Beam-stack search
Algorithms AIMech:BeamStackSearch DRAFT
A layer-based beam search augmented with a stack of admitted evaluation ranges so that backtracking systematically revisits successors previously discarded by beam-width pruning.
Identity Notes & Concept Scope
Beam-stack search combines breadth-first beam expansion with chronological backtracking and an incumbent solution-cost bound. It is distinct from permanent beam pruning, limited-discrepancy beam backtracking, and the separately named iterative-deepening BSIDA-star variant. No normalized identity match was found across the 442 AIO and 130 prior native records; ignored-inclusive text hits in the main checkout were prior discovery leads, source citations and search-order contrasts, plus incidental lightbulb and wet-bulb dependency substrings, not existing concept records.
Claims & Grounded Evidence
2 Claims
Mechanism
#search-procedure
Use an admissible cost estimate f=g+h and an incumbent upper bound U. For each expanded layer, store a range of successor evaluations admitted to the next beam. Width pruning records the smallest rejected evaluation as the upper endpoint. On backtracking, advance the deepest unfinished range and regenerate that layer’s successors. Improved solutions lower U; exhaustion of the stack ends the improvement search.
Limitation
#bound-and-incumbent-scope
The basic method starts from a bound corresponding to an actual solution, while the paper introduces a separate iterative-deepening version when such a bound is unavailable. As an implementation obligation inferred from the displayed initialization and return logic, a wrapper must preserve the initial incumbent when the improvement search finds no better path.
⚠️ Curated Limitations & Negative Results
- The paper’s termination and optimality argument assumes finite branching, a positive lower bound on operator cost and a finite initial upper bound supplied by an actual solution, together with admissible pruning. It is not a guarantee for arbitrary zero-cost cycles, an infinite bound or arbitrary duplicate policies.
- Ties require a consistent ordering, or enough beam capacity to retain the unbroken tie group; a bare numeric range with unresolved ties is insufficient.
- The baseline retains up to a beam of nodes per depth layer plus the beam stack. O(w*d) node storage does not mean a fixed total-memory cap independent of search depth.
- The initially supplied solution must remain available if no improvement is found. An upper-bound number alone is not a returned path; this record does not certify the displayed pseudocode as a complete implementation.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable AIO counterpart for this graph/state-space search procedure was identified in the inspected snapshot. Using graphs, evaluation functions or a search subroutine in an AI system does not equate the algorithm with a learned model, a learning task or a mathematical function.
Search Scope: All 442 pinned AIO labels and scoped synonyms and 130 preceding native identities were compared. Both updated checkouts were searched including hidden and ignored files. Exact CLASS nodes and edges for GraphLanguageModel, ReasoningLLM, NeuroSymbolicLLM, MachineLearningTask, MathematicalFunction and ReinforcementLearning were inspected, alongside semantic search for search, planning, heuristic, path, traversal, symbolic, reasoning and graph terminology.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:GraphLanguageModel
|
A language model operating on structured graph representations is not a graph traversal or shortest-path algorithm. |
AIO:ReasoningLLM
|
Language-task deductive inference does not denote general state-space search. |
AIO:NeuroSymbolicLLM
|
A language-model architecture combining neural and symbolic components is not the search procedure itself. |
AIO:MachineLearningTask
|
Building methods that learn from data is not the identity of an algorithm traversing a specified graph. |
AIO:MathematicalFunction
|
The search procedure is distinct from the mathematical functions it may evaluate. |
AIO:ReinforcementLearning
|
A learning task balancing exploration and exploitation is distinct from deterministic graph/state-space traversal. |
Evidence Sources & Primary Literature
2 Sources| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Rong Zhou, Eric A. Hansen
|
primary paper
full_text
|
sha256:eb2ac12fea9d…
|
memory-and-implementation
|
Rong Zhou, Eric A. Hansen
|
primary paper
full_text
|
sha256:eb2ac12fea9d…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T06:01:43Z
Updated: 2026-10-11T06:05:35Z
Source File:
data/curated/BeamStackSearch.yaml