{ "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": "2608.21690", "title": "Context as an Environment: Programmatic Context Management for Long-Horizon Agents", "summary": "Scroll replaces prompt-side history compression and fixed memory representations with an append-only event log plus a persistent sandboxed Python kernel, where the model writes code to search and materialize state and only printed projections enter the working context. It costs engineering effort to build and sandbox the kernel and eviction index, adds code-execution latency per turn, and its reported results depend on a strong proprietary backbone. It can fail if the model writes faulty state-management code, if eviction landmarks mislead retrieval of evicted regions, or if sandboxing is insufficient against unsafe exec output.", "rationale": "The approach is implementable with third-party APIs and a single machine, but its gains are demonstrated on specific benchmarks with one backbone and require workload-specific validation before production adoption.", "technique": "Programmatic context management via executable session environment", "family": "memoria_e_contexto", "recommendation": "testar", "published": "2026-08-21", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2608.21690", "independent_implementations": 6, "total_implementations": 6, "stars": 6, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "Scroll replaces prompt-side history compression and fixed memory representations with an append-only event log plus a persistent sandboxed Python kernel, where the model writes code to search and materialize state and only printed projections enter the working context. It costs engineering effort to build and sandbox the kernel and eviction index, adds code-execution latency per turn, and its reported results depend on a strong proprietary backbone. It can fail if the model writes faulty state-management code, if eviction landmarks mislead retrieval of evicted regions, or if sandboxing is insufficient against unsafe exec output.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2608.21690", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "With Qwen3.8-Max as backbone, Scroll reports 94.8% on LongMemEval_S, 73.1% on BEAM_10M (5.1 points above the best published memory system), and 86.7% on LOCA_256K (37.4 points above the best published long-horizon agent).", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2608.21690", "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": [ { "anchor": "S2.E1", "label": "(1)", "section": "2 Scroll Context Manager", "role": "proposed_method", "latex": "S_{t}=(L_{t},\\;P_{t},\\;V_{t}),", "mathml": "St=(Lt,Pt,Vt),", "context": "An agent session produces a growing sequence of events e1,e2,… (user messages, model responses, tool calls, tool results), each with an associated payload (its raw content, e.g., a full tool output). We write the session state after t agent steps as", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" } ], "equations_status": "selected", "core_kind": "formula", "equations_fetched_at": "2026-09-19", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }