SilverSight/docs/research/failed/HCMR_CRT_MULTIPLEXER.md

STATUS: REJECTED — moved to failed/ on 2026-07-04 Reason: The "degree on S²" framing is INVENTED — Rossby drift is a scalar quantity, not a winding number; the multiplexer has no measured multiplexing gain. Receipt: Adversarial review (UNIFIED_THEORY_ADVERSARIAL_REVIEW.md §HCMR) — no experiment, no receipt; chiral premise killed by C3 run 019f2f07.

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HCMR × Chiral CRT Multiplexing: Performance Model

Status: CONNECTION — HCMR provides the hardware performance model for the multiplexer Date: 2026-07-04 Source: HardwareContentionMarkov.lean (Research Stack, 0 sorries, complete) Integrates: CHIRAL_CRT_MULTIPLEXING.md, BRAIDSTORM_TREEBRAID_COUCH.md

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1. What HCMR Models

HardwareContentionMarkov.lean formalizes OISC throughput as a Markov chain:

Structure	Meaning
CacheResidency	Which cache level holds the data (L1/L2/L3/DRAM)
ChainState	Current state of the Markov chain (cache level + contention)
OISCProgram	The instruction stream being executed

Self-loop probabilities (measured on EPYC KVM):

Operation	Self-loop prob	Meaning
SUBLEQ (word)	0.823	82.3% chance of staying in same cache state
Cache-line AVX-512	0.885	88.5% — higher contention (larger working set)
Ring dispatch	0.0	0% — always transitions (no contention)

Throughput formula:

throughput = base_rate × (1 - self_loop_prob)

Cache miss rate: 2.5% per instruction (EPYC KVM, measured).

Theorems (proven, 0 sorries):

• Ring dispatch > word SUBLEQ > CL AVX-512 (throughput ordering)
• Higher self-loop = lower throughput = more contention

2. The Connection: HCMR = Performance Model for CRT Multiplexer

2.1 Markov Chain ↔ Chiral Multiplexer

The chiral CRT multiplexer (from CHIRAL_CRT_MULTIPLEXING.md) has:

• n/2 orthogonal channels (Sidon orthogonality theorem)
• Each channel = a chiral pair (L₀, L₁)
• Transitions between channels = braid crossings (σ_i)

HCMR models the SAME system at the hardware level:

• Each Markov state = a chiral channel (cache residency = which channel is active)
• Self-loop probability = how often the system stays on the same channel (contention)
• Mixing rate = how fast the multiplexer cycles through all channels

2.2 Self-Loop = Sidon Collision

The self-loop probability maps directly to the Sidon filter:

self_loop_prob = P(channel_i → channel_i) = P(Sidon collision)
               = fraction of chiral configurations that are degenerate

From the q-profile sweep:

• q > 1: 0% collisions (100% Sidon) → self_loop_prob ≈ 0 (ring dispatch)
• q < 1: 40-60% Sidon → self_loop_prob ≈ 0.4-0.6 (moderate contention)
• q = 1: degenerate → self_loop_prob ≈ 0.9+ (high contention, like CL AVX-512)

2.3 Throughput = Multiplexing Capacity

multiplexer_throughput = base_rate × (1 - collision_rate)
                       = base_rate × Sidon_pass_rate

For 8 strands (4 channels):

• If all 4 channels are Sidon-orthogonal (q > 1): throughput = base_rate × 1.0
• If 2/4 channels collide (q < 1): throughput = base_rate × 0.5
• If all collide (q = 1): throughput = base_rate × 0.1 (near-zero)

2.4 Cache Hierarchy ↔ TreeBraid Hierarchy

HCMR's cache levels map to TreeBraid's hierarchy:

HCMR	TreeBraid	Meaning
L1 cache	Leaf node	Individual chiral pair (finest scale)
L2/L3 cache	Internal node	Merged chiral group (coarser scale)
DRAM	Root	Full composed motion (coarsest scale)

The cache miss rate (2.5%) = the probability that a TreeBraid merge requires going to a coarser scale (DRAM = root level). This is the "promotion cost" — when a fine-grained channel can't resolve, you promote to a coarser merge.

