arcmix

Arcmix is a collection of generated Rust cases built around ARC — Answer, Reason, Check — producing answers, explaining them, and verifying them with explicit checks.

An ARC program does three things:

• Answer — produces the result
• Reason — provides a short witness, derivation, or explanation showing why the result follows
• Check — runs one or more explicit validations that fail loudly if an assumption is wrong, an edge case is missed, or the answer disagrees with an independent test

A useful way to read this repository is as teaching material for an LLM acting as a student programmer. The target is not a mysterious giant system that emits polished text. The target is a small program that can be written, inspected, rerun, criticized, and improved. In that teaching setup, the student should learn to produce code that answers a precise question, explains its reasoning in a compact witness, and then verifies the result with explicit checks instead of asking for trust.

That teaching angle matters because it pushes toward the right habits: keep the data explicit, keep the logic local, keep the question precise, and make correctness visible in the running artifact. The goal is not just to compute, but to compute in a way that stays easy to inspect, explain, and challenge. That is the practical value of the ARC approach: each run leaves an auditable trail instead of a black box. This follows the core ARC idea that trustworthy computation should be self-contained, repeatable, and verifiable at runtime.

ARC principles used here

These examples are organized around a few simple ARC principles:

• Data + Logic + Question — each case starts from explicit input data, explicit rules or algorithms, and a precise question to answer
• P3: Prompt → Program → Proof — in the broader ARC workflow, the important deliverable is not only an answer but a portable program whose output can be checked again and again
• Proof = Reason Why + Check — the explanation and the executable validation work together; either one alone is weaker than both together
• Runtime verification is mandatory — every case should validate key invariants during execution rather than relying on trust in the code or in the author
• Portable artifacts beat opaque sessions — a small standalone program is easier to benchmark, automate, audit, archive, and compare across implementations

In this repository, the programs are generated with GPT and iteratively refined under human guidance. They are specialized for speed, but they still follow the same ARC discipline: answer the question, explain the answer, and verify the answer. Rather than using a generic logical reasoner, arcmix uses generated, task-specific Rust cases that follow an Answer–Reason–Check discipline. That also makes them good exemplars for teaching or evaluating an LLM as a student programmer: the deliverable is not merely source code, but a runnable artifact whose reasoning and checks are visible at the interface.

Relation to Ershov’s mixed computation

There is a useful family resemblance between the ARC style used here and Andrei Ershov’s idea of mixed computation. In Ershov’s formulation, a computation can be split into a part that is carried out now, using the information already available, and a residual program that is left to run later on the still-unknown part of the input. In modern terms, that is closely related to partial evaluation and program specialization.

Arcmix is not a classical mixed-computation system, but it often follows a similar engineering instinct. Instead of shipping a large generic reasoner, it generates a smaller task-specific Rust artifact that has already absorbed much of the problem structure: the rules, the data layout, the query shape, and the expected checks. What remains is a compact executable that can run fast, expose its reasoning in a local form, and validate its own output.

The main difference is emphasis. Classic mixed computation is usually introduced as a way to specialize programs and obtain efficient residual code. ARC adds a stronger interface discipline around that specialized artifact: the program should not only compute the answer, but also present a short reason and perform explicit checks that can fail loudly. In that sense, arcmix can be read as borrowing part of the spirit of mixed computation while redirecting it toward auditable, benchmarkable, student-readable proof artifacts.

For readers who want the historical connection, see Andrei P. Ershov, Mixed computation: Potential applications and problems for study (1982), and the later literature on partial evaluation and mixed computation.

Why this style is useful

That structure has practical benefits:

• it makes each run easier to audit
• it keeps examples readable instead of opaque
• it teaches a student programmer, human or LLM, to separate result, explanation, and verification
• it encourages independent cross-checks rather than self-certification
• it makes benchmark cases useful as demonstrations, not just speed tests
• it gives each example a stable textual interface that is easy to compare across implementations
• it makes the output easier to reuse in automation, compliance-style review, and reproducible experiments

What counts as a good Check

A good ARC check is not a decorative success message. It should be a concrete test that can actually fail.

In this repository, good checks try to have one or more of these properties:

• they recompute a quantity from a different angle
• they validate a witness example separately from the summary statistic
• they test algebraic identities, conservation laws, or structural invariants
• they verify boundary cases and representative hard cases
• they stop the program with a clear error when the contract is broken

That means the Check section is meant to resist self-certification. The best cases do not merely restate the main computation path; they challenge it.

Included cases

collatz-1000

A computational check of the Collatz conjecture in src/collatz_1000.rs.

It models:

• the standard Collatz step n -> n / 2 for even n
• the standard Collatz step n -> 3n + 1 for odd n
• exhaustive verification over all starts from 1 through 10000
• the longest stopping time within that range
• the highest peak value reached within that range
• a classic witness trace summary for 27

control-system

A small rule-based control example in src/control_system.rs.

