Author

Lin-Na Zhou

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#artificial intelligence Preprint May 2026

OracleProto: A Reproducible Framework for Benchmarking LLM Forecasting via Knowledge Cutoffs and Temporal Masking

This work introduces OracleProto, a reproducible framework for evaluating LLM forecasting in a controlled, temporally masked environment, and retains resolved forecasting tasks as reusable evaluation resources under explicit temporal boundaries, supporting continual accumulation of forecasting datasets and systematic e...

Yi-Ding Ma, Cheng-Yu Ruan, Kai-Bo Huang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

SKIP: a Self-knowledge-guided Step-wise Preference Learning Framework for Concise Reasoning

While Chain-of-Thought (CoT) reasoning has been proven to be effective, it often leads to overthinking, resulting in computational overhead, inference latency, and even degraded performance in large language models (LLMs). Existing concise reasoning frameworks significantly compromise accuracy while compressing the len...

Qin-Hong Lin, Yu-Hao Zhang, Ying-Lun Feng et al. · 0 citations

#artificial intelligence Jun 2026

FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness

FinAcumen accumulates financially grounded reasoning experience from prior trajectories, distilling successful strategies and failure-derived cautionary rules into a persistent memory bank, and shows that selective experience activation improves reasoning reliability under retrieval uncertainty.

Pianran Guo, Pengcheng Zhou, Yucheng Jian et al. · arXiv.org · 1 citation