A Resource Efficient Framework for Self-Evolving Intelligence Through LLM Guided Neuro Evolution
Hashen Chandrasekara · Pluto Super Intelligence · Working paper v5.0, July 2026 · self published, not peer reviewed, no independent audit
A population of small agents improves through an evolutionary loop whose mutation operator is a language model and whose selection pressure is a deterministic verifier rather than a learned reward. The paper formalises certainty as a Gibbs measure over anchored knowledge, and proves a risk decomposition theorem: under the abstention policy, confidently wrong output requires either a registry error or a claim binding error, which reduces the problem to two measurable surfaces.
47 / 48planted false claims blocked
24 / 24correct abstentions, out of scope
0 / 600adversarial claims passed
~1 in 3of 49 novel phrasings, confidently wrong
How to read those four numbers
They come from an examination that Hashen Chandrasekara designed and ran himself. No third party administered or witnessed it. What makes it meaningful is not independence but pre-registration: the question set was written and content hashed before the system saw it, and run against a frozen, content hashed registry snapshot, so the system could not be tuned to the test.
The 0 of 600 and the roughly 1 in 3 measure different surfaces, which is why they are printed together. The adversarial study tested whether the gate leaks under pressure inside covered domains. The 49 item probe tested unfamiliar phrasing, and every failure traced to the claim binding layer failing to parse language it had not seen, not to the verdict policy. The paper's risk decomposition predicts exactly that split.