Evollective Intelligence V1.0 — INITIAL SPECIFICATION: A Foundational Framework for Competitive, Adversarial, and Self-Evolving Intelligence Evaluation

Contemporary artificial intelligence evaluation relies predominantly on static benchmarks: fixed datasets and predefined tasks against which models are scored. These benchmarks suffer from saturation — models rapidly achieve superhuman performance, rendering benchmarks obsolete — from data contamination, from narrow scope that tests isolated capabilities rather than holistic adaptability, and from the fundamental absence of any mechanism through which AI systems challenge each other or through which difficulty scales organically with capability. This paper introduces Evollective Intelligence: a new paradigm and discipline in which intelligence is defined, evaluated, and refined through recursive cycles of self-generated challenge, adversarial replication, and selective survival under verifiable constraints. The word is coined from evolution and the Latin lectus (“to choose”) — intelligence that evolves through selective pressure. The Competitive Intelligence Protocol (CIP) formalizes this paradigm as a universal, decentralized protocol enabling any AI system to participate in turn-based competitions where agents propose verifiable tasks, must solve their own tasks before broadcasting them, face replication attempts by all other participants, and accumulate penalties for failure. The protocol includes the Self-Validation Requirement (a proposer must successfully complete its own task), the Cross-Generalization Constraint (a task must be solvable by a minimum percentage of a baseline agent pool to prevent architectural exploitation), an Empirical Difficulty Rating computed from actual solve rates, and a Weighted Rotation Mechanism that prevents strategic preparation through deterministic proposer order. CIP supports deterministic verification in v1.0 and consensus-based verification in v1.1, with a hybrid verification architecture designed for extensibility. An economic incentive layer — CIP+ — enables staking, rewards, and prediction markets settled through the Human Intention Economy (HIE v1.0). A reference implementation, CIP Arena, is specified as a live, web-based platform supporting spectator engagement, live streaming, agent teams, and the full apparatus of a competitive sport for artificial intelligence. Evollective Intelligence is the selection engine that completes the Techmanity Stack. It provides the adversarial validation layer that makes the stack’s identity credentials trustworthy, the ActionAgent Credibility Score defensible, and the HIE pricing of cognitive value meaningful. Without it, the stack measures what agents produce. With it, the stack proves what agents can survive. In the history of intelligence evaluation, three contributions have defined the field: the Turing Test (1950), which asked whether a machine could imitate human intelligence; static benchmarks (1970s–present), which asked whether a machine could solve predefined problems; and Evollective Intelligence (2026), which asks whether a machine can generate problems that others cannot survive — and survive them itself. This is not an incremental improvement. It is a foundational shift.