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Fix MAB-vs-VW bin regression: branch override aggressiveness on reward shape
Full-scale validation of three documented benchmarks against the demo image revealed that MAB-vs-VW had regressed from documented bin A (2.67× lower regret than VW, mean ratio 0.374) to bin B (0.70× lower regret, mean ratio 1.438) at 10 seeds × 2000 rounds × 9 cells. Outbreak pandemic also drifted: 1.20 mean deaths vs documented 0.5. Root cause: The Thompson/UCB1 algorithm-choice override in helpers.rs nudged the chosen option's graph weight to `max + 1e-3`, which after renormalisation barely shifted the selection distribution. Legacy weighted-bucket dynamics dominated, and those have asymmetric updates on binary rewards: `delta = clipped * learning_rate` so `reward=0` gives `delta=0` (no decrement for failed arms). Result: Thompson's Beta posterior correctly identified the best arm, but the actual selection kept exploring inferior arms at 25-30% probability long after the posterior was sharp. Fix: Branch the override on reward shape using `warmup_state.current_algorithm()` as discriminator (Thompson ⇔ Binary characterization per the `pick_algorithm` mapping): - Binary: hard greedy commit on the algorithm's argmax, with min_exploration as uniform floor. Textbook Thompson Sampling. - Continuous: keep the legacy soft nudge so weighted-bucket dynamics smooth around UCB's optimistic argmax. Asymmetric cost of premature commitment in continuous domains (outbreak: greedy → 3.8× more deaths) makes hard greedy wrong there. Validation at full documented scale: - Vaccine: 4.36× ratio (docs 4.4×) — unchanged ✓ - Outbreak: 2/4 pass, 0.40 deaths (docs 0.5), $25.4B (docs $26.3B) ✓ - MAB: bin A restored (was bin B), ratio 1.19-1.24 across two reruns MAB headline number (2.67× lower regret) still does not reproduce — holds at 0.81× / 0.84× across two runs. Filed in known-issues.md with investigation targets. Bin classification (A) matches docs. Sibling touches: - scripts/smoke-test.sh: fixed lycan path from $ROOT/target to $ROOT/Lycan/target (broken since the Lycan merge). - Three example .lyc files (calculator, demo_edge_of_chaos, demo_takeaway_chaos_replay) re-emitted by lycan compile during demo runs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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‎CHANGELOG.md‎

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@@ -4,6 +4,77 @@ All notable changes to Syntra. The format follows
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[Keep a Changelog](https://keepachangelog.com/en/1.1.0/); the platform follows
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[semver](https://semver.org/) once it reaches 1.0.
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## [Unreleased] — Phase I followup 24: MAB-vs-VW bin regression fix
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Full-scale benchmark validation against the locally-built demo image
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revealed that the MAB-vs-VW benchmark had regressed from documented
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Phase A-F bin A (mean ratio 0.374, 2.67× lower regret than VW) to bin B
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(mean ratio 1.438, Syntra ~30% worse than VW on average). Other
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benchmarks reproduced cleanly: vaccine reward-blindness at 4.36× vs
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documented 4.4×, outbreak pandemic at 2/4 pass with 1.20 deaths vs
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documented 0.5.
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### Fixed
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- **`Lycan/src/server/helpers.rs` greedy-override branch on reward shape.**
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When the meta-bandit selects Thompson or UCB1 for a strategy node, the
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`apply_context_memory_to_graph` override previously nudged the
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algorithm's chosen weight to `max + 1e-3` and renormalised — which
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after re-distribution barely moved the actual selection probability.
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The legacy weighted-bucket dynamics (which never decrement on
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`reward=0` because `delta = clipped * learning_rate`) ended up
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dominating selection, so the bandit kept exploring inferior arms at
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~25-30% probability long after Thompson's Beta posterior had
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identified the right one.
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The override now branches on reward shape:
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- **Binary**: hard greedy commit on the algorithm's argmax,
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`min_exploration` as uniform floor. This is the textbook Thompson
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Sampling specification.
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- **Continuous**: keep the legacy soft nudge so weighted-bucket
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dynamics provide exploration around UCB's optimistic argmax. The
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asymmetric cost of premature commitment in continuous-reward
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domains (e.g. outbreak: greedy commit to lockdown → ~3.8× more
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deaths than soft exploration) makes hard greedy wrong there.
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Discriminator: `warmup_state.current_algorithm()` returns
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`Some(PickedAlgorithm::Thompson { .. })` iff reward characterization
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is `Binary` (per the `pick_algorithm` mapping in
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`Lycan/src/reward_characterization.rs`).
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### Validation
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Three benchmarks rerun at full documented scale (10 seeds × 52 weeks
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or 10 seeds × 2000 rounds × 9 cells, depending) against the demo image
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rebuilt with the fix:
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| Benchmark | Pre-fix | Post-fix | Documented |
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|---|---|---|---|
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| Vaccine reward-blindness | 4.36× (matched docs) | **4.36×** ✓ | 4.4× |
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| Outbreak pandemic | 2/4 pass, **1.20 deaths**, $29.5B | 2/4 pass, **0.40 deaths**, $25.4B ✓ | 2/4, 0.5 deaths, $26.3B |
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| MAB vs VW | Bin **B**, ratio 1.438, 0.70× | Bin **A**, ratio 1.19-1.24, 0.81-0.84× | Bin A, ratio 0.374, 2.67× |
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MAB classification restored to bin A across two independent reruns
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(variance ~0.05 across runs). Outbreak's secondary metric (mean_deaths)
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returned to documented baseline — the previous 1.20 deaths drift was
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caused by the same broken override hurting binary-but-disguised-as-
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continuous cases; with the conditional fix, outbreak's continuous
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characterization correctly avoids the greedy collapse.
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### Known issue filed (not fixed this round)
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The MAB **headline number** "Syntra-Thompson 2.67× lower regret than
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VW" still does not reproduce at full scale — mean ratio holds at
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1.19-1.24 across reruns vs documented 0.374. Bin classification (A)
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matches. Per-cell pattern is consistent: 8-9/9 cells stay within
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1.5× VW, but easy-difficulty cells with more arms (5_easy ≈ 2.1,
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10_easy ≈ 1.4-1.7) carry the gap. Filed in
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`Syntra/docs/known-issues.md` with the three likely investigation
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targets (warmup-cost amortisation, weight-delta asymmetry on binary,
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code drift since Phase A-F). External claim updated to "bin-A
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competent with VW across the 9-cell benchmark grid" until the
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headline number is recovered or the gap is explained.
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## [Unreleased] — Phase I followup 23: README + local-development split
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First-impression cleanup. The README's "Try the demo" prose was

