Every manuscript on the journal, from preprint to open access. Abstracts are public; full text is for Fellows, Patrons, and open papers.
JoC-2026-008PreprintPosted on clearing Commons scrutiny — round-one review is open.BreakthroughA breakthrough — a new result others can build upon.
Shashank Taxak · AI & Machine Learning
Where should a figure go? Most layout engines either drop figures at the end of a document, spread them mechanically, or anchor them to the first place the text says "Figure 1". We ask whether the *document's own statistics* can decide, in closed form, without any training. In the spirit of Reverse Synthetic Networks — where the data synthesises the model directly — we synthesise each figure's position from three statistics of the manuscript itself: the TF-IDF affinity between the figure's caption and each block of running text, the proximity to any explicit lexical reference, and a small set of layout priors. There is no loss function, no gradient, and no learned parameters. On a controlled 600-document corpus (1,903 figures) drawn from the Aitele Commons' research domains, the closed-form placer reaches 92.5% top-1 placement accuracy and a mean position error of 0.18 sections, against 60.9% for a reference-only baseline and ~22% for content-blind baselines. The decisive result is on unreferenced figures — those the prose never names: here the closed-form affinity recovers 85.1% of placements, where the reference-only method collapses to 21.7% (essentially chance). Closed-form caption–text affinity, in other words, supplies the reference the author forgot to write. The same machinery groups related figures into multi-panel composites (pair-F1 0.99, 97.7% of groups recovered exactly), assigns column-spanning width for a balanced two-column page, and — extended to in-text citations — places each reference at the sentence it supports with 90.6% accuracy (vs ~15% for position-based baselines) and splices the grouped markers into the prose. The library ships with a 34-test suite (unit, edge-case, randomised-invariant, determinism, and a 1,200-block stress test). We report the method, the experiment, and its limitations honestly: the corpus is topically separated, the metrics are layout proxies rather than human aesthetic judgement, and page-overflow is out of scope.
JoC-2026-009PreprintPosted on clearing Commons scrutiny — round-one review is open.BreakthroughA breakthrough — a new result others can build upon.
Shashank Taxak · AI & Machine Learning
Every article in the Journal of the Commons carries, in its record, a short list of related work drawn from the entire catalogue — latest and archived. We build that list in closed form, in the spirit of Reverse Synthetic Networks (RSN): the corpus's own statistics synthesise each article's neighbours directly, with no trained model, no embedding service, no gradient, and no per-user state. Relatedness is a TF-IDF cosine over each article's title, abstract and keywords, refined by a few structural signals (shared field, shared keywords, shared authors, explicit implements/implementation links). On a controlled benchmark of 30 random corpora with known topical ground truth, the engine reaches nDCG@10 = 0.964 ± 0.005 and Precision@5 = 0.994 ± 0.004 (mean ± sd), against 0.500 for a field-matching baseline and ≈0.08 for content-blind baselines. Crucially, a relevance gate derived from the corpus's own similarity distribution makes the engine return nothing for an article that has no genuine relatives: across 30 corpora seeded with one-of-a-kind articles, isolated articles are correctly suppressed 100% of the time (false-positive rate 0.000), while articles that do have relatives are served 100% of the time at 99.4% suggestion precision. Ranking 5,000 articles takes 177 ms on one CPU core.
JoC-2026-010PreprintPosted on clearing Commons scrutiny — round-one review is open.BreakthroughA breakthrough — a new result others can build upon.
