cuda-self-evolve

An agent that can modify its own bytecodes is an agent that can learn, adapt, and evolve.

Self-modification and evolution framework for FLUX agents. This crate provides the SAFE framework for self-modification — turning the FLUX VM into a Darwinian machine.

Overview

Bytecodes that perform better survive. Bytecodes that don't get rolled back. Over many generations, the agent's own code optimizes itself.

Evolution Pipeline

┌─────────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
│  Current     │───▶│  Propose  │───▶│  Apply   │───▶│  Verify  │───▶│  Score   │
│  Bytecodes   │    │ Mutation │    │ w/ Safety│    │ Validity │    │ Fitness  │
└─────────────┘    └──────────┘    └──────────┘    └──────────┘    └────┬─────┘
                                                                       │
                    ┌──────────────────────────────────────────────────┘
                    │
              ┌─────▼─────┐     ┌──────────┐
              │ Accepted?  │────▶│  Record  │───▶ Next Generation
              └─────┬─────┘     │ History  │
                    │ No         └──────────┘
              ┌─────▼─────┐
              │  Rollback  │◀── Snapshot
              │  Restore   │
              └───────────┘

Architecture

Mutation Engine (mutator.rs)

• propose() — Analyze bytecodes and propose mutations based on policy
• apply() — Apply with safety checks (size bounds, opcode validity, jump table consistency)
• rollback() — Revert to previous version if confidence drops
• verify() — Post-mutation validation (well-formed, no infinite loops, valid jumps)

Selection (selection.rs)

• fitness() — Score: (1 / cycles_per_result) × confidence × trust
• compete() — Run N variants, pick best fitness
• breed() — Crossover at instruction boundaries

Genome (genome.rs)

• Compressed representation — NOPs removed, patterns folded, loops extracted
• encode() / decode() — Convert between bytecodes and genome
• mutate() — Higher-level genome mutations (gene substitution, amplification, pruning)

History (history.rs)

• EvolutionRecord — Generation, mutation type, before/after fitness, accepted
• trend() — Detect improving / plateau / declining trajectories

Mutation Types

Type	Description
OptimizeOpcode	Replace expensive opcode with cheaper alternative
InsertNOP	Add NOP for alignment
PatchJump	Adjust jump target offsets
ReplaceSequence	Replace instruction sequences
AddInstinct	Insert SENSE+ACT instinct patterns
AdjustDecay	Tune memory decay factors
TuneThreshold	Adjust decision thresholds

Usage

use cuda_self_evolve::*;

let bytecodes = vec![Opcode::PUSH as u8, 42, Opcode::ADD as u8, Opcode::HALT as u8];
let policy = EvolutionPolicy::default();
let mut modifier = SelfModifier::new(bytecodes, policy);

modifier.metrics.trust = 0.8;
modifier.metrics.confidence = 0.9;

let mut rng = rand::thread_rng();
if modifier.evolve(&mut rng).unwrap() {
    println!("Mutation accepted! Generation {}", modifier.generation());
}

Safety Guarantees

1. Trust gating — Self-modification only when trust exceeds threshold
2. Rollback threshold — Auto-revert if fitness drops below fraction of previous
3. Snapshot-based rollback — Always possible to restore previous state
4. Jump table validation — No mutations that create dangling jump targets
5. Size bounds — Bytecodes never exceed configured maximum
6. Infinite loop detection — Non-loop cycles flagged during verification

Fitness Formula

fitness = (1 / cycles_per_result) × confidence × trust

• Speed: Fewer cycles per result = higher fitness
• Confidence: Result certainty gates survival
• Trust: Agent trustworthiness as a survival factor