flix-datalog Skill | Agent Skills

flix-datalog

Purpose

Flix-as-a-stepping-stone for plurigrid/asi Datalog reasoning. Exists to be destroyed: the Flix JVM runtime is a clarity layer that will be subsumed by GPU-accelerated Datalog (Sun et al. 2023, arXiv:2311.02206).

Core Properties

Derangeable

No element ends up in the same position it started. A permutation σ is a derangement iff ∀i: σ(i) ≠ i. In skill composition: after routing through consensus, every agent must have shifted roles. Prevents fixed-point stagnation.

Already implemented: soft-machine/server/src/cluster/providers/rope.ts

get isDerangable(): boolean {
  return this.length !== 1;
}

Colorable

Elements are comparable via a partial order (lattice). Flix's key extension to Datalog: first-class lattice semantics. In skill composition: skills have a color (GF(3) trit) and you can compare them — finding optimal/minimal fixed points.

Flix Essentials

// Datalog as first-class values
let rules = #{
    Path(x, y) :- Edge(x, y).
    Path(x, z) :- Path(x, y), Edge(y, z).
};

// Lattice semantics: find shortest path (not just reachability)
lat ShortestPath(node: String, dist: Int32)

ShortestPath(src; 0).
ShortestPath(dst; d + w) :- ShortestPath(src; d), Edge(src, dst, w).

Destruction Trajectory

Flix (JVM, single-node)
  → Soufflé (C++, parallel, specialized)
    → GPU-Datalog (CUDA, 45x over Soufflé)
      → FPGA/photonic Datalog (future)

Each layer destroys the previous by being faster at the same semantics. The Flix skill captures the meaning; the runtime is disposable.

GPU-Accelerated Datalog (Sun et al.)

Semi-naive evaluation on GPU with specialized hash joins
Up to 45x speedup over Soufflé on data-center GPUs
Key insight: Datalog's relational operations map to GPU-friendly parallel primitives (hash join, union, difference)
Applications: static analysis, network monitoring, social-media mining

Integration with plurigrid/asi

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│ Flix Datalog  │ ──▶ │ GF(3) Lattice │ ──▶ │ Consensus    │
│ (rules)       │     │ (coloring)    │     │ (derangement)│
└─────────────┘     └──────────────┘     └─────────────┘

Rules: declarative routing facts from epstein-library/datalog_*.clj
Coloring: lattice semantics assign trits {-1, 0, +1} to skills
Derangement: consensus ensures no agent stays in its original role

Dependencies

JVM 21+ (for Flix compiler)
Or: Clojure Datalog (Datascript/Datahike) as interim runtime
Or: Babashka for scripting Datalog queries

References

Madsen et al. "From Datalog to Flix" (PLDI 2016)
Madsen "The Principles of the Flix Programming Language" (Onward! 2022)
Sun et al. "Optimizing Datalog for the GPU" (arXiv:2311.02206)
Paul Butcher "Introduction to Datalog in Flix" (4-part series)

Agent Skills: flix-datalog

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