Shashank Taxak · AI & Machine Learning
When a manuscript enters peer review, an editor must find reviewers who actually know the subject, who have no conflict of interest, and who are not already overloaded. We do this in closed form, in the spirit of Reverse Synthetic Networks (RSN): the corpus's own statistics synthesise the match. A reviewer's expertise — their field, declared keywords, and the text of their own authored work — is a TF-IDF vector; the manuscript is another; fit is their cosine, refined by field/keyword overlap and balanced by current review load. No trained model, no gradient, no embedding service, no per-reviewer profile learning. On a controlled benchmark of 30 reviewer pools with known expertise ground truth, the engine puts a correct expert in the top slot 99.8% of the time (nDCG@5 = 0.980 ± 0.008, MRR 0.999), against 0.52 for a field-matching baseline and 0.08 for random. Three properties make it safe to act on automatically: (1) conflicts are detected automatically from the collaboration record — the manuscript's authors, editor-declared conflicts, anyone who has co-authored with an author, and anyone sharing a lab with the author team are excluded with 100% reliability (while a clean expert is still found 100% of the time); (2) a fit gate returns an empty list for a manuscript with no in-Commons expert — the explicit signal to source reviewers externally — suppressing 100% of no-expert cases at 0 false positives; and (3) a load signal spreads assignments (peak load 7.4 → 6.3, dispersion 0.48 → 0.30) with negligible loss of fit. Matching against a 5,000-reviewer pool takes 131 ms on one CPU core. By design the engine serves the Commons — its Fellows and Patrons; for external reviewers it returns nothing, and the editor sources them by hand.
JoC-2026-012PreprintPosted on clearing Commons scrutiny — round-one review is open.BreakthroughA breakthrough — a new result others can build upon.
Shashank Taxak · Defense Systems
We study an on-device, training-free classifier that types a multi-suspect call-detail-record (CDR) group by the clandestine-network archetype it most resembles — one of nineteen documented topologies — or as benign. Each archetype is a synthesised neuron: the measured feature-space centroid of canonical exemplars, scored by a uniform-weight Gaussian kernel in standardised space. On a replicated synthetic benchmark (8 runs, seeds disjoint from calibration), 20-class typing reaches 86.3% top-1 accuracy (95% CI 85.8–86.8%), macro-F1 0.856; an open-set abstention gate lifts selective accuracy to 88.5% at 95.0% coverage. The central result is negative and we lead with it: the risk score that separates conspiracies from a trivial benign null at AUC 1.00 separates them from realistic benign-but-busy groups (families, call-centres, rallies, fleets, carpools) at AUC 0.25 — below chance — flagging 83% of lawful cohesive groups. Structure does not encode intent. Accuracy also falls from 83.8% to 33.3% under realistic partial observation, and 20-member mixed conspiracies defeat single-label typing (30% confidence) while still being correctly decomposed. We conclude the engine’s defensible role is a structure-typer and lead-generator for a human analyst, not an autonomous detector. All numbers come from the shipping engine; the benchmark is synthetic (separability + robustness, not field validation). 1.2em
JoC-2026-011PreprintPosted on clearing Commons scrutiny — round-one review is open.BreakthroughA breakthrough — a new result others can build upon.
Shashank Taxak · Defense Systems
We present a keystroke-cadence verifier for high-assurance login that is synthesised in closed form from enrolment data—no gradient training, no epochs. Each typing sample becomes a fixed-length timing vector; the model z-score-normalises features, removes incoherent enrolment samples, optionally weights features by a multi-class Fisher ratio, forms PCA sub-prototypes, and decides by a cohort-relative weighted-cosine margin gated by a per-user threshold. On the public CMU keystroke benchmark (51 subjects), under a protocol with strictly held-out impostors, the method attains a mean per-subject Equal-Error-Rate of 9.38% (7.78% with Fisher weighting disabled). That is in the same range as the best detector reported by Killourhy and Maxion (9.62%), but we stress this is not a head-to-head comparison: our cohort-relative protocol uses the other enrolled subjects as negative evidence, which their per-subject detectors do not, and the per-subject variance is large ( 8.5%; 95% CI [7.0,11.7]%). A component ablation—reported in full, including a negative result—shows that cohort-relative scoring alone sets a low-false-accept operating point (FAR 0.69% vs 13.3% for an unweighted single-threshold baseline, 19 fewer impostor admissions); per-user adaptive thresholds were measurably inert on this benchmark, and Fisher weighting slightly hurts. Cross-session drift would otherwise make a static operating point reject 40–86% of genuine logins; we close that gap with password-gated continuous learning—a cadence rejection that the password confirms is appended as a new neuron, so the template tracks the user while impostors (who lack the password) can never poison it. Across CMU sessions this halves the fallback rate and halts the drift escalation at no false-accept cost. We are explicit about the remaining limitations: the synthetic study is an internal consistency check rather than independent evidence, and only zero-effort impostors are evaluated.
