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

Ziya Hosseynzadeh; Farjad Fallah; Farnoosh Fallah; Soroush Gooran; Mohammad Amanloo; Hossein Sameti

cs.AIOct 23, 2026

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

Authors: Ziya Hosseynzadeh, Farjad Fallah, Farnoosh Fallah, Soroush Gooran, Mohammad Amanloo, Hossein Sameti

Organizations: Speech and Language Processing Laboratory (SLPL), Sharif University of Technology, Tehran, Iran · School of Electrical and Computer Engineering, University of Tehran, Iran · Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

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.

Figures & tables

Fig 1: The CEMIR enrichment pipeline. For each action step $t$ , a scene description $s_{t}$ (Qwen3-8B) is used both as text and as the prompt for an image $I_{t}$ (FLUX.1 [dev]); together with the unchanged raw action $a_{t}$ , they form the enriched step.

Model	n	FISER SR%	CEMIR SR%	$\Delta$ exact_acc (CEMIR $-$ FISER)	95% CI	$p$ -value	$p$ -value (Holm)	Significance
GPT-5	400	28.67	29.08	0.42	[-1.25, 2.25]	0.7147	1.0000	N.S. (Not Significant)
GPT-5-MINI	400	27.21	26.25	-1.08	[-2.83, 0.67]	0.2489	0.9954	N.S.
GPT-5-NANO	400	19.83	19.27	-0.58	[-2.50, 1.33]	0.5971	1.0000	N.S.
Qwen2.5‑Omni‑3B	400	8.83	9.58	0.75	[-1.59, 3.00]	0.5762	1.0000	N.S.

Table 1: Aggregate Success Rate (SR%) comparison between FISER and CEMIR across different LLM backbones.

\Delta

denotes CEMIR

-

FISER. P-values from a cluster-level paired permutation test (episode as the independent unit) with Holm-Bonferroni correction; CIs are cluster-bootstrap 95% intervals.

Fig 2: Aggregate success rates of FISER vs. CEMIR across different models.

Level	n	FISER SR%	CEMIR SR%	$\Delta$ exact_acc (CEMIR $-$ FISER)	95% CI	$p$ -value	$p$ -value (Holm)	Significance
1	100	60.88	59.58	-1.31	[-3.50, 0.80]	0.2589	1.0000	N.S. (Not Significant)
2	100	11.58	13.17	1.58	[-1.08, 4.17]	0.2766	1.0000	N.S.
3	100	6.17	5.42	-0.75	[-1.92, 0.50]	0.2822	1.0000	N.S.
4	100	5.92	5.94	0.02	[-1.50, 1.61]	1.0000	1.0000	N.S.

Table 2: Comparative Success Rates (SR%) by difficulty level.

\Delta

represents the performance margin (CEMIR

-

FISER). All

p_{\mathrm{holm}}

values exceed the 0.05 threshold.

Fig 3: Success Rate (SR%) of FISER and CEMIR across four difficulty levels.

Model	Level	n	FISER (%)	CEMIR (%)	$\Delta$ (pp)	95% CI	$p$ -value	$p$ -value (Holm)	Significance
GPT-5	1	100	81.33	83.00	1.67	[-1.67, 5.33]	0.4936	1.0000	N.S. (Not Significant)
	2	100	18.00	18.33	0.33	[-4.67, 5.33]	1.0000	1.0000	N.S.
	3	100	8.00	7.00	-1.00	[-3.33, 1.00]	0.5627	1.0000	N.S.
	4	100	7.33	8.00	0.67	[-1.67, 3.33]	0.8078	1.0000	N.S.
GPT-5-MINI	1	100	77.26	77.00	-0.33	[-3.67, 3.00]	1.0000	1.0000	N.S.
	2	100	15.00	16.00	1.00	[-3.67, 5.67]	0.7827	1.0000	N.S.

Table 3: Detailed Success Rates (SR%) and statistical significance for each LLM backbone across four difficulty levels.

Fig 4: Model-specific comparison of Success Rates between FISER and CEMIR for Level 1 (left) and Level 2 (right).

Fig 5: Model-specific comparison of Success Rates between FISER and CEMIR for Level 3 (left) and Level 4 (right).

Level	Modality	n	FISER SR (%)	Modality SR (%)	$\Delta$ (pp)	95% CI	$p$ -value	$p$ -value (Holm)	Sig.
1	Raw Text + Generated Narrative	100	21.67	27.00	5.33	[-0.67, 11.33]	0.0967	0.5799	N.S. (Not Significant)
	Raw Text + Image	100	21.67	16.67	-5.00	[-12.00, 2.00]	0.1861	0.6078	N.S.
2	Raw Text + Generated Narrative	100	6.00	9.67	3.67	[-0.67, 8.33]	0.1408	0.6078	N.S.
	Raw Text + Image	100	6.00	13.33	7.33	[2.00, 12.67]	0.0090	0.0716	N.S.
3	Raw Text + Generated Narrative	100	4.00	6.00	2.00	[-1.00, 5.33]	0.3067	0.6078	N.S.
	Raw Text + Image	100	4.00	2.00	-2.00	[-4.33, 0.34]	0.2140	0.6078	N.S.

