Evolutionary symbolic compression engine — discovers mathematical abstractions by rewarding compression and novelty over expression trees.

As of 2026-04-18 (post-Phase U), the machine traverses mathscape autonomously, with self-containing compute, and now a self-tuning meta-loop on top that observes its own learning, proposes its own next training recipe, and sails out when the reachable territory for a seed is exhausted:

• Given any sufficiently-diverse corpus set, it discovers primitives, reinforces them via retroactive reduction across a shared forest substrate, and climbs the proof-status lattice to Axiomatized on cross-corpus empirical evidence alone. No human approval is in the loop. No external prover assists. No hook fakes the gate.

• More input makes the machine more efficient, not less. (node, rule) memoization + content-addressable hash-consing turn per-corpus cost into O(new_nodes × library_size) — measured at ~0.84 ms/corpus from 10,000 corpora up through 100,000, on a commodity darwin-arm64 workstation.

• The lynchpin invariant holds at every tested scale: every rule in the final library earns cross-corpus retroactive support ≥ 2. No corpus artifacts survive.

• Phase T (wall-clock efficiency): R37 early-stop on library plateau cuts post-saturation iterations automatically — 1.80× on single M0, 3.97× on 4-phase scenarios; autonomous-traversal medium 96ms (vs 150 expected), stress 321ms (vs 500 expected).

• Phase I (subterm-paired anti-unification): law discovery no longer limited to root-level pattern matches. Unblocks Phase H's rank-2 meta-inception when paired with Phase J's empirical validity (pending).

• Phase U (self-tuning meta-loop): MetaLoop<Executor, Proposer> drives an observe → propose → execute → observe cycle. LearningObservation captures what each scenario taught; HeuristicProposer encodes Phase T's findings as decisions; AdaptiveProposer LEARNS per-archetype performance over time and biases its picks toward empirical winners. Fully Lisp-residential (R32 spec, R33 scenario, R10 policy, U.1 observation), meta-loop attestation is BLAKE3 over the chain of chain-attestations.

• Phase V (fix-point motor): the loop closes on itself. LearningObservation.staleness_score() detects when the environment has stopped producing novelty; SpecArchetype::AdaptiveDiet mutates the DIET (routes through AdaptiveCorpusGenerator, which reads library state and synthesizes residue-inviting terms). Observed running: default corpus saturates at 4 rules over 3 phases, motor fires AdaptiveDiet on phase 3, library grows to 5 — a rule the default corpus could not reach. The model reacts to its own saturation by expanding its environment. Trained on bare math: staleness, intervention, and reward are all pure functions of the observation stream.

• Phase V.map (the machine has a map of itself): MathscapeMap is a typed, attestable view of the library — core_rules, union_rules, mutation_edges, seed metadata, per-rule cross-corpus support, and a BLAKE3 Merkle root over canonical rule serialization. save_to_path / load_from_path persist it; library_merkle_root produces deterministic attestation.

• Phase V.events (the machine narrates itself): MapEvent enum with typed variants — NovelRoot, RootMutated, CoreGrew, StalenessCrossed, RuleCertified, RuleRejectedAtCertification, BenchmarkScored. MapEventConsumer trait + BufferedConsumer implementation form the default event bus. Every architectural transition is an event the downstream consumers see in order.

• Phase V.certify (reactive promotion): CertificationLevel state machine (Candidate → Validated → ProvisionalCore → Certified → Canonical) with Certifier trait, DefaultCertifier, and CertifyingConsumer — a chainable consumer that watches the event stream, promotes rules through the lattice on confirmed empirical support, and emits RuleCertified / RuleRejectedAtCertification downstream.

• Phase V.stream (never-destroy streaming trainer): StreamingPolicyTrainer wraps LinearPolicy in RefCell and implements MapEventConsumer. Every event produces a reward signal; reward × feature vector applies an online SGD step. The trainer never resets between phases — it is the persistent policy head across the whole session.

• Phase V.benchmark (labeled-data ingress / report card): MathProblem, ProblemResult, BenchmarkReport, canonical_problem_set() (12 problems), harder_problem_set() (6 symbolic-identity problems needing discovered rules), run_benchmark(), and BenchmarkConsumer::benchmark_now(library, downstream) that scores the library and emits MapEvent::BenchmarkScored { solved_fraction, delta_from_prior }. The streaming trainer rewards improvement asymmetrically: +3× for gains, −5× for regressions — don't break what worked.

• Phase V.shed (neuroplasticity): per-weight activation counts and cumulative contributions tracked inside the streaming trainer. prune(magnitude_threshold, min_activations) zeros weights that are both small and rarely-activated and marks them pruned; rejuvenate(index, initial_value) un-prunes and re-seeds. Pruned weights are skipped on future updates. The policy sheds dead dimensions while the stream continues to form new ones — neuroplasticity applied to the policy head.

