Current K12 online math tutoring platforms share a common weakness: they tend to identify student errors only at a surface level, and when it comes to feedback, most systems simply hand out the correct answer. Over time, this combination chips away at the pedagogical promise of intelligent tutoring. The present work tackles both problems through a lightweight diagnostic-feedback pipeline that pairs MathBERT for semantic analysis with SymPy for symbolic verification. Why two engines? Because MathBERT reads the meaning behind a student’s response — catching conceptual slips that pattern-matching would miss — while SymPy checks whether two mathematical expressions are genuinely equivalent, which eliminates the well-documented hallucination tendencies of language models in algebraic contexts. For the feedback component, parameter-efficient fine-tuning (PEFT) first injects math-domain knowledge into a compact language model, after which direct preference optimization (DPO) steers the model toward Socratic-style questioning instead of answer-revealing behavior. Tested against the MathDial corpus, the pipeline reaches 99.17% on answer verification accuracy, posts an F1 of 0.9139 for error localization, and keeps the direct-answer rate down to 4.2% — beating every baseline on all three counts. These numbers suggest that symbolic checking paired with preference alignment offers a practical path toward tutoring systems that are both reliable and genuinely instructive, even on modest hardware.
Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constraints absent from regular Transformer stacks. We propose YOLO-PEFT, a structure-aware framework that formulates adapter placement as an auditable constraint-planning problem. Given a detector graph, a PEFT request, and a resource budget, YOLO-PEFT assigns operator and semantic roles, evaluates explicit operator-validity, detector-semantic, graph-interface, and deployment predicates, records a reason code for each excluded module, and either emits a budgeted target-module plan or returns Refuse before training. Under the official VOC07+12 trainval-to-VOC07 test protocol, planner-selected RS-LoRA reaches 0.7138 and 0.7307 mAP50-95 on YOLO11s and YOLO12s, respectively, compared with 0.6428 and 0.6662 for Full-SFT. On RT-DETR-L, all seven evaluated LoRA-family configurations cross the predefined catastrophic threshold, supporting a calibrated Refuse-to-Full-SFT decision within the evaluated coverage. A controlled YOLO11 audit further shows that LoRA reduces peak training memory by 43.9 percent, although training takes 1.72 times longer. Within the evaluated detector families, placement policies, and calibration coverage, YOLO-PEFT replaces manual target-module trial and error with explicit, inspectable planning while preserving verified train-save-merge-export paths; refusal on unseen detector architectures remains an open validation problem. Project Page: github.com/Tencent/YOLO-Master
Xuchen Lin, Wenjie Nie, Jinlong Peng et al.· 0 citations
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