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docs: reorganise -- current reference gathered, finished work archived, superseded and dead ideas marked as such

Browse Source 
docs/CLAUDE.md Phase 2, 2026-10-14.  All moves by git mv; every archived
file carries a header saying what it is, why, and where to look instead.

- docs/reference/ (new): roadmap and versioning policy, M7.1, word-ACL
  design, kernel args, hosted acceptance test, dictionary, primitives,
  ontology, L8 table -- previously spread over docs/, docs/lithosananke/
  and docs/03-architecture/.
- docs/working/archive/historical/: finished work (session plan, test-gap
  audit, birthing plan, fix write-ups, the January architecture draft).
- docs/working/archive/superseded/: the Hermes-VM Tripod docs and July
  Hermes designs (-> FABRIC-3.5/3.6), the StarForth roadmap, every working
  doc already flagged superseded or obsolete.
- docs/working/archive/dead/: the hal_* target contract (never adopted),
  L4Re, metrics-and-knobs.
- Papers and proposals into docs/working/papers/.
- References rewritten across the repo (not logs/, not docs/patent/);
  CLAUDE.md's documentation section rewritten.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
rajames
2026-10-14 16:03:56 -04:00
co-authored by Claude Opus 5.5
parent 1a4b163861
commit 7aed588f78
116 changed files with 305 additions and 175 deletions
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docs/working/architecture/03-architecture/physics-engine/feedback-loops-analysis.md
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<!-- Moved from docs/03-architecture/physics-engine/metrics-and-knobs.md to docs/working/architecture/03-architecture/physics-engine/metrics-and-knobs.md on 2026-06-16 (docs reorg Phase 2) -->
# StarForth Metrics & Knobs: GAPS ONLY
## What's NOT Wired (Things to Add)
### DEAD CODE / NEVER CALLED
| What | Location | Status | Comment |
|------|----------|--------|---------|
| Bucket reordering | `physics_hotwords_cache.c:201-215` | Defined but NEVER CALLED | Bubble sort exists but no automatic trigger. Is this dead code or forgotten? |
---
### STACK METRICS (ZERO COLLECTION)
| Metric | File | Missing Since | Impact | Knob Needed |
|--------|------|---|---|---|
| Data stack peak depth | `stack_management.c` | Day 1 | Can't tell if workload needs larger stack | STACK_SIZE tuning blind |
| Return stack peak depth | `stack_management.c` | Day 1 | Can't measure colon definition nesting | Tail-call optimization impossible |
| Underflow attempts | `arithmetic_words.c:59-62` | Day 1 | Can't detect fragile code paths | Could optimize checks if zero underflows observed |
| Overflow attempts | `arithmetic_words.c` | Day 1 | Can't detect stack exhaustion | Early warning system missing |
---
### DICTIONARY METRICS (ZERO VISIBILITY)
| Metric | File | Missing Since | Impact | Why Matters |
|--------|------|---|---|---|
| Bucket load histogram | `vm.c: dict lookup` | Day 1 | First-char bucketing (26 buckets) may be unbalanced | Affects lookup latency, collision clustering |
| Dictionary growth rate | `memory_management.c` | Day 1 | No visibility into `here` pointer velocity | Can't predict "dictionary full" events |
| FORGET usage frequency | `dictionary_words.c` | Day 1 | No trace of word deletion patterns | Fragmentation unknown |
| Lookup failure rate | `vm.c` | Day 1 | How often is a word not found? | Error path frequency unknown |
| Word name length distribution | `vm.c` | Day 1 | Average name length unknown | Affects memory efficiency |
---
### BLOCK I/O METRICS (COMPLETELY DARK)
| Metric | File | Missing Since | Impact | Phase |
|--------|------|---|---|---|
| Block read frequency | `block_subsystem.c` | Day 1 | How often is BLOCK called? | DoE phase 1 |
| Block write frequency | `block_subsystem.c` | Day 1 | How often is UPDATE/FLUSH called? | DoE phase 1 |
| Writeback frequency | `block_subsystem.c:198-200` | Day 1 | When are dirty blocks flushed? | DoE phase 1 |
| Cache hit rate (disk) | `block_subsystem.c` | Day 1 | Blocks served from RAM cache % | DoE phase 1 |
| Dirty block ratio | `block_subsystem.c` | Day 1 | Blocks pending flush at time T | DoE phase 1 |
| I/O latency | `block_subsystem.c` | Day 1 | Time per read/write operation | DoE phase 2 |
| Fragmentation ratio | `block_subsystem.c` | Day 1 | Gaps in allocated blocks | DoE phase 2 |
---
### PER-WORD EXECUTION METRICS
| Metric | File | Missing Since | Impact | Use Case |
|--------|------|---|---|---|
| Per-word execution frequency | `vm.c: inner loop` | Day 1 | Which words are actually hot? | Profiling, hotspot detection |
