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Evaluating and Improving LLM Self-Modeling

Paper recorded by Signals 4 on 2026-08-31 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

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 o

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#259 most recent of 300 cs.AI papers we have recorded · ↑ newer: Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-T · ↓ older: MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI
Cite this page: Evaluating and Improving LLM Self-Modeling: the #259 most recent of 300 cs.AI papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/evaluating-and-improving-llm-self-modeling.html
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