4 papers
Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
Xiaojie Gu, Sherry T. Tong, Aosong Feng +8
Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks witho…
Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
Jialin Chen, Aosong Feng, Harshit Verma +7
Financial markets are characterized by extreme non-stationarity, low signal-to-noise ratios, and strong dependence on external information such as news, company fundamentals, and m…
Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
Leyao Wang, Yanan He, Peng Chen +5
Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, and synthesis. Their growing r…
Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction
Ngoc Bui, Hieu Trung Nguyen, Arman Cohan +1
The key-value (KV) cache is a major bottleneck in long-context inference, where memory and computation grow with sequence length. Existing KV eviction methods reduce this cost but…