{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2610.11152",
  "title": "Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning",
  "summary": "The paper evaluates, rather than proposes, a technique: it tests whether appending raw trajectory-reward pairs to an LLM's context functions as inference-time RL, and finds it does not; reward signals are read but flipping, randomizing, or removing them leaves improvement curves nearly unchanged, and even corrupted trajectories help as much as real ones. It replaces nothing directly, but its finding implies that engineering effort spent on reward shaping, reward-aware prompting, and exploration instructions for context-based agent memory is largely wasted, and should instead go toward input distribution and demonstration quality as in standard ICL. The cost of acting on this finding is low, but it can fail to transfer: the experiments cover four benchmarks and six models in the direct ICRL setting, so results may not hold for structured memory architectures, larger models, or domains where reward semantics are genuinely informative.",
  "rationale": "This is a diagnostic negative result rather than an adoptable method, so the reader should use it to deprioritize reward-centric context design while validating the conclusion on their own agent workloads before restructuring memory systems.",
  "technique": "Direct in-context reinforcement learning with reward-conditioned trajectories",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11152",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.11152",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "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": ""
}