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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

arXiv:2610.32022v1cs.AIcs.RO

TL;DR

The paper examines whether multimodal enrichment helps resolve pragmatic ambiguity when the environment is already described in text. It compares generated narrative-and-image enrichment with structured reasoning across four models and four difficulty levels, finding no statistically significant difference in any of the sixteen comparisons. The conclusions are scoped to HandMeThat v2 and the evaluated enrichment configuration.

  • Problem

    Reasoning and enrichment approaches had not been compared on pragmatic intent ambiguity in a text-complete setting, leaving it unclear whether enrichment helps when the missing information is a person’s unstated intent.

    Source: Introduction · p. 2, Research Gap · p. 5

  • Method

    The study compares FISER’s structured reasoning over episode text with CEMIR, which augments that text with generated narrative and imagery, across four language models.

    Source: Introduction · p. 2, Models and Configurations · p. 7

  • Results

    Across all sixteen model-by-difficulty comparisons, CEMIR’s performance was statistically indistinguishable from FISER’s, while success rates varied primarily with backbone capability and task difficulty.

    Source: Analysis and Discussion · p. 12, Conclusion · p. 17

  • Takeaways & Limitations

    Within HandMeThat v2 and this implementation, self-generated narrative and imagery did not yield a measurable advantage over structured reasoning on the same text.

    Source: Analysis and Discussion · pp. 16–17

    The evaluation is limited to HandMeThat v2 and a single enrichment configuration, so the conclusions may not extend to perceptual-uncertainty benchmarks or alternative multimodal strategies.

    Source: Analysis and Discussion · p. 16

Abstract

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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.

Introduction

The paper asks whether generated multimodal enrichment can match or exceed structured reasoning when ambiguity concerns unstated human intent rather than missing scene information. It introduces CEMIR and compares it with FISER on HandMeThat v2 across model scales and difficulty levels.

Introduction

  • Prior work offers two strategies: structured reasoning over prior actions and instruction text, or enriching model input with additional context before reasoning.FISER exemplifies structured reasoning on HandMeThat, while enrichment methods have reported gains on other language-understanding tasks.Source: Introduction · p. 2
  • Reasoning-based and enrichment-based approaches had not been compared under pragmatic ambiguity where the environment is fully described and the human’s intent remains uncertain.HandMeThat v2 provides a setting for this comparison because its textual interface withholds no perceptual information.Source: Introduction · p. 2
  • The study introduces CEMIR, which adds generated narrative and imagery from episode text, and evaluates it against FISER on HandMeThat v2.The comparison spans four language models and uses a within-system ablation of CEMIR’s narrative and visual components.Source: Introduction · pp. 2–3

Related Work

Prior work separates reasoning over supplied text from enrichment with additional context, and the two paradigms had not been compared on pragmatic intent ambiguity. This section reviews both approaches and identifies the resulting research gap.

Reasoning-Based Intent Recognition

  • Reasoning-based methods progress from Bayesian intent inference with hand-specified goal spaces to LLM pipelines; FISER divides inference into social and embodied reasoning over text.The earlier Bayesian and POMDP approaches depend on hand-specified goal spaces and transition models, limiting their applicability to open-ended natural-language instructions.Source: Reasoning-Based Intent Recognition · p. 3

Enrichment-Based Approaches to Ambiguity Resolution

  • Enrichment-based approaches add context before reasoning, using either independently observed environmental signals or synthesized or retrieved content.The distinction is whether added content has independent access to the environment.Source: Enrichment-Based Approaches to… · pp. 3–4

Grounding in Directly Observed Perceptual Signals

  • Direct-perception methods use signals such as robot state, scene clutter, or user gaze to resolve ambiguity that language alone leaves open.These signals originate from observation of the physical world rather than the instruction text.Source: Grounding in Directly Observed… · p. 4