| Per-word latency distribution | `vm.c` | Day 1 | Median/P99 time per word | Performance regression detection |
| Word category breakdown | `word_source/*.c` | Day 1 | Arithmetic vs. Stack vs. Control dominance | Workload characterization |
| Colon vs. native ratio | `vm.c` | Day 1 | User-defined word execution frequency | Code complexity analysis |
---
### HEAT DYNAMICS (PHASE 2 NOT ACTIVE)
| Metric | File | Status | Comment |
|--------|------|--------|---------|
| Heat decay trace | `physics_metadata.c: TODO` | Phase 2 placeholder | Need to measure if decay causes pattern loss |
| Heat percentile distribution | `physics_metadata.c` | Not updating dynamic | Stored but never dynamically recalculated |
| Heat concentration ratio | `physics_metadata.c` | Not tracked | (Top 10 words heat) / (total heat) |
| Heat velocity | `physics_metadata.c` | Not tracked | Rate of heat change per unit time |
| Heat plateau detection | `physics_metadata.c` | Not tracked | When does word heat level off? |
---
### PIPELINING METRICS (PHASE 1 INSTRUMENTATION, NOT USED)
| Metric | File | Status | Phase 2 Use |
|--------|------|--------|---|
| Prediction accuracy by depth | `physics_pipelining_metrics.c` | Not measured | Binary chop TRANSITION_WINDOW_SIZE |
| Context transition frequency | `physics_pipelining_metrics.c` | Counted but not reported | Validate context patterns |
| Speculation ROI histogram | `physics_pipelining_metrics.c` | Not aggregated | Tune SPECULATION_THRESHOLD |
| Misprediction cost (actual) | `physics_pipelining_metrics.c` | Measured but not validated | Calibrate cost model |
| Pipeline stall reduction | `physics_pipelining_metrics.c` | Not measured | Throughput impact unknown |
---
### CACHE POLLUTION & CYCLES
| Metric | File | Missing Since | Impact |
|--------|------|---|---|
| Cache eviction/re-promotion cycles | `physics_hotwords_cache.c` | Day 1 | Are we thrashing? |
| False negatives (words evicted before promotion) | `physics_hotwords_cache.c` | Day 1 | Should promotion threshold be lower? |
| Cache eviction candidate value | `physics_hotwords_cache.c` | Day 1 | Was LRU the right choice? |
| Bucket scan depth (position of hit) | `physics_hotwords_cache.c` | Day 1 | Is bucket order actually helping? |
| Promotion wait time | `physics_hotwords_cache.c` | Day 1 | How long does word stay in bucket before cache? |
---
## Priority for "Twisting the Dragon's Tail"
### 🔴 **CRITICAL (Do First)**
1. **Stack peak depth** - 5 minutes to add, massive insight
2. **Block I/O metrics** - Writeback behavior unknown (could be performance killer)
3. **Dictionary bucket load** - Unbalanced bucketing = latency tail
### 🟠 **HIGH (Phase 1 DoE)**
4. **Per-word execution frequency** - Validation for cache/window decisions
5. **Heat decay trace** - Validate Phase 2 feature before enabling
6. **Block I/O hit rate** - Complete the I/O picture
### 🟡 **MEDIUM (Phase 2 DoE)**
7. **Pipelining accuracy by depth** - Input to window size binary chop
8. **Colon definition nesting depth** - Tail-call optimization opportunities
9. **Dictionary growth velocity** - Predict "full" events
### 🟢 **NICE-TO-HAVE (Phase 3)**
10. Heat concentration ratio
11. Word category breakdown
12. Speculation ROI histogram
---
## Implementation Effort Estimate
| Category | Files | LOC | Time | Priority |
|----------|-------|-----|------|----------|
| Stack metrics | stack_management.c, vm.h | ~30 | 30 min | 🔴 |
| Dictionary metrics | vm.c, memory_management.c, vm.h | ~50 | 45 min | 🔴 |
| Block I/O metrics | block_subsystem.c, vm.h | ~80 | 1 hr | 🔴 |
| Per-word metrics | vm.c, physics_metadata.c, vm.h | ~100 | 1.5 hr | 🟠 |
| Heat dynamics | physics_metadata.c, vm.h | ~60 | 1 hr | 🟠 |
| Pipelining accuracy | physics_pipelining_metrics.c, vm.h | ~40 | 45 min | 🟡 |
---
## Bucket Reordering Mystery
**File**: `physics_hotwords_cache.c:201-215`
**Code**: `hotwords_bucket_reorder()` - bubble sort by execution_heat
**Status**: Function defined but **NEVER CALLED** from lookup path
**Question**: Is this dead code or forgot to hook it?
**If enabled, metrics needed:**
- Reorder frequency
- Cost (bubble sort latency)
- Benefit (does reordering actually improve hit latency?)
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> **⚠ OBSOLETE (flagged 2026-10-09).** Conceptual precursor to the L8 Jacquard mode-selector
> system; it never mentions Jacquard/L8, the attractor buckets, or the six named modes, and
> was never reconciled with the implemented `ssm_jacquard.c` system. The canonical current
> reference for the physics/feedback model is `feedback-loops.md`. Kept for historical record.
