Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Backend data + intelligence layer for competitive ruliology (strategy graphs, payoff/fitness storage, GNN opponent models) #546

Open

Description

Companion issue (ruvnet/ruflo): ruvnet/ruflo#2314

Summary

Following Stephen Wolfram's Games Between Programs: The Ruliology of Competition,
there's a companion effort in Ruflo to add competitive execution to swarms — arenas,
tournaments, and co-evolution of program strategies (see the Ruflo issue). That work produces a
lot of structured, queryable intelligence: strategy spaces, opponent behavior, payoff
landscapes, fitness curves, and evolutionary histories.

This proposes that RuVector own the data + intelligence layer for it, while Ruflo owns
execution and all visualization. RuVector ships no UI — only data and compute services
(MCP tools, query endpoints, RVF artifacts).

What's proposed

  1. Strategy graphs + opponent modeling (GNN) — model strategies as nodes and
    beats/loses-to/mutated-from edges in a property graph (ruvector-graph, Cypher); embed
    strategies via ruvector-gnn/ruvector-attention into HNSW for "who plays like X" queries;
    per-opponent next-move models; proof-gated lineage via ruvector-verified. (ADR-196)
  2. Payoff / fitness / evolution storage — competitive arrays as sparse matrices
    (ruvector-solver), fitness curves as downsampled time series, evolutionary lineage as an
    append-only log, packaged as portable RVF run artifacts for replay/sharing. (ADR-197)
  3. Backend data + tooling for visualization — versioned read-model APIs, streaming feeds,
    and server-side graph layout/downsampling that feed the Ruflo dashboards — explicitly
    no rendering, no front-end, no auth UI in RuVector. (ADR-198)

Cross-cutting (shared with Ruflo — see companion issue)

  • Verified distillation of winning LLM strategies into compact programs (coherence-checked,
    proof-gated). (ADR-199 ↔ Ruflo ADR-151)
  • Integration layer — ingestion APIs, canonical schemas + shared ID namespace, query/stream
    endpoints, RVF exchange. (ADR-200 ↔ Ruflo ADR-152)
  • Safe compute governance — bounded budgets, proof-gated mutation so evolved strategies
    can't corrupt verified state, validated ingestion, WASM sandbox for untrusted RVF. (ADR-201 ↔ Ruflo ADR-153)
  • Visualization split + governance — RuVector = data/intelligence; Ruflo = UI; paired
    ADRs, versioned contracts. (ADR-202/203 ↔ Ruflo ADR-154/155)

Why RuVector

This is squarely RuVector's wheelhouse — the graph-transformer stack (ADR-046), proof-gated
mutation (ADR-047), verified training (ADR-049), HNSW + quantization, RVF cognitive containers
(ADR-029/030), and the pi.ruv.io brain. The Ruflo prototype currently fakes this layer with a
local file/AgentDB RunStore; the intent is to graduate it to RuVector once there's appetite.

Proposed phasing

  • Phase 1: define the read-model + ingestion contract (with Ruflo) so the Ruflo prototype
    can target it; store competitive arrays + fitness curves (RVF). (ADR-197/200)
  • Phase 2: strategy graph + GNN embeddings + opponent models. (ADR-196)
  • Phase 3: verified distillation + safe-compute governance. (ADR-199/201)

Questions for maintainers

  1. Is a competitive-ruliology data/intelligence layer something you'd want in RuVector?
  2. New crate (e.g. ruvector-arena) vs. composing existing crates (ruvector-graph +
    ruvector-gnn + ruvector-solver)?
  3. RVF as the run-artifact format — right call, or prefer another store?
  4. Preferred contract surface to Ruflo — MCP tools, HTTP endpoints, or both?

Full ADR set (RuVector ADR-196–203) and the cross-repo map are drafted in the ruliad
meta-repo (INTEGRATION-ADRS.md). Scope is flexible — happy to start with just the storage
contract if that's the most useful first step.

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

    Milestone

    No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions

        AltStyle によって変換されたページ (->オリジナル) /