Verified Inference Between Adversaries

A datacenter asserts that it ran a 400-billion-parameter model on your prompt. It might have run a smaller one, an older checkpoint, or a quantized copy that costs a fraction as much. Ordinary logging cannot settle this, because the logs are produced by the party whose behavior is in question. Neither can a hash of the weights: the operator can hold the right weights and still run something else.

Similarly, assume that a treaty between two governments is reached regarding AI research and development. Both governments claim that they are using particular models that meet certain thresholds, have been approved by regulatory boards, etc. The same problem applies; there is not any obvious way for one party to such an agreement to verify the claims of the other.

This remains an unresolved gap wherever the verifier cannot trust the operator, or the attestation stack the operator controls. Evaluations measure a model under conditions you control, and say nothing about the model served to somebody else afterward. Hardware-backed attestation can say a great deal about platform and software state — but it buys that by requiring trust in a hardware root-of-trust ecosystem, and it still does not yield an operator-independent statement about this computation producing that output. What is missing is a way to replace trust in the operator’s execution environment with a proof an independent verifier can check on hardware it controls.

VerInf is a research prototype addressing that gap, led by James Petrie at the Future of Life Institute. I work on it as a MARS V fellow. This living document details what the system does and does not currently prove, where we think it is heading, and my future ambitions for this line of work.

What the proof certifies

The naive framing — chiefly, “prove the model produced this output” — is the wrong one. Frontier inference runs in floating point, on nondeterministic kernels, across hardware that does not reproduce bit-for-bit. Demanding exact reproduction would make the problem intractable, and even worse, it isn’t really even addressing the right problem.

VerInf instead bounds the unexplained information in an output stream: the number of bits in the output that the committed model does not account for. If the operator swapped in a different model, the outputs it produced would be poorly predicted by the model it committed to, and the bound rises. A cheap substitution is therefore expensive to hide.

The intended end-to-end certificate combines three pieces, held to different standards — and one of them is not yet integrated:

• a transcript anchor, binding the committed token streams to digests recorded independently at generation time, so the certificate is about the run that actually happened rather than a convenient reconstruction. The AES and SHA-256 circuits for this are implemented and tested; they are not yet wired end to end against recorded digests. The demonstrated runs below are therefore internally consistent — the scored tokens are the tokens the proven forward pass consumed — but not yet externally anchored to a record made at generation time;
• the forward pass, where every claim must admit exactly one satisfying assignment. Slack in an intermediate value would propagate through the remaining layers in directions nobody can analyze;
• the surprisal bound itself, where freedom is permitted, provided every free direction pushes the reported number up rather than down.

Upstream of the logits the prover must have no room at all; downstream, in the short arithmetic from logits to the reported bound, the prover may have room, so long as every rounding is forced upward. In an elegant twist, cheating can only make your own number worse.

A related move governs our predictor. The bound is computed against a predictor of the deployment’s outputs that the prover supplies. By Gibbs’ inequality the resulting sum is a valid upper bound for any predictor, so the choice can indeed be left to the prover entirely.

Underneath, matrix products are checked with Freivalds projections over Ligero commitments, which are hash-based — so there is no trusted setup, and the construction is plausibly post-quantum^aa Future research intends to make this statement of “plausible” post-quantum security into one of “definitive” post-quantum security..

Where trust is required

The prover wants confidentiality: weights, activations, and both token streams must not leak. The verifier wants soundness: the reported bound must be genuine. Their interface is a public claim list stating what kind of computation was performed — which reveals the model architecture, though not the weights.

Under the protocol’s soundness assumptions and a correct verifier implementation, the prover need not be trusted for correctness: prover deviations can cause false acceptance only within the protocol’s soundness error, and otherwise cause verification to fail. Those are real assumptions, not decoration — soundness rests on the cryptographic primitives, the challenge generation, the verifier’s own parser boundaries, and the per-challenge bound of the chosen configuration, which is a deployment parameter rather than a fixed property. The verifier’s trusted base is deliberately small and shares no code with the prover, which narrows that surface without eliminating it. VerInf has not had a full security audit.

The arrangement this enables between mutually distrustful parties: each runs its own verifier on its own hardware, and neither has to trust the datacenter where the proving happened.

Results on record

First, the single-chip results, which predate my involvement.

All on a single NVIDIA DGX Spark. The committed witness for the Maverick run is roughly 7.2 TB, streamed at the working set rather than held.

At 0.880 bits per token against a 202,048-token vocabulary, the proof accounts for about 95% of the information a token could carry. That is not “the model produced this”; it is “very little here is unexplained by the model that was committed.”

What I have contributed thus far

Parallelizing the prover across GPUs is named future work in the paper, and it is the direction I am working in. It runs into an immediate problem: you cannot measure a cluster you do not yet have access to, and partitioning decisions have to be made before the hardware arrives.

My first deliverable was the measurement scaffolding that parallelism needs. I built a dry-run profiler that predicts a proving run’s time, memory, bandwidth, and proof size before running it, from a workload manifest plus measured hardware constants, and exposes the dependency structure a future scheduler will consume:

• a manifest contract — one record per tape operation, with two independent producers (an exact tape-walker that runs where the prover runs, and closed-form builders in pure Python that cross-check it);
• a cost model in per-claim accounting form, carrying the production expressions from the paper’s cost appendix;
• a claim-level dependency DAG with critical path and width profile, which is what tells you how much parallelism actually exists;
• a partition scorecard that maps claims onto N shards under competing strategies — contiguous tape ranges, pipelined layers, expert-sharded — and scores them.

