The Smoking Gun: How a Job ID Collision Caused Silent Proof Corruption in a Distributed Proving System

In distributed systems debugging, the most elusive bugs are those that produce consistent failures under one configuration and perfect results under another, with no obvious difference in the code path. The user's message at index 1977 in this opencode session is a textbook example of a developer presenting the critical experimental evidence that breaks open a stubborn production bug. After days of chasing binary mismatches, GPU flakiness, and timing races, the user delivers the data that pinpoints a job ID collision in the ProofShare proving pipeline—and does so with the elegance of a controlled experiment.

The Message in Full

The user's message is a dense firehose of diagnostic output from the CUZK GPU proving daemon, followed by a concise analytical conclusion:

CUZK_TIMING: prep_msm_ms=2338
CUZK_TIMING: gpu_tid=0 batch_add_ms=2923
CUZK_TIMING: gpu_tid=0 tail_msm_ms=209 gpu_total_ms=6528
CUZK_TIMING: b_g2_msm_ms=4150 num_circuits=1
TIMELINE,174024,GPU_PICKUP,ps-porep-1000-1,worker=0,partition=1
2026-03-13T09:06:00.738220Z  INFO gpu_worker{worker_id=0 gpu=0 job_id=ps-porep-1000-1 proof_kind=porep-c2 batch_size=1 partition=Some(1)}: cuzk_core::engine: GPU worker picked up synthesized proof batched=false partitioned=true partition=Some(1)
TIMELINE,174024,GPU_START,ps-porep-1000-1,worker=0,partition=1
CUZK_TIMING: split_vectors_ms=0 setup_to_split_ms=0
2026-03-13T09:06:00.740342Z  INFO cuzk_core::pipeline: GPU prove complete (split) proof_count=1 proof_bytes=192 gpu_ms=12890
TIMELINE,174026,GPU_END,ps-porep-1000-1,worker=0,partition=0,gpu_ms=12890

thread 'tokio-runtime-worker' panicked at cuzk-core/src/pipeline.rs:1910:9:
partition 0 already inserted

The message continues with all ten partition proofs being marked INVALID, and then the user's crucial experimental result:

Ran curio with proofshare max tasks set to 1 (meaning 1 only running in parallel) -> Correct proof! So possibly some pipeline partition keying issue, where the keys need to be more unique like request uuid?

This single sentence—"Correct proof!"—is the breakthrough. After days of debugging, the user has found a configuration that works: limiting the system to a single concurrent proofshare task. And they've already formed the correct hypothesis: the job_id is derived from minerid-sectorid, and since all proofshare challenges target the same bench sector (miner=1000, sector=1), concurrent tasks produce identical job IDs, causing the partition assembler to mix results across different proofs.

The Context: A Week of Production Debugging

To understand why this message is so significant, we need to trace the debugging journey that preceded it. The conversation leading up to message 1977 is the culmination of an extended investigation into why the CUZK GPU proving engine was producing invalid proofs in production.

The story begins in an earlier sub-session where the assistant implemented PCE (Pre-Compiled Constraint Evaluator) extraction for all proof types and fixed a WindowPoSt crash. This work introduced a partitioned proving pipeline for SnapDeals, allowing overlapping synthesis and GPU proving. The pipeline worked correctly in isolation—benchmarks passed, individual proofs validated.

But when the system was deployed to production on a remote vast.ai host with the Curio proving framework, something went wrong. The ProofShare system, which coordinates distributed proof generation across multiple workers, began producing invalid proofs. Every single one of the ten PoRep partitions was failing verification.

The assistant initially suspected a binary mismatch. The production host was running a cuzk binary built via Dockerfile.cuzk-rebuild (a minimal rebuild Dockerfile), while benchmarks ran inside a full Dockerfile.cuzk container. The hashes differed: 34295ce6 (bad) vs e6b3ed80 (good). The assistant extracted the working binary from the container and deployed it to the host ([msg 1967]). But even with the "good" binary, all ten partitions still failed.

