Arrow Research search

Author name cluster

Kaiyuan Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
2 author rows

Possible papers

2

AAAI Conference 2026 Conference Paper

SAPO: Self-Adaptive Process Optimization Makes Small Reasoners Stronger

  • Kaiyuan Chen
  • Guangmin Zheng
  • Jin Wang
  • Xiaobing Zhou
  • Xuejie Zhang

Existing self-evolution methods overlook the influence of fine-grained reasoning steps, which leads to the reasoner-verifier gap. The computational inefficiency of Monte Carlo (MC) process supervision further exacerbates the difficulty in mitigating the gap. Motivated by the Error-Related Negativity (ERN), which the reasoner can localize error following incorrect decisions, guiding rapid adjustments, we propose a Self-Adaptive Process Optimization (SAPO) method for self-improvement in Small Language Models (SLMs). SAPO adaptively and efficiently introduces process supervision signals by actively minimizing the reasoner-verifier gap rather than relying on inefficient MC estimations. Extensive experiments demonstrate that the proposed method outperforms most existing self-evolution methods on two challenging task types: mathematics and code. Additionally, to further investigate SAPO's impact on verifier performance, this work introduces two new benchmarks for process reward models in both mathematical and coding tasks.

PDF Details DOI

ICML Conference 2024 Conference Paper

CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding

  • Kaiyuan Chen
  • Xingzhuo Guo
  • Yu Zhang
  • Jianmin Wang 0001
  • Mingsheng Long

Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theroy, emulating its two core mechanisms: Correcting predictions from residuals and Hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks, and ignore the Precision Weighting mechanism of PC theory. Precision weight posits that the brain allocates more attention to signals with lower Precision, contributing to the the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM) which demonstrates the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models, and allocate the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights is an estimator of model’s predictability on the rigid body and fluid motion dataset. We also apply CogDPM to real-world prediction tasks using the U. K. precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models in providing more proficient forecasting.

Details
v2026.09.13