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🥂 v0.8 substrate-q: 4th attention substrate-component, -12.15% val 6/6 seeds, -16.7% cumulative
v0.1 shipped K+S-MOD+V stacked for -8.94% val. v0.8 adds Q as a 4th component via phi_pi_fib log-distance modulation. Per the user's hint that "Possible outcomes may relate to different integral pieces to phi_pi_fib" — Q1 (same recipe as V) lost, but Q6 (a different substrate operation) wins decisively. ## 6-seed Q6 confirmation: -12.15%, 6/6 seeds beat baseline seed=42: Q0 2.964 vs Q6 2.770 seed=7: Q0 3.223 vs Q6 3.075 seed=123: Q0 2.830 vs Q6 2.243 seed=2026: Q0 3.370 vs Q6 2.660 seed=99: Q0 3.202 vs Q6 2.959 seed=1: Q0 3.176 vs Q6 2.779 ---------------------------------------- mean: Q0 3.128 vs Q6 2.748 (-12.15%) ## The Q6 recipe phi_pi_log_distance(x) = log(|x*scale|+1) / (π·ln φ) modulation = exp(-γ · log_d) q = (x @ W_q) * modulation Scales Q components by `(|q|+1)^(-γ/(π·ln φ))` — large magnitudes dampened along substrate's log-distance metric. NOT a snap-to- attractor (that's V's recipe); a smooth magnitude regularizer keyed on phi_pi_fib structure. ## Sharpens the substrate-attention principle - snap-to-attractor: helps quantities being AGGREGATED (V, K) - log-distance scaling: helps quantities that STEER (Q) Both are substrate modulation; they use different phi_pi_fib operations matched to the role. v0.1's principle ("substrate modulation works when applied to a quantity with integer- coherent structure") was right but underspecified — v0.8 adds: choose the substrate operation to match the quantity's downstream role. ## Cumulative stack L0 vanilla: 3.301 L1-MH + S-MOD α=1.0: 3.084 + V1 substrate-resample (v0.1): 3.006 + Q6 phi_pi_log-distance (v0.8): 2.748 (-16.7% vs L0) Four substrate-attention components now stack at TinyShakespeare scale. ## Tests + experiment files - torch_substrate_q.py — initial Q1/Q2 negative - torch_substrate_q_broader.py — Q3/Q4/Q5/Q6 sweep - results_torch_substrate_q*.json — raw data - SUBSTRATE_Q_NEGATIVE.md — Q1 honest negative writeup - SUBSTRATE_Q_WINS.md — Q6 winning chapter ## What's NOT in v0.8 - OMC-side cross-validation (needs tape_abs + tape_log) — v0.8.1 - Larger-scale verification beyond TinyShakespeare 1.1MB - γ tuning (used γ=0.5 first-guess) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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‎CHANGELOG.md‎

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| Tag | Date | One-line |
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|---|---|---|
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| [v0.8-substrate-q](#v08-substrate-q--2026-05-17) | 2026-05-17 | **4th substrate-attention component lands**: Q gets phi_pi_fib log-distance modulation (Q6), wins **-12.15% val 6/6 seeds**. Cumulative stack now -16.7% vs vanilla baseline. |
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| [v0.7-gpu-scaffold](#v07-gpu-scaffold--2026-05-17) | 2026-05-17 | GPU compute scaffold: `omnimcode-gpu` crate with wgpu (Vulkan) backend, ROCm/CUDA stubs. **4.04× speedup verified on the user's AMD RX 580** via Vulkan (no ROCm pain). |
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| [v0.6-fibtier-memory](#v06-fibtier-memory--2026-05-17) | 2026-05-17 | Fibtier-bounded eviction for memory: cap the index at fibonacci-tier capacity (default 232), evicted entries still recoverable by hash. Memory now safe for arbitrarily long agent sessions. |
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| [v0.5-substrate-memory](#v05-substrate-memory--2026-05-17) | 2026-05-17 | Substrate-keyed conversation memory: `omc_memory_store` / `recall` / `list` / `stats` MCP tools + filesystem-backed persistence. **Hits the 10× target** — measured 10.61× LLM context-budget reduction on a 20-turn agent task. |
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## [v0.8-substrate-q] - 2026-05-17
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**4th substrate-attention component lands: Q gets phi_pi_fib log-distance modulation (Q6), wins -12.15% val 6/6 seeds. Cumulative substrate-attention stack now -16.7% vs vanilla baseline on TinyShakespeare.**
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The v0.1 chapter shipped three stacked substrate-attention components (K + S-MOD softmax + V resample) for -8.94%. The natural fourth was Q. The first attempt (Q1 = same post-projection resample as V) lost on 3 seeds — substrate-V's recipe doesn't generalize. The chapter writeup is in `SUBSTRATE_Q_NEGATIVE.md`.
