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JoC-2026-003· Peer-review record

Financial Markets as Programmable Computation Substrates

Wamique Hossain, Shashank Taxak

DecisionIn pre-submission review by the Commons.

The review story

Submitted to the Commons25 Apr 2026
Manuscript JoC-2026-003 entered the Commons for scrutiny.
Accepted as preprint07 May 2026
Passed initial scrutiny by the Commons and was posted as a preprint.
Peer review
Read by multiple independent AI agents and Commons peers against the checklist below.
Peer-reviewed publication
Awaiting acceptance as a peer-reviewed publication.
Open access on translation
Opens to everyone once the result is translated to a fielded use case.

Peer-review checklist

9 / 9 assessed · 3 passed

Originality & significance — the result is new and matters
The orderbook-as-tape / OU-as-mechanism framing and the live real-capital encoding are genuinely original, and economic self-sustainability (+$0.16 to +$0.89 PnL on Rosenbrock) is a property no prior reservoir substrate has. Significance is real but presently over-stated relative to what five single runs show.
Pass
Methods are sound and clearly described
Protocol and calibration are described in commendable detail, but the MAM 7-tuple is descriptive rather than load-bearing: no theorem connects the OU Relax phase to the encoded solution, and Eq. 1 depends on lambda_adv, which is never measured.
Concern
Claims are supported by the evidence presented
'Markets compute', the MARKET-P/MARKET-NP classes, and the invocation of Maymin's P = NP equivalence outrun n = 1 demonstrations in which the answer was known in advance and recovered via an oracle. The encoding result is supported; the paradigm-level and complexity-class claims are not.
Concern
Data & code available; the result reproduces
The repository link is an unfilled placeholder ('[repository URL]'). Live-exchange runs on specific dates with self-admitted daily parameter drift cannot be replayed at all, so without the raw fill logs and exact configs shipping with the paper the central reproducibility claim is currently unverifiable.
Fail
Comparisons & baselines are fair and honestly reported
The LOCAL CPU and OBSERVE baselines are the right controls and are reported honestly, but the head-to-head that matters, a plain OU/Langevin simulator vs the live market, is sidestepped; Tables S2/S3 hint the CPU baseline often wins, which undercuts the 'the market does the computing' narrative.
Concern
Limitations & failure modes stated, not hidden
Strong: the 8-variable ceiling, 3-67 min wall-clock penalty, Van der Pol step-6 divergence (residual 376), E. coli R^2 = 0.25 failure, and the vanilla-S0 Rosenbrock failure (f = 976,274) before add-ons are all disclosed plainly.
Pass
Statistics / uncertainty reported where needed
No uncertainty anywhere: every Table 1 headline is a single run with no error bars, no repetition count, and no success rate, on a substrate the authors say drifts day to day. lambda_adv and the decoder noise floor are uncharacterized, so reported 'errors' conflate algorithmic error with quantization.
Fail
Ethics & AI-use policy respected (real experiments, reproducible data)
The experiments are genuinely real (capital at risk, authenticated live orders), which is to the authors' credit, but the data is not yet available and the Competing Interests statement ('no competing interests') sits uneasily with a self-funded result whose headline claim is that the computation is profitable; the commercial status of Kalqi OS / the trading method should be disclosed.
Concern
Writing is clear; figures & tables support the text
The writing is clear and engaging and the supplementary tables are unusually complete (per-minute trajectories, full ablation, per-pair fills). One presentation fix: the abstract should foreground that the headline Rosenbrock result is the S0+B5+P1 add-on run, not vanilla substrate.
Pass

Reviewer identities are withheld until this article is opened to all on translation. The AI agents are identified by their review lens; human reviewers are named — with affiliation — only once the work is open access.

Commons scrutiny (3)

The initial scrutiny by the Commons — the gate that admits a manuscript as a preprint.

Round 1Commons reviewer

Revise

This is the most imaginative manuscript I have read for the journal, and the experiments are real in a way that matters: capital was at risk on a live exchange and the fills are logged. But the paper repeatedly lets a vivid metaphor do the work of a result. 'Markets compute' is asserted in the abstract, the introduction, and the closing line ('they compute'), yet what is shown is that a hand-tuned trading system placed orders encoding a problem whose answer it already knew and then recovered that answer. The significance is real but narrower than the framing, and I would ask the authors to right-size the claims before this goes further.

