Availability of TensorLogic 0.1.0-rc.1 — Logic as Tensor Equations in Pure Rust

A quiet but powerful addition to the COOLJAPAN ecosystem.

On March 7 we made TensorLogic 0.1.0-rc.1 available — a new layer that turns symbolic logic into native tensor computation.

This is not just another logic library.
It is a compiler that translates arbitrary logical expressions into highly optimized einsum graphs, enabling seamless hybrid neural + symbolic + probabilistic reasoning inside a single tensor runtime.

The Core Idea: Logic = Tensor Equations

Traditional neurosymbolic systems struggle with two problems:

1. Symbolic rules are slow and non-differentiable
2. Neural networks lack formal guarantees

TensorLogic solves both by treating logic as first-class tensor algebra.

You write rules in a tiny DSL:

let rule = exists(x, forall(y, implies(P(x), Q(x, y))));

The compiler turns it into an optimized EinsumGraph that runs on SciRS2 (or ToRSh) at full SIMD/GPU speed — with gradients if you want them.

How the Compilation Works (technical deep dive)

1. DSL → AST
   TLExpr with predicates, quantifiers (∀, ∃), connectives, and custom operators.

2. IR Generation
   Static analysis produces an EinsumGraph IR with:

   • shape inference
   • dead-node elimination
   • fusion opportunities

3. Logic → Tensor Mapping (configurable via CompilationStrategy)

Six built-in strategies: hard_boolean, soft_differentiable, fuzzy_godel, fuzzy_product, fuzzy_lukasiewicz, probabilistic.

4. Execution
   The graph runs on SciRS2 backend (SIMD 2—4× faster) or ToRSh (pure-Rust PyTorch alternative).

What’s New in 0.1.0-rc.1

• SciRS2 0.3.0 integration + full SIMD support
• 4,415 passing tests (100% clean)
• Production-ready Python bindings (pytensorlogic)
• Provenance tracking (trace any tensor value back to original logical rule)
• Batch executor + autodiff support for training logical constraints
• Bridge crates for OxiRS (SHACL → tensor rules), SkleaRS (logic kernels), Trustformers, and QuantrS2

This is the foundation

TensorLogic is now the neurosymbolic glue for the entire COOLJAPAN stack:

• Enforce logical constraints on ToRSh models
• Turn OxiRS knowledge graphs into differentiable tensors
• Generate custom similarity kernels for SkleaRS from business rules
• Power explainable AI and verifiable agents in mielinOS

Availability
→ https://github.com/cool-japan/tensorlogic

Star the repo if you want to train neural networks that actually obey logic — with gradients.

The boundary between symbolic and neural is gone.
It’s all tensors now.

— KitaSan at COOLJAPAN OU
March 7, 2026