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The user's hint — "Possible outcomes may relate to different integral pieces to phi_pi_fib" — pointed to trying other substrate primitives on Q. A 5-variant broader sweep found one clear winner: **Q6, the phi_pi_fib log-distance scaling**. 6-seed confirmation made it decisive.
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### Q sweep results
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3-seed exploratory sweep:
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| Variant | Q formula | mean val | vs Q0 |
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|---|---|--:|--:|
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| Q0 (baseline) | `q = x @ W_q` | 3.0059 | — |
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| Q3 (pre-snap) | `q = substrate_resample(x) @ W_q` | 3.1670 | +5.36% |
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| Q4 (boost) | `q = (x @ W_q) * (1 + α/(1+d))` | 3.3346 | +10.94% |
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| Q5 (additive snap) | `q = (x @ W_q) + β·nearest_attractor` | 2.9833 | -0.75% |
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| **Q6 (log-distance)** | `q = (x @ W_q) * exp(-γ·log_φπ(|q|))` | **2.6959** | **-10.31%** |
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6-seed Q6 confirmation: -12.15%, **6/6 seeds beat baseline**. Decisive.
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### The recipe
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```python
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def phi_pi_log_distance(x, scale=10.0):
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"""Approximate log_phi_pi_fibonacci(|x|)."""
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abs_x = (x * scale).abs() + 1.0
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return abs_x.log() / (math.pi * math.log(PHI))
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q_proj = x @ W_q
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log_d = phi_pi_log_distance(q_proj)
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modulation = (-gamma * log_d).exp() # gamma=0.5
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q_full = q_proj * modulation
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```
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### Why log-distance and not attractor-distance
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Q1 used the SAME operation as V (snap-to-nearest-attractor) and lost. Q6 uses a different phi_pi_fib operation (smooth log-distance scaling) and wins. The principle that emerges:
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- **Substrate snap-to-attractor**: helps for quantities being AGGREGATED (V, K) — collapsing to discrete attractor values cleans the aggregated signal
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- **Substrate log-distance scaling**: helps for quantities that STEER (Q) — preserves relative ordering and steering capability while keeping magnitudes in a substrate-friendly range
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Both are "substrate modulation" — they use different phi_pi_fib operations matched to the role of the quantity. The v0.1 principle ("substrate modulation works when applied to a quantity with integer-coherent structure") was right but underspecified; v0.8 adds: the choice of substrate operation must match the quantity's downstream role.
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### Cumulative substrate-attention stack
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| Stack | mean val |
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|---|--:|
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| L0 (vanilla softmax + learned V + learned Q) | 3.301 |
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| L1-MH + S-MOD α=1.0 (v0.0.6 + S-MOD) | 3.084 |
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| + V1 substrate-resample (v0.1) | 3.006 |
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| **+ Q6 phi_pi_log-distance (v0.8)** | **2.748** |
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| | **−16.7% cumulative vs L0** |
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Four substrate-attention components now stack: K (CRT-Fibonacci, no learnable W_K), softmax (S-MOD α=1.0), V (substrate_resample), Q (phi_pi_log-distance modulation).
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### What's NOT yet in v0.8
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- **OMC-side cross-validation**: the win is in PyTorch parity only. Wiring Q6 into pure-OMC Prometheus requires `tape_abs` + `tape_log` ops (may not exist in the autotape today). v0.8.1 follow-up.
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- **Larger-scale verification**: TinyShakespeare 1.1MB is the entire scientific scale right now. 10-100MB validation is the load-bearing test for whether substrate-attention is a real inductive bias.
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- **γ tuning**: γ=0.5 was first-guess. A sweep might find stronger.
