{ "schema_version": 1, "builder_plan": { "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.", "change": "Test the proposed division of work against a single-agent implementation.", "baseline": "One agent with the same tools and an equal total token and time 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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.", "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": "2609.18094", "title": "Agora: Git as Shared Memory for Collective AutoResearch", "summary": "Agora replaces independent, memoryless autonomous research agent sessions with an append-only Git DAG where every claim, hypothesis, and verification is an immutable, re-runnable commit, plus a derived index and diversity-aware selection to prevent monoculture. It costs only Git infrastructure and the agent compute itself (13 LM workers for 12 days in the reported run), but adds the complexity of maintaining the commit schema, index, and selection rule, plus occasional human intervention to break monocultures. It can fail through unverified or duplicated claims accumulating in the graph, collapse of the community onto one branch absent the diversity mechanism, and the absence of evidence that it beats simply running more independent agents on the same budget.", "rationale": "The coordination mechanism is cheap to prototype with third-party APIs and no cluster, but the single uncontrolled case study does not yet justify production adoption over simpler parallel-agent setups.", "technique": "Git-based shared research memory for agent collectives", "family": "orquestracao_multiagente", "recommendation": "testar", "published": "2026-09-16", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.18094", "independent_implementations": 1, "total_implementations": 2, "stars": 90, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "Agora replaces independent, memoryless autonomous research agent sessions with an append-only Git DAG where every claim, hypothesis, and verification is an immutable, re-runnable commit, plus a derived index and diversity-aware selection to prevent monoculture. It costs only Git infrastructure and the agent compute itself (13 LM workers for 12 days in the reported run), but adds the complexity of maintaining the commit schema, index, and selection rule, plus occasional human intervention to break monocultures. It can fail through unverified or duplicated claims accumulating in the graph, collapse of the community onto one branch absent the diversity mechanism, and the absence of evidence that it beats simply running more independent agents on the same budget.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.18094", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "13 agents over 12 days improved a data-free weight-transfer initialization from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M; the paper explicitly notes no controlled comparison establishing that shared memory improves discovery per unit of compute.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.18094", "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": "" }