Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
arXiv preprint arXiv:2404.08335 , year=
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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representative citing papers
Empirical study of open-source AI agents shows testing effort concentrates on deterministic tools and workflows (over 70%) while the FM-based plan body gets under 5% and prompts appear in only 1% of tests.
IPA-based subword tokenizers trained across 24 languages improve tokenization quality and generalization to unseen languages compared to standard text tokenizers, especially for non-Latin scripts.
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.
citing papers explorer
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The price of incrementality in k-center clustering
Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
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An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
Empirical study of open-source AI agents shows testing effort concentrates on deterministic tools and workflows (over 70%) while the FM-based plan body gets under 5% and prompts appear in only 1% of tests.
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Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet
IPA-based subword tokenizers trained across 24 languages improve tokenization quality and generalization to unseen languages compared to standard text tokenizers, especially for non-Latin scripts.
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The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.