Math notes (agent drafts) [uts-016Q]

Notes on formulating discrete-induced physics [fdip-0001]

Physics often models length, time and field values as continuous (infinitely divisible) quantities. Measurements have finite resolution, so they do not establish that this continuum is fundamental. The real numbers are its usual mathematical form. The hypothesis is that geometry, time and matter can be induced from finite carriers and exact algebra, with continuum physics recovered at large scales. A classical field assigns values to points of space; in this discrete setting, the points are vertices or edges of a finite graph. Finitely many coordinates need not mean finitely many possible values: later Gaussian probability laws are laws on a real inner-product space. What a structure induces is kept separate from what must be supplied. A finite incidence presentation records the finite vertex set, edge set and endpoint maps; the alternating four-cycle of Example [fdip-0003] makes the additional weights explicit. Reordering vertices changes a representation; changing weights changes the model. Gaussian elimination in Elimination and retained observables uses a specified quadratic action and reference measure.

Induced geometry provides one motivation. On a fixed background, one-loop matter fluctuations generate an Einstein-Hilbert term in the effective action [visser2002sakharov, Section 1, Eq. (1)], so the gravitational action need not be assumed: it can be induced by the matter content. Keeping supplied microscopic data, state assumptions and induced structure apart is then a distinction with content. Conjecture [fdip-003X] asks for an arithmetic scheme with a Clifford sheaf and a finite-group action, for a map from a moduli stack controlling the continuum limit and the background dependence, and for quantum-field-theory parameters given by special L-values. Spacetime, physical parameters, the Standard Model equations and gravitational equations stay outside those finite constructions. Discrete induction and its open interfaces records the comparisons that remain open: the physical environment map, and the continuum question, which is asked and not answered. The groupoid and the realization do not supply a site, a stack, or a descent. No moduli map is constructed.

These notes develop finite constructions and do not propose a physical theory. The parts run from fields on finite carriers through Gaussian laws, symmetry and Clifford algebras, to arithmetic families and a fixed-mode comparison on a supplied circle. Geometric realization then asks which information survives when weights are forgotten. Selected backgrounds, gauge fields, Euclidean time and lattice fermions give the finite constructions that the structure chapters and the closing § [fdip-003W] compare. Remark [fdip-004E] maps the parts. The alternating four-cycle returns as the example that ties a later construction to specified fields, weights and observables.

Notes on Rubik's abstract polytopes [frap-0001]

These are the accompanying mathematical notes for the experimental research project FRAP, Formalizing Rubik's Abstract Polytopes. The notes ask how the mathematics of abstract polytopes, incidence actions and group constraints can explain the concrete practice of solving Rubik puzzles. The longer motivation, history and scope are recorded in why these notes connect polytopes and puzzles.

The route is staged so each abstraction earns a concrete consequence. We begin with incidence and shape symmetry, apply local facet moves to a tetrahedron and a cube, then pass to layered cubes, orientations and reachability. The later notes return to human methods, computer search, replay and play. How the notes are organized is the working route through those chapters; the companion repository is still an early research project, so formalization and executable solver work remain part of the scope.

Notes on formulating theoretical intelligence potential [ftip-0001]

Formulating Theoretical Intelligence Potential (FTIP) studies how much reliable capability post-training and agent procedures can obtain from a specified pretrained model, and how that capability depends on information, feedback, computation and evaluation. The question is comparative: what improves, through which mechanism, at what cost, and on which tasks? It concerns behavior under specified conditions, rather than an intrinsic intelligence number assigned to a model.

Post-training studies how demonstrations, preferences, rewards and interaction change a model's policy. Fine-tuning, direct preference optimization, and reinforcement learning with human or verifiable feedback differ in their objectives, feedback and update procedures. Agent systems add another source of change: tool use, search, persistent state and retained context can alter behavior even with fixed model weights. Their computation, failure modes and reliability are part of the capability being studied.

Evaluation and finite limits ask which improvements survive independent testing, what transfers to other task laws, and what follows from assumptions about sampling, support, feedback and proxy error. Architecture and optimization ask how the model's computation, parameterization and optimizer affect achievable capability and resource tradeoffs. Its mechanism map asks which bottleneck a design changes and what evidence would demonstrate that change, without ranking architectures. Matched comparisons connect these questions without identifying a higher training reward, a successful rollout and a transferable capability as the same outcome.

Conceptual discovery across generations extends the study to successive learned artifacts, reusable representations, curricula and contributions from independently developed researchers. It asks when a system can discover a method and acquire the ability to use it on fresh problems, with development and learning costs included. Mathematical research supplies concrete settings for that question; the proposed discovery-cost separation remains open. The chapter's research route makes the capability profile explicit: follow the lineage and acquisition criterion, then use the mathematical research records and assays to separate retrieval, transfer, remix, construction, consequence, verification and retention.

Civilization and economic reproduction ask how learning resources arise from productive capacity, finance and the renewal of knowledge. Economic development can constrain a campaign or expand its future resources. The chapter proves conditional bounds for specified models and studies reliable contributions and shared preparation costs, alongside alternative models that sustain continued learning. These results make the budget part of the mathematical question; the general discovery lower bound remains open.

The shared model, task and evaluation interfaces support these different lines of inquiry. The notes combine published empirical observations, specified mechanisms, finite mathematical results and open research questions. Each result has its own assumptions and scope; a capability claim depends on the starting artifact, the intervention, independent evaluation and resource bounds.

Notes on formalizing counterexamples to Connes' rigidity conjecture [connes-0001]

These notes treat the OpenAI, Zhou, and Anthropic counterexamples to Connes' rigidity conjecture as three distinct formalization targets. All three proofs are in scope; the current detailed development follows Zhou's construction.

Notes on formalized Clifford algebra programme [fcap-0001]

These are the accompanying mathematical notes for the experimental research project FCAP. FCAP is read “F-cap,” and studies whether Lean can serve both as a formal language for the abstract and concrete mathematics of Clifford algebras and as an implementation language for efficient symbolic and numerical computation. It is also a coined acronym for “Formalized Clifford Algebra Programme.”

FCAP's formalization is still at an early spike-test stage. The main storyline follows its own coordinate representations and orthogonal-product transport. The appendix is a mathematical companion to Tau Ceti's Clifford and Spin work: it connects the filtered exterior model, reflection and Pin/Spin constructions, bivector lifts, real signatures, and spinor modules. Those results supply context and possible tools for FCAP's longer program; the appendix does not claim they are FCAP formalizations.

Notes on foundational group algebras (for) physics [fgap-0001]

These are the accompanying mathematical notes for the experimental research project FGAP. FGAP is read “F-gap,” for the foundational gap between established mathematical tools and open questions about physical structures, especially beyond the Standard Model. It is also a coined acronym for “Foundational Group Algebras (for) Physics.”

The project studies mathematical structures that may help us understand physical structures more deeply. It does not construct or claim a physical theory. The current storyline has been strongly shaped by agent exploration of one spike-test slice of that research domain. It follows the project's approach: turn a structural hypothesis into a small, source-grounded vertical slice, then use explicit calculations and formalization to expose its assumptions.

This slice begins with Hamilton's Quaternions and the quaternionic realization of the binary tetrahedral group, because they give a small and explicit route into finite real Group Algebras. For the human-curated storyline of the notes, see § [spin-0010].

For the mathematics, we follow the Hurwitz-unit treatment in [voight2021quaternion, ch. 11]. We keep the mathematics and the physical suggestions separate.