Author name cluster

Canran Xiao

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards

Zhiming Lin
Kai Zhao
Sophie Zhang
Peilai Yu
Canran Xiao

Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on costly annotations. We introduce CEC-Zero, a zerosupervision reinforcement learning framework that addresses this by enabling LLMs to correct their own mistakes. CEC-Zero synthesizes errorful inputs from clean text, computes cluster-consensus rewards via semantic similarity and candidate agreement, and optimizes the policy with PPO. It outperforms supervised baselines by 10–13 F1 points and strong LLM fine-tunes by 5–8 points across 9 benchmarks, with theoretical guarantees of unbiased rewards and convergence.CEC-Zero establishes a label-free paradigm for robust, scalable CSC, unlocking LLM potential in noisy text pipelines.

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AAAI Conference 2026 Conference Paper

From Points to Coalitions: Hierarchical Contrastive Shapley Values for Prioritizing Data Samples

Canran Xiao
Jiabao Dou
Zhiming Lin
Zong Ke
Liwei Hou

How should we quantify the value of each training example when datasets are large, heterogeneous, and geometrically structured? Classical Data-Shapley answers in principle, but its O(n!) complexity and point-wise perspective are ill-suited to modern scales. We propose Hierarchical Contrastive Data Valuation (HCDV), a three-stage framework that (i) learns a contrastive, geometry-preserving representation, (ii) organizes the data into a balanced coarse-to-fine hierarchy of clusters, and (iii) assigns Shapley-style pay-offs to coalitions via local Monte-Carlo games whose budgets are propagated downward. HCDV collapses the factorial burden to O(T∑ℓKℓ) = O(TKmax log n), rewards examples that sharpen decision boundaries, and regularizes outliers through curvature-based smoothness. We prove that HCDV approximately satisfies the four Shapley axioms with surplus loss O(η log n), enjoys sub-Gaussian coalition deviation Õ(1/√T), and incurs at most kε∞ regret for top-k selection. Experiments on four benchmarks — tabular, vision, streaming, and a 45 M-sample CTR task — plus the OpenDataVal suite show that HCDV lifts accuracy by up to +5 pp, slashes valuation time by up to 100×, and directly supports tasks such as augmentation filtering, low-latency streaming updates, and fair marketplace payouts.

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AAAI Conference 2026 Conference Paper

Seeing through the Conﬂict: Transparent Knowledge Conﬂict Handling in Retrieval-Augmented Generation

Hua Ye
Siyuan Chen
Ziqi Zhong
Canran Xiao
Haoliang Zhang
Yuhan Wu
Fei Shen

Large language models (LLMs) equipped with retrieval—the Retrieval-Augmented Generation (RAG) paradigm—should combine their parametric knowledge with external evidence, yet in practice they often hallucinate, over-trust noisy snippets, or ignore vital context. We introduce TCR (Transparent Conflict Resolution), a plug-and-play framework that makes this decision process observable and controllable. TCR (i) disentangles semantic match and factual consistency via dual contrastive encoders, (ii) estimates self-answerability to gauge confidence in internal memory, and (iii) feeds the three scalar signals to the generator through a lightweight soft-prompt with SNR-based weighting. Across seven benchmarks TCR improves conflict detection (+5–18 F₁), raises knowledge-gap recovery by +21.4 percentage points and cuts misleading-context overrides by –29.3 percentage points, while adding only 0.3% parameters. The signals align with human judgements and expose temporal decision patterns.

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ICRA Conference 2025 Conference Paper

Diffusion-Based Self-Supervised Imitation Learning from Imperfect Visual Servoing Demonstrations for Robotic Glass Installation

Canran Xiao
Liwei Hou
Ling Fu
Wenrui Chen

Heavy-duty glass installation is a high-risk, precision-critical task in modern construction, traditionally performed through labor-intensive and error-prone manual methods. This paper presents a novel robotic framework that leverages diffusion-based self-supervised imitation learning from imperfect visual servoing demonstrations to achieve safe and precise glass installation. Specifically, our approach employs noisy and suboptimal demonstration data obtained via visual servoing to train a Denoising Diffusion Probabilistic Model (DDPM). This model iteratively refines installation trajectories, transforming them into smooth, precise, and collisionfree movements. Extensive experiments demonstrate that our method significantly surpasses conventional visual servoing and standard imitation learning baselines in terms of success rate, precision, and installation efficiency, while markedly improving operational safety. Our results establish a new benchmark for automating complex, high-risk tasks in construction robotics.

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NeurIPS Conference 2024 Conference Paper

Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data

Xiaohong Chen
Canran Xiao
Yongmei Liu

Federated learning has become a pivotal distributed learning paradigm, involving collaborative model updates across multiple nodes with private data. However, handling non-i. i. d. (not identically and independently distributed) data and ensuring model consistency across heterogeneous environments present significant challenges. These challenges often lead to model performance degradation and increased difficulty in achieving effective communication among participant models. In this work, we propose Confusion-Resistant Federated Learning via Consistent Diffusion (CRFed), a novel framework designed to address these issues. Our approach introduces a new diffusion-based data harmonization mechanism that includes data augmentation, noise injection, and iterative denoising to ensure consistent model updates across non-i. i. d. data distributions. This mechanism aims to reduce data distribution disparities among participating nodes, enhancing the coordination and consistency of model updates. Moreover, we design a confusion-resistant strategy leveraging an indicator function and adaptive learning rate adjustment to mitigate the adverse effects of data heterogeneity and model inconsistency. Specifically, we calculate importance sampling weights based on the optimal sampling probability, which guides the selection of clients and the sampling of their data, ensuring that model updates are robust and aligned across different nodes. Extensive experiments on benchmark datasets, including MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and NIPD, demonstrate the effectiveness of CRFed in improving accuracy, convergence speed, and overall robustness in federated learning scenarios with severe data heterogeneity.

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NeurIPS Conference 2024 Conference Paper

Swift Sampler: Efficient Learning of Sampler by 10 Parameters

Jiawei Yao
Chuming Li
Canran Xiao

Data selection is essential for training deep learning models. An effective data sampler assigns proper sampling probability for training data and helps the model converge to a good local minimum with high performance. Previous studies in data sampling are mainly based on heuristic rules or learning through a huge amount of time-consuming trials. In this paper, we propose an automatic swift sampler search algorithm, SS, to explore automatically learning effective samplers efficiently. In particular, SS utilizes a novel formulation to map a sampler to a low dimension of hyper-parameters and uses an approximated local minimum to quickly examine the quality of a sampler. Benefiting from its low computational expense, SS can be applied on large-scale data sets with high efficiency. Comprehensive experiments on various tasks demonstrate that SS powered sampling can achieve obvious improvements (e. g. , 1. 5% on ImageNet) and transfer among different neural networks. Project page: https: //github. com/Alexander-Yao/Swift-Sampler.

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