Fuzzy Logic and Control Systems

Neueste Paper

• MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller

  Zhibin Wen, Tao Han, Lei Bai, Can Li, Yang Xu · 30. September 2026

  Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not always lead to better results and can introduce substantial redundant reasoning on simple problems. Conversely, aggressively shortening reasoning ca…

• SR4-Fit: A Unified Interpretable Rule-Based Machine Learning Framework for Informative and Trustworthy Decision-Making

  Shyam Sundar Murali Krishnan, Dean Frederick Hougen · 29. September 2026

  In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions. These explanations are often unreliable because they do not reflect the model's actual computations, limiting accountability and trust. A natural alter…

• Selecting Diverse SFT Traces Improves Post-RL Generalization

  Dylan Zhang, Mingyuan Wu, Jinning Li · 29. September 2026

  Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route diversity, the variation in the sequences of reasoning steps in supervised fine-tuning (SFT) data, and propose a lightweight, rule-based fingerprint to …

• Auditability Is Not One Property: Rule Overlap, Behavioural Agreement, and Composition in Reinforcement Learning

  Liu Hung Ming · 25. September 2026

  Reinforcement-learning (RL) policies are often distributed as opaque neural checkpoints, while training logs show that a run occurred without explaining what the policy learned. We study whether independently trained policies can be represented and composed through auditable discrete behavioral rule…

• Differentiable Fuzzy Inference Layer: A Monotone, Compositional Ordinal Reasoning Head for Large Language Models

  Zhen Zhang, Amr Alanwar · 23. September 2026

  A state-of-the-art language model asked to interpret "most of most students passed" typically answers "most," though composing two instances of "most" yields a proportion closer to "some." We trace this failure to an architectural choice rather than a data deficit: standard classifier heads treat or…

• RLVR$^{2}$: Reinforcement Learning with Verifiable Rubric-based Ranking

  Hao Li, Zhengkun Zhang, Gangqiang Hu, Zhen Zhang, Yude Gao, Dai Dai, Jing Liu · 22. September 2026

  Reinforcement Learning with Verifiable Rewards (RLVR) is expanding from tasks with well-defined correctness signals, such as mathematics and code, toward multifaceted quality requirements specified by multi-dimensional rubrics. Since policy optimization consumes one scalar per rollout, rubric-based …

• SoftTri: Smooth Triangular Membership Functions for Adaptive Fuzzy Inference Systems

  Babak Sarani, Rahman Ardakanian, Ali Mousavi · 18. September 2026

  Triangular membership functions (MFs) are widely used in fuzzy systems because of their interpretability, low parameterization complexity, and strong locality properties. However, their inherent nondifferentiability at knot points limits the effectiveness of gradient-based optimization in adaptive n…

• Why Sample What You Can Enumerate? Exact Policy Optimization for Genomic Tool Selection

  Haoyue Liu, Xiaoyu Ma, Ye Chen, Zhichao Wang, Xiaoying Tang · 10. September 2026

  Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe becomes structurally mismatched in specialist scientific settings where the complete tool-subset space is enumerable. There, a small set of recurrin…

• Gradients Know What Outcomes Don't: Unlocking Reinforcement Learning for LLM Reasoning with Gradient-Aligned Rewards

  Leqi Zheng, Jinbo Su, Fang Niu, Chaokun Wang, Weiping Wang, Jiajun Zhang, Shannan Yan, Jie Wu, Zhaolu Kang, Rong Fu, Hang Zhang · 4. September 2026

  Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct trajectories. Existing dense reward alternatives, from surface heuristics to process reward models, either ignore the exper…

• AgentRM: Enhancing Agent Generalization with Reward Modeling

  Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun · 4. September 2026

  Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning the policy model with more diverse tasks to improve the generalizability. In this work, we find that finetuning a reward …

• A Survey on Rubric-Guided Reinforcement Learning for Language Models

  Zifei Shan, Fangning Shao · 31. August 2026

  Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large language models (LLMs) with human preferences. However, traditional RLHF relies on scalar reward signals that lack interpretability and fail to capture the multifaceted nature of response quality. R…

• GRIP: Granular Reward-Guided Parameter Interpolation for Efficient Reasoning

  Lam So, Canhui Wu, Han Lin · 27. August 2026

  Reasoning-oriented large language models often achieve strong problem-solving performance by generating long chains of thought, but this behavior substantially increases inference cost and latency. In contrast, instruction-tuned models tend to answer more concisely, yet often lack comparable reasoni…

• iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration

  Xiaowei Jiang, Daniel Leong, Beining Cao, Nan Zhou, Yingtao Ren, Yu-Cheng Chang, Thomas Do, Chin-Teng Lin · 18. August 2026

  Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an interpretable fuzzy rule-based learning framework that preserves human-under…

• HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry

  Haoran Pei, Zhao Su, Zetao Lin, Haoran Li, Jun Shen, Qi Zhu, Lan Guo, Qingguo Zhou, Binbin Yong · 13. August 2026

  The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring transparent reasoning. However, existing ANFIS models generally construct rule antecedents and perform inference in Euc…

• Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

  M. Sajid, A. Quadir, A. Rahaman, P. N. Suganthan, M. Tanveer · 12. August 2026

  The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples uniformly, which limits their robustness and effectiveness when applied to real-world datasets containing noise and outl…

• MedCalc-R1: Knowledge-Guided Reward Framework for Medical Mathematical Reasoning

  Haotian Wang, Lian Yan, Xingzhi Yao, Fanshu Meng, Ye He, Jingchi Jiang, Yi Guan · 11. August 2026

  In Reinforcement Learning with Verifiable Rewards (RLVR) frameworks for mathematical reasoning tasks, floating-point results are typically evaluated using a tolerance-based reward. However, this strategy suffers from challenges such as difficulty in threshold calibration, unstable training dynamics,…

• Interpretable reinforcement learning with decision-tree pruning

  Mark Leon Ringer, Michel Tokic · 10. August 2026

  Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness. Converting a trained policy into explicit decision-tree rules improves transparency and the resulting artifacts often remain too complex for human understanding. We present a prunin…

• Evidential Rule Learning for Interpretable Classification with Abstention

  Javier Fumanal-Idocin, Javier Andreu-Perez · 7. August 2026

  Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abstain when they cannot decide reliably. We introduce Fast Evidential Rule Learning (FERL), a method that learns interpreta…

• Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library

  Cayan Deniz Kucuktopana, Javier Fumanal-Idocin, Richard Pitts, Javier Andreu-Perez · 23. Juli 2026

  Machine learning models achieve high predictive accuracy in regression tasks, but their deployment in safety-critical and regulated domains requires interpretability. While fuzzy rule-based systems offer transparent, linguistically explicit interpretable models, Mamdani-style fuzzy regression remain…

• Regression Language Models for Code

  Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed S. Abdelfattah · 28. Mai 2026

  We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (R…

• Mediative Fuzzy Logic: From Type-1 Foundations to Type-2, Type-3 and Quantum Extensions

  Oscar Montiel Ross · 25. Mai 2026

  Mediative Fuzzy Logic was conceived as a practical scheme for reconciling hesitant or conflicting assessments in fuzzy control and decision-making. However, its logical and semantic foundations remain underdeveloped, especially beyond operational type-1 settings. This article develops a unified acco…

• Latent Linear Quadratic Regulator for Robotic Control Tasks

  Yuan Zhang, Shaohui Yang, Toshiyuki Ohtsuka, Colin Jones, Joschka Boedecker · 22. April 2026

  Model predictive control (MPC) has played a more crucial role in various robotic control tasks, but its high computational requirements are concerning, especially for nonlinear dynamical models. This paper presents a $\textbf{la}$tent $\textbf{l}$inear $\textbf{q}$uadratic $\textbf{r}$egulator (LaLQ…

• xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification

  Ertugrul Kececi, Tufan Kumbasar · 17. April 2026

  Recent advances in Deep Learning (DL) have boosted data-driven System Identification (SysID), but reliable use requires Uncertainty Quantification (UQ) alongside accurate predictions. Although UQ-capable models such as Fuzzy ODE (FODE) can produce Prediction Intervals (PIs), they offer limited inter…

• A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot

  Mohsen Jalaeian-Farimani, Mohammad-R Akbarzadeh-T, Alireza Akbarzadeh, Mostafa Ghaemi · 16. April 2026

  To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive s…

• Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs

  Wenjing Tang, Xinyu He, Yongxi Huang, Yunxiao Xiao, Cewu Lu, Panpan Cai · 3. März 2026

  Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, and open-vocabulary object types, leading to significant open-ended uncertainty and a boundlessly large planning space. To…