• Phase W (perpetual self-optimizing model): four research-grade mechanisms absorbed from continual-learning and dynamic-sparse-training literature.

  • W.1 RigL phantom gradients: pruned weights still accumulate |would-be-delta|; auto_rejuvenate picks up phantom-active weights automatically. The shed+grow loop is now fully autonomous (Evci et al. 2020).
  • W.2 EWC Fisher-weighted stability: per-weight Fisher EMA; anchor on benchmark improvement; Fisher-weighted pullback on regression events protects load-bearing weights from drift while marginal weights stay plastic (Kirkpatrick et al. 2017).
  • W.3 Learning-progress intrinsic reward: benchmark events get a +4× bonus for current - min(last K scores) positive improvement — the agent is rewarded for improving itself (Schmidhuber / Oudeyer).
  • W.stall Corrupted/stalled pruning: prune_dormant_or_corrupted sheds weights that went silent after being active or that stay zero despite high Fisher (pressure-flattened).
  • W.4 EventHub + motor translator: synchronous reentrant- safe pub/sub spine; publish_outcome_events translates MetaLoopOutcome → MapEvent stream fanned out to every subscriber. The full perpetual loop runs end-to-end in one subscription (tests/perpetual_loop.rs).

• Phase X (Mathematician's Curriculum): 32 problems across 6 subdomains — arithmetic-nat, arithmetic-int, symbolic-nat, tensor-algebra, compound, generalization — with per-subdomain scoring. Observed real-motor result: 56% → 88% (Δ +31.2%), 4 subdomain masteries, 2 regressions surfacing rule-vs-kernel priority issue the coarse benchmark hid.

• Phase Y.0 (LiveInferenceHandle): query the running model without freezing the stream. infer, current_competency, policy_snapshot, library_snapshot, library_size — all non-blocking, dashboard-friendly, Lisp-morphable.

• Phase Y.1 (OpenAPI spec + normalized pipeline): apis/mathscape-inference/openapi.yaml — 3.0.3, 5 paths, 23 schemas — single source of truth for REST, gRPC, GraphQL, MCP, SDKs, docs, completions. Sekkei → takumi → forge-gen renders every transport.

• Phase Y.2 (mathscape-inference-api crate): typed DTOs matching the spec field-for-field, InferenceService trait all backends route through, HandleAdapter default impl wrapping LiveInferenceHandle. JSON-over-wire full round-trip tested (11/11).

See docs/arch/autonomous-traversal.md for the milestone doc, docs/arch/self-tuning-meta-loop.md for the Phase U+V+W frame, docs/arch/perpetual-improvement.md for the fixed-point synthesis, and docs/arch/landmarks.md for the full canonical map (phases A–Y).

cd mathscape

# Run the orchestrated autonomous-traversal suite (4 tests):
cargo test -p mathscape-axiom-bridge --test autonomous_traverse

# Scale probe — 10,000 procedural corpora, ~8s in release:
MATHSCAPE_TRAVERSE_BUDGET=10000 \
  cargo test -p mathscape-axiom-bridge --test autonomous_traverse \
    autonomous_traverse_medium --release -- --nocapture

# Mega probe — 100,000 procedural corpora, ~85s in release:
MATHSCAPE_TRAVERSE_BUDGET=100000 \
  cargo test -p mathscape-axiom-bridge --test autonomous_traverse \
    autonomous_traverse_medium --release -- --nocapture

Or via the reserved skill (from any repo):

/mathscape-traverse

Mathematical understanding is compression. Mathscape automates that compression: given a minimal computational substrate and a reward signal that favors shorter descriptions of more phenomena, a search process rediscovers known mathematics — and may find new compressions humans haven't seen. The self-containing compute property means this is tractable at arbitrary input scale: the machine's developed tools (symbols, meta-rules) compact each new corpus using work already done, so the effective traversal cost stays bounded as the reachable territory expands.

• CLAUDE.md — conventions, architecture, crate structure.
• docs/arch/machine-synthesis.md — the canonical picture of the five architectural objects, ten gates, five forces, three regimes.
• docs/arch/autonomous-traversal.md — the milestone + self-containing compute findings.
• Full arch-doc list in CLAUDE.md.

• Read-only observation: use the mathscape-traverse skill.
• Code changes: edit crates directly, then rerun autonomous_traverse to confirm the lynchpin invariant still holds.
• Anything that breaks autonomous_traverse_deterministic_replay is a serious regression — some source of non-determinism has leaked into the loop.