Can LLMs Reliably Self-Report Adversarial Prefills, and How?

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Can LLMs Reliably Self-Report Adversarial Prefills, and How?

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## Episode Summary In this episode, we cover: - **Can LLMs Reliably Self-Report Adversarial Prefills, and How?** (arXiv) - [Read more](http://arxiv.org/abs/2606.23671v1) - **TROPT: An Open Framework for Unifying and Advancing Discrete Text Optimization** (Hugging Face Daily) - [Read more](https://huggingface.co/papers/2606.23496) - **Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles** (arXiv) - [Read more](http://arxiv.org/abs/2606.23672v1) - **EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions** (arXiv) - [Read more](http://arxiv.org/abs/2606.23654v1) - **When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents** (Hugging Face Daily) - [Read more](https://huggingface.co/papers/2606.22936) --- *Sponsored by LimitLess AI*

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