Study finds no performance difference between FISER and CEMIR on HandMeThat v2
Pragmatic intent recognition is essential for human‑robot interaction, allowing agents to resolve ambiguous natural‑language instructions by inferring the speaker’s underlying goals. Recent research has focused on two paradigms: structured symbolic reasoning and multimodal input enrichment. The present study compares these approaches to determine whether enrichment provides an advantage when…
Key points
- FISER uses structured social and embodied reasoning to resolve ambiguous instructions.
- CEMIR synthesizes narratives and photorealistic images from textual prompts for multimodal enrichment.
The study evaluates FISER, a framework that uses structured social and embodied reasoning, against CEMIR, a method that synthesizes narratives and photorealistic images from textual prompts. Four large language models—Qwen2.5‑Omni‑3B, GPT‑5, and two others—were tested on the HandMeThat v2 benchmark.
Statistical analysis, using multiple‑comparison correction, found no statistically significant difference between the two approaches across any model or difficulty level. The result held across all sixteen model‑by‑difficulty comparisons and in a targeted ablation that isolated CEMIR’s narrative and visual components.
These findings suggest a boundary condition for text‑derived multimodal enrichment: when the relevant task constraints are already present in the textual instruction, synthetic multimodal augmentation may not provide measurable benefit over structured reasoning on the same text.
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