A profiler is only as good as its predictions, so validation came in two rounds. The first was retrodictive: predict the archived Maverick run from a synthetic manifest plus the development box’s measured constants, then compare against what that run actually recorded.

Prove time is deliberately a bracket, not a point estimate. The floor is the bandwidth-bound target the design should reach after planned reorganization; the aggregate is calibrated on today’s code. The gap between bracket and measurement largely reflects itemized implementation overhead, and so functions as a work-list rather than an unexplained residual.

The second round I ran end to end myself, on hardware the tooling had never seen: a single rented B200, the class of machine the multi-GPU effort actually targets. One morning and about eleven dollars of GPU time later:

• the calibration suite filled a complete machine profile from scratch, every constant measured on the box with its provenance recorded (headline: 29× the development box’s memory bandwidth);
• the exact tape-walker ran against real tapes for the first time and cross-checked clean against the closed-form builders — zero flags: every claim count, witness slot, linear constraint, and quadratic product matched within the documented expected set, and the per-row witness layout agreed with the prover’s own accounting row for row;
• the extracted Maverick manifest reported 109,273,513 witness rows, against the production run’s measured 109.27 M — the profiler reproducing reality, not merely its own model of it;
• the first cost bracket priced on measured Blackwell constants put the full Maverick proof at a 376-second floor on one B200 — roughly 28× the development box, tracking the measured bandwidth ratio almost exactly, which is what the cost model’s scaling story says should happen when the dominant terms ride memory bandwidth.

I also corrected the cost model’s RMSNorm row to the wrap-free bracket constants, bringing it into line with the paper’s own analysis.

A retraction

The same B200 session produced a finding I later had to withdraw: an apparent interconnect bottleneck that turned out to be almost entirely an artifact of my own traffic model. Nothing was wrong with the machine; something was wrong with what I had assumed about it.

I record it because it is the discipline the whole exercise exists to enforce. A profiler that only ever confirms its author is not a measurement instrument. Predictions go out before the measurement, and get corrected in public when the measurement disagrees — otherwise the numbers above would be worth very little.

What is upstream, and what is not

Merged code — the dry-run profiler and its parts: manifest contract, extractor, cost model, execution DAG, partition scorecard; and the RMSNorm cost-model correction.

Publicly reported validation — the B200 tape extraction and the measurements that came out of it, recorded in the profiler pull request itself.

Pending upstream — the calibration suite, the machine-profile tooling, and the remaining B200 artifacts. Until those land, treat the figures above as reported by me rather than independently inspectable.

On agent assistance. Implementation was agent-assisted. I owned the research direction, the cost-model derivation and checking, the experimental design, the validation, and the review; generated code was kept only after it was tested and cross-checked. This is project-wide practice rather than a personal one — agent co-authorship is recorded on the great majority of commits in the repository, across contributors, as part of how the fellowship’s compute is used. I mention it because it is visible in the commit trailers, and a reader who finds it there rather than here would be right to wonder why it went unsaid.

Where this goes next

The immediate step is a port to 2–8 GPUs, and the reason is not that more GPUs sound better. Multi-device proving is what lifts the ceiling on model scale, context length, and mixture-of-experts breadth — the dimensions along which a single box runs out first.

The honest projection carries the same bracket as everything else here. The anchor is a 376-second floor for the full Maverick proof on one B200: a floor, not today’s code, and it assumes the reorganization the cost model already itemizes. Distributing that across 2–8 devices puts a 400B proof in the low minutes if the partition scales cleanly, and under a minute at the optimistic end. Both numbers are projections from a model validated against one archived run and one live machine — good enough to plan against, not yet a measurement.

Beyond that, fleets of 64–128 GPUs are what an order-of-magnitude larger model would require. That figure comes from the same cost model and has not been validated at that scale.

Current Limitations

While VerInf looks promising, there are currently limitations:

• No security audit. It is a research prototype.
• Proof Size — 93.6 GB at full scale. This is a real deployment obstacle, not a rounding error.
• 0.880 bits per token is not zero. For some applications that residue is fine; for others it is not, and the paper is explicit that tightening it is open work.
• Soundness is a per-challenge bound (about 2⁻¹⁶·⁶ in the demonstrated configuration), raised by opening more columns at a measured cost in verification time. It is a deployment choice, not a fixed property.
• The claim list reveals the architecture.
• The transcript anchor is not yet demonstrated end to end. The AES and SHA-256 circuits are implemented and tested; binding them against independently recorded digests is not yet shown.

Why this matters for assurance

The reason to care is not that datacenters are presumed dishonest. It is that “trust us, we ran the model we said” is the current state of the art, and it does not scale to a world where the stakes of that claim keep rising — third-party evaluation, regulatory audit, compute governance, any arrangement where an inference claim carries weight and the parties are not aligned.

Verified inference is one approach to a general question: how do you establish trustworthy claims about an AI system when the system, the operator, and the evaluator may each be untrusted? Cryptography attacks it from below. Evaluations attack it from above. Neither is sufficient alone.