Each proof point was run, checked, and documented with real output. Grouped by the question it answers.
01
Live Discovery
16/16
repos discovered, mined, and stored in one command
PULSE Knowledge Loop
Discovers 18 repos from live X feeds, filters for novelty, clones and mines 16 novel repos into 86 new methodologies.
cam pulse scan
16/16 assimilated
86 methodologies
0 failures
Live Scan Results (March 22, 2026)
Discovered: 18 repos from X
Novel: 16 (2 already known)
Assimilated: 16/16 (0 failed)
Methodologies: 86 new patterns stored
JSON repair: 100% recovery (16/16 repaired)
02
Cross-Repo Synthesis
3→1
repos synthesized into one working module, 5/5 tests
Plugin Event System
Retrieves patterns from 3 mined repos and builds a cohesive event system: bus, middleware, plugin loader, loop detection. 258 lines.
3 repos → 1 module
5/5 tests
full attribution
Live Results
Retrieved=3 | Used=3 | Attributed=3 | Quality=0.82
258 source lines across 5 modules
Event bus + middleware + plugin loader + loop detection
Knowledge compounds: Build A patterns inform Build B
03
Agent Self-Repair
10/10
tests passing after self-correction
Inner Correction Loop
On failure, byte-level restore resets the workspace and re-prompts the agent with violations plus test output. Drift 0.868, quality 0.76, lifecycle transition embryonic → viable.
workspace restore
violation feedback
28 tests
up to 3 retries
Live Run Results (March 24, 2026)
Run 1: Correction loop triggered 3x (restore + feedback confirmed)
Run 2: First-attempt success — 10/10 tests | Drift: 0.868 | Quality: 0.76
Knowledge: 2 PULSE patterns injected | Lifecycle: embryonic → viable
What Can CAM Build From Learned Patterns?
04
Knowledge Application
PULSE Usage Proof
Mined knowledge is retrieved, injected into agent prompts, and used to produce working code with passing tests. Retrieved=3, Used=3, Attributed=3.
Live Run Results (March 23, 2026)
Task: Pre-tool-call guardrail system
Agent output: 157 lines, 4 files, quality 0.85
Tests: 4/4 passing | Attribution: 3 patterns traced
05
Self-Evolution
Self-Enhancement Pipeline
CAM clones its own source, enhances the copy, validates through 7 gates, and atomically swaps the live install. Proven: quality 0.97, all 3,734 tests pass on the enhanced copy.
7-gate validation
atomic swap
quality 0.97
06
Smart Mining
Multi-Pass Mining Pipeline
Three-pass mining: domain classification, knowledge overlap assessment, and focused LLM extraction with adaptive token budget. Nine repos, 52 methodologies.
Pipeline Stages
Pass 1: Domain classification (10 categories, 0 cost)
Pass 2: KB overlap (overlap_score, suggested_focus)
Pass 3: LLM mining with structured context
07
Knowledge Application
Repo Upgrade Advisor
Ranked improvement recommendations with confidence scores from mined knowledge. Evaluates a repo and produces enhancement plans backed by methodology evidence.
cam evaluate
confidence scores
08
Escalating Complexity
Expectation Ladder
Five levels of escalating complexity: health check → build → validate → mine → self-improve. Each level exercises more of CAM's pipeline.
5 levels
progressive validation
09
Distributed Intelligence
CAM Swarm — Ganglion Federation + Drive-Ops
Multiple specialist CAM ganglia federate into a single CAM Brain. The Drive-Ops ganglion mined 1.5TB of local repos in 16 batches, producing 1,046 methodologies from 63 unique repos with 82 duplicates caught by content-hash dedup. Read-only FTS5 cross-queries pull knowledge across siblings when local confidence is low.
Drive-Ops Results
1,046 methodologies | 63 repos | 1.5TB drive scanned | 82 duplicates caught
16 batches | 3.5 hours | cross-query proven | brain manifests live
federation
drive-ops ganglion
content dedup
FTS5 cross-query
10
Structured Verification
Metric Expectations
Natural-language specs become structured verification gates. "90% coverage" can be enforced as a hard gate. 51 tests cover extraction and enforcement.
