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.

Automated identification of bacterial flagellar motors in cryo-electron tomography (cryo-ET) remains a significant challenge due to low signal-to-noise ratios, imaging artifacts, and the complexity of capturing both local and global structural contexts. In this work, we propose MSVT+AUM, a hybrid framework that combines a Multi-Scale Volumetric Transformer (MSVT) with Adaptive Uncertainty Modeling (AUM) to address these limitations. MSVT employs a 3D ResNet-50 backbone to extract hierarchical volumetric features, which are integrated through a volume-level self-attention mechanism and refined via 3D Vision Transformer blocks with positional encoding. To enhance discriminative capability in densely packed molecular environments, we introduce a Contextual Spatial Attention module that adaptively reweights spatial and channel-wise representations. To reduce false positives and provide calibrated confidence estimates, AUM jointly models aleatoric and epistemic uncertainties using heteroscedastic regression and Monte Carlo dropout, with detection thresholds dynamically adjusted based on uncertainty estimates. Evaluation on the BYU flagellar motor dataset and an extended subset of the CryoET Data Portal shows that our method achieves an F 2 score of 0.891, representing a 1.5 % improvement over existing approaches, while reducing in-ference time by 23%. MSVT+AUM exhibits robust generalization across diverse bacterial species and noise conditions, offering a scalable and reliable solution for high-throughput structural analysis in cryo-ET.

Instruction fine-tuning (IFT) has emerged as a ubiquitous strategy for specializing large language models (LLMs), yet it implicitly assumes a single, coherent "ground-truth" preference behind all human-written instructions. In practice, annotators differ in the styles, emphases, and granularities they prefer, introducing preference bias that can erode both robustness and generalization. We propose Dynamic Cross-Layer Preference Correction (\textsc{DCPC}), it couples (i) a preference-sensitive similarity estimator that detects mismatched instructional cues, (ii) cross-layer prefix alignment to reconcile semantic representations across transformer layers, and (iii) a lightweight Preference Correction Module (PCM) that dynamically adjusts hidden states to honor the inferred dominant preference. On five Super/GLUE tasks and the Alpaca set—plus six preference-shifted variants—DCPC boosts accuracy/F1-EM by 4.0–6.7 points and gpt-score by +0.7, while cutting inter-seed variance up to 35% on LlaMA-2 13B and Mistral-7B, setting a new state of the art for robust instruction tuning.