{ "schema_version": 1, "builder_plan": { "scenario": "A self-hosted model uses more compute or memory than your workload can justify.", "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.", "baseline": "The original unpruned model at the same precision, context, and load.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Task quality, actual GPU memory, serving throughput, and preparation cost.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.29402", "title": "Resource-Aware Model Selection for Scalable Indoor Localization on HPC Platforms", "summary": "The method replaces exhaustive inference over hundreds of pretrained local autoencoder models for WiFi fingerprint localization with coarse-to-fine hierarchical selection and temporal-locality filtering that executes only spatially plausible models. The cost is added system complexity: a building-floor-spot model hierarchy must be maintained, and trajectory-aware pruning depends on stateful tracking of user movement. It can fail when a user's first fix has no trajectory history, when movement violates spatial-locality assumptions (teleportation, floor changes via elevators), or when hierarchical misclassification at coarse levels eliminates the correct fine-grained model.", "rationale": "The technique targets a niche WiFi-localization ensemble workload rather than mainstream LLM inference, so it is only relevant if the reader operates modular multi-model serving, where the candidate-pruning pattern could transfer with workload-specific validation.", "technique": "Hierarchical and trajectory-aware model selection pruning", "family": "esparsidade_e_poda", "recommendation": "observar", "published": "2026-09-24", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.29402", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces exhaustive inference over hundreds of pretrained local autoencoder models for WiFi fingerprint localization with coarse-to-fine hierarchical selection and temporal-locality filtering that executes only spatially plausible models. The cost is added system complexity: a building-floor-spot model hierarchy must be maintained, and trajectory-aware pruning depends on stateful tracking of user movement. It can fail when a user's first fix has no trajectory history, when movement violates spatial-locality assumptions (teleportation, floor changes via elevators), or when hierarchical misclassification at coarse levels eliminates the correct fine-grained model.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.29402", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Hierarchical Candidate Pruning reduces inference from 735 spot-model evaluations to 67; Trajectory-Aware Pruning reduces it to 10, cutting model executions by 98.6% with no reported loss in localization quality.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.29402", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [], "equations_status": "not_fetched", "core_kind": "", "equations_fetched_at": "", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }