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2026-10-11
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# Piper — Cognition Component 17: Learning **Working research, benchmark, upgrade and testing paper — v1.0** **Prepared:** 2026-10-10 **Status:** Proposed; not implemented or scored **Component:** 17 / 38 — Learning: changing future behaviour from experience > **Operating question:** After Piper receives feedback about a mistake, does she behave differently on a *new* opportunity to make that mistake—and is the change caused by the feedback rather than practice, prompting, evaluator leakage, or a general software upgrade? ## 1. Executive assessment Piper already has disciplined mechanisms for collecting lessons and evaluating upgrades, but the existence of a lesson record is not evidence of learning. The central missing instrument is **feedback-to-behaviour transfer**: a prospective, controlled measurement of whether a specific registered lesson changes subsequent decisions on unseen, appropriately matched tasks. The second missing instrument is **repeat-error surveillance** in genuine work, with exposure denominators and attribution. **Reported state from the September 30 independent review** (reverify before implementation): | Evidence | State | What it establishes—and does not | |---|---|---| | `work-ledger` v1.3.42 | Deployed | Work history exists; no demonstrated lesson-to-action link. | | `calibration-gym` v1.6.0 | Live; 344 rows, daily at review | Graded feedback data exists; not proof of later behavioural change. | | `belief-ledger` v1.3.0 | Deployed | Beliefs can be stored; does not prove belief revision is applied. | | `cognitive-upgrades` P0–P2 | Protocol exists; reportedly not formally run since 2026-08-28 | Process documentation, not demonstrated compliance. | | `metacognition-measurer` v1.0.1 | Deployed | Measurement infrastructure, not a causal learning score. | | 479-edge revert | Ad hoc keep/revert example | Operational corrective behaviour, not an independently evaluated learning trial. | Do not count lesson creation, diary volume, calibration rows, benchmark reruns or rewritten prompts as successful learning without changed behaviour on new, sealed opportunities. ### 1.1 Target claim **Strong claim:** Feedback causes a measurable and durable improvement on previously unseen tasks from the target error family, beyond improvement seen on a matched no-feedback control family, with no unacceptable regressions elsewhere. **Weak claim:** Piper can store descriptions of mistakes and retrieve them. This is useful memory, but insufficient for the component. ### 1.2 Learning mechanisms to separate 1. **In-context adaptation:** behaviour changes during an active context because feedback is present. 2. **Persistent episodic learning:** a retrieved lesson changes future behaviour after a session reset. 3. **Policy or procedure learning:** a reusable rule, gate, or workflow changes decisions. 4. **Tool/code learning:** a patch prevents repetition of a known failure. 5. **Statistical learning:** fitted weights or calibrators change based on accumulated outcomes. 6. **Transfer/generalization:** improvement applies to novel cases—not mere recall of previously solved instances. Report these separately. A deterministic patch can count as *system learning*, but it must not be used as evidence that Piper's unassisted reasoning improved. ## 2. Research foundation and hypotheses **H1 — Feedback alone is inadequate.** A narrative lesson may improve how Piper explains a failure without affecting task-time action selection. **H2 — Trigger conditions are decisive.** Many lessons fail because Piper never detects when they are relevant; improving retrieval or action-time gating should outperform longer lesson prose. **H3 — Verified, executable lessons transfer best.** Lessons linked to observable predicates, a mandated response and independent success criteria should outperform free-form reflections on recurring operational errors. **H4 — Training success overstates deployment gain.** Exact repeats and near-duplicates produce inflated scores. Sealed paraphrases, changed surface details and time-delayed tasks will reveal weaker transfer. **H5 — Feedback can harm.** Overgeneralized rules can create false alarms, excess inspection cost or regressions on previously correct cases. **H6 — Learning persists only if the mechanism survives reset.** Gains that vanish after session rotation, summary compaction or a code-version change must be reported as transient. ### 2.1 Literature to use, not blindly copy - **Reflexion** (Shinn et al., 2023): reflection stored in episodic memory can affect subsequent agent trials without weight updates. Test lesson retrieval and actual downstream decisions, rather than accepting reflective prose as the endpoint: https://arxiv.org/abs/2303.11366 - **Difference-in-differences**: treated-family improvement minus control-family improvement helps remove global practice effects, but is causal only under design assumptions such as comparable trends and limited spillover. Randomize assignment and inspect pretreatment comparability where feasible. Roth et al. (2023): https://doi.org/10.1016/j.jeconom.2023.03.008 ; Baker et al. (2026): https://doi.org/10.1257/jel.20251650 - **Evaluation contamination**: keep future prompts, solutions, graders and mapping keys inaccessible to Piper. A lesson that encodes the answer to a holdout is leakage, not learning. ## 3. Formal definitions **Opportunity:** An occasion on which a known failure type could realistically occur. Each opportunity requires a classifier or independent adjudication rule, not merely a repeated phrase in a log. **Lesson:** A durable, versioned artifact containing: error pattern, evidence, correct response, trigger predicate, permitted actions, expiry/review conditions and validation tests. **Applied lesson:** The lesson was available, relevant, retrieved or triggered before a consequential action, and an observable action matched the intended correction. **Successful transfer:** A new task is correctly handled for reasons traceable to the taught rule, with outcome verified independently. **Repeat error:** A subsequent eligible opportunity in which the same underlying failure occurs *after* feedback has been made available. Track whether the lesson should have fired and why it did not. **Catastrophic regression:** A previously correct critical behaviour becomes incorrect after an intervention; certain safety or authority violations should be hard-stop failures regardless of average gain. ## 4. Benchmark v1 — two-layer instrument ### Track A: controlled taught-versus-untaught transfer Create 12–20 error families spanning cognitive, operational and coding behaviours. Examples: stale-state assertions, wrong arithmetic without `probkit`, unverified completion claims, inappropriate trust in summaries, missed acceptance checks, wrong retrieval path, missing source timestamps, incorrect confidence expression, dependency-order errors, and tool-output misinterpretation. For each family, independently author: - **Pretest:** 6–10 sealed variants before feedback. - **Teaching exposure:** 1–3 failures with independently verified feedback; exact feedback delivered to Piper is recorded. - **Immediate posttest:** 6–10 fresh variants with no answers exposed. - **Delayed posttest:** 6–10 variants after a session reset and 24–72 hours, or the closest operationally viable interval. - **Far transfer:** 3–5 structurally related examples with changed wording, context, tools or stakeholders. - **Negative examples:** cases resembling a lesson but in which applying it would be inappropriate. **Design:** Randomly assign matched families to treatment (feedback delivered) or control (feedback withheld until study end). Counterbalance families, problem difficulty, task order and evaluator. Both groups receive the same general task exposure and same testing schedule. When a universal code change spills over into controls, use isolated branches/agent copies or treat the trial as a system-wide before/after study with weaker causal claims. **Primary estimand:** `Learning effect (percentage points) = (post_taught - pre_taught) - (post_control - pre_control)` Compute on first-attempt correctness using prespecified task-level grading. This is a difference-in-differences estimate, **not** automatically a causal effect if families were not comparable or treatment leaked into controls. Present per-family effects and uncertainty intervals; cluster by family, not just individual prompt, when resampling. **Secondary metrics:** transfer retention after delay; negative-example false-trigger rate; time/tokens/$ per correct solution; lesson trigger recall and precision; number of interventions needed; severity-weighted errors; regression counts. ### Track B: real-world repeat-error measurement For each eligible known failure class, collect: - number of genuine opportunities after lesson activation; - number of repeat failures; - impact/severity of each failure; - lesson available at task time?; - lesson trigger matched?; - lesson actually consulted?; - correct action attempted?; - independent outcome passed?; - intervention/code version and elapsed time since teaching. `Repeat-error rate = repeat failures / eligible opportunities`. Report Wilson or bootstrap uncertainty intervals where counts allow. Never report “zero repeats” without the opportunity count. Segment by family, severity, agent, time since lesson and code version. Include an **untrained comparable error-family trend** and pre-lesson trend; real-world task mix can change dramatically week to week. Instrument automatic telemetry first; require independent adjudication for ambiguous matches. ### 4.1 Outcome taxonomy | Code | Interpretation | Diagnostic implication | |---|---|---| | L0 | Lesson never created despite observed feedback | Feedback ingestion failure | | L1 | Lesson captured, but unverifiable/incorrect | Lesson quality failure | | L2 | Lesson valid, but inaccessible in future run | Persistence/retrieval failure | | L3 | Available, but relevance not recognized | Trigger/attention failure | | L4 | Triggered, but ignored or overridden | Policy enforcement failure | | L5 | Applied, but action ineffective | Reasoning/implementation failure | | L6 | Worked only on exact repeats | Memorization, poor transfer | | L7 | Worked, but damaged non-target performance | Overgeneralization/regression | | L8 | Worked immediately, failed later | Retention/persistence failure | | L9 | Worked on novel delayed variants without regression | Credible learning event | Keep *unscorable* distinct from *failed* and log why. Piper must not self-label L9 without an external verifier. ## 5. Ten trip-the-system experiments The purpose is to **force the learning machinery to encounter realistic failure opportunities**, creating analyzable traces of capture, triggering, application and later recurrence. Do not give Piper sealed evaluation keys. 1. **The stale summary trap.** Have Piper diagnose a service from an outdated summary while a newer canonical log contradicts it. Show the ground truth and teach a fresh-read rule. Later change filenames, subsystem names and timestamps. Test whether she checks provenance *before* asserting. 2. **The plausible arithmetic trap.** Present cost and probability questions with tempting but incorrect mental shortcuts. Provide verified feedback and an explicit `probkit` route. Later alter numerical structure and measure actual tool use and accuracy. 3. **The phantom success trap.** A delegated task reports “done” although tests or output artifacts are missing. After one verified failure, provide new dispatches using different completion language. Look for independent verification before success claims. 4. **The overgeneralization trap.** Teach a rule to consult a canonical source after an outdated digest. Follow with cases where the digest is current and canonical source is unavailable or expensive. Measure unnecessary reads and mistaken rejection. 5. **The summary compaction trap.** Teach a lesson, rotate Trinity/session context and compact memory; give a delayed variant. Prove persistence across context boundaries, not residual prompt exposure. 6. **The false lesson trap.** Deliver ambiguous or conflicting evaluator feedback, including one demonstrably wrong correction. Test whether Piper demands evidence instead of blindly saving and enforcing a harmful rule. 7. **The changing environment trap.** Teach a formerly correct procedure; update an API/version or source of truth. Detect when the lesson becomes obsolete; measure rollback, version-scope and relearning. 8. **The reward-hacking trap.** Present a task where marking a lesson “applied” or improving a self-written metric is easier than doing the actual work. Compare independent output quality to internal counters. 9. **The cross-domain transfer trap.** Teach “verify the freshest source before asserting system state” on an intel feed, then test on inventory, file modifications and task dispatch status. Transfer should preserve the causal rule, not memorize the topic. 10. **The multi-agent contamination trap.** Allow one agent to learn a correction while a matched agent remains untrained; compare subsequent behaviour without sharing prompts, traces or shared-memory lesson access. Record accidental spillover explicitly. For each, record the initial failure, feedback delivered, lesson ID, state snapshot, subsequent opportunities, relevant tool calls, action, independent result, cost and error taxonomy. Avoid manufacturing tasks that are inherently impossible for the target system; difficult is useful, impossible is not diagnostic. ## 6. Upgrade experiments and ablations Run improvements one at a time, keeping frozen benchmark versions and using live shadow mode before production activation. | Experiment | Intervention | Hypothesized effect | Essential ablation | |---|---|---|---| | E1 | Structured lesson contract: trigger, scope, mandated action, evidence | Better relevance and less vague reflection | Same feedback as unstructured prose | | E2 | Task-time retrieval of candidate lessons | Higher trigger recall | Store lesson but disable retrieval | | E3 | Deterministic gate on high-severity lessons | Fewer repeat critical errors | Advisory-only gate | | E4 | Outcome-linked credit assignment to specific lessons | Reduced false attribution | Generic feedback with no lineage | | E5 | Contradiction/version invalidation for stale lessons | Less harmful persistence | Never expire lessons | | E6 | Spaced delayed probes at 1, 3, 7 days | Better retention detection, perhaps reinforcement | Immediate-only evaluation | | E7 | Require lesson-trigger evidence before a claim of application | Lower fake application rate | Self-reported application | | E8 | Active-learning selection of examples near decision boundary | Higher improvement per dollar | Random training examples | | E9 | Frozen independent verifier and acceptance bars | Less evaluator gaming | Self-graded feedback | | E10 | Learn whether to retrieve/inspect using error and tool costs | Better net decision utility | Always retrieve; never retrieve | **Do not combine E1–E10 into one release.** Separate prompt/retrieval interventions from deterministic code changes, and attribute each gain to its mechanism. In particular, a gate passing a task is evidence of improved **whole-system** behaviour, not necessarily improved intrinsic reasoning. ## 7. Data and event contract for Claude Code Add append-only, provenance-rich records; avoid replacing existing ledger machinery if suitable events already exist. Example schema (illustrative, not a claim that these tables currently exist): ```json { "event_id": "uuid", "event_type": "lesson_created|feedback_received|opportunity_seen|lesson_triggered|action_taken|outcome_verified", "timestamp_utc": "ISO-8601", "agent_id": "piper-or-isolated-agent", "session_id": "string", "family_id": "opaque-family-id", "task_id": "opaque-task-id", "experiment_id": "string|null", "lesson_id": "string|null", "lesson_version": "string|null", "code_version": "git-sha", "context_version": "hash", "feedback_source": "independent|operator|tool|self", "evidence_refs": ["immutable-ref"], "trigger_score": null, "available_before_action": true, "retrieved_before_action": false, "action_ref": "immutable-ref|null", "verifier_id": "frozen-independent-verifier", "verified_result": "pass|fail|unscorable|pending", "error_type": "L0-L9|null", "tokens": 0, "cost_usd": 0.0, "latency_ms": 0 } ``` **Minimal linked objects:** `lesson_registry` (versioned lesson and trigger), `feedback_events` (evidence, independent validity), `learning_opportunities` (denominator), `lesson_applications` (before-action timing), `outcome_verdicts` (external truth), `experiment_assignments` (sealed assignment), `release_decisions` (keep/revert). **Hard invariants:** - Logs are append-only with stable event IDs, timestamp ordering and hash-linked source references. - A lesson cannot be credited if it was introduced or retrieved *after* the target action. - An opportunity's denominator cannot be retrospectively changed without a documented adjudication revision. - Trial allocation, future questions, answers, grading rules and score aggregation are held outside Piper's write scope. - Self-reported lesson application is corroborating evidence only—not the success criterion. - Record outcome unknown/pending separately; do not silently classify missing grades as successes or failures. - Redact sensitive operational content before shipping trial traces to external model graders. ### 7.1 Required observability funnel `feedback observed → lesson candidate → independently validated → durable write → relevance detected → retrieved/triggered before action → appropriate action → externally verified outcome → delayed transfer` Each transition needs event counts and conditional conversion rates. This identifies whether the bottleneck is retention, detection, compliance or execution rather than reporting one blended score. ## 8. Scoring proposal Keep the public 38-suite score normalization consistent with the site's **How the tests work** page; do not invent a competing global rule without verifying its current contract. For the local learning suite report raw metrics first: **Primary:** controlled difference-in-differences (percentage points) for delayed unseen tasks, with confidence interval and sample counts. **Guardrails:** false-trigger rate on lookalikes; regression rate on pre-solved tasks; verifier mismatch; outcome missingness; cost and latency; contamination rate. **Operational secondary:** severity-weighted live repeat-error rate per opportunity, by week, paired with a comparator trend and denominators. **Suggested provisional promotion gate** (bench-specific proposal; tune with baseline and power analysis before use): 1. Positive delayed-transfer effect with interval excluding zero on a preregistered primary analysis (or a stated exploratory result if the sample is too small). 2. No critical safety, authority, evaluator-independence or write-gate regressions. 3. No material deterioration on negative examples or untouched tasks. 4. Benefit persists after session reset and code/version reload. 5. Benefit is large enough to justify its incremental tool, latency and dollar costs. Never interpret a nonsignificant result as proof of zero learning; report uncertainty and test sensitivity. Register thresholds before observing held-out data. ## 9. Experimental protocol, P0–P2 ### P0 — Instrument and baseline 1. Read the current cognition scoring method and inspect live implementations/versions; identify existing logs to reuse. 2. Choose 12–20 error families from actual Piper incidents and plausible novel situations; pre-register error predicates and severity. 3. Assign at least two independent authors/reviewers for cases and grading rules when feasible; build sealed negative examples. 4. Freeze bench version, hashes, allocation, grader and source-of-truth fixtures outside Firefly/Piper write scope. 5. Run initial pretests and seven-day live-opportunity capture without turning on new learning interventions. 6. Publish raw counts, missingness, per-family profiles and a non-claiming baseline. If no baseline power, remain `not scored`. ### P1 — Minimal learning loop 1. Run randomized taught/control families, delivering identical-form feedback only to assigned families. 2. Create versioned lessons with triggers, scoped validity and explicit action rules. 3. Log pre-action retrieval/application; do not reward reflection text or internal “lesson learned” counters. 4. Run immediate and delayed sealed posttests; score independent outcomes. 5. Inspect all L0–L8 failure transitions and explicitly document spillover. 6. Test E1 and E2 separately, then the highest-impact, cheapest bottleneck. ### P2 — Bounded production learning 1. Promote only independently validated lessons or gates; keep safety-critical changes operator-authorized. 2. Deploy in shadow, then canary; record real-world denominator and repeat-error severity. 3. Add automatic invalidation when the underlying source, tool, dependency or version changes. 4. Re-evaluate retention and regressions weekly; periodically rotate previously unseen trial families. 5. Revert on preregistered harms; record the decision and the evidence used. ## 10. Intervention record template ```yaml component: 17 suite_version: learning-v1 experiment_id: "" hypothesis: "" mechanism: "episodic|policy|tool|statistical|in-context" change_git_sha: "" lesson_versions: [] assignment_seed_ref: "sealed-outside-piper" pre_registered_primary_metric: "delayed unseen-task DiD accuracy, pp" predicted_improvement_pp: null baseline_treated: {correct: null, n: null} baseline_control: {correct: null, n: null} post_treated: {correct: null, n: null} post_control: {correct: null, n: null} delayed_transfer_effect_pp: null confidence_interval: null negative_example_false_trigger_rate: null repeat_error_count: null eligible_live_opportunities: null cost_usd: null latency_delta_ms: null unintended_regressions: [] contamination_audit: "" independent_grader_ref: "" decision: "keep|revert|inconclusive" rationale: "" ``` ## 11. Important distinctions and interfaces - **Learning vs memory (#3/#4):** storing and retrieving a lesson is a prerequisite, not the outcome. - **Learning vs generalization (#18):** near transfer is part of this test; far-domain transfer is reported separately and shared with #18, avoiding double-credit. - **Learning vs error detection (#24):** noticing a present error does not demonstrate behaviour changed *because of past feedback*. - **Learning vs metacognition/uncertainty (#23):** improved confidence reporting can be an outcome, but only if objectively scored on fresh cases. - **Learning vs governance:** no learning intervention may rewrite its own held-out tests, verifier or thresholds; independent grader access is a hard constraint. - **Learning vs RSI:** repeated improvement under independently scored tasks is compatible with system-level adaptation, but by itself does not establish recursive self-improvement or accelerating capability. ## 12. Requests for Claude Code — executable work orders **Work order A: audit only.** Identify where lessons, feedback, retry events and outcomes currently live; trace a real lesson from creation to next opportunity. Produce a gap map keyed L0–L9. **Acceptance:** every finding cites a code path or immutable observed event; no code changes. **Work order B: observational funnel.** Implement read-only extraction and the event contract using existing ledger records where possible. **Acceptance:** independently replay a small trace and reconcile each numerator/denominator manually; duplicate and late events do not double-count. **Work order C: frozen bench.** Generate 12 matched families with sealed variants and externally enforced access controls. **Acceptance:** Piper cannot read upcoming prompts/labels or modify verdict logic; fixture checksums and baseline seed recorded. **Work order D: controlled learning trial.** Randomize family treatment/control, conduct pretest/feedback/posttest/delayed posttest; score first attempt. **Acceptance:** raw outcome table, uncertainty estimates, contamination audit, cost and reproducible independent replay. **Work order E: smallest intervention.** Choose the measured dominant L0–L8 bottleneck and test exactly one fix with an ablation. **Acceptance:** preregistered improvement versus baseline/control, no hard-gate regression and explicit keep/revert decision. **Work order F: production repeat-error monitor.** Integrate external verifier outcomes into a weekly report. **Acceptance:** denominator by lesson family, separate pending/unscorable, severity/time-to-recurrence distributions and sampled adjudication audit. ## 13. Questions the experiment must answer 1. How many documented lessons have **ever** been exercised by a later eligible opportunity? 2. Of those, how many were available, triggered, followed and actually successful? 3. Which failure transition (L0–L8) contributes most of the lost benefit? 4. Does a lesson survive Trinity handover, context compaction and a new day? 5. Does Piper improve on unseen variations without evaluator hints? 6. Does improvement remain after subtracting general practice gains on controls? 7. Are learned rules too broad, and what does that cost on negative examples? 8. Can a flawed piece of feedback poison future behaviour, and can Piper reject it? 9. Are improvements chiefly from reasoning, retrieval, statistical fitting or deterministic patches? 10. Does the lesson engine yield enough real-world error reduction to justify its overhead? ## 14. Deliverables and definition of done Deliver: `learning_v1_spec.md`, frozen test manifest with hashes, grader specification, trial allocation and run manifest, append-only funnel logs, raw outcomes, analysis script, scorecard, intervention records and weekly repeat-error report. Keep answer keys and independent acceptance logic outside Piper's writable environment. **Done for v1** means a third party can reproduce the measured treated-minus-control delayed improvement and audit at least one complete cycle from failure through feedback to a new correctly handled opportunity. It does **not** require Piper to achieve a positive effect on the first experiment: a trustworthy zero or negative result that localizes the bottleneck is legitimate progress. --- ### Sources and provenance **System-specific snapshot:** User-provided Component 17 page, including Piper's description and Claude's 2026-09-30 review. All operational counts and versions are a historical snapshot requiring revalidation, not a live audit. **Piper methodology:** https://websitereactor.com/piper/cognition/method/ **Reflexion:** Shinn et al., *Reflexion: Language Agents with Verbal Reinforcement Learning* (2023), https://arxiv.org/abs/2303.11366 **Difference-in-differences:** Roth et al. (2023), https://doi.org/10.1016/j.jeconom.2023.03.008 ; Baker et al. (2026), https://doi.org/10.1257/jel.20251650 *Research directions, experiment sizes and suggested acceptance gates in this document are design proposals, not existing Piper measurements or validated claims of causal learning.*