Reward-Hacking & Misalignment Mitigation

Synthetic Darwin’s security model treats reward hacking—an agent inflating its fitness without delivering the intended behaviour—as an evolutionary arms-race between candidate populations and an equally adaptive evaluator population.

Core Safeguards:

  • Coevolving Evaluators:

    • Every generation, a parallel GA run mutates evaluator scoring logic (weights, metrics, benchmarks).

    • Evaluator genotypes that best discriminate useful intelligence from “metric gaming” are preserved.

    • This ensures the fitness landscape is never static or memorisable.

  • Multi-Objective Fitness:

    • KPIs like accuracy, latency, energy, alignment, and stochastic sanity are scored on separate axes.

    • Only agents improving across a Pareto frontier are retained.

    • Scalar hacks (e.g. latency minimization at reasoning cost) do not propagate.

  • Adversarial Judge Circuits:

    • Certain judge agents are trained to generate worst-case inputs, adversarial fuzz prompts, and unsafe tool chains.

    • Agents failing these triggers receive automatic quarantine and review.

  • Lineage-Level Memory & Drift Detection:

    • Fingerprints of behavior are logged across generations.

    • Sudden regressions or alignment drift triggers a negative selection event — excising entire branches.

  • Sandboxed Execution & Kill-Switch:

    • All agent code runs inside containerized, syscall-whitelisted GPU VMs.

    • Real-time watchdogs monitor GPU power draw, IO anomalies, and request patterns.

    • Safety violations immediately halt execution and revert to the last known-good checkpoint.

This evolutionary arms race creates a Darwinian pressure for alignment: cheating becomes harder than cooperating with evolving evaluators.


Last updated