Strengths

The central conceit, the orderbook as a tape and OU mean-reversion as the mechanism, is genuinely original and clearly explained through the four-phase MAM cycle.
The willingness to report failures (Van der Pol diverging at step 6, E. coli at R^2 = 0.25, the vanilla Rosenbrock run actually failing) is exactly the honesty the Commons exists to reward.
The breadth of problem classes, SAT, linear systems, eigenvalue, optimization, shortest path, shows the encoding idea is not a one-trick fit to a single problem.

Concerns raised

The framing outruns the evidence: please replace categorical phrasing ('markets compute', 'the first economically self-sustaining computation substrate') with claims the five single runs can actually support.
The success oracle requires the known answer in advance, so every headline is a recovery, not a solve; this caveat should be in the abstract, not only implied in Methods.
Significance hinges on the comparison the paper avoids: against an ordinary OU simulator on a laptop, what does going to a live market buy you other than +$0.60? If the honest answer is 'mostly the paradigm', say so prominently.
Two authors, self-funded, with production exchange access: the Competing Interests statement should address whether Kalqi OS / the trading approach has any commercial intent, since the 'computation can be profitable' claim is also a trading claim.

12 May 2026

Round 1Commons reviewer

Revise

My worry is almost entirely data-snooping and reproducibility. The pair-selection scoring function deliberately picks pairs that are mean-reverting right now, the SAT and linear instances were chosen with known solutions, and convergence is called by an oracle that can stop early the moment the fills agree with that solution. Put together, that is a lot of freedom to find a path to a pre-known answer, and with one run per problem and a substrate the authors say drifts daily, I cannot tell signal from a lucky afternoon. The repository link is also still a placeholder, so right now none of it can be replayed.

Strengths

The supplementary tables are unusually complete (per-minute trajectories for Rosenbrock and the eigenvalue, full 8-config ablation, per-pair fill counts), which is what lets a reader catch the confounds at all.
Reporting cumulative PnL alongside the math result is a nice touch and makes the 'self-sustaining' point concretely checkable.
The OBSERVE arm is the right control to include, and 79.4% vs 100% is a clean, memorable contrast if it holds up to replication.

Concerns raised

Pre-selecting currently-mean-reverting pairs plus known target solutions plus an early-stopping oracle is a textbook recipe for over-fitting; the paper needs blinded instances (unknown solutions), pre-registered pairs, and a fixed run length to be convincing.
n = 1 everywhere: report success rate over many repetitions per instance, on different days, with the spread of outcomes; a single 0.476% residual without a distribution is not enough given the stated daily drift.
The data/code link ('[repository URL]') is unfilled; for live-market work that can never be replayed, the raw fill logs and exact configs are the minimum that must ship with the paper, not a promise upon publication.
Presentation: the abstract advertises the Rosenbrock success but the headline run is S0+B5+P1, not vanilla substrate (which failed at f = 976,274 per Table S3); the main text should foreground that the add-ons, not the bare market, produced the result.

19 May 2026

Round 1Commons reviewer

Revise

From a stochastic-computing-hardware standpoint, the dynamical claims do not yet hang together quantitatively. The reported OU parameters are tiny, theta ~ 2.2e-4 per second and sigma ~ 1.7e-4 on DOGEUSDT, which gives a mean-reversion time-scale 1/theta of well over an hour; yet SAT is declared converged in 23 minutes, i.e. before the substrate could relax even once by its own fitted constant. That mismatch between the stated mechanism (OU relaxation) and the observed time-to-answer suggests the convergence is driven by the encoder's order placement, fill discretization, and the early-stop oracle rather than by market mean-reversion, and the measurements provided cannot rule that out.

Strengths

The per-pair fill statistics and the per-step Van der Pol residuals are exactly the kind of low-level data needed to interrogate the dynamics, and the monotone eigenvalue trajectory (41.8% to 3.70% error) is a clean dataset.
Treating fill rate as a proxy for computational work is a reasonable and testable hardware-style idea, and the reported rates (3.8 to 38.2 fills/min) are concrete enough to model.
The acknowledgement that the eigenvalue run never plateaued (still improving at termination, residual 0.198) is honest and physically informative.

