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CEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognition

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Briefings

A comparative study evaluated two intent recognition frameworks, FISER and CEMIR, using sixteen LLMs on the HandMeThat v2 benchmark. Statistical analysis found no significant performance difference between the methods, indicating that structured reasoning may be as effective as multimodal enrichment when ambiguity stems from unstated intent rather than missing visual data. The study concludes that backbone capability and ambiguity difficulty are more critical factors than synthetic multimodal augmentation.

Key points

• FISER and CEMIR showed no statistically significant difference in performance across sixteen model-by-difficulty comparisons on the HandMeThat v2 benchmark.
• Performance was primarily determined by backbone capability and the inherent difficulty of the ambiguity, not by the addition of synthetic multimodal components.

Context

This research addresses the debate on whether text-derived multimodal enrichment improves intent recognition when ambiguity involves unstated human goals rather than missing scene information. The findings clarify the boundary conditions for using synthetic multimodal augmentation in human-robot interaction tasks.

Limitations

The study relies on a single preprint (arXiv:2610.32022v1) and uses multiple-comparison correction for statistical significance. Results are limited to the HandMeThat v2 benchmark and do not account for real-world deployment constraints or variations in LLM capabilities beyond the tested models.

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