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Proofs of useful work for capacity accounting and Bandwidth limits and compartmentalization

No new hardware needed

Proofs of useful work for capacity accounting

Attack testing records published testing for this use. It does not by itself show independent review, a formal proof or that a deployed system is secure.

• Proofs of useful work for capacity accounting: Analysis
• Bandwidth limits and compartmentalization: Analysis

Open significant failures

3 failures in 2 mechanisms

• Known shortcuts let a miner claim somewhat more work than it did in Proofs of useful work for capacity accounting

  Pearl's specification lists known mining speedups: crafted inputs, precision shortcuts, seed grinding, work reuse, and faster kernels or hardware. A policy check caps the summands a miner may skip at one-sixteenth of those in a tile. For capacity bounding, any gap between work proven and work possible leaves spare capacity. ⁴

  Known failure · Theoretical argument · Significant · Open. On the record

• Operator control of pod routing collapses the bound in Bandwidth limits and compartmentalization

  Lucid's analysis finds that if the operator can freely assign pods to routers, it could dedicate a whole cell of 100 or more pods to one pipeline stage. The bound then falls to about 90–220x uncompressed and as low as about 25x with compression. The proposed mitigation, auditor-controlled random assignment that is periodically re-randomized, has not been implemented. ⁷

  Known failure · Theoretical argument · Significant · Open. On the record

• Parallel scale-up switches are hard enforcement points in Bandwidth limits and compartmentalization

  In GB200 topologies, GPUs reach GPUs in other nodes through NVSwitches without a NIC on the path. Amodo notes that limits are hard to enforce there because many switches work in parallel, so compromising one or two would bypass the limit. ¹⁰

  Known failure · Theoretical argument · Significant · Open. On the record

Scope limitations

• Proves that work was done, not that no capacity remains in Proofs of useful work for capacity accounting

  Proof-of-work accounting bounds unmonitored compute only relative to an estimate of what the actor has. Attestable states that the verifier "needs a credible estimate of the compute available" to the actor, and that a proof "cannot discover a datacenter that was never declared". ¹

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Chip registries and manufacturing records: A registry of chips is one basis for the estimate of available compute that the flaw's source says the verifier needs. A pointer, not evidence that this failure is mitigated. Add

  Related mechanism Proposed Remote detection of data centres: Looks for data centres that were never declared, which a proof cannot discover. A pointer, not evidence that this failure is mitigated. Add

• Undeclared local storage raises per-pod capacity in Bandwidth limits and compartmentalization

  More memory or storage per pod helps an adversary. Lucid requires per-pod storage to be declared, capped and physically inspected. ⁷

  Scope limitation · Theoretical argument. On the record

• Training within one pod is not covered in Bandwidth limits and compartmentalization

  Lucid's bounds concern pre-training models larger than the pods are sized for. Training models that fit in one pod, fine-tuning and reinforcement-learning post-training within one pod are outside the modelled threat. ⁷

  Scope limitation · Open question. On the record

Open questions

• Security rests on new hardness assumptions in Proofs of useful work for capacity accounting

  Komargodski and Weinstein base security on hardness assumptions about batches of low-rank random linear equations, and list PoUW "from more standard or well-studied assumptions" as an open problem. Pearl's floating-point variant introduces a further "quantized-subspace hardness" assumption. ^{3 4}

  Open question · Open question. On the record

• Low-communication training reduces the bandwidth training needs in Bandwidth limits and compartmentalization

  DiLoCo matched fully synchronous training on 8 workers while communicating 500 times less. Rahman writes that this family of methods theoretically allows large-scale training with less than 100 Mbps. Lucid includes these methods in its bounds, but notes that extreme activation compression, architectures with unusually small inter-layer widths, or modular paradigms could erode the margin. ^{7 11 12}

  Open question · Theoretical argument. On the record

Not yet demonstrated

Proposed Proofs of useful work for capacity accounting

Mechanisms on the map that are not in the proposal. Pointers, not recommendations. ?

• Research demo Tamper evidence for verifier devices Add
  • Bandwidth limits and compartmentalization waits on it. Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU.
• Proposed Network taps and certifiers Add
  • Bandwidth limits and compartmentalization waits on it. The verifier must know that all traffic leaving a pod crosses the capped, monitored links.

Missing prerequisites

• Research demo Tamper evidence for verifier devices needed by Bandwidth limits and compartmentalization Add

Blockers

7 blockers recorded

• Proofs of useful work for capacity accounting
  • Bounding spare capacity needs a credible estimate of the compute available to the actor, including third-party access. Capacity bounds ¹
  • Proofs of work cannot find facilities that were never declared. Coverage & hidden compute ¹
  • As of September 2026 no implementation, demonstration or independent evaluation of proofs of work for capacity bounding has been published. Adversarial validation
• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ⁷
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ⁸
  • Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU. Hardware trust. Waits on Tamper evidence for verifier devices ^{7 10}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{7 11 12}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Proofs of useful work for capacity accounting.

Not involved: Bandwidth limits and compartmentalization.

Inputs and outputs

Depends on the design for Proofs of useful work for capacity accounting.

Not involved: Bandwidth limits and compartmentalization.

Training data

Not involved: Proofs of useful work for capacity accounting and Bandwidth limits and compartmentalization.

Proofs of useful work for capacity accounting

• Operational use Pearl proof-of-useful-work blockchain Open-source project, Pearl Research Labs

Bandwidth limits and compartmentalization

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• Proposed RAND secure inference data center (SIDC) design Proposed architecture, RAND

1. Pacing AI Requires Proof, Attestable (2026). Original
2. Mechanisms to Verify International Agreements About AI Development, A. Scher & L. Thiergart (2025). Original
3. Proofs of Useful Work from Arbitrary Matrix Multiplication, I. Komargodski & O. Weinstein (2025). Original
4. Pearl Floating Point Scheme Specification, Pearl Research Team (2026). Original
5. pearl: Monorepo for the Pearl network, Pearl Research Labs (2026). Original
6. Verification Plan, R. Dean (2026). Original
7. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
8. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
9. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
10. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
11. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
12. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original