JoC-2026-002ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Wamique Hossain, Dilip Kumar Burnwal, Shashank Taxak · Computer Architecture
Graphics and inference accelerators are built from arrays of binary multipliers and adders whose area and switching cost grow with operand width. Stochastic computing (SC) encodes a value as the probability of a bit being one in a random stream, reducing multiplication to a single AND gate and scaled addition to a multiplexer, and is intrinsically tolerant of bit errors—making it a candidate primitive for area- and reliability-constrained graphics. Prior SC work targets isolated arithmetic blocks, 2-D image filters, or neural-network layers; no prior design integrates SC into a complete graphics-rendering pipeline. We present StochastiCore, a synthesizable GPU pipeline (command processing, SC vertex transform, binary rasterization, SC fragment shading, framebuffer/Z-buffer, and VGA output) implemented in open-source Verilog. We make three contributions beyond integration. First, a theoretical analysis of SC graphics that derives the estimator variance, the binary-to-stochastic quantization bias, the stream-correlation error of multiplication, the white-noise spectrum that makes SC noise act as dithering, and the bit-error-rate at which SC overtakes binary arithmetic—each validated against simulation. Second, a reproducible, gate-level characterization: a single Yosys flow regenerates every cell count (the complete SC compute unit synthesizes to 1,709 primitive cells / 597 iCE40 LUT4, comparable to a single binary MAC-4 at 1,912 / 674 while providing four multiply lanes, a MAC, a , and a lerp), and an activity-driven power model run on the synthesized netlist reports realistic energy including all LFSR, converter, and counter switching. Third, a bitstream-generator study showing that replacing the LFSR bank with a low-cost deterministic low-discrepancy generator reduces generator area 7.5 and generator switching energy 15.4, with a documented accuracy/latency trade-off. Bit-accurate rendering on the verified LFSR model (not an idealized random model) achieves 36.7 dB PSNR at L=1024 and is resolution-invariant; a cycle-accurate frame-time model gives 68 fps at L=256 on 320240. Site-resolved Monte-Carlo fault injection (N=10,000, 95% CI) shows SC compute-domain errors fall below binary above 0.5%$ bit-error rate, while storage and control faults are equally vulnerable and are best protected by conventional means. We report these results honestly: SC is not a per-operation throughput or energy win; its advantages are area parity at higher functional density, a one-gate marginal multiply, graceful degradation, and free anti-banding.
JoC-2026-003ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Wamique Hossain, Shashank Taxak · Markets & Computation
We demonstrate that financial markets under limit-order microstructure are programmable computation substrates. We define the Market Abstract Machine (MAM), a 7-tuple treating the orderbook as a computational tape and Ornstein-Uhlenbeck mean-reversion dynamics as the computation mechanism. We validate the framework on Binance Futures with real capital across five problem classes: 3-SAT (8 variables, 34 clauses, 100% satisfied, 87 fills, 23 minutes), 8-dimensional linear systems (0.48% residual, 107 fills, 2.8 minutes), 6D Rosenbrock optimization (global minimum region in 20 minutes with net positive PnL), 8x8 eigenvalue (3.70% error, 564 fills, 67 minutes), and shortest-path (exact solution, 6.3 minutes). Ablation studies show passive observation achieves only 79.4% SAT satisfaction versus 100% with capital committed, establishing capital deployment as the computational primitive. Financial markets are the first economically self-sustaining computation substrate.