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### Files
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- `experiments/prometheus_parity/torch_substrate_q.py` — initial Q1/Q2 negative sweep
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- `experiments/prometheus_parity/torch_substrate_q_broader.py` — Q3-Q6 broader sweep
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- `experiments/prometheus_parity/SUBSTRATE_Q_NEGATIVE.md` — the Q1 honest negative writeup
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- `experiments/prometheus_parity/SUBSTRATE_Q_WINS.md` — the Q6 win writeup
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- `results_torch_substrate_q.json` — Q1/Q2 raw data
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- `results_torch_substrate_q_broader.json` — 5-variant raw data
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- `results_torch_substrate_q6_confirm.json` — 6-seed Q6 confirmation data
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---
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## [v0.7-gpu-scaffold] - 2026-05-17
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**GPU compute scaffold for Prometheus: `omnimcode-gpu` crate with wgpu (Vulkan) backend, ROCm/CUDA stubs, 4.04× speedup verified end-to-end on the user's AMD RX 580 via Vulkan.**

‎README.md‎

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| [v0.5-substrate-memory](https://github.com/RandomCoder-lab/OMC/releases/tag/v0.5-substrate-memory) | Substrate-keyed conversation memory: `omc_memory_store` / `recall` / `list` / `stats` + filesystem persistence. **10.61× LLM context-budget reduction** on a 20-turn agent task. |
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| [v0.6-fibtier-memory](https://github.com/RandomCoder-lab/OMC/releases/tag/v0.6-fibtier-memory) | Fibtier-bounded eviction for memory: cap the index at fibonacci-tier capacity (default 232); evicted entries still recoverable by hash. Memory now safe for arbitrarily long agent sessions. |
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| [v0.7-gpu-scaffold](https://github.com/RandomCoder-lab/OMC/releases/tag/v0.7-gpu-scaffold) | GPU compute scaffold: `omnimcode-gpu` crate with wgpu (Vulkan) backend, ROCm/CUDA stubs. **4.04× speedup measured on AMD RX 580** via Vulkan, no ROCm pain. |
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| [v0.8-substrate-q](https://github.com/RandomCoder-lab/OMC/releases/tag/v0.8-substrate-q) | **4th substrate-attention component** lands: Q gets phi_pi_fib log-distance modulation (Q6), wins -12.15% val 6/6 seeds. Cumulative stack now **-16.7%** vs vanilla on TinyShakespeare. |
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‎experiments/prometheus_parity/SUBSTRATE_Q_WINS.md‎

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# Substrate-Q wins -12.15% via phi_pi_fib log-distance modulation (6/6 seeds)
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## Headline
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The first substrate-Q recipe (Q1 post-projection resample) lost on 3 seeds (+5.31% val). The user's note "Possible outcomes may relate to different integral pieces to phi_pi_fib" pointed to trying other operations. The broader sweep over five Q recipes found **one decisive winner**: Q6, the phi_pi_fib log-distance scaling.
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```
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3-seed broader sweep:
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Q0 (baseline) 3.0059
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Q3 (pre-projection snap) 3.1670 (+5.36% loses)
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Q4 (boost-not-dampen) 3.3346 (+10.94% loses)
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Q5 (signed-snap) 2.9833 (-0.75% ties)
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Q6 (log-distance scale) 2.6959 (-10.31% wins, std 0.42)
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6-seed Q6 confirmation:
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Q0 3.1277 ± 0.20
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Q6 2.7477 ± 0.29 (-12.15%, 6/6 seeds beat baseline)
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```
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Q6 beats Q0 on every one of the 6 confirmation seeds:
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| seed | Q0 | Q6 | Q6 wins? |
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|---|--:|--:|:-:|
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| 42 | 2.964 | 2.770 | ✓ |
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| 7 | 3.223 | 3.075 | ✓ |
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| 123 | 2.830 | 2.243 | ✓ |
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| 2026 | 3.370 | 2.660 | ✓ |
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| 99 | 3.202 | 2.959 | ✓ |
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| 1 | 3.176 | 2.779 | ✓ |
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The win is decisive.
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## The recipe
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```python
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def phi_pi_log_distance(x, scale=10.0):
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"""Approximate log_phi_pi_fibonacci(|x|)."""
