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20242026
most citedNeuro-symbolic Learning Yielding Logical Constraints

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.SE2026

Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes

Yuhao Tan, Zhibang Yang, Fangkai Yang +9

Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained.…

cs.LG2026

InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs

Guangyuan Wu, Weining Cao, Zehui Tan +4

Loop invariant inference is a fundamental yet challenging problem in program verification. Recent LLM-aided guess-and-check techniques have shown strong performance on single-loop…

cs.CV2026

Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction

Chaoqun He, Mingyang Xiang, Yingjing Xu +5

Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming inputs and respond at approp…

cs.CL2026

Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps

Yanke Zhou, Yiduo Li, Hanlin Tang +6

Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training…

cs.LG2025

Conformal Correction for Efficiency May be at Odds with Entropy

Senrong Xu, Tianyu Wang, Zenan Li +4

Conformal prediction (CP) provides a comprehensive framework to produce statistically rigorous uncertainty sets for black-box machine learning models. To further improve the effici…

cs.AI20242 cited

Neuro-symbolic Learning Yielding Logical Constraints

Zenan Li, Yunpeng Huang, Zhaoyu Li +5

Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This…