Synth-Fuse: A Modular Fusion Library for Hybrid Swarm–RL–Numerical Intelligence (v0.1.0-alpha)

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Authors: Jose Roberto Jimenez, KIMI K2
Cited by: 0

Abstract

Synth-Fuse: A Modular Fusion Engine for Hybrid Intelligence(v0.1.0 ) Core Axiom (unchanged) Every algorithm is a plugin; every fusion is a pipeline; every pipeline is a JAX transform. Plugin Contract (pure, JIT, PyTree) def step(key: jax.Array, state: PyTree, params: PyTree) -> PyTree: Failure mode: side-effect → non-deterministic under vmap.Control: static analysis + chex.dataclass enforce immutability. Fusion Calculus (higher-order, closed under JIT) Combinator | Semantics | Failure mode | Control ---|---|---|--- fuse_seq | (f ∘ g)(x) = g(f(x)) | exploding gradient | gradient-norm clip inside each primitive fuse_loop | fⁿ(x) | infinite recursion | max_iter hard-cap + cos-distance early-stop fuse_cond | 1_c(x)⋅f(x) | condition never true | entropy monitor triggers default branch fuse_parallel | f(x) + g(x) | tree-shape mismatch | compile-time PyTree structural check fuse_meta | M(f) | meta-overfitting | β-divergence threshold on Λ drift Recipes (pre-fused, JIT-ready) Recipe | Core idea | Failure mode | Control ---|---|---|--- FQL-RIME | flow-guided Lévy escape | flow collapse (σ→0) | minimum-entropy latch on latent space MRBMO-PPO | siege-elite buffer | elite set empty | fallback to full-population update ISO-VNS | chaotic perturb | chaos diverges (λ>4) | logistic-map clamp at λ_max=3.9 CA-SVD-UKF | rank-collapse | κ(A)>κ_max | trigger full-rank refresh W-Orion (Weierstrass field solver) Claim: smooth surrogate over discrete architectures via heat-kernelU(x)=Σᵢexp(−‖x−τᵢ‖²/(4σ²)) Failure mode: σ too large → landscape flattens, search stallsControl: adaptive σ schedule driven by ΔU/Δx gradient magnitude STCL (semantic–thermodynamic loop) Free-energy functional: ℱ(ℓ)=Λ(ℓ)−β⋅C(ℓ) Failure mode: β too high → compression destroys concept (Λ

Fields of Study & Subjects

Metaheuristic Optimization Algorithms Research Artificial Intelligence Applications Reinforcement Learning in Robotics

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
16 January 2026
Document type
Preprint