Enrichment from Self-Generated or Retrieved Content

  • Generated or retrieved content derived from text has improved performance on classification and QA tasks, although it supplies no signal beyond the source text.Theoretical debate about grounding contrasts with reported modest benefits on these tasks.Source: Enrichment from Self-Generated or… · p. 4

Research Gap

  • The two paradigms had not been evaluated on pragmatic ambiguity, where the environment is fully described but a person’s unstated intent remains uncertain.HandMeThat v2 enables a comparison of self-generated enrichment with structured reasoning under this condition.Source: Research Gap · p. 5

Proposed Method

CEMIR enriches the same textual episode used by FISER with generated narrative and visual context. It processes each action independently and provides the original trajectory alongside a narrative and image for every step.

Proposed Method

  • Each action is enriched independently, producing one narrative and one visual frame per step to preserve localized cues such as object attributes and spatial relations.For an episode with N actions, the pipeline produces N narrative descriptions and N corresponding frames.Source: CEMIR Framework · p. 5
  • At inference, CEMIR presents the original textual trajectory together with each step’s generated narrative and image through the models’ vision-language interfaces.The raw action remains unchanged in the enriched step.Source: CEMIR Framework · p. 6, Figure 1 · p. 6
  • CEMIR uses Qwen3-8B to generate a scene description for each action step and FLUX.1 [dev] to turn that description into an image.The scene description is also supplied as text, so each step adds a narrative and visual modality to the original action.Source: CEMIR Framework · p. 5, Figure 1 · p. 6

Dataset and Experimental Setup

The study uses a balanced 400-episode HandMeThat v2 subset and compares prompted FISER with CEMIR across four model backbones. It applies a single-attempt evaluation protocol, audits generated-content fidelity, and tests reduced CEMIR variants on one backbone.

Dataset

  • The HandMeThat v2 subset contains 400 episodes, balanced at 100 per ambiguity level across 25 goal templates.The four levels range from literal grounding to ambiguity that cannot be fully resolved without strong human-preference priors.Source: Dataset · p. 6

Baseline (FISER Framework)

  • FISER uses its original prompted LLM protocol with the evaluated backbones, taking the raw textual episode as input rather than a specially trained architecture.Its textual input includes the world-state description, human trajectory, and instruction.Source: Baseline (FISER Framework) · p. 7

Content Fidelity Verification

  • Two annotators rated 469 of 498 audited action steps as consistent with the episode ground truth, with 96% agreement.Ratings covered 40 stratified episodes; stylistic or non-task-relevant embellishments were not counted against a Pass.Source: Content Fidelity Verification · p. 7

Models and Configurations

  • The comparison covers Qwen2.5-Omni-3B, GPT-5-Nano, GPT-5-Mini, and GPT-5 using a two-shot protocol.Exemplars were sampled randomly from the 400-episode subset, excluding the evaluation instance; sampling was independent by condition and run.Source: Models and Configurations · p. 7

Evaluation Protocol and Metrics

  • Each episode allows one action-sequence attempt within seven steps, and success rate is the percentage of episodes with the correct target object identified within that budget.Episodes were evaluated over three independent runs; method differences were tested using episode-level paired permutation tests with Holm–Bonferroni correction.Source: Evaluation Protocol and Metrics · pp. 7–8

Modality Ablation

  • Two Qwen2.5-Omni-3B ablations retain either generated narrative or generated imagery while omitting the other modality.Both variants keep CEMIR’s prompting structure and inference protocol and are evaluated against FISER.Source: Modality Ablation · p. 8

Results

Across the tested backbones, CEMIR showed no statistically significant aggregate difference from FISER. Success rates declined as difficulty increased, while neither method significantly changed performance across difficulty tiers.

Comparative Analysis Across LLM Backbones

Impact of Environmental Complexity

1. Performance Correlation with Backbone Capability:

Higher-capability backbones were associated with higher success for both methods, especially in low-complexity environments.