It models:

• the source measurements, observations, and targets as typed Rust enums
• the derived helper rule measurement10/2
• the two control1/2 rules for actuator1 and actuator2
• the final existential query true :+ control1(_, _) as query satisfied

deep-taxonomy-100000

A specialized forward-chaining taxonomy benchmark in src/deep_taxonomy_100000.rs.

It models:

• one seed fact: Ind has class N(0)
• 100,000 chain rules: N(i) -> N(i+1), I(i+1), J(i+1)
• one final class rule: N(100000) -> A2
• one goal rule: A2 -> goal reached

This version is specialized for speed and does not use a slower generic triple engine.

delfour

A Rust translation of Ruben Verborgh’s Delfour Insight Economy phone/scanner demo in src/delfour.rs.

It models:

• desensitizing a household condition into a neutral low-sugar need
• deriving a scoped, expiring insight envelope for shopping assistance
• signing that envelope with HMAC-SHA256 over canonical JSON
• authorizing scanner use under a purpose-limited ODRL-style policy
• suggesting a lower-sugar alternative for the scanned product
• verifying minimization, authorization, and duty-timing checks

euler-identity

An exact arithmetic version of Euler’s identity in src/euler_identity.rs.

This version uses direct integer arithmetic over a small ExactComplex type.

It mirrors the mathematical structure:

• construct exp(i*pi) exactly as (-1, 0)
• add (1, 0) to obtain (0, 0)
• verify the phase modulus squared is 1
• certify that the identity holds exactly

fibonacci

A direct Fibonacci computation in src/fibonacci.rs.

This version computes the requested values with iterative Rust and BigUint.

It prints:

• F(0)
• F(1)
• F(10)
• F(100)
• F(1000)

goldbach-1000

A computational check of Goldbach’s conjecture in src/goldbach_1000.rs.

It models:

• prime generation up to 1000 with a sieve
• exhaustive verification for every even target from 4 through 1000
• enumeration of unordered prime-pair decompositions n = p + q
• the hardest targets with the fewest decompositions
• the richest target with the most decompositions
• a balanced witness pair for 1000

gps

A route-planning example in src/gps.rs.

It models:

• four route descriptions
• recursive path chaining
• duration and cost summation
• belief and comfort multiplication
• route filtering against goal constraints
• human-readable route output

The translation uses Rust concepts like City, Action, Stage, Description, and Route.

kaprekar-6174

A computational proof of Kaprekar’s constant in src/kaprekar_6174.rs.

It models:

• the four-digit Kaprekar routine with leading zeros preserved
• the exclusion of repdigits such as 1111 and 0000
• exhaustive verification over all remaining four-digit starts
• proof that every valid start reaches 6174
• verification of the standard <= 7 iteration bound
• readable witness traces, including the leading-zero case 2111 -> 0999 -> ... -> 6174

matrix-mechanics

A toy matrix-mechanics example inspired by Werner Heisenberg’s matrix formulation in src/matrix_mechanics.rs.

It models:

• observables as exact 2x2 matrices
• a Hamiltonian with two discrete energy levels
• a second observable that swaps the energy basis states
• explicit computation of HX, XH, and the commutator [H, X]
• checks that the spectrum is correct and that matrix order matters

pn-junction-tunneling

A toy PN-junction tunneling example inspired by tunnel-diode / Esaki-diode behavior in src/pn_junction_tunneling.rs.

It models:

• an ordinary junction and a heavily doped junction with different depletion widths
• filled N-side states and empty P-side states as small exact integer energy sets
• a forward-bias shift that changes the overlap window for tunneling
• a current proxy computed as the exact overlap count at each bias step
• checks that the overlap rises to a peak and then falls, illustrating a negative-differential region

transistor-switch

A toy transistor-switch example inspired by a BJT used as an ON/OFF switch in src/transistor_switch.rs.

It models:

• an NPN transistor as a low-side switch rather than as a linear amplifier
• exact millivolt and microamp arithmetic for the base and collector paths
• cutoff for a low control input and saturation for a high control input
• collector current as the smaller of the gain-limited and load-limited currents
• checks that the OFF state carries no load current and the ON state is load-limited in saturation

polynomial

A quartic polynomial consistency check in src/polynomial.rs.

It models the two quartic outputs shown in the original example material and verifies:

• the exact source coefficients for each reported polynomial
• the exact roots for the real and complex quartics
• polynomial reconstruction from those roots
• direct zero-evaluation of each root against its source polynomial
• that every reported example is internally consistent

odrl-dpv-ehds-risk-ranked

An ODRL + DPV + EHDS risk-ranking example in src/odrl_dpv_ehds_risk_ranked.rs.

