Unsupervised contrastive learning has attracted significant attention in natural language processing for learning sentence embeddings. Existing methods primarily constrain the orientation distribution of representations in high-dimensional semantic space via cosine-based objectives, yet the embedding vector also posses...
Tian-Yu Zong, Bingkang Shi, Hong-Zhu Yi et al.· Neural Networks· 0 citations
Graph neural networks (GNNs) have emerged as a powerful tool for learning circuit representation, but largely ignore the dynamic nature of logic optimization (LO), leading to a severe ”domain gap” where embedding quality degrades after heuristic transformations. To address this, we propose CircuitFlow, a framework for...
Miao Liu, Xin-Hua Lai, Li-Wei Ni et al.· IEEE Transactions on Compute...· 0 citations
ModelEquivBench is presented, a certifying, multi-relational evaluation system that reports a per-pair semantic profile E0--E6 that contains relation-appropriate, independently re-checkable evidence: replayable traces or explicit maps for E0--E1, exact-rational certificates for positive E2--E6 conclusions, and explicit...
Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workf...
Penglin Zhu, Linhai Zhang, Jungang Xu et al.· 0 citations
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