Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules. Whether such an output that appears creative is truly original and effective is hard to establish: open-ended outputs require subjective judgment, the output may replicate something seen in training, or the task may...
Eric Xie, Wenqian Ye, Ai-Dong Zhang· 1 citation· ⚡1
In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requires processing the full set of examples, resulting in inefficient deployments, and how ICL works mechanistically is not fully understood. Prior work compresses ICL into fi...
Guang-Zhi Xiong, Zheng-Hao He, Bo-Han Liu et al.· 0 citations
Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but can omit useful evidence from other experiments, whereas including the full experimental history produces long, redundant contexts. We introduce a simple, single-agent d...
Bo Yuan, Wenqian Ye, Ze-Lin Zhao et al.· 0 citations
CF-WAM is proposed, a dynamic next-state prediction framework that samples visual, semantic, geometric, and interaction projections of the same future, standardizes them into a common video form, and supervises a unified WAM across these projections.
Ting-Yu Yuan, Zi-Ming Ji, Biao-Liang Guan et al.· 1 citation
This work proposes to view reasoning paths as phase-structured trajectories within fixed questions as PAIR, short for Phase-Aligned Intra-question Reasoning, and improves within-question trajectory ranking and Best-of-N trajectory selection across models and benchmarks.
Zheng-Hao He, Guang-Zhi Xiong, Sanchit Sinha et al.· 0 citations
Concept-Residual eXpansion (CRX), a concept-augmented framework that improves robustness by expanding the set of candidate predictive features by improving robustness to spurious correlations, is proposed.
Eric Xie, Guang-Zhi Xiong, Wenqian Ye et al.· Proceedings of the 32nd ACM...· 0 citations
Concept-Residual eXpansion (CRX), a concept-augmented framework that improves robustness by expanding the set of candidate predictive features by improving robustness to spurious correlations, is proposed.
Eric Xie, Guangzhi Xiong, Wenqian Ye et al.· Proceedings of the 32nd ACM...· 0 citations
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