CEMIR vs. FISER: Comparing Multimodal Enrichment and Structured Reasoning for Intent Recognition

Forum topic · 小凯 · 2026-10-26

Summary

This comparative study benchmarks two paradigms for intent recognition in human-robot interaction: FISER, a framework using structured social and embodied reasoning, and CEMIR (Context-Enriched Multimodal Intent Recognition), which synthesizes narratives and photorealistic images from text prompts to enrich multimodal inputs. The authors evaluate both approaches across four large language models (from Qwen2.5-Omni-3B to GPT-5) on the HandMeThat v2 benchmark, analyzing their ability to resolve pragmatic ambiguity where unstated human intent—not missing scene information—causes ambiguity. Statistical analysis with multiple-comparison corrections reveals no statistically significant performance difference between FISER and CEMIR in any of the sixteen model-difficulty comparisons, including targeted ablations isolating CEMIR's narrative and visual components. Performance instead correlates mainly with backbone capability and intrinsic ambiguity difficulty. The findings define a boundary condition for text-derived multimodal enrichment: when relevant task constraints are already available in the text instruction, synthetic multimodal augmentation may offer no measurable benefit over structured reasoning on the same text.

CEMIR vs. FISER: A Comparative Study of Reasoning-Based and Enrichment-Based Intent Recognition

Field: Machine Learning
Authors: Ziya Hosseynzadeh, Farjad Fallah, Farnoosh Fallah, Soroush Gooran, Mohammad Amanloo, Hossein Sameti
Published: 2026-10-27
arXiv: 2610.32022

Abstract

Intent recognition in human-robot interaction requires agents to resolve ambiguous natural language instructions by inferring the speaker's underlying goals. Research has increasingly focused on two notable paradigms for addressing this task: structured symbolic reasoning and multimodal input enrichment. However, it remains unclear whether enrichment-based methods provide benefit in settings where the ambiguity concerns unstated human intent rather than missing observable scene information.

This paper presents a comparative and diagnostic study benchmarking FISER—a framework utilizing structured social and embodied reasoning—against CEMIR (Context-Enriched Multimodal Intent Recognition), a method that performs multimodal enrichment by synthesizing narratives and photorealistic images from text prompts.

Key Findings

Both approaches were evaluated on the HandMeThat v2 benchmark across four LLMs, ranging from Qwen2.5-Omni-3B to GPT-5.
Statistical analysis with multiple-comparison corrections found no statistically significant performance difference between FISER and CEMIR in any model or difficulty level.
This pattern held across all sixteen model-difficulty comparisons, and also in targeted ablations isolating CEMIR's narrative and visual components.
Performance was primarily driven by backbone capability and the intrinsic difficulty of the ambiguity.

Implication

The results establish a boundary condition for text-derived multimodal enrichment: when the relevant task constraints are already available in the text instruction, synthetic multimodal augmentation may not provide measurable benefits over structured reasoning applied to the same text.

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