GRAVITY: A Problem-State-Driven Adaptive Computational Intelligence Fabric

Overview GRAVITY: A Problem-State-Driven Adaptive Computational Intelligence Fabric proposes a constrained framework for allocating computational intelligence, execution strategies, resources, and verification according to the requirements of each problem instance. Modern AI systems frequently frame computation as a model-selection problem: which model should handle a request? GRAVITY extends this framing by considering the broader computational trajectory required to solve a problem, including problem-state construction, context management, capability selection, execution planning, resource allocation, verification, stopping, and escalation. Core Contribution The paper formulates adaptive intelligence allocation as a constrained, multi-objective sequential decision problem. Its central principle, Minimum Sufficient Intelligence (MSI), is to select the least-cost feasible computational strategy expected to satisfy explicit requirements for quality, reliability, risk, and latency. The proposed architecture coordinates heterogeneous computational capabilities, including deterministic algorithms, statistical and machine-learning models, large language models, tools, agents, and human reviewers. It separates hard feasibility and governance constraints from the optimization of competing objectives. The framework provides a common conceptual basis for investigating contextual bandits, adaptive routing, multi-objective optimization, resource-aware scheduling, sequential decision processes, execution orchestration, and outcome verification within a unified computational fabric. Preliminary Evaluation The paper reports a preliminary deterministic Track B benchmark summary using seed 42. In the supplied harness results, GRAVITY reported 100% task success and verification reliability, compared with 50% for the GRAVITY-STATIC control and 100% for the named GPT-OSS-120B comparator. GRAVITY reported 134 tokens per task, compared with 1,270 for the comparator. These figures are descriptive results from the supplied benchmark summary. Task-level logs, sample counts, complete configuration traces, and confidence intervals were not available for independent audit. The results therefore do not establish statistical significance or general superiority across workloads, models, providers, or operating conditions. Research Scope and Future Work The paper identifies a research direction for integrating adaptive computational intelligence into existing enterprise systems without requiring the replacement of their underlying systems of record. Future work includes reproducible multi-workload evaluation, ablation studies, uncertainty-aware decision policies, improved cost and resource accounting, and empirical validation of the proposed architecture. GRAVITY is presented as a research framework and testable architectural proposition. The paper distinguishes its proposed system-level contribution from the established algorithms and methods on which its design draws. Intended Audience This work is intended for researchers and practitioners in adaptive computation, artificial intelligence systems, computational intelligence, model routing, sequential decision-making, multi-objective optimization, resource allocation, AI orchestration, and enterprise AI architecture. Feedback, critical review, and independent reproduction are welcome.