It models:

• an EHDS secondary-use agreement as specialized Rust data rather than a generic RDF engine
• four patient-rights expectations as weighted needs
• four agreement clauses and their corresponding ODRL permissions
• missing safeguards for data permits, opt-out respect, secure processing, and statistical anonymisation
• score calculation as base risk + need importance, capped at 100
• ranked findings with explicit mitigations and independent checks on score recomputation and ordering

path-discovery

A path-finding example in src/path_discovery.rs.

It models:

• the full airport graph from the source data
• airport labels as Rust lookup data
• direct flights as (from, to) edges
• adjacency-map construction for traversal
• depth-limited DFS over simple paths
• a query for all routes from Ostend-Bruges International Airport to Václav Havel Airport Prague with at most 2 stopovers

sudoku

A generic Sudoku solver in src/sudoku.rs.

It models:

• a standard 9×9 Sudoku grid as 81 cells
• row, column, and box legality with bitmask tracking
• repeated naked-single propagation
• minimum-remaining-values branching with backtracking
• a default puzzle and its completed grid
• independent checks for clue preservation, unit validity, legality replay, and uniqueness

Files

• src/main.rs — CLI dispatcher
• src/report.rs — structured ARC report model used for JSON output
• src/collatz_1000.rs — Collatz conjecture benchmark translation
• src/control_system.rs — control system benchmark translation
• src/deep_taxonomy_100000.rs — specialized taxonomy benchmark
• src/delfour.rs — Delfour phone/scanner insight example
• src/euler_identity.rs — Euler identity benchmark translation
• src/fibonacci.rs — Fibonacci benchmark translation
• src/goldbach_1000.rs — Goldbach conjecture benchmark translation
• src/gps.rs — GPS benchmark translation
• src/kaprekar_6174.rs — Kaprekar 6174 proof example
• src/matrix_mechanics.rs — matrix mechanics toy example
• src/odrl_dpv_ehds_risk_ranked.rs — ODRL/DPV/EHDS risk-ranking case
• src/path_discovery.rs — path discovery benchmark translation plus generated airport and flight data
• src/pn_junction_tunneling.rs — PN-junction tunneling toy example
• src/polynomial.rs — polynomial benchmark translation
• src/transistor_switch.rs — transistor as on/off switch toy example
• src/sudoku.rs — generic Sudoku solver

Run

The package name is arcmix, so a release build produces target/release/arcmix.

Default case:

cargo run --release

Explicit cases:

cargo run --release -- collatz-1000
cargo run --release -- control-system
cargo run --release -- deep-taxonomy-100000
cargo run --release -- delfour
cargo run --release -- euler-identity
cargo run --release -- fibonacci
cargo run --release -- goldbach-1000
cargo run --release -- gps
cargo run --release -- kaprekar-6174
cargo run --release -- matrix-mechanics
cargo run --release -- path-discovery
cargo run --release -- pn-junction-tunneling
cargo run --release -- polynomial
cargo run --release -- transistor-switch
cargo run --release -- sudoku

Structured JSON output for one case:

cargo run --release -- collatz-1000 --format json

Structured JSON output for the whole suite:

cargo run --release -- --all --format json

Snapshot management from the repository root:

cargo run --release -- show sudoku
cargo run --release -- show sudoku json
cargo run --release -- refresh
cargo run --release -- check

Stable output and snapshots

arcmix supports two stable output forms:

• the normal human-readable ARC text output
• a structured JSON report produced with --format json

The recommended workflow is:

1. keep the human-readable text output for people
2. keep JSON as the canonical machine-checkable form
3. store checked-in snapshots for both
4. refresh snapshots only when a case intentionally changes

The arcmix binary handles this directly when you run it from the repository root. Because snapshots are regular files in the repository, intentional output changes show up as normal diffs in version control. If you add or grow a case, run cargo run --release -- refresh, review the snapshot diff, and commit it together with the code change.

cargo run --release -- refresh
cargo run --release -- check

What it does:

• runs the current binary from the repository root
• writes per-case text snapshots under snapshots/text/
• writes per-case JSON snapshots under snapshots/json/
• writes all.txt, all.json, and list.txt
• compares fresh output against the checked-in snapshots during check

That gives a practical separation between:

• computation — the Rust code that derives the answer and checks it
• rendering — the human-facing text output
• regression control — snapshot files that show when a case changed

This is especially useful for ARC programs because the output is part of the artifact: the answer, the reason why, and the executable checks are all meant to stay auditable and reproducible.

Snapshot layout

snapshots/
  text/
    <case>.txt
    all.txt
    list.txt
  json/
    <case>.json
    all.json

List available cases:

cargo run --release -- --list

Run all cases in sequence:

cargo run --release -- --all

ARC output style

Each case prints a short three-part story:

• Answer — the result in a compact, human-readable form
• Reason Why — the main witness, derivation, or explanation
• Check — concrete validations and cross-checks that fail loudly on contradiction

Where possible, the check section uses more than one line of evidence, so the program does not rely on a single computation path to certify its own output.

Each case run also reports elapsed time in milliseconds on stderr, with ANSI color for the timing line.

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