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arXiv Machine Learning By Tailia Malloy, Prateek Kumar Rajput, Serge Lionel Nikiema, Cleotilde Gonzalez, Tegawend\'e F. Bissyand\'e

Metacognitive Reasoning in Energy Based Models using Instance Based Learning Theory

Read the original on arXiv Machine Learning →

The paper introduces MERITED, a framework that combines Instance-Based Learning Theory (IBLT) with Energy Based Models (EBMs) to enable metacognitive reasoning about computational effort. It allows an EBM to dynamically allocate resources based on uncertainty estimates, addressing limitations of large language models that cannot predict uncertainty before responding. The authors present a 191‑million‑parameter reasoning EBM and demonstrate how MERITED uses an IBL model for efficient, uncertainty‑driven compute allocation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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