mlsanityData to 14 Oct 2026

LLM Agents as Computational Typologists

arXiv 2609.07791 ↗First posted 7 Sep 2026cs.CLcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Linguistic typology relies on expert analysis of reference grammars across languages, making large-scale crosslinguistic comparison labor-intensive and unscalable. We introduce AUTOTYPOLOGIST, an LLM agent for evidence-grounded typological analysis over reference grammars. The agent is capable of retrieving relevant grammar sections, analyzing interlinear glossed text (IGT), and iteratively reasoning over typological hypotheses using a ReAct-style workflow. We evaluate the system on TYPOLOGICAL FEATURE CODING against expert annotations and TYPOLOGICAL HYPOTHESIS TESTING with typological universals using 25 open-source reference grammars. Operating under different information constraints in TYPOLOGICAL FEATURE CODING, the agent can synthesize information from reference grammar prose but still faces challenges with only IGTs in the target language. In TYPOLOGICAL HYPOTHESIS TESTING, the agent can synthesize crosslinguistic evidence and identify both supporting cases and counterexamples. These findings suggest that LLM agents can support scalable and inspectable typological analysis, while still requiring expert validation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 16 of its 44 references that are in the map. First posted on arXiv 7 Sep 2026.

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16 of 44 references.

References · 16

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 425
  4. 2023A Benchmark for Learning to Translate a New Language from One Grammar Bookcited by 75
  5. 2026How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Taskscited by 33
  6. 2024LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languagescited by 28
  7. 2024Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?cited by 23
  8. 2023Large Linguistic Models: Investigating LLMs' metalinguistic abilitiescited by 18
  9. 2020PuzzLing Machines: A Challenge on Learning From Small Datacited by 15
  10. 2024Back to School: Translation Using Grammar Bookscited by 11
  11. 2025A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysiscited by 11
  12. 2020Automatic Extraction of Rules Governing Morphological Agreementcited by 9
4 more
  1. 2025Explicit Learning and the LLM in Machine Translationcited by 6
  2. 2025LingGym: How Far Are LLMs from Thinking Like Field Linguists?cited by 6
  3. 2021When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selectioncited by 5
  4. 2026Speaking of Language: Reflections on Metalanguage Research in NLPcited by 1

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  3. 2025LingBench++: A Linguistically-Informed Benchmark and Reasoning Framework for Multi-Step and Cross-Cultural Inference with LLMs
  4. 2026Agent-Driven Corpus Linguistics: A Framework for Autonomous Linguistic Discovery
  5. 2026Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages
  6. 2026Typological Feature Prediction with Large Language Models: An In-Context Learning Approach
  7. 2025IOLBENCH: Benchmarking LLMs on Linguistic Reasoning
  8. 2025Creating ConLangs to Probe the Metalinguistic Grammatical Knowledge of LLMs