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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.08150 |
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| _version_ | 1866911709572104192 |
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| author | Lall, Supriya Farrell, Christian Pathanjaly, Hari Pavic, Marko Chezhian, Sarvesh Asai, Masataro |
| author_facet | Lall, Supriya Farrell, Christian Pathanjaly, Hari Pavic, Marko Chezhian, Sarvesh Asai, Masataro |
| contents | Reasoning is a fundamentally algorithmic task. Yet current work on LLM-based reasoning relies on free-form generation whose theoretical guarantees (soundness, completeness, complexity, optimality) remain poorly understood. We argue that we should not treat them as general-purpose reasoners, and as an alternative, we propose a paradigm we call \emph{verbalized algorithms} (VAs), which combines LLMs and various algorithms with established guarantees. Instead of betting on LLM's ability to solve a reasoning task, VAs limit their scope by decomposing the task down to simple elementary operations on strings that they can answer reliably. For example, sorting a list of natural language strings could be done by using an LLM as a binary comparison oracle in a parallel or approximate sorting algorithm. We push the accuracy-runtime Pareto front with \emph{verbalized maximum}, \emph{sorting}, \emph{clustering}, and \emph{submodular maximization}, for numerical reasoning, topic clustering, Wi-Fi access point optimization, and multi-hop Q\&A RAG task. These results suggest improving LLM-based reasoning through standard algorithmic analysis is a feasible and better grounded research direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Verbalized Algorithms: Classical Algorithms are All You Need (Mostly) Lall, Supriya Farrell, Christian Pathanjaly, Hari Pavic, Marko Chezhian, Sarvesh Asai, Masataro Computation and Language Reasoning is a fundamentally algorithmic task. Yet current work on LLM-based reasoning relies on free-form generation whose theoretical guarantees (soundness, completeness, complexity, optimality) remain poorly understood. We argue that we should not treat them as general-purpose reasoners, and as an alternative, we propose a paradigm we call \emph{verbalized algorithms} (VAs), which combines LLMs and various algorithms with established guarantees. Instead of betting on LLM's ability to solve a reasoning task, VAs limit their scope by decomposing the task down to simple elementary operations on strings that they can answer reliably. For example, sorting a list of natural language strings could be done by using an LLM as a binary comparison oracle in a parallel or approximate sorting algorithm. We push the accuracy-runtime Pareto front with \emph{verbalized maximum}, \emph{sorting}, \emph{clustering}, and \emph{submodular maximization}, for numerical reasoning, topic clustering, Wi-Fi access point optimization, and multi-hop Q\&A RAG task. These results suggest improving LLM-based reasoning through standard algorithmic analysis is a feasible and better grounded research direction. |
| title | Verbalized Algorithms: Classical Algorithms are All You Need (Mostly) |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.08150 |