Papers with Event Segmentation Theory

2 papers
Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning (2026.findings-acl)

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Challenge: Existing methods for storing and retrieving memory are limited by shallow semantic retrieval.
Approach: They propose a memory mechanism that organizes and retrieves past experiences to support decision-making.
Outcome: Experiments on LoCoMo and NarrativeQA show that CompassMem improves retrieval and reasoning performance across multiple backbone models.
Action Boundary Blindness: When LLM Agents Cannot Tell Where One Action Ends and Another Begins (2026.acl-long)

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Challenge: Large language model agents exhibit action boundary blindness, granularity confusion, scope creep and boundary ambiguity . Explicit boundary prompting improves ABS by 0.08–0.13 across all models .
Approach: They propose four automatic metrics that require no human annotation to detect boundary blindness . they propose to use a multi-label attribution framework to validate the models .
Outcome: Experiments with seven large language model agents show that the best model achieves only 0.424 ABS . Explicit Boundary Prompting improves ABS by 0.08–0.13 across all models .

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