This ruled out the simplest explanation. The binary wasn't the problem.

What the Message Reveals: The Panic and the Pattern

The user's message contains several layers of evidence that collectively tell the story of the bug.

The Panic. At pipeline.rs:1910, the Rust runtime panics with "partition 0 already inserted". This is the smoking gun. The partition assembler, which collects GPU-proved partition results and assembles them into a final proof, is trying to insert partition 0 into a data structure that already contains partition 0. In a correctly functioning system, each partition result is inserted exactly once. A duplicate insertion means two different proof jobs are sharing the same assembler slot.

The Invalid Partitions. Following the panic, the verification phase runs and finds all ten partitions invalid:

PER-PARTITION VERIFICATION: 0/10 valid — likely a num_circuits=1 GPU proving bug

The assistant's log message attributes this to a num_circuits=1 GPU proving bug—a reasonable guess given the symptom, but ultimately a red herring. The real issue is more subtle: the partitions being verified don't belong to the same proof. They're a scrambled mix of results from different concurrent proofshare challenges.

The Timing Anomaly. The timeline shows GPU workers finishing after verification has already completed for some partitions. The "assembler not found, discarding partition result" warning indicates that the assembler was cleaned up (because verification failed) before all GPU workers had finished. This is a secondary symptom of the root cause: the wrong partitions were assembled, verification failed prematurely, and the assembler was destroyed while GPU workers were still processing legitimate partitions.

The Controlled Experiment: max_tasks=1

The most powerful evidence in the user's message is the controlled experiment. By setting proofshare max tasks to 1, the user ensured that only one proofshare challenge was active at a time. With no concurrent tasks, there could be no job ID collision. And indeed, the result was "Correct proof!"

This is a textbook debugging technique: isolate the variable you suspect (concurrency) by eliminating it, and observe whether the symptom disappears. The experiment is clean, the result is unambiguous, and the conclusion is inescapable.

The user's reasoning is worth quoting in full:

job-id is derived from minerid-sectorid? Challenge sectors are all the same sector. Tho in benchmark we also use the same IDs, so not sure why it would work (maybe reused seed between challenges = partitons interchangable)?

This shows the user thinking through the puzzle. The benchmark also uses miner=1000, sector=1, but it works. Why? The user correctly hypothesizes that the benchmark might reuse the same seed across challenges, making partitions interchangeable—so even if they're mixed up, the resulting proof is still valid. In production, each proofshare challenge has a unique random seed, so mixing partitions from different challenges produces garbage.

Input Knowledge Required to Understand This Message

To fully grasp the significance of this message, one needs:

  1. Understanding of the CUZK proving pipeline. The system works in phases: synthesis (CPU, generating constraint system data), GPU proving (running the Groth16 prover on GPU), and verification (checking the resulting proof). Partitions are independent chunks of work that are synthesized, GPU-proved, and then assembled into a final proof.
  2. Knowledge of the ProofShare system. ProofShare is a protocol for distributing proof generation across multiple workers. The Curio framework manages tasks and communicates with the CUZK daemon. Each proofshare challenge generates a RequestId that becomes the job_id in CUZK.
  3. Awareness of the bench sector setup. For testing purposes, all proofshare challenges use a fixed bench sector: miner ID 1000, sector number 1. This is fine for sequential testing but creates a collision hazard under concurrency.
  4. Familiarity with the partition assembler architecture. The assembler in engine.rs uses job_id as the key for collecting and assembling partition results. If two jobs have the same job_id, their partitions will be merged into a single assembler instance.
  5. Debugging methodology. The user's experiment—reducing concurrency to 1 to test the collision hypothesis—requires understanding that the bug is concurrency-dependent and that eliminating concurrency should eliminate the symptom if the hypothesis is correct.

