/frontier/query-matching projections of ten large embedded historical documents for1500–1706. It does not claim fresh reading of all unrelated embedded science. All911 main and394 nested frontier objects were queried recursively; root read all19 matching full objects, ten neighboring identity/definition projections and full WalkSAT scientific projection. Existing owner295 is the selected source-discovery owner;296,300,301 and all other owners remain independent. The original2001 mechanism is supported, while the2004 discovery URL remains contextual and its statistic wording differs. This is bounded current-corpus identity clearance, not universal novelty or discovery saturation.
Claims & Grounded Evidence
5 Claims
Scope
#fixed-noise-preprocessing-controller
Auto-Walksat is a preprocessing controller for a CNF formula and a supplied stochastic SAT algorithm with a noise parameter. It probes settings, estimates a setting from a proxy statistic, and then runs the solver with that selected setting; the defining experiments instantiate Walksat-SKC.
Mechanism
#terminal-unsuccessful-score-ratio
For Walksat-SKC the objective is the number of unsatisfied clauses. The stated invariant ratio is the mean divided by the standard deviation of objective values at the ends of several unsuccessful attempts made with different random seeds.
Mechanism
#short-repeated-probes
Figure 2 estimates the ratio at a fixed noise setting with repeated short attempts, collecting objective statistics after 2,000 flips plus one flip for every atom of the formula. It states a mean-confidence-interval stopping test whose exact displayed numerical convention is unresolved.
Mechanism
#bracketing-and-parabolic-search
The described controller brackets noise over the displayed 0–100 scale, uses a parabolic interpolation proposal when the parabola is well formed, and otherwise takes a golden-section step toward the larger side before probing again and shrinking the bracket.
Scope
#post-minimum-heuristic-and-failure
The discussion and conclusion describe increasing the ratio-minimizing noise by ten percent before the final solver run as an empirical heuristic. The same paper reports that its logistics example instead calls for decreasing noise and says how to choose the direction is unresolved.
⚠️ Curated Limitations & Negative Results
- The terminal statistic is defined over unsuccessful attempts, while the probe pseudocode does not specify treatment of successful attempts, a minimum retained count, a standard-deviation estimator, or what to do when the retained set is empty or has zero spread. These are source specification gaps, not observed implementation failures.
- Figure 2 visually prints 0.05 with superscript 3 and no corresponding footnote visible on that page. Its exact exponent/footnote meaning, interval width convention, replication rule and statistical construction are unresolved. Even a precise mean-only interval does not establish precision of mean divided by a near-zero standard deviation.
- Figure 2 leaves endpoint values initially undefined, gives only a general bracket reassignment instruction, and does not fully specify how the last proposal n relates to a retained best point. The prose’s one-percent refinement criterion does not identify its exact comparison quantity or zero boundary. Finite noisy probes and this description do not prove a global ratio minimum or an optimal solver noise setting.
- The prose’s ten-percent increase is absent from Figure 2. The logistics example explicitly needs the opposite direction. Its 39–69 to 9–39 numbers illustrate a 30-percentage-point decrease for that example, without establishing a general relative/additive offset or clipping convention. The original does not give an automatic rule for choosing the direction.
- The supplied algorithm must expose the relevant noise parameter and objective observations. The detailed evaluated move rule is Walksat-SKC; tuning one parameter need not fix instances difficult for that solver. This is preprocessing followed by a chosen fixed setting, not a specification of generic within-run adaptation. A finite failed SAT search is not an UNSAT certificate.
- Illustrated curves, four DIMACS problem experiments, short tuning overhead and logistics results are bounded observations. They do not establish universal speedup, calibrated success probability, optimality for every formula/variant, or a theorem that the invariant ratio predicts performance. No benchmark or author implementation was run here.
