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cs.CL2026

FinanceHarness: Autonomous Financial Deep Research Framework

Yijia Xiao, Rujun Han, Yanfei Chen +8

The paper introduces FinanceHarness, a framework that uses large language models and autonomous agents to automate end‑to‑end financial deep research, and presents FinanceGym, a be…

cs.CL2026

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards

Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang +9

Training deep research agents, namely systems that plan, search, evaluate evidence, and synthesize long-form reports, pushes reinforcement learning beyond the regime of verifiable…

cs.CL2026

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning

Yihe Deng, I-Hung Hsu, Jun Yan +7

Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLV…

cs.CL2025

Towards Compute-Optimal Many-Shot In-Context Learning

Shahriar Golchin, Yanfei Chen, Rujun Han +7

Long-context large language models (LLMs) are able to process inputs containing up to several million tokens. In the scope of in-context learning (ICL), this translates into using…

cs.CL2025

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents

Zhen Tan, Jun Yan, I-Hung Hsu +12

Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limi…

cs.CL2025

Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling

Wenda Xu, Rujun Han, Zifeng Wang +7

Recent advances in knowledge distillation (KD) have enabled smaller student models to approach the performance of larger teacher models. However, popular methods such as supervised…