Embedding Inefficiency Awareness and Consequence Sensitivity into AI Cognition

Large language models are effective problem solvers but lack internal awareness of their own reasoning costs and the alternative strategies they did not take. IMCR reframes reasoning so that multiple semantically distinct paths are generated for each task; each path is assigned a predicted consequence vector (dimensions include resource cost, logical risk, coherence drift, shortcut bias, and learning potential). The system selects a path using a valuation function that weighs both task objectives and the predicted cognitive costs. After execution, predicted consequence vectors are compared to observed outcomes; prediction errors update a meta-reasoning memory that gradually builds cross-task heuristics for efficient cognition. The paper provides formal definitions, algorithmic pseudocode, experimental setups and metrics (Inefficiency Divergence Index, Path Rationalization Accuracy, Consequence Prediction Error), an appendix with equations, and an accompanying Python simulation demonstrating predicted vs actual consequence dynamics. Full manuscript and code included. What’s new / contributions: Introduces consequence-vector embedding as a first-class representation of reasoning path costs. Presents a meta-reasoning memory that accumulates inefficiency patterns over time. Formalizes semantic path diversity and latent decision-point extraction. Proposes novel evaluation metrics (IDI, PRA, CPE) and experimental protocol. Includes reproducible simulation code and an appendix with equations and ablation plans.