Supported Metrics
min_coverage_pct | min_test_count
min_files_changed | max_files_changed
Operators: gte, gt, lte, lt, eq | Hard/soft gates
How Does the Discovery Pipeline Work?
11
Knowledge Maintenance
Repo Freshness Monitor
Phase 1 uses cached metadata checks. Phase 2 scores significance. Only important repo changes trigger a re-mine.
Significance Formula
commits * 0.3 + new_release * 0.4
+ readme_changed * 0.2 + size_delta * 0.1
12
Supply Chain Defense
Secret Scanning
Two-gate TruffleHog plus regex fallback. Every repo is scanned before mining. Critical findings block the pipeline.
Verified
cam security scan src/ → CLEAN (0 findings)
cam security status → TruffleHog AVAILABLE
13
Autonomous Evolution
Self-Enhancement — CAM Improves Its Own Code
After mining new knowledge, CAM clones itself, runs its own build pipeline against the copy using newly learned patterns, validates through 7 gates (syntax, config, imports, DB schema, CLI smoke, full pytest, diff summary), and swaps only if ALL gates pass. Protected files (verifier, factory, engine, schema, config) require human review even if gates pass. Latest run: knowledge application demo produced a working async microservice with 3 PULSE patterns injected per attempt, drift 0.894, 12 files created. Self-enhancement: all 7 gates passed, 3,734 tests in 203.4s, atomic swap completed.
Latest Validation Run (March 2026)
7 gates PASSED | 3,734 tests | 203.4s | 5 protected files
Knowledge demo: 3 PULSE patterns injected | drift 0.894 | 12 files
clone → enhance → validate → swap → post-swap → rollback-ready
self-enhance
7-gate validation
compiler-bootstrap
protected files
atomic swap
3,734 tests
14
Live Knowledge Application
End-to-End Knowledge Injection — From Mined Patterns to Architecture-Informed Code
CAM retrieves 3 PULSE patterns from 3,590 stored methodologies and injects them into the agent prompt alongside 12 evaluation hints. The agent produces architecture-informed code: endpoint separation, idempotent request tracking, and async SQLite persistence. The verifier checks drift alignment (0.894) and executes tests. On success, the EMA fitness feedback loop records the outcome, strengthening the patterns that contributed. Patterns traced back to mined source repos: Aegis_Atlas, CLI-Anything, and ClawTeam.
Live Run Results (March 2026)
Retrieved: 3 PULSE patterns from 3,590 methodologies
Prompt: 12 evaluation hints + 3 injected patterns
Output: async microservice | 12 files | endpoint separation
Verification: drift 0.894 | tests passing | idempotent tracking
Fitness: EMA feedback recorded | lifecycle transition confirmed
Attribution: Aegis_Atlas, CLI-Anything, ClawTeam
knowledge injection
3 patterns retrieved
3,590 methodologies
drift 0.894
EMA fitness
full attribution
15
Adaptive Intelligence
Bayesian Kelly Agent Routing
Sukhov (2026) Bayesian Kelly Criterion sizes agent task allocation from Beta posteriors.
Kappa-shrinkage prevents overconfidence with small samples. Uncertainty discount reduces
fitness scores for unreliable agents' methodologies up to 30%. Adaptive A/B margins demand
bigger effects from thin data — no premature conclusions.
kelly.py + dispatcher.py — real routing data
f* = (p̄ - (1-p̄)/b) × n_eff / (n_eff + κ)
Kelly routing: architecture → claude 37.6% | gemini 26.1% | grok 26.1% | codex 10.2%
Kelly routing: analysis → claude 61.5% | codex 17.7% | gemini 17.7% | grok 3.1%
39 tests | kappa=10.0 | f_max=0.40 | exploration_floor=2%
bayesian
kelly-criterion
agent-routing
39 tests
16
A/B Knowledge Impact
7/8
quality checks won by KB-equipped agent
Retry Logic A/B Test — Qualitative Proof
Same task, two configurations. Run A: empty KB. Run B: full KB with 3,590 methodologies. Task: add retry logic with exponential backoff. Run B retrieved 5 battle-tested patterns from 4 repos in 1.4s and won 7 out of 8 quality checks.