Output Knowledge Created by This Message

This message creates several critical pieces of knowledge:

  1. The root cause is identified. The job ID collision hypothesis is now supported by experimental evidence. This transforms the debugging effort from open-ended investigation to targeted fix.
  2. The fix is suggested. The user explicitly proposes making the job ID "more unique like request uuid." This gives the assistant a concrete direction: add the harmony task ID (or another unique identifier) to the RequestId format string.
  3. The binary mismatch theory is ruled out. Since the same binary produces correct proofs with max_tasks=1, the binary itself is not defective. This saves the assistant from further investigation of build environment differences.
  4. The benchmark vs. production discrepancy is explained. The benchmark works because it either runs sequentially or uses a consistent seed that makes partition mixing harmless. Production fails because each challenge has a unique seed.
  5. A reproducible test case is established. The max_tasks=1 configuration can be used as a control in future testing, and the max_tasks>1 configuration as the test case for the fix.

The Thinking Process Visible in the Message

The user's message reveals a sophisticated debugging thought process:

Step 1: Observe the symptom. All ten partitions are invalid. This is a systematic failure, not a random fluke.

Step 2: Look for patterns in the diagnostic output. The panic at "partition 0 already inserted" is a strong signal. It's not a GPU computation error; it's a data structure invariant violation.

Step 3: Form a hypothesis. The job_id is ps-porep-1000-1—derived from the proof type, miner ID, and sector ID. Since all challenges use the same sector, concurrent tasks produce identical job IDs.

Step 4: Design an experiment. Reduce concurrency to 1. If the hypothesis is correct, the collision disappears and proofs become valid.

Step 5: Run the experiment and observe the result. "Correct proof!" The hypothesis is confirmed.

Step 6: Propose the fix. Make the job ID unique per invocation by incorporating a request UUID or task ID.

This is textbook scientific method applied to production debugging. The user doesn't just dump logs and ask for help; they present structured evidence, describe their reasoning, and propose a solution. The message is as much a diagnosis as it is a question.

Why This Message Matters

Message 1977 is a turning point in the conversation. Before this message, the assistant was chasing binary mismatches and GPU proving bugs. The assistant's previous summary ([msg 1976]) concluded that the Dockerfile.cuzk-rebuild binary was defective and needed to be replaced. But the user's experiment proves that the binary is fine—it's the job ID scheme that's broken.

This is a classic lesson in distributed systems debugging: when a component works perfectly in isolation but fails under concurrency, look for resource collisions, not computational errors. The GPU wasn't producing bad proofs; the partition assembler was mixing proofs from different jobs because they shared the same key.

The fix is elegant: add the harmony task ID to the RequestId format string, changing it from ps-porep-%d-%d (proof type, sector ID) to ps-porep-%d-%d-%d (proof type, sector ID, task ID). This ensures that even when multiple proofshare challenges target the same sector, each gets a unique job_id and its own partition assembler instance.

The broader lesson is about the importance of unique identifiers in concurrent pipelines. When a system uses a derived key (like minerid-sectorid) as a job identifier, it implicitly assumes that no two concurrent jobs will have the same key. This assumption is valid for normal proving (where each sector is unique) but breaks for proofshare challenges (which all target the same bench sector). The fix makes the key explicitly unique rather than relying on implicit uniqueness of the input data.

Conclusion

The user's message at index 1977 is a masterclass in production debugging. It combines raw diagnostic output, a critical panic signal, a controlled experiment, and a clear hypothesis into a single coherent narrative. The message transforms the debugging effort from speculation to certainty, identifies the root cause (job ID collision), and proposes the fix (add a unique task ID to the job identifier).

In the subsequent messages, the assistant implements this fix, overcomes Go build cache issues in Docker to deploy it, and audits all other CUZK RequestId callers to ensure no similar vulnerabilities exist. The fix is consolidated into a single commit, and the final Docker image is pushed. But none of that would have happened without the user's message 1977—the moment when the smoking gun was found.