{ "schema_version": 1, "builder_plan": { "scenario": "Your workload might fit a smaller or differently structured model.", "change": "Check for available weights and a compatible runtime before planning a product trial.", "baseline": "Your current model on identical held-out tasks and the same resource budget.", "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 success, latency, memory, context limits, and migration 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": "2610.26379", "title": "CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries", "summary": "CTLite replaces learned temporal encoders for irregular multivariate time series with a parameter-free continuous-time moment encoder whose summaries are cached and reused across optimization steps and repeated queries, leaving only a small damped-extrapolation plus low-rank cross-variable readout to train. It costs almost nothing in parameters or latency and runs on a single small GPU or even CPU-class hardware, but it adds a preprocessing and caching layer and its accuracy claim is only best-or-second-best rather than uniformly superior. It can fail where the moment-based local level and trend summaries are a poor fit for the dynamics, on datasets unlike the four evaluated benchmarks, or when histories change frequently enough to negate the reuse benefit.", "rationale": "The method fits constrained infrastructure well and shows strong efficiency claims, but it targets the niche of irregular multivariate time series forecasting and its accuracy advantage is workload-dependent, so it warrants a pilot on the reader's own data rather than unconditional adoption.", "technique": "Reusable parameter-free continuous-time summaries for irregular time series", "family": "arquitetura_eficiente", "recommendation": "testar", "published": "2026-10-19", "as_of": "2026-10-20", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.26379", "independent_implementations": 0, "total_implementations": 1, "stars": 0, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "CTLite replaces learned temporal encoders for irregular multivariate time series with a parameter-free continuous-time moment encoder whose summaries are cached and reused across optimization steps and repeated queries, leaving only a small damped-extrapolation plus low-rank cross-variable readout to train. It costs almost nothing in parameters or latency and runs on a single small GPU or even CPU-class hardware, but it adds a preprocessing and caching layer and its accuracy claim is only best-or-second-best rather than uniformly superior. It can fail where the moment-based local level and trend summaries are a poor fit for the dynamics, on datasets unlike the four evaluated benchmarks, or when histories change frequently enough to negate the reuse benefit.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.26379", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "18.6-87.3% lower inference latency than recent models at batch size 32 with only 88-2,537 trainable parameters; reusing summaries cuts total runtime ~52% over 16 forecast calls on the same history, with best or second-best MSE/MAE on four benchmarks.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.26379", "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": "" }