{ "schema_version": 1, "builder_plan": { "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.", "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.", "baseline": "Your current agent with the same tool schemas and task 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": "Correct completed actions, invalid calls, retries, latency, and cost per completed task.", "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": "2609.29837", "title": "PUBG Ally: A Conversational Embodied Agent as an AI Teammate", "summary": "The method replaces scripted or rule-based game NPCs with a language-model agent that uses a controlled tool interface to inspect game state, interpret voice, and issue high-level actions to a faster low-level control layer. It costs a full live-service data loop (nearly 39k gameplay sessions for iterative training), plus model compression, context compaction, safety training, runtime guardrails, and memory redaction to meet latency and player-safety requirements. It can fail through speech-action desynchronization under latency pressure, unsafe or inappropriate player-facing utterances, and divergence between offline evaluation metrics and actual player preference.", "rationale": "The architecture pattern (LLM agent over a fast control layer) is instructive, but the approach depends on live-service scale, proprietary gameplay data, and on-device deployment infrastructure that a constrained team cannot replicate, so it is worth tracking rather than adopting.", "technique": "LLM agent with tool use steering a real-time game control layer", "family": "uso_de_ferramenta", "recommendation": "observar", "published": "2026-09-24", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.29837", "independent_implementations": 1, "total_implementations": 2, "stars": 235, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces scripted or rule-based game NPCs with a language-model agent that uses a controlled tool interface to inspect game state, interpret voice, and issue high-level actions to a faster low-level control layer. It costs a full live-service data loop (nearly 39k gameplay sessions for iterative training), plus model compression, context compaction, safety training, runtime guardrails, and memory redaction to meet latency and player-safety requirements. It can fail through speech-action desynchronization under latency pressure, unsafe or inappropriate player-facing utterances, and divergence between offline evaluation metrics and actual player preference.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.29837", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "In a live-service survey across 141 countries, positive responses exceeded negative ones by 25.1 percentage points on whether players would recommend Ally (among respondents with confirmed gameplay); this is a preference-margin claim, not a multiplicative factor.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.29837", "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": "" }