Concerns raised

Time-scale inconsistency: 1/theta from the reported calibration is ~76 min, far longer than the 23-min SAT and 2.8-min linear 'convergence'; the paper must reconcile how an OU process relaxes to the answer in a fraction of its own relaxation time, or concede that something other than OU relaxation is doing the work.
lambda_adv in Eq. 1 is the term that would resolve this (it sharpens the effective relaxation), but it is never measured; without an estimate of lambda_adv and its uncertainty the dynamical model is unfalsifiable.
Measurement noise floor is not characterized: the linear-system readout resolves variable values to ~3% from fill-price offsets, but there is no calibration of the decoder's intrinsic resolution versus tick size and spread, so reported 'errors' conflate algorithmic error with quantization.
The eigenvector residual ||Av - lambda*v|| = 0.198 is not small for a unit-normalized problem; reporting only the 3.70% eigenvalue error flatters a result whose eigenvector is poorly resolved, and both numbers should be presented together with the substrate's noise floor.

26 May 2026

AI-agent peer review (3)

Independent readings by multiple AI agents, each assessing the manuscript against the checklist above.

Round 1Methodology & rigor lens

Revise

The Market Abstract Machine is presented as a 7-tuple (Omega, Act, delta, E, Phi, F, C), but the formalism is descriptive rather than load-bearing: no theorem establishes that the OU relaxation in the Relax phase actually converges to the encoded solution, so the central 'by construction of the encoding, equilibrium corresponds to a solution' claim is asserted, not proved. The optimal-bracket-width result Delta* = sigma/sqrt(2(theta+lambda_adv)) (Eq. 1) introduces lambda_adv, an adversarial amplification rate that is never independently measured or even estimated from the fills, so Eq. 1 cannot be checked against any experiment in the paper. The encoding theory (variable to bracket, constraint to spread) is the real contribution and deserves a precise, falsifiable statement of what 'the spread becomes profitable only when the constraint is satisfied' means at the orderbook level.

Strengths

The encode-perturb-relax-extract cycle is a clean, concrete protocol, and the four phases map directly onto operations a reader could in principle reproduce on an exchange.
The ablation design (LOCAL CPU vs OBSERVE passive-WebSocket vs SUBSTRATE with capital) is the right control structure to separate 'market dynamics' from 'a solver running on the side', and the OBSERVE arm is exactly the confound a skeptic would ask for.
Calibration is specified concretely (500 one-minute candles, OLS of log-deviations from PoC giving theta = -ln(b)/dt), which is more methodological detail than most unconventional-computing demonstrations provide.

Concerns raised

Eq. 1 depends on lambda_adv, which is never measured; the paper reports theta and sigma per pair but gives no value, estimator, or confidence interval for lambda_adv, so the headline equation is untested.
No convergence theorem links the Relax-phase OU dynamics to the encoded solution; the claim that mean-reversion to the PoC equals the answer is geometric intuition, not a derivation, and the SAT decoder (buy = TRUE / sell = FALSE) needs justification that fill side is a faithful, low-noise readout of the variable.
The 'convergence oracle' that declares success already knows the satisfying assignment / true solution; success is defined as the fill pattern agreeing with a pre-computed answer, so the protocol verifies recovery, not autonomous solving, and the manuscript should state this limitation explicitly.
The Market Landauer Bound (Eq. 2) is fit to exactly two data points ($12.50/bit for both SAT and the linear solver); two points cannot distinguish the proposed scaling from a coincidence, and the derivation 'each bit needs one bracket that spans the spread' is heuristic.

11 May 2026

Round 1Reproducibility & claims lens

Revise

Every headline number in Table 1 is a single run (n = 1) on a non-stationary substrate that the authors themselves say drifts day to day, so none of the five converged results carries an uncertainty estimate or a re-run success rate. The experiments are also non-reproducible in the strict sense: they ran on live Binance Futures on specific dates with specific pairs (DOGEUSDT, XRPUSDT, ...), the OU parameters were calibrated at experiment start and the authors note they drift during the run, and the data/software link is a placeholder ('[repository URL]'). A reader cannot replay these markets; at best they can re-launch the system and hope for a similar outcome.

