Dimensions overall score 4.0

• Energy-Based Reasoning via Structured Latent Planning (EBRM) models reasoning as gradient-based optimization of a multi-step latent trajectory under a learned energy function.method 95%
• On CNF logic satisfaction, latent planning degrades accuracy from approximately 95% to approximately 56%.result 95%
• The performance degradation on logic tasks arises from a distribution mismatch where the decoder is trained on encoder outputs but evaluated on planner outputs that drift into unseen latent regions.technical 90%
• Dual-path decoder training and latent anchoring are proposed fixes to address the distribution mismatch problem.method 90%
• On arithmetic tasks, final energy correlates with absolute error at r = 0.073, indicating energy does not predict answer quality.result 90%
• The energy function decomposes into per-step compatibility, transition consistency, and trajectory smoothness terms.technical 90%
• A six-part ablation protocol was designed covering component contributions, trajectory length, planner dynamics, initialization, decoder training distribution, and anchor weight.method 85%
• Energy-based models avoid normalization requirements, allowing flexible architecture design.technical 80%

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