Large Language Models (LLMs) have demonstrated remarkable performance in code intelligence tasks such as code generation, summarization, and translation. However, their reliance on linearized token sequences makes them brittle to long-range program dependencies and superficial lexical shifts such as identifier renaming. Existing structure-aware approaches typically treat structure as serialized text prompts or auxiliary training objectives, which often inflate context length or rely on internalized structural priors, failing to provide explicit guidance during inference. To address these limitations, we propose CGBridge, a novel plug-and-play method that enhances LLMs with Code Graph information through an external, trainable Bridge module. It aligns Code Property Graph structure with code semantics and compresses them into compact soft-prefixes, decoupling structural reasoning from textual generation without updating the backbone. Experiments across multiple code LLM backbones and scales show consistent gains over both text-only adaptation and graph-augmented baselines. Furthermore, CGBridge remains robust under identifier renaming and enables over 4× faster inference than LoRA-tuned models, demonstrating both effectiveness and efficiency in structure-aware code understanding.

Compute cost in automated essay scoring is usually treated as engineering rather than measurement. This paper introduces the Cost-Aware Measurement Design (CAMD) framework, defines cost per defensible score (CPDS), and argues that AES systems should be compared by quality-cost sufficiency at a declared scoring volume.

Compact distillation preserved clean essay-scoring agreement across three student models and six compressed teacher targets, but the best target differed by model and margins were small. Perturbation, distribution-shift, and subgroup-bootstrap analyses dominated those differences, indicating that resource-constrained educational scoring requires uncertainty-aware evaluation rather than clean held-out comparison alone.

Using PERSUADE 2.0 discourse segments, this study examines whether effectiveness labels carry stable linguistic meaning across discourse types. Traditional NLP features and latent components show robust type-by-effectiveness interactions. Length decomposition indicates much of this signal reflects elaboration, while smaller length-robust patterns remain, cautioning against context-free diagnostic feedback in automated writing evaluation.

This study examines whether NLP transfer learning techniques, specifically BERT, can be used to develop prompt-generic AES models for practice writing tests. Findings reveal that fine-tuned DistilBERT, without further pre-training, achieves high agreement (QWK ≈ 0.89), enabling scalable, robust AES models in statewide K-12 assessments without costly supplementary pre-training.

Generalizability Theory with entropy-derived stratification optimized automated essay scoring reliability. A G-study decomposed variance across 14 encoders and 3 seeds; D-studies identified minimal ensembles achieving G ≥ 0.85. A hybrid of one medium and one small encoder with two seeds maximized dependability per compute cost. Stratification ensured uniform precision across

Natural Language to Visualization (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from large-scale data. Recent advancements in Large Language Models (LLMs) show promise in automating code generation to transform tabular data into accessible visualizations. However, they often struggle with complex queries that require reasoning across multiple tables. To address this limitation, we propose a collaborative agent workflow, termed nvAgent, for NL2Vis. Specifically, nvAgent comprises three agents: a processor agent for database processing and context filtering, a composer agent for planning visualization generation, and a validator agent for code translation and output verification. Comprehensive evaluations on the new VisEval benchmark demonstrate that nvAgent consistently surpasses state-of-the-art baselines, achieving a 7.88% improvement in single-table and a 9.23% improvement in multi-table scenarios. Qualitative analyses further highlight that nvAgent maintains nearly a 20% performance margin over previous models, underscoring its capacity to produce high-quality visual representations from complex, heterogeneous data sources. All datasets and source code are available at: https://github.com/geliang0114/nvAgent.