Synthetic network generators preserve cross-flow correlations enabling source-level membership inference, shown via the TraceBleed attack across five datasets and six generators.
Language mod- els are few-shot learners
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CodeMind evaluates ten LLMs on four benchmarks using three new code reasoning tasks, finding performance varies by model size and drops with complexity while showing no correlation with bug repair ability.
Presents a hierarchical energy-aware framework with UCB-DUAL bandit for decentralized rank scheduling in multi-task federated fine-tuning for IoV networks.
RouteFormer is a transformer-RL hybrid for single-agent graph routing that reports 10% and 7% shorter distances than Concorde and LKH-3 on mission-like graphs by incorporating constraints the solvers ignore.
MulFSA combines micro-level firm sentiment, meso-level industry sentiment, and duration-aware smoothing from PLMs/LLMs to extract a daily sentiment index that reduces credit spread forecast errors by 10.25% MAE and 11.94% MAPE on a 1.35M-text Chinese bond corpus.
Survey of 188 engineers using SEM finds that UTAUT2 constructs influence LLM adoption differently across five SE purposes, with some factors showing negative effects when examined in isolation.
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
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CodeMind: Evaluating Large Language Models for Code Reasoning
CodeMind evaluates ten LLMs on four benchmarks using three new code reasoning tasks, finding performance varies by model size and drops with complexity while showing no correlation with bug repair ability.
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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.