aiqbench analysis
TechPulse take: This research introduces Energy-guided Recursive Models (ERM), a novel framework that leverages Hopfield-type memories to assign intrinsic energies to candidate trajectories in recursive models. By using these energy values to guide selection and determine optimal recurrent depths, the method significantly improves reasoning accuracy on complex tasks like Sudoku and mazes while maintaining marginal inference overhead. The study establishes energy guidance as a practical criterion for enhancing test-time scaling without relying solely on deeper recurrence.
Energy-guided Recursive Model
arXiv:2607.10128v3 Announce Type: replace Abstract: Recursive models show promise on reasoning and language tasks, yet their test-time scaling lacks a principled criterion for selecting trajectories or determining recurrent depth. We introduce \textbf{Energy-guided Recursive Model (ERM)}, which uses Hopfield-type memories of valid local and global structures to assign intrinsic energies to candidate trajectories. These energies guide candidate selection and suggest an effective range of recurrent depths, implying that deeper recurrence does not necessarily improve reasoning accuracy. They also enable sampling methods such as parallel tempering to improve exploration. For reasoning tasks, ERM achieves optimal solutions on Sudoku ($98.97\%$), Pencil Puzzle Bench (PPBench, $88.04\%$) and Maze ($99.30\%$), reaching the best accuracy in recursive modeling. On language modeling, ERM reduces RedPajama-V2 perplexity by $1.74\%$ with marginal inference overhead. The results support energy guidance as a practical framework for improving test-time scaling in recursive models.
Abstract:Recursive models show promise on reasoning and language tasks, yet their test-time scaling lacks a principled criterion for selecting trajectories or determining recurrent depth. We introduce \textbf{Energy-guided Recursive Model (ERM)}, which uses Hopfield-type memories of valid local and global structures to assign intrinsic energies to candidate trajectories. These energies guide candidate selection and suggest an effective range of recurrent depths, implying that deeper recurrence does not necessarily improve reasoning accuracy. They also enable sampling methods such as parallel tempering to improve exploration. For reasoning tasks, ERM achieves optimal solutions on Sudoku ($98.97\%$), Pencil Puzzle Bench (PPBench, $88.04\%$) and Maze ($99.30\%$), reaching the best accuracy in recursive modeling. On language modeling, ERM reduces RedPajama-V2 perplexity by $1.74\%$ with marginal inference overhead. The results support energy guidance as a practical framework for improving test-time scaling in recursive models.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2607.10128 [cs.LG]
(or arXiv:2607.10128v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.10128 Focus to learn more arXiv-issued DOI via DataCite
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