Table 4: Ablation study on Qwen2.5‑Omni‑3B: Success Rate (SR%) of FISER vs. two modality conditions (Raw Text + Generated Narrative; Raw Text + Image) across four difficulty levels.

\Delta

= Modality SR

-

FISER SR. N = 100 episodes per cell.

Fig 6: Conceptual framework illustrating the primary task bottlenecks.

Multimodal Intent Recognition

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Apr 27, 2026

IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models

Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding. This challenge is particularly critical in multimodal settings, where agents must integrate heterogeneous signals including text, visual cues to form a coherent interpretation of user intent. This paper presents IntentVLM, a novel two-stage video-language framework designed for open-vocabulary human intention recognition. The approach is inspired by forward-inverse modeling in cognitive science by decomposing intention understanding into goal candidate generation followed by structured inference through selection, effectively reducing hallucinations in latent reasoning. Evaluated on the IntentQA and Inst-IT Bench datasets, IntentVLM achieves state-of-the-art results with up to 80% accuracy, notably surpassing the baseline performance by 30% and matches human performance. Our findings demonstrate that this structured reasoning approach enhances open-vocabulary intention understanding without catastrophic forgetting, offering a robust foundation for human-centered robotics.

Hamed Rahimi, Clemence Grislain, Adrien Jacquet Cretides +2

Institute of Intelligent Systems and Robotics (ISIR), Sorbonne University

Intent Classification Video-Language Models VLM Reasoning Human-Robot Interaction

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Jun 19, 2025

Measuring Intent Comprehension in LLMs

People judge interactions with large language models (LLMs) as successful when outputs match what they want, not what they type. Yet LLMs are trained to predict the next token solely from text input, not underlying intent. Because written language is an imperfect proxy for intent, and correlations between phrasing and desired outcomes can break down in training data, models that rely too heavily on surface cues may respond inconsistently to semantically equivalent prompts. This makes it essential to evaluate whether LLMs can reliably infer user intent-especially in high-stakes settings where robustness and generalization are critical. We introduce a formal framework for assessing intent comprehension in LLMs: whether a model demonstrates robust understanding of user intent by producing consistent outputs across semantically equivalent prompts while differentiating between prompts with distinct intents. Our evaluation approach is based on a variance decomposition of model responses into three components: variability due to user intent, user articulation, and model uncertainty. Models that understand what users want, and are not overly sensitive to textual cues, should attribute most output variance to intent differences, rather than articulation style. Applying this framework across diverse domains, we find that, within the five LLaMA and Gemma models we evaluate, larger models typically assign a greater share of variance to intent, indicating stronger comprehension of intent, although gains are uneven and often modest with increasing model size. These results motivate moving beyond accuracy-only benchmarks toward semantic diagnostics that directly assess whether models understand what users intend.

Nadav Kunievsky, James A. Evans

Knowlesdge Lab, University of Chicago, Chicago, Illinois, USA · Knowledge Lab, Data Science Institute, Department of Sociology, University of Chicago, Chicago, Illinois, USA.

Prompt Sensitivity LLM Evaluation Intent Classification Language Model Robustness

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May 7, 2026

IntentGrasp: A Comprehensive Benchmark for Intent Understanding

Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capability of LLMs. Derived from 49 high-quality, open-licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source datasets curation, intent label contextualization, and task format unification. IntentGrasp contains a large-scale training set of 262,759 instances and two evaluation sets: an All Set of 12,909 test cases and a more balanced and challenging Gem Set of 470 cases. Extensive evaluations on 20 LLMs across 7 families (including frontier models such as GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7) demonstrate unsatisfactory performance, with scores below 60% on All Set and below 25% on Gem set. Notably, 17 out of 20 tested models perform worse than a random-guess baseline (15.2%) on Gem Set, while the estimated human performance is ~81.1%, showing substantial room for improvement. To enhance such ability, this paper proposes Intentional Fine-Tuning (IFT), which fine-tunes the models on the training set in IntentGrasp, yielding significant gains of 30+ F1 points on All Set and 20+ points on Gem Set. Tellingly, the leave-one-domain-out (Lodo) experiments further demonstrate the strong cross-domain generalizability of IFT, verifying that it is a promising approach to substantially enhancing the intent understanding of LLMs. Overall, by benchmarking and boosting intent understanding ability, this study sheds light on a promising path towards more intentional, capable, and safe AI assistants for human benefits and social good.

Yuwei Yin, Chuyuan Li, Giuseppe Carenini

Department of Computer Science, University of British Columbia

LLM Evaluation Intent Classification

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Abstract

Multimodal Intent Recognition

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