Papers with fine-grained diagnosis

3 papers
Understanding Generalization in Role-Playing Models via Information Theory (2026.findings-acl)

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Challenge: Existing methods to characterize RPM generalization are inadequate . existing methods do not provide a fine-grained diagnosis of distribution shifts .
Approach: They propose a reasoning-based effective mutual information difference (R-EMID) to measure RPM performance degradation in an interpretable way.
Outcome: The proposed model predicts the worst-case generalization performance of RPMs and reveals how shifts contribute to degradation.
Beyond Ranking: Fine-Grained Diagnostics and Self-Improvement for MLLMs (2026.acl-long)

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Challenge: Current paradigms rely on holistic scoring and static leaderboards to disentangle fine-grained competencies.
Approach: They propose a framework to shift the focus from ranking to fine-grained diagnosis.
Outcome: The proposed framework surpasses the strongest baseline by 7.92%.
Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs (2026.findings-acl)

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Challenge: Existing models fail to recall and accurately apply designated persona knowledge without explicit cues . memory-driven role-playing paradigms are attracting significant interest .
Approach: They propose a memory-driven role-playing paradigm that frames persona knowledge as the LLM's internal memory store and a prompting architecture that guides structured memory retrieval and response generation.
Outcome: The proposed paradigm provides a comprehensive diagnostic for four-stage role-playing abilities across 12 LLMs.

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