The Moment of Synthesis: Discovering Parameter Gaps in Filecoin's Proof Pipeline

In the course of designing a pipelined SNARK proving daemon called cuzk, an assistant reached a critical inflection point during an exhaustive investigation of Filecoin's existing proof infrastructure. Message 85 captures a moment of synthesis — a "now I see the full picture" realization that reshaped the architectural assumptions underpinning the entire cuzk design. What makes this message remarkable is not the complexity of its commands, but the quiet pivot it represents: from exploring what the system should have, to verifying what it actually has.

The Context of Discovery

By message 85, the assistant had already spent considerable effort reading seven prior optimization proposals, exploring every layer of the existing proof system — from the ffiselect child process model for GPU proving, through SRS/parameter loading paths across all proof types (PoRep, SnapDeals, WindowPoSt, WinningPoSt), to the supraseal C2 C++/CUDA API surface and bellperson prover internals. The assistant had also studied GPU inference engine architectures (vLLM, Triton, TensorRT-LLM) for patterns in model loading and scheduling, drawing analogies between model weights and SRS parameters.

The immediate preceding messages (79–84) had been a systematic exploration of the filesystem: listing the golden test data in /data/32gbench/, examining the cache directory with its 34 GB layer files, inspecting the parameter files in /var/tmp/filecoin-proof-parameters/, and probing the lotus-bench simple command surface. Message 84 had revealed the actual parameter files present on the system — a listing that showed only "8-0-0" parameters (like v28-proof-of-spacetime-fallback-merkletree-poseidon_hasher-8-0-0-...) and conspicuously absent "8-8-0" parameters for full 32 GiB PoRep proofs.

The Insight: What's Missing

The message opens with a direct statement of discovery:

Now I see the full picture. Only small-sector (8-0-0) params are actually downloaded, not the full 32 GiB (8-8-0) params. Let me check what curio fetch-params looks like and check the existing c1.json is actually 32G.

This sentence carries significant weight. The "8-0-0" versus "8-8-0" distinction in Filecoin's parameter naming convention encodes the Merkle tree structure used for different proof types. The "8-0-0" parameters are associated with smaller proofs — WindowPoSt and WinningPoSt, which operate on smaller subsets of sector data. The "8-8-0" parameters, by contrast, are required for the full 32 GiB StackedDrg (PoRep) proofs — the most memory-intensive operation in the pipeline, consuming approximately 200 GiB of peak memory.

The realization that only the smaller parameters are present on the system has profound architectural implications. The cuzk daemon, designed as a persistent proving service, would need to handle 32 GiB PoRep proofs. If the required parameters aren't pre-downloaded, the daemon must either fetch them on demand (adding latency) or pre-warm them during initialization (adding startup time and storage requirements). This directly affects the SRS memory manager design that the assistant was developing — the three-tier hot/warm/cold system with explicit budget control.

Verifying Through Data: The Python Scripts

Rather than proceeding on assumption, the assistant immediately writes two Python one-liners to verify the golden test data. This is characteristic of the systematic, evidence-driven approach throughout the investigation.

The first script decodes /data/32gbench/pc1out.txt, a base64-encoded JSON file representing the PreCommit1 output:

import json, base64, sys
with open('/data/32gbench/pc1out.txt', 'r') as f:
    b64 = f.read().strip()
data = base64.b64decode(b64)
obj = json.loads(data)
print('registered_proof:', obj.get('registered_proof', 'N/A'))
print('keys:', list(obj.keys())[:10])

The output confirms the proof type: registered_proof: StackedDrg32GiBV1_1. This is the 32 GiB PoRep proof variant — the most computationally intensive proof in Filecoin's ecosystem. The keys reveal the data structure: _lotus_SealRandomness, comm_d, config, labels, registered_proof. Each of these fields feeds into the C2 (Commit2) proof generation pipeline that the assistant had been analyzing in excruciating detail across the prior seven proposals.

