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

A comparative study of FISER, a structured social and embodied reasoning framework, and CEMIR, a multimodal enrichment method that synthesizes narratives and photorealistic images from text prompts, found no statistically significant performance difference between the two approaches on the HandMeThat v2 benchmark. The researchers evaluated both methods across four Large Language Models, from Qwen2.5-Omni-3B to GPT-5, and found the null result held across all sixteen model-by-difficulty comparisons and in a targeted ablation isolating CEMIR's narrative and visual components. Performance instead tracked backbone model capability and the inherent difficulty of the ambiguity, suggesting a boundary condition for text-derived multimodal enrichment when task constraints are already present in the textual instruction.

arXiv:2610.32022v1 Announce Type: new 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. In this paper, we present 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 textual prompts. By evaluating both approaches across four Large Language Models LLMs , ranging from Qwen2.5-Omni-3B to GPT-5, on the HandMeThat v2 benchmark, we analyze their capability to resolve pragmatic ambiguities. Our statistical analysis, employing multiple-comparison correction, reveals no statistically significant difference in performance between FISER and CEMIR across any model or difficulty level. This pattern holds across all sixteen model-by-difficulty comparisons, as well as in a targeted ablation isolating CEMIR's narrative and visual components. Instead, performance is primarily associated with backbone capability and the inherent difficulty of the ambiguity. These findings suggest a boundary condition for text-derived multimodal enrichment: when the relevant task constraints are already available in the textual instruction, synthetic multimodal augmentation may not provide measurable benefit over structured reasoning on the same text.