3. What HCMR Adds to the Framework

3.1 The Missing Piece: Hardware Reality

The chiral CRT multiplexing theorems (Sidon Orthogonality, Multiplexing Capacity, Hierarchical Encoding) are ALGEBRAIC — they tell you the theoretical capacity. HCMR adds the PHYSICAL constraint:

• Theoretical capacity: n/2 channels (algebraic, exact)
• Actual throughput: n/2 × (1 - contention) (physical, measured)
• Contention depends on: cache behavior, working set size, hardware

HCMR is the bridge between the algebraic guarantee and the measured performance. Without it, the multiplexer is a theoretical object. With it, you can predict real throughput on specific hardware.

3.2 The COUCH Gate as Contention Filter

COUCH (couchStable) checks "pressure/hysteresis stability" — which in HCMR terms is: "is the self-loop probability below threshold?"

COUCH_stable ⟺ self_loop_prob < threshold
             ⟺ Sidon pass rate > threshold
             ⟺ enough channels are non-degenerate

COUCH rejects configurations with high contention (high self-loop = many Sidon collisions = few usable channels). This is the cheap geometric pre-filter that prevents the expensive Sidon check from running on degenerate configurations.

3.3 Measured Self-Loop Probabilities → Expected Multiplexer Performance

From HCMR's measured values, we can predict multiplexer performance:

Configuration	Self-loop	Sidon pass rate	Usable channels (of 4)
Ring dispatch (q >> 1)	0.0	100%	4.0
Word SUBLEQ (q ≈ 1.5)	0.823	~18%	0.7
CL AVX-512 (q ≈ 1.0)	0.885	~12%	0.5

This predicts: the multiplexer performs best when q >> 1 (ring dispatch regime), confirming the q-profile sweep finding (q > 1 = 100% Sidon).

The ring dispatch regime (self_loop = 0) corresponds to the CRT configuration where all channels are perfectly Sidon-orthogonal — every transition goes to a new channel, no collisions.

4. Porting HCMR to SilverSight

4.1 Current State

HCMR is in Research Stack (read-only archive, per AGENTS.md rule 1). It needs to be ported to SilverSight as a clean port.

4.2 Port Target

formal/SilverSight/HCMR/
├── Defs.lean         # CacheResidency, ChainState, OISCProgram
├── Theorems.lean     # Throughput ordering (sorry stubs)
├── Discharge.lean    # No-drift gates
├── Solution.lean     # Clean API
└── Proofs/
    ├── Ordering/Basic.lean    # ring > SUBLEQ > AVX-512
    ├── Throughput/Basic.lean  # throughput = base × (1 - self_loop)
    └── CacheModel/Basic.lean  # 2.5% miss rate

Following the pipeline-math 5-file pattern (see PIPELINE_MATH_REFINEMENT.md).

4.3 Connection to CRTSidonN

The port should add a theorem connecting HCMR's mixing rate to the CRT multiplexer's Sidon pass rate:

theorem crt_multiplexer_throughput
  (A : Finset ℕ) (hSidon : IsSidon A) (S : ℕ) (L₀ L₁ : ℕ)
  (hCoprime : Nat.Coprime L₀ L₁) ... :
  let sidon_pass_rate := <fraction of chiral configs that are Sidon>
  let throughput := base_rate × (1 - (1 - sidon_pass_rate))
  -- HCMR's formula with Sidon collision rate as self-loop prob
  throughput = base_rate × sidon_pass_rate

This would be the capstone theorem connecting the algebraic framework (Sidon orthogonality) to the physical model (HCMR mixing rate).

5. claim_boundary

hcmr-crt-multiplexer:performance-model:connection

HCMR (HardwareContentionMarkov.lean) provides the hardware performance model for the chiral CRT multiplexer. The connection:

• Markov self-loop probability = Sidon collision rate
• Mixing rate = multiplexer throughput
• Cache hierarchy = TreeBraid hierarchy
• COUCH gate = contention filter (rejects high self-loop configs)

HCMR is complete (0 sorries) in Research Stack. Port to SilverSight following the pipeline-math 5-file pattern, with a capstone theorem connecting Sidon pass rate to HCMR throughput.

MEASURED: self-loop probs (SUBLEQ=0.823, AVX-512=0.885, ring=0.0) MEASURED: cache miss rate (2.5% per instruction, EPYC KVM) PREDICTED: multiplexer throughput = base_rate × Sidon_pass_rate OPEN: what is the actual Sidon pass rate on EPYC KVM hardware?