# 1. Introduction
A Steady State Machine (SSM) is a novel computational model in which the system’s operational state is defined not by discrete symbolic transitions, but by convergence to an optimal runtime equilibrium.
Unlike traditional models such as Finite State Machines (FSMs), Pushdown Automata, or Turing Machines — where “state” is explicit and symbolic — a Steady State Machine derives its behavior from continuous thermodynamic variables such as:
* execution_heat
* entropy (diversity)
* pipeline readiness
* heat decay
* steady-state slope
* localized temperature gradients
* adaptive strategy selection
An SSM observes itself, adapts, and settles into stable performance basins called steady states.
This makes the Steady State Machine the first computational model where the attractor defines the machine, not the symbol.
# 2. Core Definition
Definition 1 — Steady State Machine
> A Steady State Machine is a computational system defined by:
A set of internal physical metrics
* 𝐻 = execution_heats
* 𝐸 = entropy/diversity measures
* 𝑃 = pipeline activation thresholds
* Θ = heat decay profile
* 𝑊 = sliding execution window
A set of adaptive policies
* lookup strategies
* caching regimes
* bucket reorganizations
* pipeline decisions
* inference-based mode switches
* A state-space composed of attractor basins, not symbolic states
* steady attractor
* quasi-steady attractor
* transient attractor
* chaotic attractor
* A governing dynamic
### 𝑀𝑡+1=𝑓(𝑀𝑡,Δ𝐻,Δ𝑊,Δ𝐸) where 𝑓 pushes the machine toward a stable basin.
The machine is defined by its convergence behavior, not its instruction sequence.
# 3. The Fundamental Insight
In an SSM, performance is the state.
The system is constantly measuring:
* how hot words are
* how often patterns recur
* how pipeline-able a sequence is
* how diverse the window is
* how much the dictionary should reorganize
And as these stabilize, the machine settles into a steady state — a self-maintaining optimal regime.
This regime is preserved until the environment changes (e.g., new workloads, diversity spikes), at which point the SSM moves into a transient or chaotic regime and eventually finds a new equilibrium.
# 4. Phases of a Steady State Machine
## 1. Transient Phase
- System warms up
- Execution patterns are sporadic
- Heat gradients are noisy
- Pipeline inactive or unstable
## 2. Quasi-Steady Phase
- Some patterns emerge
- Lookups begin to bias
- Hot words form local attractors
- Dictionary reorders stabilize
## 3. True Steady State
- Heat surface smooth
- Pipeline active
- Diversity window predictable
- Lookups converge to optimal
- Runtime > baseline VM
- System remains stable unless perturbed
## 4. Chaotic/Disruption Phase
### Triggered by:
- major workload shift
- dictionary mutation
- extreme entropy spike
- cold-cache shock
- SSM falls out of equilibrium temporarily.
## 5. Axioms of Steady State Machines
>Axiom 1 — Convergence
Every sustained workload induces the SSM to converge toward a stable attractor unless external entropy forces divergence.
>Axiom 2 — Locality of Heat
Execution heat reflects both locality and temporal relevance. Locality produces stability.
>Axiom 3 — Adaptive Reordering
Reordering is not symbolic mutation; it is thermodynamic self-optimization.
>Axiom 4 — Stability Maximizes Throughput
The steady state is always faster than the cold state, and usually faster than the naive baseline VM.
>Axiom 5 — Perturbation Response
When disrupted, the SSM seeks a new steady state; it does not thrash indefinitely.
## 6. Relationship to Other Computational Models
>FSM vs SSM
FSM: discrete symbolic states
SSM: emergent attractors in thermal space
>Turing Machine vs SSM
TM: behavior defined by tape symbols and transitions
SSM: behavior defined by adaptation toward equilibrium
>Markov Chain vs SSM
MC: probabilistic transitions between explicit states
SSM: deterministic drift toward stable basins, driven by observation
>Neural Network vs SSM
NN: learns weights
SSM: reorganizes runtime structures, not parameters
## 7. Why This Breaks New Ground
### A Steady State Machine is the first:
- runtime-thermodynamic computing model
- adaptive VM with convergence properties
- load-dependent reordering machine
- self-optimizing dictionary machine
- phase-shifting execution system
- state-machine whose state is emergent, not explicit
- This is a fundamental new category of computation.
## 8. Canonical Example: StarForth
### StarForth implements the first real SSM with:
- execution_heat
- rolling diversity windows
- phase-based lookup strategies
- adaptive dictionary ordering
- pipeline activation
- DOE-calibrated inference loop
- background physics monitor
- It is, quite literally, the reference implementation.
## 9. Formal Summary Statement
>A Steady State Machine (SSM) is a computational system in which the operational state is defined by attractor convergence in thermodynamic runtime space rather than by discrete symbolic transitions. An SSM continuously measures workload characteristics, adjusts internal structures, and stabilizes into performance-optimized steady states. When perturbed, it transitions through transient or chaotic phases before finding a new equilibrium. This model defines a new class of adaptive computational systems with properties distinct from finite state machines, pushdown automata, and Turing Machines.