7 papers
From the Task Boundaries of Narrative Text to Structural Anchoring, Uncertainty Triggers, and Cross-Calibration
Bowen Deng, Jiaqi Zou, Kexin Zhang +1
Causal graphs represent structural relationships among variables, yet users must still interpret direction, mechanism, and adjustment conditions in relation to the task at hand. Pr…
CompEvo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting
Yuxuan Zhang, Yangyang Feng, Yong Guan +6
News-driven time series forecasting uses evolving textual events together with historical observations to predict future values, supporting applications such as market risk monitor…
Learning to Reason with Insight for Informal Theorem Proving
Yunhe Li, Hao Shi, Bowen Deng +8
Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language models' (LLMs) strength in n…
Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture
Tianhao Fu, Xinxin Xu, Weichen Xu +6
Market making (MM) through Reinforcement Learning (RL) has attracted significant attention in financial trading. With the development of Large Language Models (LLMs), more and more…
Two-way Evidence self-Alignment based Dual-Gated Reasoning Enhancement
Kexin Zhang, Junlan Chen, Daifeng Li +4
Large language models (LLMs) encounter difficulties in knowledge-intensive multi-step reasoning (KIMSR) tasks. One challenge is how to effectively extract and represent rationale e…
Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?
Yuxuan Zhang, Yangyang Feng, Daifeng Li +3
Multi-agents-based news-driven time series forecasting is considered as a potential paradigm shift in the era of large language models (LLMs). The challenge of this task lies in me…
Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations
Junlan Chen, Kexin Zhang, Daifeng Li +3
The emergence of large language models offers new possibilities for structured exploration of scientific knowledge. Rather than viewing scientific discovery as isolated ideas or co…