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Sung Ju Hwang

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#artificial intelligence Preprint Sep 2026

EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents

Experiments show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed-policy LLM baselines, achieving the improved SR and CR in most evaluated settings, and suggest that adapting the reusable procedure governing tool use is a key direction for building more robust LLM trading agents.

Sehee Kim, Yumin Choi, Minki Kang et al. · 0 citations

Learning diverse attacks on large language models for robust red-teaming and safety tuning

This work proposes to use GFlowNet fine-tuning followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts, and finds that the attacks generated by the method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer we...

Seanie Lee, Minsu Kim, Lynn Cherif et al. · 62 citations · ⚡8

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