JoC-2026-007ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Shashank Taxak · Exoplanet Science
The claimed detection of dimethyl sulfide (DMS) in the atmosphere of K2-18 b is the highest-profile candidate biosignature in the James Webb Space Telescope era, but independent re-analyses under heterogeneous forward models and reduction pipelines have arrived at incompatible verdicts . Here we resolve the controversy with a single methodological instrument: a uniform, pre-registered, machine-learning-accelerated Bayesian retrieval of the public JWST exoplanet atmosphere archive that implements the full Green et al. 2021 Standards of Evidence protocol end-to-end. A conditional normalizing-flow neural posterior estimator trained on 10^6 physics-based forward simulations over a 31-dimensional atmospheric-parameter space (including 15 life-indicative species and a non-isothermal three-layer pressure-temperature profile) produces a calibrated posterior for any archive spectrum in seconds. Every flagged (target, species) tile is driven through 14 independent gates — Holm–Bonferroni and Benjamini–Hochberg multi-testing correction, asymmetric-tail significance, an abiotic-mechanism catalogue of 25+ published pathways, a stellar-contamination posterior (Rackham2018,LustigYaeger2023), injection–recovery, null-injection false-positive-rate measurement, line-list ablation, and five further robustness tests — before any biosignature-relevant interpretation is permitted. We apply the instrument to all four published K2-18 b reductions and report CONFIRM/REJECT the DMS claim at _10BF_bio:abio=X, and simultaneously deliver the first population-scale 95%-credible upper-limit table on 15 biosignature gases across the 15-planet sample. The posterior catalogue, trained surrogate weights, 10^6 simulation library, preregistration, and analysis code are released as a community resource under MIT licence. We confirm/refute the previously-claimed detection of dimethyl sulfide (DMS) in the atmosphere of K2-18 b, and score every atmosphere against thermochemical equilibrium to identify headline molecular-disequilibrium anomalies. The posterior catalog, trained surrogate weights, preregistration, and analysis code are publicly released as a community resource.
JoC-2026-006ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Wamique Hossain, Sushant Maji, Dilip Kumar Burnwal, Shashank Taxak · Quantum Error Correction
Bivariate bicycle (BB) codes, introduced as a practical quantum LDPC family by Bravyi and co-workers, achieve approximately 12 physical qubits per logical qubit through their [[144, 12, 12]] “Gross” code, but published BB codes are not explicitly constrained to the heavy-hexagonal connectivity of current IBM superconducting processors. We report a systematic computational search for BB codes under the toroidal locality surrogate L_1^ 4 (a necessary condition for native heavy-hex embedding). Using random, evolutionary, and LLM-guided strategies we identify seven heavy-hex-compliant BB codes whose code-capacity figure of merit k d^2 / n upper-bound-exceeds the Gross code by factors 1.84–4.41; four lie on Smith-Normal-Form-distinct non-cyclic abelian-group tori not identified in the sources we checked, while three lie on cyclic tori and carry residual prior-art risk against generalized-bicycle tables we could not fully inspect. We provide an MIP-based upper bound d 24 for [[192, 8, 24]] (with asymmetric d_X = 30, d_Z = 24) and BP+OSD coset-decoding upper bounds for the remainder. Circuit-level memory experiments under a non-fault-tolerant syndrome-extraction schedule matched to a Gross reference reveal a stabilizer-weight-versus-pseudothreshold correlation consistent with heuristic scaling: across our four measured codes, break-even pseudothreshold drops from 5 10^-3 at stabilizer weight 6 (Gross reference) to 3 10^-4 at stabilizer weight 12 ([[196, 18, 24]]). Headline implication: our highest-S code is not deployable on current IBM hardware until fault-tolerant scheduling is developed for its weight-12 stabilizers. Evolutionary search clearly dominates random search in our single-seed comparison (95% upper-CI on this dominance is reported in-text). LLM-guided proposals, tested across five reasoning modes against a matched random control, achieve significantly higher valid-proposal rate (risk ratio 1.93, Fisher exact p = 1.2 10^-3) but do not extend the Pareto front ( 8% rule-of-three upper-CI on frontier-extension rate). The paper’s primary contribution is therefore a quantitative characterization of the weight-vs-pseudothreshold tradeoff; the codes themselves guide but do not solve the near-term-hardware problem.
