feat(ModularBot): 6 guns, pattern matcher, melee modules, adversarial bots

- New guns: guess-factor (GF histogram), pattern-matcher (movement tape replay)
- New modules: minimum-risk melee movement, spinning melee radar
- New test bots: PatternMover, RandomMover, WaveSurfer
- Fixed: FeedbackEvent now carries actualX/actualY for proper GF learning
- Fixed: TM gun warmup gating + directional residuals
- Fixed: circular gun integrated formula + multi-bin omega cache
- Fixed: oscillator wall-bounce lockout
- Fixed: phantom meteor perpendicular body orientation
- 6/6 battle wins across all enemy types

BNNBot_garage/analysis/journal.md

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+# Research Journal
+
+## 2026-09-18 — Session 1: Foundation
+
+### Input Engineering
+- Stripped Hebbian learning, kept 690-bit encoding
+- Removed self-state (was 40 bits, not needed for aiming)
+- Compressed wall distances: 4×7bit → 2×7bit XY position (saved 140 bits)
+- Evaluated sin/cos vs Gray-code angles: sin/cos kept (no wraparound discontinuity, worth 180 extra bits)
+- Final: 690 bits = 10 frames × 69 bits
+
+### Wave Feedback System
+- Built circular wave system: 10 power levels, expanding at bullet speed
+- Hit detection: ±18px tolerance, 95.6% hit rate
+- CSV logging: binary + decimal, 181 columns, toggled via BNNBOT_CSV env var
+- Provides ground truth for any future learning method
+
+### Data Analysis
+- 324 rows from battle_1, 277 fully resolved
+- Enemy moves in straight lines (97.8% heading stability)
+- Linear extrapolation is strong baseline (15-27px MAE)
+- Velocity is constant, acceleration negligible
+
+### Predictor Backtest
+- Tested 3 predictors: pure linear, weighted multi-frame, linear + Hebbian residual
+- Hebbian residual wins: 14-21% MAE reduction, converges within one battle
+- Only 10/24 table cells activate — sparse problem
+
+### Theoretical Exploration
+- XOR layers collapse (associative) — can't stack for depth
+- AND with fixed masks also linear over GF(2)
+- Non-linearity requires combining input-dependent signals: popcount + threshold
+- Depth needs credit assignment through layers — open problem without gradients
+
+### Key Insight
+The input engineering IS the deep feature hierarchy. The learnable part should be shallow unless we find a learning rule that can genuinely exploit depth under our constraints (no supervised learning, no gradient descent).
+
+### Next Steps
+- Research non-gradient, non-supervised learning methods across domains
+- Parallel exploration: biology, electronics, discrete math, unconventional CS
+- Prototype top candidates against CSV data
+
+## 2026-09-18 — Session 1 (continued): Prototype Results
+
+### Domain Research (4 parallel explorations)
+- **Biology**: Three-factor Hebbian + eligibility traces is the universal pattern. Immune clonal selection fits binary weights. Key insight: the wave system provides delayed reward, but eligibility traces (recording which weights contributed) are the missing piece.
+- **Electronics**: WNN/WiSARD (LUT RAM nodes) — each neuron is a lookup table, O(1) inference, learning = table write. ΣΔ correction accumulators.
+- **Discrete Math**: The correction signal has only ~4-5 bits of entropy (Shannon analysis). Current 24-cell table is near-optimal for this opponent. Hamming k-NN over full 690 bits could replace hand-picked features.
+- **Unconventional CS**: Tsetlin Machines — purpose-built for binary inputs + reinforcement. Regression TM handles continuous prediction. "Goes Deep" paper (2025) adds multi-layer hierarchy.
+
+### Prototype Backtests (277 rows, online learning)
+
+| Method | Avg MAE | vs Baseline | Convergence |
+|--------|---------|-------------|-------------|
+| P1 Linear extrapolation | 12.24 | — | Instant |
+| P3 Hebbian residual | ~10.45 | −14.6% | Fast (50 rows) |
+| WiSARD K=12, 276 bits | 9.93 | −18.9% | Fast (50 rows) |
+| TM Regression (warm) | 12.45 | Still converging | Needs 1000+ rows |
+| TM last-50 only | 9.37 (p1.07) | Strong | Learning curve active |
+
+### Key Decisions
+- WiSARD wins on limited data — simplest, fastest convergence, best MAE
+- TM has potential but is data-hungry — needs diverse enemy battles
+- XOR temporal features don't help — raw bits are sufficient
+- K=12 tuple size is optimal for 277-1000 row datasets
+
+### Bot Changes Today
+- **fix(hebbian)**: symmetric learning — both active and inactive outputs learn
+- **feat(BNNBot)**: shaped reward based on miss distance — every shot teaches something
+- **style(BNNBot)**: remove 870-bit binary dump from output
+- **fix(hebbian)**: random weight init to escape zero local minimum
+
+### Still TODO
+- Generate battle CSV against diverse enemy types (agent ran out of tokens)
+- Re-run both prototypes on multi-enemy data
+- Implement WiSARD in Nim bot
+- Test TM with more data to see if it surpasses WiSARD