Measured Results (April 2026)
Retrieved: 5 patterns from 4 repos
Run B wins: 429 awareness, jitter, bounded delay, error classification,
shared helper, error context, structured logging, fast-fail
Scorecard: KB-equipped 7/8 | Base 0/8
A/B test
7/8 quality wins
qualitative
17
Statistical A/B Proof
+33.6%
composite quality lift — 3/6 dimensions p<0.05
SkyDate SWE A/B Test — Blind, Statistical, 6-Dimensional
Full-stack SWE code generation on a Next.js history app. Blind 50/50 routing: control suppresses ALL knowledge (past_solutions + CAG corpus). 23 tasks, 6 evaluation phases, fully autonomous. KB-equipped arm: 100% success rate (8/8) vs 67% control (10/15). Near-zero variance (±0.001) proves consistent quality, not luck.
Statistical Results (April 2026)
Composite: 0.699 vs 0.523 — +33.6% | Cohen's d = 0.843 (large)
D1 Functional Correctness: p = 0.039 (significant)
D2 Structural Compliance: p = 0.024 (significant)
D6 Expectation Match: p = 0.039 (significant)
Success rate: 100% vs 67% | Variant variance: ±0.001
6 dimensions: Functional, Structural, Intent, Correction, Token, Expectation
blind A/B
p<0.05
Cohen's d 0.843
100% success
6-dim SWE metric
23 tasks
zero variance
18
RL Core Infrastructure
40 tests
Epsilon-greedy + Thompson sampling — wired into every task
RL Method Tournament — Bandit Selection, Forbidden-on-Retry, Thompson Graduation
Every task now goes through an RL bandit that selects the best methodology from 3,590 mined patterns. 90% exploit (pick the best), 10% explore (discover hidden gems). After 5+ observations per (methodology, task_type), graduates from epsilon-greedy to Thompson sampling using Beta posteriors. Methods that fail twice on a task are forbidden on retry, forcing iteration through the ranked list. Cold-start protection gives under-tested methods 20% exploration.
Live Proof (April 2026)
3 queries against real DB (3,590 methodologies, real embeddings)
PRIMARY + CONTEXT selection verified on each query
Bandit outcomes recorded: 3 rows in methodology_bandit_outcomes
Forbidden-on-retry: 2 failures → excluded on next evaluate()
Relevance floor: 0.3 min score, skip KB if nothing qualifies
Infrastructure failures do NOT penalize methodologies
epsilon-greedy
Thompson sampling
forbidden-on-retry
cold start 20%
40 tests
live DB proof
wired into core
19
Cross-Brain Intelligence
40 results × 5 brains
One query. Seven brains. Patterns no single brain could find alone.
Cross-Brain Pattern Atlas — "Design defense-in-depth security for a multi-tenant AI agent gateway"
That single query searched 3,590 methodologies across all 5 brains (General, Drive-ops, Rust, TypeScript, Agentic-memory, Go, Misc) simultaneously. Result: Rust’s WASM sandbox with dual metering + taint tracking. Go’s five-layer permission lattice + AES-GCM encrypted secret storage. TypeScript’s defense-in-depth API gating + tiered AI gateway. Python’s pre-ingestion secret scanning + RLS multi-tenancy. Two universal patterns discovered automatically (multi-tenant isolation appeared in both Python and Go; MCP proxy appeared in both Rust and TypeScript). 108 transferable insights generated — each one says “this brain’s technique has no equivalent in that brain.” An 8-layer composition assembled the best pattern from each domain into a layered architecture. No LLM hallucination. Every result is a real methodology mined from a real repo.
