Best-first beam search with a global frontier cap
Algorithms AIMech:GlobalFrontierBeamSearch DRAFT
A best-first search procedure that limits the entire open frontier to a fixed number of nodes by discarding the worst-ranked frontier nodes when that capacity is exceeded.
Identity Notes & Concept Scope
The qualified name identifies the global frontier truncation described in the 2005 and 2010 search papers. The unqualified phrase best-first beam search is intentionally not an identity alias: Meister, Vieira and Cotterell use that phrase in 2020 for a decoding procedure with a per-sequence-length path limit. A shared name does not equate those pruning rules. 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
3 Claims
Mechanism
#search-procedure
Select the best-ranked node from the current open frontier and add its eligible successors. Whenever the frontier exceeds the chosen capacity, discard its worst-ranked nodes until it fits. Candidates from different depths can therefore compete for the same finite frontier slots.
Scope
#historical-scope
The beam-stack paper distinguishes its layer-based beam from the broader usage that truncates a best-first open list after adding successors.
Scope
#name-disambiguation
The later NLP paper controls how many paths of each sequence length are considered. That per-length counter is a different rule from limiting the total open frontier, so the same phrase does not identify the global-cap procedure without qualification.
⚠️ Curated Limitations & Negative Results
- Global frontier pruning can permanently remove a needed path; the cap alone establishes neither completeness nor optimality.
- The cap applies to the open frontier, not automatically to closed tables, predecessor storage, generated successors or the total program memory.
- No equivalence to breadth-first beam output, monotonic quality with larger width, or universal runtime advantage is asserted.
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
3 Sources| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Christopher Wilt, Jordan Thayer, Wheeler Ruml
|
primary paper
full_text
|
ID: doi:10.1609/socs.v1i1.18182
sha256:cfc3cf8f422b…
|
earlier-description
|
Rong Zhou, Eric A. Hansen
|
primary paper
full_text
|
sha256:eb2ac12fea9d…
|
nlp-name-boundary
|
Clara Meister, Tim Vieira, Ryan Cotterell
|
primary paper
full_text
|
ID: doi:10.1162/tacl_a_00346
sha256:46728395bd4f…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T06:01:43Z
Updated: 2026-10-11T06:01:43Z
Source File:
data/curated/GlobalFrontierBeamSearch.yaml