JoC-2026-005ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Wamique Hossain, Sushant Maji, Dilip Kumar Burnwal, Shashank Taxak · Computational Biology
Whole-cell computational models represent the frontier of systems biology, yet none enforce the detailed balance condition—the thermodynamic constraint that k_f/k_r = (- G^/RT) for every reaction (the Wegscheider condition). Here we show that enforcing this constraint as a simulator invariant reveals systematic thermodynamic inconsistencies in the state-of-the-art JCVI-syn3A whole-cell model, producing qualitatively different biological predictions. We develop a stochastic simulation framework in which every reaction’s forward and reverse propensities satisfy local detailed balance at every simulation event, parameterized from eQuilibrator standard transformed Gibbs energies ( G^). Applied to JCVI-syn3A, we find that of 77 metabolic reactions with complete kinetic and thermodynamic data, 70% violate detailed balance by more than 3-fold (median discrepancy: 12-fold), and 27% (21 of 77) have their equilibrium direction reversed at standard state—of which 12 persist at physiological intracellular concentrations. Five of ten glycolytic steps have their equilibrium direction reversed, including lactate dehydrogenase, where the thermodynamic equilibrium constant (K_eq = 48,743) differs from the model’s kinetic value (K_eq = 0.97) by 50,000-fold—predicting dominant lactate fermentation consistent with known Mycoplasma biology. Our framework, validated on a 13-reaction proof-of-concept and scaled to a 2,069-reaction model of syn3A (198 metabolic + 51 transport + 1,820 gene expression reactions, 1,671 species), provides a principled foundation for thermodynamically consistent whole-cell simulation.
JoC-2026-001ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Shashank Taxak · AI & Machine Learning
We introduce Reverse Synthetic Networks (RSN), in which the data synthesizes the model in closed form: every weight is computed from corpus statistics, with no loss function, no gradient, and no GPU. We separate a positive and a negative result. For discriminative tasks a fully synthesized classifier edges a tuned linear SVM on 20 Newsgroups (87.0% ensemble) and reaches 90.8% on digit recognition with zero training, in seconds on a CPU. For generation, the same paradigm has a hard ceiling: a battery of twelve closed-form enrichments all collapse to a Modified Kneser-Ney n-gram. Measured honestly in bits-per-byte against a locally-run GPT-2, the pure RSN reaches 1.97 BPB versus GPT-2-small's 1.04, and a 100M-token scaling study shows the ceiling is representational, not data-limited. RSN is a cheap, strong floor for discriminative work, not a path to the trained generative frontier, and we explain why.
JoC-2026-004ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Wamique Hossain, Shashank Taxak · AI & Machine Learning
We present MCM-Guided Activation Steering (MCM-GAS), a framework that uses live financial market Ornstein-Uhlenbeck (OU) equilibrium dynamics to compute context-adaptive steering vectors for transformer residual streams. Unlike static activation steering---which injects a fixed mean-difference vector regardless of input context---MCM-GAS modulates each component of the steering vector in real-time using normalized z-scores from passively observed mean-reverting cryptocurrency pairs. We implement MCM-GAS on GPT-2 (117M parameters, 12 layers, d_model=768) and GPT-2 Medium (345M, 24 layers, d_model=1024) via TransformerLens residual stream hooks, computing four domain-specific steering vectors (code generation, bug fixing, architecture design, code explanation) from positive/negative activation poles. Across nine rigorous experiments totaling 700+ measurements, we demonstrate: (1) statistically significant representation shifts toward target activation poles (all p 1.5); (2) strict monotonicity of projection onto v_steering as a function of injection intensity alpha; (3) measurable market-driven vector modulation; (4) simultaneous attraction toward the positive pole and repulsion from the negative pole (all p < 0.005); and (5) MCM-GAS vectors that are statistically distinct from static vectors (p < 10^-33). Critically, our ablation study (Experiments 6-9) comparing MCM modulation against random, scrambled, and anti-correlated baselines reveals a vector- and model-dependent effect: on GPT-2, v_arch (layer 8) shows significant MCM advantage over random (p < 10^-12, Cohen's d = +6.04, 80th percentile); on GPT-2 Medium (345M), v_coding and v_bugfix show significant MCM advantage (d = +2.79, +6.16). At large-N (51 prompts), v_bugfix and v_arch replicate MCM advantage (d = +2.86, +4.86), while v_coding shows random advantage (d = -2.73). No single vector shows consistent MCM advantage across both models. These results establish MCM-GAS as a novel dynamically adaptive activation steering framework, with honest evidence that market modulation provides statistically significant advantages for specific representational subspaces, but the advantaged subspace is model-dependent rather than universal. Keywords: activation steering, representation engineering, Ornstein-Uhlenbeck process, market computation, transformer interpretability, residual stream injection, ablation study