Live Output (April 2026)
$ cam federate "defense-in-depth security for a multi-tenant AI agent gateway"
Brains queried: 7 | Results: 40 | Coverage: 100%
UNIVERSAL PATTERNS (same concept, multiple languages):
• Multi-Tenant Dataset Isolation [python, go] (overlap: 0.32)
• MCP Proxy for Multi-Repo Support [rust, typescript] (overlap: 0.30)
UNIQUE INNOVATIONS (10 per brain):
• [rust] WASM Sandbox with Dual Metering (from openfang)
• [rust] Merkle Hash-Chain Audit Trail (from openfang)
• [rust] Information Flow Taint Tracking (from openfang)
• [go] Five-Layer Permission Lattice (from goclaw)
• [go] AES-GCM Encrypted Secret Storage (from goclaw)
• [go] Security Audit with Risk Scoring (from skillshare)
• [typescript] Defense-in-Depth API Gating (from dram-quest)
• [typescript] Secure AI Gateway + Tiered Models (from dram-quest)
• [general] Pre-Ingestion Secret Scanning (from a_aSatzClaw)
• [general] Four-Technique PHI Redaction (from a_a_betaQ)
8-LAYER COMPOSITION:
L1 [rust] AI integration → L2 [general] architecture
L3 [rust] CLI UX → L4 [rust] code quality
L5 [go] cross-cutting → L6 [go] data processing
L7 [typescript] design patterns → L8 [general] security
108 transferable insights | 3,734 tests passing | 0 regressions
5 brains queried
40 real results
2 universal patterns
108 transferable insights
8-layer composition
3,590 methodologies
zero hallucination
20
Paired A/B Proof — Statistically Significant
p = 0.015
Wilcoxon signed-rank — 26 paired within-subject comparisons
Knowledge Ablation: Same Task, Same Agent, Blind A/B
The definitive test: each of 26 coding tasks was run twice on the same agent — once with the full knowledge base (3,590 methodologies), once with knowledge suppressed. Neither the agent nor the verifier knows which arm it’s in. The paired design eliminates agent confounding and task difficulty variance, requiring 4× fewer samples than unpaired experiments. Result: knowledge-equipped agents succeeded on 92.3% of tasks vs 73.1% control. On the 7 discordant pairs (one arm succeeded, one failed), the variant won 6 out of 7. When a task is borderline — hard enough that the agent might fail — the knowledge base tips it toward success.
Statistical Results (April 7, 2026 — 39 min, 5 agents, 26 pairs)
Success: 92.3% variant vs 73.1% control | +19.2 pp lift
Composite: 0.804 vs 0.660 — +0.144 mean paired diff
Wilcoxon signed-rank (one-sided): W=122, p=0.0153
Paired t-test (one-sided): t=2.248, p=0.0168
Bootstrap 95% CI: [+0.023, +0.270] (excludes zero)
Cohen’s dz = 0.45 (medium effect)
McNemar discordant: 6 variant wins / 1 control win
Win/Tie/Loss: 9 / 15 / 2
Per agent: codex +0.116 | local +0.182 | grok +0.275 | claude +0.017 | gemini +0.138
All 5 agents show positive mean diff — KB effect is agent-independent
21
Real CLI Tool — Built From Mined Knowledge
1,346,855 files
indexed in a single real-world scan — 97.55 GB, zero dependencies
TidyHome — The Tool You’ll Actually Use Tomorrow
Not a benchmark. Not a meta-tool. Not a synthetic A/B report. A real Python CLI that cam create --execute built end-to-end from CAM-PULSE’s file-management, CLI, scanning, and dedup methodology families. Then verified against an actual home directory: 1.35 million files, 97.55 GB, 360 permission errors handled gracefully. Smart suggestions surfaced 4.8 GB clearly reclaimable (cache + installers), 27.9 GB in ML model files, 63,300 files untouched for 1+ year. 38 tests. 90% coverage. 16/16 validation steps pass in the harness.
Verified end-to-end (April 10, 2026)
Source: 8 modules, 459 statements, 0 pip deps (Python stdlib only)
Tests: 38 passed, 0 failed, 90% code coverage
Harness: 16/16 validation steps pass, exit 0
Real scan: 1,346,855 files indexed / 360 skipped
Total size: 97.55 GB across 1,211 unique extensions
SQLite index: 529 MB at ~/.tidyhome/index.db
Smart suggestions:
[CACHE] 202,918 files 3.5 GB reclaimable
[INSTALLER] 623 files 1.3 GB reclaimable
[ARCHIVE] 6,051 files 4.0 GB reclaimable
[MODEL] 411 files 27.9 GB (redownloadable)
[STALE-1Y] 63,300 files untouched 1+ year
KB sources: MiroFish, abacus_FileSearch, CLI-Anything,
app_organizer, AMM + Rust/Go/Python brain resilience