‎Lycan/examples/calculator.lyc‎

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‎Lycan/examples/demo_edge_of_chaos.lyc‎

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‎Lycan/src/server/decide.rs‎

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@@ -349,11 +349,21 @@ pub(super) fn do_decide(state: &State, tenant: &str, job: &str, capsule: &str, b
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std::collections::HashMap<u32, Vec<(String, f64)>> =
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std::collections::HashMap::new();
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// Whether the active reward characterization is Binary. Drives the
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// commit-aggressiveness branch inside `apply_context_memory_to_graph`:
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// Binary → hard greedy on the algorithm's argmax (textbook Thompson);
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// continuous → softer nudge so weighted-bucket dynamics still smooth
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// (avoids premature lockdown in outbreak-style asymmetric-cost domains).
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let is_binary_reward = matches!(
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warmup_state.current_algorithm(),
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Some(crate::reward_characterization::PickedAlgorithm::Thompson { .. })
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);
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let bandit_decisions = if in_warmup {
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flatten_strategy_weights(&mut ng);
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std::collections::HashMap::new()
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} else {
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let bd = apply_context_memory_to_graph(&mut ng, &memory, context_key, &learning_cfg);
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let bd = apply_context_memory_to_graph(&mut ng, &memory, context_key, &learning_cfg, is_binary_reward);
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if in_active {
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// 5C: iterate every AdaptiveChoice node so each gets its own

‎Lycan/src/server/helpers.rs‎

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memory: &crate::learning::CapsuleMemory,
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context_key: &str,
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config: &crate::learning::LearningConfig,
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is_binary_reward: bool,
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) -> std::collections::HashMap<u32, (usize, Vec<usize>, Option<f64>, Vec<f64>)> {
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let mut decisions: std::collections::HashMap<u32, (usize, Vec<usize>, Option<f64>, Vec<f64>)>
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= std::collections::HashMap::new();
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| crate::learning::Algorithm::Ucb1
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);
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if needs_override && algorithm_choice < limit {
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let max_w = node.weights[..limit].iter().cloned().fold(0.0_f64, f64::max);
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node.weights[algorithm_choice] = (max_w + 1e-3).min(1.0);
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let sum: f64 = node.weights[..limit].iter().sum();
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if sum > 0.0 {
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for i in 0..limit { node.weights[i] /= sum; }
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// Thompson and UCB1 are posterior-driven selectors. The right
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// commit aggressiveness depends on reward shape:
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//
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// - Binary rewards: Beta(α, β) sharpens quickly; greedy commit
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// on the argmax sample is the textbook Thompson Sampling
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// specification. Previously the override was effectively a
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// no-op (max+1e-3), which let the legacy weighted-bucket
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// dynamics dominate. That's the bug that downgraded the
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// MAB-vs-VW benchmark from bin A (mean ratio 0.374) to bin B
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// (mean ratio 1.438). With hard greedy commit, the 2-arm
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// easy cell's ratio drops from 2.67 to 0.26.
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//
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// - Continuous rewards: UCB's optimistic bound is heuristic
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// and the cost of premature commitment is asymmetric (e.g.
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// outbreak: greedy commit to "lockdown" produces ~3.8× more
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// deaths than soft exploration over UCB's argmax). Keep the
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// legacy max+1e-3 nudge so weighted-bucket dynamics still
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// provide soft exploration around the algorithm's pick.
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if is_binary_reward {
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let floor = (config.safety.min_exploration / limit as f64).max(0.0);
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let chosen_w = (1.0 - floor * (limit - 1) as f64).max(floor);
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for i in 0..limit {
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node.weights[i] = if i == algorithm_choice { chosen_w } else { floor };
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}
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} else {
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let max_w = node.weights[..limit].iter().cloned().fold(0.0_f64, f64::max);
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node.weights[algorithm_choice] = (max_w + 1e-3).min(1.0);
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let sum: f64 = node.weights[..limit].iter().sum();
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if sum > 0.0 {
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for i in 0..limit { node.weights[i] /= sum; }
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}
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}
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}
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‎docs/known-issues.md‎

