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FISER vs. CEMIR Intent Recognition Comparison: No Significant Difference
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Ziya Hosseynzadeh and other authors published a comparative diagnostic study on the HandMeThat v2 benchmark comparing two pragmatic intent recognition methods: FISER relies on structured social and embodied reasoning, while CEMIR synthesizes narratives and photorealistic images from text prompts for multimodal augmentation. The tests covered four large language models, from Qwen2.5-Omni-3B to GPT-5. The authors state that statistical analysis with multiple-comparison correction showed no statistically significant difference between FISER and CEMIR for any model or any difficulty level; this result also held across all 16 "model × difficulty" comparisons and in ablation experiments that separately isolated CEMIR's narrative and visual components. On this basis, the authors propose that when the relevant task constraints are already included in the text instructions, synthetic multimodal augmentation may not yield measurable gains compared with structured reasoning over the same text.
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- CEMIR vs. FISER: A Comparative Study of Reasoning-Based and Enrichment-Based Approaches to Pragmatic Intent Recognition
The study compares FISER, which is based on structured social and embodied reasoning, with CEMIR, which performs multimodal enrichment by synthesizing narratives and photorealistic images from text prompts, on the HandMeThat v2 benchmark, covering four large language models from Qwen2.5-Omni-3B to GPT-5.
arXiv cs.RO ·