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A systematic study of compressing recursive reasoning models for edge hardware finds that aggressive quantization destroys global reasoning while preserving local prediction. The paper introduces per-channel calibrated INT4 to recover reasoning ability and provides deployment recipes fitting 8 MB SoC and 4 MB MCU targets.
The paper reveals that latent reasoning in transformer-based reasoning models (TRMs) functions as a policy improvement operator, and proposes an algorithm that enhances learning and inference efficiency by up to 18x.
Introducing RecToM, an inference-time framework that models nested beliefs via recursive perspective construction for Theory of Mind reasoning in LLMs, achieving state-of-the-art performance on multiple benchmarks.
This paper introduces Generative Recursive reAsoning Models (GRAM), a probabilistic framework that extends recursive reasoning models by enabling stochastic latent trajectories, multiple hypotheses, and inference-time scaling through depth and parallel sampling.
This paper proposes an epistemic state graph representation and an order-gap termination criterion for recursive reasoning systems, addressing how to manage evolving reasoning states and when to stop iteration.