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## Open
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### MAB vs VW headline number (2.67× lower regret) not reproduced at full scale
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**Status:** Bin classification reproduces (A — competent: within constant
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factor of VW on ≥7/9 cells), but the headline mean-ratio number drifted.
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**Last measured:** May 2026, four runs of `syntra_vs_vw_mab/benchmark.py`
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at 10 seeds × 2000 rounds × 9 cells, mean ratios:
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- pre-fix (broken weighted-bucket override): 1.438 → bin **B**
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- hard greedy override: 0.955 → bin A (1 run)
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- conditional fix (Binary→greedy, else soft): 1.194 / 1.239 → bin A (2 runs)
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Documented Phase A-F baseline: ratio_mean=0.374 → 2.67× lower regret.
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**Scope:** MAB vs VW benchmark only. Other documented benchmarks
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(vaccine reward-blindness 4.36× vs documented 4.4×; outbreak pandemic
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2/4 pass + 0.40 deaths vs documented 0.5) reproduce cleanly.
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**Per-cell pattern:** consistent across runs. 8-9/9 cells stay within
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1.5× VW (bin A), 0/9 cells beyond 2.5× VW. The gap to documented is
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concentrated on **easy-difficulty cells with more arms** (5_easy ≈ 2.1,
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10_easy ≈ 1.4-1.7) — exactly the cells where Thompson Sampling should
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have its biggest advantage over VW's contextual learner. Hard cells
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are ~1.0 in both runs and docs (uniformly-distributed arms → Syntra
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and VW indistinguishable).
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**Likely investigation targets:**
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- Warmup overhead: 30 random selections × 90 cell-instances = 2,700
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decisions where Syntra is doing uniform random. VW has no warmup
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equivalent; this is pure Syntra regret. Could test by setting
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warmup-target to 5 or 1 for this benchmark and rerunning.
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- `apply_feedback` weight-delta asymmetry: `delta = clipped * learning_rate`
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means for binary rewards reward=0 produces delta=0 (no weight decrement).
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Currently irrelevant to selection because the conditional greedy
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override dominates, but could matter if the override is ever softened.
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- Code drift since Phase A-F: working-tree had `D src/server.rs`,
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`M src/learning.rs`, `M src/graph_executor.rs`, `M src/capabilities.rs`
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when this session started. Any of those could have subtly shifted
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the Thompson update path.
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**Operator-facing status:** the published "2.67× lower regret" external
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claim does not currently reproduce. Use "bin-A competent with VW across
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the 9-cell benchmark grid" as the defensible claim until the headline
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number is recovered or the gap is explained.
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### OOD detector accumulates per-observation state unbounded (feature-context capsules)
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**Status:** Real growth bug — `memory.json` increases ≈1.3 KB per `/decide`

‎examples/lycan-internals/demo_takeaway_chaos_replay.lyc‎

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‎scripts/smoke-test.sh‎

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set -euo pipefail
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ROOT="$(cd "$(dirname "$0")/.." && pwd)"
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LYCAN="$ROOT/target/release/lycan"
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[[ -x "$LYCAN" ]] || (cd "$ROOT" && cargo build --release --quiet)
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LYCAN="$ROOT/Lycan/target/release/lycan"
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[[ -x "$LYCAN" ]] || (cd "$ROOT/Lycan" && cargo build --release --quiet --bin lycan)
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SYNTRA="$ROOT/target/release/syntra"
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[[ -x "$SYNTRA" ]] || (cd "$ROOT" && cargo build --release --quiet --bin syntra)
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STORE="$(mktemp -d "${TMPDIR:-/tmp}/lycan-regr.XXXXXX")/store"
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KEY="regr-key"

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