The second script examines c1.json, the output of the Commit1 phase:

import json
with open('/data/32gbench/c1.json', 'r') as f:
    data = json.load(f)
if isinstance(data, dict):
    print('keys:', list(data.keys())[:10])
    print('registered_proof:', data.get('registered_proof', 'N/A'))
elif isinstance(data, list):
    print('top-level list, len:', len(data))

The output reveals a top-level dictionary with keys SectorNum, Phase1Out, and SectorSize. Notably, registered_proof is N/A — the C1 output doesn't carry the proof type directly; it's implicit in the sector size and the structure of Phase1Out. This is a subtle but important detail for the cuzk API design: the daemon must infer the proof type from context rather than reading it from a single field.

Assumptions and Their Corrections

Several assumptions are visible in this message, some validated and one corrected:

The corrected assumption: The assistant had implicitly assumed that the full 32 GiB parameters would be pre-downloaded on a development/production machine. The filesystem exploration revealed otherwise — only the smaller 8-0-0 parameters were present. This is a realistic discovery: in a development environment, engineers may only download the parameters needed for their immediate testing, and the full 32 GiB SRS files are enormous (multiple gigabytes each).

The validated assumption: The golden test data in /data/32gbench/ genuinely represents 32 GiB PoRep proofs. The pc1out.txt decode confirmed StackedDrg32GiBV1_1, and c1.json showed SectorSize as a key (though its value isn't printed, the proof type confirmation is sufficient).

The emergent question: "Let me check what curio fetch-params looks like" — this reveals an assumption that there's a standard mechanism for fetching the missing parameters. The assistant is probing the Curio orchestration layer to understand how parameters are managed in production.

Input and Output Knowledge

Input knowledge required to understand this message includes:

The Thinking Process

The reasoning visible in this message follows a pattern of hypothesis → verification → architectural implication. The assistant forms a hypothesis ("only small-sector params are downloaded"), immediately verifies it ("check the existing c1.json is actually 32G"), and the verification serves dual purposes — confirming the data is genuine 32 GiB PoRep while also revealing the parameter gap.

The choice of Python one-liners over more complex tooling is deliberate. These are quick, disposable scripts that answer specific questions without ceremony. The assistant could have used jq for JSON parsing or file for type detection, but Python's base64 module is necessary for the base64-decoded file, and using a single language for both inspections reduces cognitive overhead.

The error handling (2>/dev/null || echo "Not valid base64/json") shows awareness that the data might not conform to expectations — a prudent approach when exploring unfamiliar artifacts.

Broader Architectural Significance

This discovery ripples through the cuzk architecture design. The SRS memory manager, which the assistant would document in cuzk-project.md, must account for the fact that 32 GiB parameters may not be immediately available. The three-tier hot/warm/cold system gains a new dimension: not just eviction policy, but also a fetch policy that determines when and how to download missing parameters.

For the testing utility (cuzk-bench) that the user requested, this means test scenarios must include parameter preloading steps. The assistant's note about using /data/zk/params/ as the parameter storage path (from the user's instructions in message 78) now takes on additional meaning — it's not just a convention, but a necessary part of the workflow.

The discovery also validates a key design decision in the cuzk architecture: the Persistent Prover Daemon model (Proposal 2 from earlier work) that keeps SRS parameters resident in GPU memory across proof jobs. If parameters must be fetched and loaded anyway, a persistent daemon that keeps them warm amortizes that cost across multiple proofs. The parameter gap discovery strengthens the case for daemon-based architecture over one-shot proof generation.

Conclusion

Message 85 is a microcosm of the engineering investigation process: a moment of insight, immediately tested against reality, with findings that reshape the architectural landscape. The assistant's realization that only small-sector parameters are present on the system is not a bug or a failure — it's a realistic constraint that any production proving service must handle. By catching this early, the cuzk design can incorporate parameter management as a first-class concern rather than an afterthought.

The message also demonstrates a methodological principle that runs throughout the entire investigation: verify everything. The assistant could have assumed the golden data was correct and the parameters were complete, but instead chose to inspect the actual bytes on disk. That discipline — testing assumptions against evidence — is what transforms a good architecture into one that survives contact with reality.