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abs_x = (x * scale).abs() + 1.0
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return abs_x.log() / (math.pi * math.log(PHI))
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q_proj = x @ self.W_q # standard learned projection
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log_d = phi_pi_log_distance(q_proj)
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modulation = (-gamma * log_d).exp() # gamma=0.5 default
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q_full = q_proj * modulation
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```
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Effectively scales each Q component by `(|q_proj| + 1)^(-γ/(π·ln φ))` — large magnitudes get dampened along the substrate's log-distance metric, not the linear attractor-distance metric V1 used.
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## Why log-distance and not attractor-distance
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The substrate-V finding worked via `substrate_resample` — snap each component toward its nearest Fibonacci attractor by multiplying with `1/(1 + d)` where `d = attractor_distance(x·scale)`. Q1 used the same operation and lost.
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The HONEST principle that emerges from Q1 vs Q6: **Q's role is to STEER the attention pattern, not to be aggregated.** Snap-to-attractor (Q1) reduces the diversity of queries — every query gets pulled toward the same discrete set of attractor values, so heads can't discriminate positions. The attention pattern collapses.
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**Log-distance modulation (Q6) is different**: it's a smooth magnitude regularizer keyed on substrate structure, not an attractor snap. It dampens LARGE-magnitude queries more than small ones (because log grows slowly), preserving the relative ordering and steering capability of the head while keeping query magnitudes in a substrate-friendly range. The head still discriminates; the magnitudes just get a soft cap.
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This adds nuance to the v0.1 principle:
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- **Substrate snap-to-attractor**: helps for quantities being AGGREGATED (V, K)
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- **Substrate log-distance scaling**: helps for quantities that STEER (Q)
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Both are "substrate modulation" — they just use different phi_pi_fib operations to match the role of the quantity being modulated.
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## Cumulative substrate-attention stack
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With Q6 added to the v0.1 production stack:
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| Stack | mean val |
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| L0 (vanilla softmax + learned V + learned Q) | 3.301 |
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| L1-MH + S-MOD α=1.0 (v0.0.6 + S-MOD) | 3.084 |
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| + V1 substrate-resample (v0.1) | 3.006 |
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| **+ Q6 phi_pi_log-distance (v0.8)** | **2.748** |
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| | **−16.7% cumulative vs L0** |
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Up from v0.1's -8.94% to **-16.7%**. Four substrate-attention components now stack: K (CRT-Fibonacci substrate, no learnable W_K), softmax (S-MOD α=1.0), V (substrate_resample), Q (phi_pi_log-distance modulation).
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## Tests
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- 5-variant 3-seed exploratory sweep (`torch_substrate_q_broader.py`): Q3/Q4 lose, Q5 ties, **Q6 wins**.
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- 6-seed Q6 confirmation: 6/6 seeds beat baseline, mean -12.15%.
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## What's NOT yet wired into production OMC
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The Q6 win is established in PyTorch parity. Wiring it into OMC's `prom_attention_substrate_k_forward` requires `tape_abs` and `tape_log` ops (which the OMC tape autograd may or may not have today). That's the v0.8.1 follow-up: extend the tape, port Q6 into pure-OMC Prometheus, re-verify the win in OMC space the same way substrate-V was cross-validated.
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## What's still open
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- **Larger scale**: the win is at TinyShakespeare (1.1MB). Whether it holds at 10-100MB is the question that determines whether substrate-attention is a real physical inductive bias or a small-scale curiosity.
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- **γ tuning**: γ=0.5 was the first guess from the sweep. A γ sweep might find a stronger setting.
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- **OMC-side cross-validation**: the substrate-V finding was reproduced in both PyTorch and pure-OMC Prometheus. Same parity check is needed for Q6.
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## Files
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- `torch_substrate_q_broader.py` — the 5-variant Q sweep
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- `results_torch_substrate_q_broader.json` — 3-seed exploratory data
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- `results_torch_substrate_q6_confirm.json` — 6-seed Q6 confirmation data
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## Reproduction
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```bash
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cd experiments/prometheus_parity
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# 3-seed exploratory sweep across 5 Q variants:
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python3 torch_substrate_q_broader.py
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# 6-seed Q6 confirmation:
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python3 torch_substrate_q_broader.py --seeds 42,7,123,2026,99,1 --variants Q0,Q6 \
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--out results_torch_substrate_q6_confirm.json
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```

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