Author

Seong Joon Oh

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#small language model Preprint Oct 2026

Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?

This work introduces BOTTLED, a benchmark in which agents receive an entire unlabelled workload and must complete it under fixed time, compute and LLM API budgets, and finds that strong zero-shot task performance does not reliably translate into strong bottling capabilities.

Ankit Sonthalia, Haritz Puerto, Alexander Rubinstein et al. · 0 citations

#artificial intelligence Preprint Sep 2026

TULIP: Targeted LLM Unlearning at Layers Identified Per-Input

A hijacking experiment is designed that grafts hidden states of the target model into an oracle trained only on the retain set, which serves as a plug-and-play component that further improves existing methods.

Yejin Kim, William F. Shen, Seokwon Jung et al. · 0 citations