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#large language models Open access

Race all the way down, race all the way up: A unifying vocabulary for bounded-commit dynamics across quantum, classical, biological, and computational substrates

Tomas Pødenphant Lund
Aug 2026 · Open MIND
Quantum Mechanics and Applications

Abstract

The same bounded-commit dynamics recur, unnamed, across quantum decoherence and einselection, classical Onsager–Machlup path integrals with Kramers escape, race-model accumulators in decision neuroscience, and autoregressive language models — literatures whose citation patterns leave the shared structure invisible. This paper proposes race-architecture as the vocabulary and empirical anchor that makes that common structure explicit. It is a synthesis, not new physics. The connected literatures include: quantum decoherence and einselection (Zurek 1981+); Onsager-Machlup classical path integrals + Kramers escape; race-model accumulators in cognitive psychology and decision-neuroscience (Vickers; Ratcliff; Usher-McClelland; Cisek-Kalaska); Wallace's biocognition rate-distortion / Yerkes-Dodson programme; 1/f noise across condensed matter, neural avalanches, and financial markets; large-language-model CR-signal dynamics (Pødenphant Lund 2026d). These literatures share an underlying structure - competing processes resolving under a finite-time budget - but use mutually incompatible vocabulary. Race-architecture is captured by R1+R2+R3 (parallel candidates with non-trivial competitive interference + bounded resources + irreversible commit), refined to five axioms A1-A5 for compatibility with Schwinger-Keldysh formalism. The Section 1.5 structural prediction is kernel-conditional (Wallace counterexample). The Schwinger-Keldysh formalism admits a race-axiomatisation under three assumptions; Feynman path integral and Onsager-Machlup are exhibited as parameter-regimes. The LLM CR-signal is a substrate-mapping providing empirical access. Companion papers in the friction-theory series: Paper 0 - Behavioural Friction Theory (concept DOI 10.5281/zenodo.19462499); Paper 1 - Friction as the cost of probabilistic computation (10.5281/zenodo.20012654); Paper 3 - Friction-guided inference (10.5281/zenodo.20014121); Paper 13 - Operational Friction Theory (10.5281/zenodo.20059876). v4.3 changelog (July 2026): a prior-art and positioning revision; no result is altered and no new empirical claim is made. (1) Section 8.1 previously surveyed adjacent unification programmes while omitting the two most prominent contemporary cross-substrate ones. Constructor theory (Deutsch 2013; Deutsch & Marletto 2015) and assembly theory (Sharma et al. 2023, Nature) are now cited and differentiated, and Wolpert (2019) is credited for the stochastic-thermodynamics bridge to computation. Because both added programmes are themselves cross-substrate, the section's closing differentiator is rewritten: cross-substrate scope alone no longer distinguishes this proposal, and the distinction is relocated to the organising primitive (modal / historical / dynamical) together with the consequences that follow from it. (2) Section 2.2 now states explicitly that a commit-event is, mathematically, a first-passage event, credits first-passage theory as already a substrate-agnostic cross-domain unification, and concedes that no new results in that formalism are claimed. The kernel-conditional non-monotone rate-shape is explicitly withdrawn from the paper's residual claims, since it has close antecedents in resonant activation, optimal stochastic resetting and hazard-shape effects; it is presented as a substrate-crossing synthesis only. (3) Section 4.3 anchors the computational substrate in the statistical-mechanics-of-learning literature (Gardner 1988; Engel & Van den Broeck 2001), with Shan, Li & Sompolinsky (PNAS 2025) cited as independent warrant rather than as a finding of this paper. (4) Abstract and scope statements tightened, and strawman disclaimers removed (the paper no longer disavows claims a reader would not attribute to a vocabulary proposal). Nine references added, all verified. Revisions made after external review. v5 (August 2026) — the abstract now leads with the measured result. The body is unchanged. What was buried is that the per-token count carries no detectable memory beyond the adjacent token: first-lag autocorrelation ratio at most 0.105 across five substrates, and 0.004 and −0.028 on the two ratio-instrumented ones. Memorylessness beyond one step is what a Markov process looks like, and the section reporting it had called this the stronger form of the reading all along while the abstract carried the exponential fit instead. The same sentence oversold the fit and undersold the finding; both halves are corrected. The structural mapping onto the equal-time Keldysh component is stated with the three assumptions that actually govern it — Gaussian approximation, discretization, Markovian modes — and as an analogy under those assumptions rather than a strict parameter-limit reduction. The cross-substrate predictions are stated with the tests that would bear on them, including the superconducting-qubit joint diagnostic and its falsifier, which had not appeared in the abstract at all. The operational-time claim now cites the point-process literature it had ceded in prose (Daley & Vere-Jones, 2003). A three-denial paragraph became one scope clause carrying the same content. Earlier versions remain in the version history.

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#large language models Open access Aug 2026

A Pattern Language for Production LLM Platforms: Governed Routing, Agent Orchestration, and AI-Native Delivery

A production platform built on large language models makes two kinds of decision, and most of its trouble comes from writing both into one clause. An optimization decision improves an objective: lower latency, lower cost, higher quality, fewer tests run. A boundary decision fixes a constraint that may not be relaxed for any gain: a residency rule, a least-privilege scope, a human-review threshold. When the two share a clause, improving one silently erodes the other, which is why efficiency and accountability are so often reported as a trade. This specification is built on one invariant: a boundary is a clause the optimizer may not cross, and everything else is optimization. The contribution is a cross-layer architectural method for separating non-negotiable constraints from adaptive optimization and binding both to reconstructable evidence, applied identically across model routing, agent orchestration and AI-native delivery. The seventeen patterns are instances of that method rather than the contribution itself. Each pattern is specified in the classical pattern form and carries three architectural declarations: the boundary it fixes, the optimizer it frees, and the evidence proving the boundary held. Every boundary is assigned to one of five classes covering data, authority, decision, resource and process constraints. Section 3 states the derivation method by which candidates were admitted or rejected, and publishes the rejections alongside the admissions so that the criterion can be examined rather than trusted. Three mechanisms make the language operate as a language rather than a list. A pattern relationship graph names which pattern supplies the artifact, evidence or authority another depends on, including the single cycle by which a workflow improves from its own structural record and the economic chain running the full height of the stack. A normative event identity, with rules for causal parentage, retries, provider boundaries and retention, turns the requirement that evidence be joinable into something an implementation can satisfy or fail. And per-pattern applicability conditions replace categorical requirements, so that a pattern governing a mechanism an institution does not operate is out of scope rather than a gap. Conformance is self-declared and published as a profile carrying the environment, the applicable set, per-pattern status, an evidence date and documented gaps. It is not a certification scheme, and no conformity assessment body operates against it. The contribution is architectural rather than empirical. Every pattern carries an evidence level, and no pattern reaches the highest level, because no implementation unconnected to the author has been evaluated. Nothing has been measured. The specification separates what would falsify the invariant from what would falsify an individual pattern and from what would falsify the composition and adoption sequence, poses six research questions, and records the absence of a real implementation profile as a known deficiency of version 1.0. An appendix reconciles the pattern identifiers with the names used across the author's papers and companion book series, including the acronyms PEVG and PARA, so that the two bodies of work can be cited as one. Version 1.1 names two constructs the specification already contained. The central proposition is named the Boundary Invariant, and the three architectural declarations required of every pattern are together named the BOE Declaration. Neither carries a trademark, both are offered for use with attribution under this document's licence, and neither changes any requirement: the proposition, its wording and its priority date are those of version 1.0. Section 11 gains the two-family naming convention and a precedence rule fixing which document governs where this specification and the Defensible AI Framework Registry describe the same relationship.

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