Author

Linsey Pang

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#artificial intelligence Preprint Oct 2026

A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering

A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers have to be exactly right: over a table of transactions, a total is either right or wrong....

Yun-Shiuan Chuang, Yi-Rou Ge, Yi-Xi Li et al. · 0 citations

#artificial intelligence Preprint Oct 2026

CM-DPO: Constraint-Margin Direct Preference Optimization for LLM Planning

Direct Preference Optimization (DPO) treats all constraint violations equally: a $1 budget overshoot and a $1,000 overshoot induce the same training signal. It is also susceptible to length and style bias when preference pairs come from different model families. We introduce Constraint-Margin DPO (CM-DPO), which replac...

Rabimba Karanjai, Qun-Hui Gu, Hemanth Hegadehalli Madhavarao et al. · 0 citations

#machine learning Preprint Aug 2026

CatchBench: When Can an Agent Failure Be Caught?

CatchBench puts one auditor's question to three information states: the declared configuration before a run (PRE), a growing prefix of its trace (LIVE), and the finished trace (POST), which none scores all three under one task-method interface.

Yue Zhao, Meng-Yuan Li, Ruo-Lin Li et al. · 4 citations · ⚡1