AGCI Benchmark: Evaluating Long-Term and Adaptive Intelligence in AI Systems (www.dropstone.io)

Blankline Research released the AGCI (Artificial General Coding Intelligence) benchmark: a model-agnostic, longitudinal evaluation framework that measures adaptive, long-term cognitive capabilities rather than isolated task performance. AGCI assesses seven cognitive dimensions—perception, memory (including cross-session persistence), reasoning, learning, adaptability, self-reflection/metacognition, and theory of mind—via naturalistic, multimodal task batteries (150–200 tasks per dimension drawn from 1,200+ scenarios). Systems run through a 7-day continuous evaluation with preserved session state and are scored by a normalized composite across dimensions (weights validated by cross-model consistency and human experts). All participants interface through a standardized REST API (identical JSON inputs/outputs), with architectural neutrality enforced by constraints (32,768-token max context, 100 queries/hour, no task-specific fine-tuning or adaptive hinting). For the AI/ML community, AGCI’s significance is twofold: it fills a gap left by static benchmarks by testing long-term memory, transfer, meta-reasoning, and robustness to distribution shift; and it provides a fair, longitudinal reference for research, deployment decisions, and policy oversight. Technical implications include emphasis on cross-modal coherence, few-shot generalization, adversarial robustness, and explicit uncertainty estimation. Adaptive difficulty (progression after ~85% accuracy), anti-contamination protocols, and architecture-neutral scoring mean AGCI can evolve with future paradigms (transformers, neuromorphic, symbolic hybrids) and push developers to build systems that truly maintain, adapt, and reason over time rather than merely pattern-match.