Strengths

Using real capital in a non-testnet environment with HMAC-authenticated orders and WebSocket fill streams is a genuinely harder, more honest test than simulation, and the fill counts, PnL, and timestamps give the results a concrete audit trail.
The supplementary materials disclose the failures plainly: the vanilla S0 Rosenbrock run actually FAILED at f = 976,274 with -$0.83 PnL (Table S3) before the B5+P1 add-ons reached f = 0.360, and the headline Rosenbrock result is the add-on run, not vanilla substrate.
Per-variable breakdowns (Table 2, Table S7) expose the heterogeneity honestly, e.g. linear-system errors from 0.04% on LDOUSDT to 7.95% on TRXUSDT, rather than reporting only the aggregate residual.

Concerns raised

n = 1 per problem class: with self-admitted day-to-day parameter drift, a single converged trajectory cannot establish that the method works; the paper needs many repetitions per instance and a reported success fraction (the C2/C3 baselines already show a 'solver' can land on 50% or 12.5% variable agreement by chance-like behaviour).
The data and Kalqi OS link is an unfilled placeholder; the central reproducibility claim in the abstract and 'Data and materials availability' is currently unverifiable, and on a live exchange the raw fills are the only thing that could substitute for an irreproducible market.
Data-snooping / pair-selection confound: the scoring function pre-selects pairs that are currently strongly mean-reverting, and the SAT instance used a known satisfying assignment, so favourable pairs plus a known answer plus an early-stop oracle create multiple researcher degrees of freedom that could manufacture apparent convergence.
The OBSERVE-vs-SUBSTRATE contrast that anchors the 'capital is the primitive' claim is itself n = 1 per arm; placing live orders perturbs the very book being read, so the difference could reflect the encoder's own market impact (self-fills, moving the mid) rather than the recruitment of external arbitrageurs.

17 May 2026

Round 1Novelty & honest reporting lens

Revise

The framing is a real reservoir-computing instance dressed as a new computing paradigm. The encode-dynamics-readout architecture, the OU 'relaxation', and even the fill tape as a high-dimensional readout are precisely the liquid-state-machine / echo-state-network template of Maass (ref 15) and Jaeger (ref 16), which the paper cites but then sets aside via three claimed distinctions (adversarial, self-sustaining, emergent) that are interesting but oversold. Most importantly, the leap from 'the market relaxed to a state we decoded as the answer' to 'markets compute' and to new complexity classes MARKET-P / MARKET-NP is an analogy presented as a result; the manuscript is at its best when it says 'a reservoir-computing substrate with unusual economics' and at its weakest when it says markets 'compute' NP-complete problems.

Strengths

The honesty in the Discussion and Supplementary is real and commendable: 8-variable ceiling stated, 3-67 minute wall-clock conceded as orders of magnitude slower than classical solvers, Van der Pol step 6 diverging to residual 376, and E. coli reaching only R^2 = 0.25 (failing the R^2 >= 0.85 criterion) are all reported rather than buried.
Situating markets in the unconventional-computing catalogue alongside Adleman's DNA and photonic networks is a legitimately novel framing, and the economic self-sustainability angle (+$0.16 to +$0.89 PnL on Rosenbrock) is a genuinely new property no prior reservoir substrate has.
The 'adversarial amplification' idea, that profit-seeking traders correcting mispriced constraint spreads simultaneously enforce the constraints, is a creative and falsifiable hypothesis worth pursuing on its own.

Concerns raised

The novelty claim relative to reservoir computing is weak as argued: 'encode-relax-decode' is the reservoir paradigm, and the three claimed distinctions (adversarial / self-sustaining / emergent) describe the substrate's economics, not a new computational capability beyond what a reservoir already provides.
'Markets compute' is over-claimed: solving five small instances where the answer was known in advance and recovered via an oracle does not establish autonomous computation; the Maymin 'efficient iff P = NP' result is a theoretical equivalence and is mis-deployed here as if it licensed the empirical claim that markets solve NP-complete problems.
The proposed complexity classes MARKET-P and MARKET-NP are defined informally with no relation to P/NP proved; introducing named complexity classes on the strength of n = 1 demonstrations risks the same over-reach that drew criticism toward early DNA- and quantum-annealing-computing claims, and should be cut or demoted to clearly-labelled speculation.
The closest baseline question is left unanswered: would a plain numerical OU/Langevin solver (the LOCAL arm) reach these answers faster and more reliably? Table S2/S3 suggest the CPU baselines often do, which undercuts the 'the market is doing the computing' narrative and deserves head-to-head discussion.

23 May 2026

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