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    <abstract>We study self-modeling: an LLM’s ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a prompt edit would change the model’s final answer. To measure this capability, we introduce a benchmark that tests diverse types of self-modeling questions. Current models show non-trivial but limited self-modeling skill, and make systematic mistakes on simple counterfactual questions about their own behavior. To improve self-modeling skill, we develop a scalable synthetic-data pipeline that produces self-modeling training data, and show that reinforcement-learning can improve aggregate self-modeling skill across three open-source model families with some transfer to held-out tasks. These gains, however, do not seem to constitute introspection consistently: improved self-modeling may not arise from privileged access to the model’s internal decision process. We release our evaluation and training code on GitHub.</abstract>
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%0 Conference Proceedings
%T Evaluating and Improving LLM Self-Modeling
%A Zeng, Siqi
%A Assis, Andre N.
%A Wang, Rowan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zeng-etal-2026-evaluating
%X We study self-modeling: an LLM’s ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a prompt edit would change the model’s final answer. To measure this capability, we introduce a benchmark that tests diverse types of self-modeling questions. Current models show non-trivial but limited self-modeling skill, and make systematic mistakes on simple counterfactual questions about their own behavior. To improve self-modeling skill, we develop a scalable synthetic-data pipeline that produces self-modeling training data, and show that reinforcement-learning can improve aggregate self-modeling skill across three open-source model families with some transfer to held-out tasks. These gains, however, do not seem to constitute introspection consistently: improved self-modeling may not arise from privileged access to the model’s internal decision process. We release our evaluation and training code on GitHub.
%U https://aclanthology.org/2026.emnlp-main.522/
%P 11978-12062

Markdown (Informal)

[Evaluating and Improving LLM Self-Modeling](https://aclanthology.org/2026.emnlp-main.522/) (Zeng et al., EMNLP 2026)

• Evaluating and Improving LLM Self-Modeling (Zeng et al., EMNLP 2026)

ACL

• Siqi Zeng, Andre N. Assis, and Rowan Wang. 2026. Evaluating and Improving LLM Self-Modeling. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11978–12062, Budapest, Hungary. Association for Computational Linguistics.