Divide-and-conquer beam-stack search
Algorithms AIMech:DivideConquerBeamStackSearch DRAFT
Aliases: DCBSS
A beam-stack search implementation that retains a small set of search layers and relay information, regenerates missing layers for backtracking, and reconstructs a solution through recursive subproblems.
Identity Notes & Concept Scope
DCBSS is the divide-and-conquer implementation of beam-stack search described by Zhou and Hansen. Unlike divide-and-conquer beam search without backtracking, it retains beam-stack range information that permits previously discarded layers to be regenerated and searched further. 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
Retain the current, previous, successor and relay layers, with relay references used to divide path reconstruction into smaller searches. If chronological backtracking reaches a layer no longer stored, replay from the start using the saved beam-stack ranges to rebuild it. For the general non-unit-cost case in the paper, retain the best goal information and delay path reconstruction until backtracking begins.
Scope
#disk-variant-boundary
A separately described external-memory option stores old layers on disk instead of regenerating them. Restoring a layer also requires the previous layer for duplicate detection and repairing relay references; disk use is an optional implementation extension, not part of every DCBSS execution.
⚠️ Curated Limitations & Negative Results
- Four retained node layers do not imply constant total storage: the beam stack still has depth-dependent entries, and path output, temporary successors and reconstruction state also require memory.
- Discarding layers trades storage for regeneration and reconstruction work; no universal runtime improvement is implied.
- The underlying beam-stack pruning, tie handling and bounded-search assumptions still matter. Layered duplicate detection must be matched to the graph setting; the paper distinguishes directed and undirected behavior.
- The paper summarizes recursive solution recovery and refers to earlier work for implementation details. This record supports the described mechanism, not a complete executable reconstruction 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…
|
external-memory-boundary
|
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: 2026-10-11T06:01:43Z
Source File:
data/curated/DivideConquerBeamStackSearch.yaml