- This assessment uses the nine-page May 2001 author preprint layout, with publication identity corroborated separately. The 2004 owner-URL description uses different shorthand for the statistic and is context only. Publisher-byte equivalence, later code behavior, the original cited invariants theory and full Brent-method assumptions were not verified, so their details are not silently imported.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No informative positive AIO mapping was established for the complete source-defined SAT probing and fixed-noise selection controller among the seven inspected targets. The assessment is bounded to the pinned snapshot and selected semantic neighborhood.
Search Scope: Auto-Walksat denotes the Patterson–Kautz 2001 fixed-noise SAT preprocessing controller, distinct from its supplied Walksat-SKC solver and later online-adaptation variants. Fresh collection on 15 October 2026 at22:42:01–22:42:36 UTC followed actual merged base4f3b4047856ef4391d42fe9de4d1e191e9d1aa86. All785 native and442 pinned AIO record identities were compared (4,873 field occurrences,3,721 normalized values), with zero candidate collisions and17 context-only fields in ten neighboring records. Four complete ignored/hidden-inclusive searches retained37,418 occurrences in1,707 distinct contents. The attributed assessment accounts for every group; three supporting partitions read complete matched contents0–1499, while root read full small contents and identity/frontier/query-matching projections of ten large embedded historical documents for1500–1706. It does not claim fresh reading of all unrelated embedded science. All911 main and394 nested frontier objects were queried recursively; root read all19 matching full objects, ten neighboring identity/definition projections and full WalkSAT scientific projection. Existing owner295 is the selected source-discovery owner;296,300,301 and all other owners remain independent. The original2001 mechanism is supported, while the2004 discovery URL remains contextual and its statistic wording differs. This is bounded current-corpus identity clearance, not universal novelty or discovery saturation. Seven exact pinned AIO contexts,53 incident-edge entries and their generated records retain the separately accepted primary/mapping reading; current collector confirms the unchanged pinned/native baseline. The no-match is bounded to MathematicalFunction, MetaLearning, MachineLearningTask, TrainingStrategy, ReinforcementLearning, MarkovChain and Network; empirical SAT parameter selection does not itself establish their defined learning/model identities.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:MathematicalFunction
|
The pinned definition is a general input–output mathematical rule, not a scalar-only class. A ratio statistic is a component, but the complete repeated-probe, bracket-search and solver-execution controller is not informatively identified with that component or generic functional representability. |
AIO:MetaLearning
|
The pinned class learns from metadata about machine-learning experiments. These instance-level SAT solver probes are not themselves machine-learning experiments or a learned cross-task model. |
AIO:MachineLearningTask
|
The procedure uses empirical observations to tune a symbolic satisfiability solver, but it need not construct a machine-learning method or learned predictive model as specified by the pinned class. |
AIO:TrainingStrategy
|
The class concerns strategies for training machine-learning models. Preprocessing a SAT formula to select the solver noise parameter does not supply that training relation. |
AIO:ReinforcementLearning
|
Feedback from SAT probes and exploration/exploitation terminology do not by themselves make this a learned policy or value-based reinforcement-learning task. The source specifies numerical parameter search, not an RL update. |
AIO:MarkovChain
|
The specified identity is the full tuning controller, not a chain model. A possible augmented-state Markov description would not establish an informative mapping to the pinned network/hidden-layer context. |
AIO:Network
|
Graph representability of a CNF or computation is insufficient; this procedure does not constitutively specify the interconnected computational-node mechanism described by the pinned class. |
Evidence Sources & Primary Literature
2 Sources| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
patterson2001
|
Donald J. Patterson, Henry Kautz
|
primary paper
full_text
|
ID: doi:10.1016/S1571-0653(04)00333-6
sha256:cc1ea78774e8…
|
kautz2004-context
|
Henry Kautz, Bart Selman, David McAllester
|
author technical report
full_text
|
sha256:525f46d2f9fd…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-15T22:55:49Z
Updated: 2026-10-15T22:55:49Z
Source File:
data/curated/AutoWalksat.yaml