1. Performance Correlation with Backbone Capability:

  • At Level 1, both methods’ success rates rose from approximately 20% with Qwen2.5-Omni-3B to over 80% with GPT-5.Source: §1 · p. 11

2. Sensitivity to Task Complexity Across Models:

Increasing environmental difficulty was associated with lower success rates across both methods and all model sizes.

2. Sensitivity to Task Complexity Across Models:

  • Even GPT-5 fell from approximately 83% success at Level 1 to approximately 8% at Level 4, a trend mirrored across models and methods.Source: §2 · p. 11

3. Comparative Stability

Across 16 model-by-difficulty conditions, CEMIR remained statistically indistinguishable from FISER; a targeted Qwen2.5-Omni-3B ablation likewise found no significant improvement from either modality alone.

3. Comparative Stability

  • Across 16 model-by-difficulty conditions, CEMIR remained statistically indistinguishable from FISER, with no consistent improvement or degradation.The comparison spans four models and four difficulty levels.Source: §3 · p. 11

Ablation Study: Modality Contributions (Qwen2.5-Omni-3B)

  • Neither generated narratives alone nor generated images alone significantly improved on FISER at any difficulty level in the Qwen2.5-Omni-3B ablation.Raw Text + Image at Level 2 showed an unadjusted +7.33 pp difference (p = 0.0090), but it did not survive Holm–Bonferroni correction (pHolm = 0.0716).Source: Ablation Study: Modality Contributions… · p. 12

Analysis and Discussion

On HandMeThat v2, CEMIR and FISER performed similarly, while success varied with model capability and task difficulty. The discussion attributes the comparison’s scope to text-derived enrichment and identifies perceptual grounding and alternative implementations as unresolved questions.

Analysis and Discussion

  • Across evaluated conditions, CEMIR remained statistically indistinguishable from FISER, while success varied primarily with backbone capability and environmental difficulty.Table 3 covers four backbones across four difficulty levels.Source: Analysis and Discussion · p. 12, Table 3 · p. 13
  • HandMeThat v2 states all relevant object properties in text, so resolving pragmatic ambiguity depends on processing that text rather than receiving additional scene information.The “dusty one” example requires linking a linguistic descriptor to an object using scene context and prior actions.Source: Analysis and Discussion · pp. 13–14, Figure 6 · p. 15
  • CEMIR’s generated narrative and imagery added no measurable accuracy gain over FISER, despite requiring an additional language model and diffusion model for each action step.FISER operates directly on the textual episode without those additional model calls.Source: Analysis and Discussion · p. 14
  • The comparison tests self-generated enrichment from episode text, not enrichment grounded in directly observed perceptual signals, which the benchmark does not provide.Whether perceptual grounding would help under pragmatic ambiguity remains untested.Source: Analysis and Discussion · p. 16
  • The evaluation is limited to HandMeThat v2 and one enrichment configuration; modality ablations were restricted to Qwen2.5-Omni-3B by computational constraints.The authors also note that the paradigm-level comparison does not isolate each component’s contribution.Source: Analysis and Discussion · p. 16
  • Future work could test perceptually ambiguous benchmarks, larger backbones, alternative fusion strategies, and richer perceptual encodings.These directions are proposed to probe when multimodal integration may become advantageous.Source: Analysis and Discussion · p. 16

Conclusion

The paper compares structured reasoning with text-derived multimodal enrichment for pragmatic intent ambiguity on HandMeThat v2. Across four backbones and four difficulty levels, it finds no statistically significant difference, with conclusions limited to the evaluated benchmark and implementation.

Conclusion

  • The study compares FISER’s social and embodied reasoning over episode text with CEMIR’s generated narrative and imagery across four backbones and four difficulty levels.A targeted ablation examines CEMIR’s narrative and visual components individually.Source: Conclusion · p. 17
  • CEMIR and FISER were statistically indistinguishable in all 16 model-by-difficulty comparisons, while success was primarily associated with backbone capability and task difficulty.No result reached significance across the 32 tests spanning the main comparison and ablation.Source: Conclusion · p. 17
  • The conclusion is scoped to enrichment synthesized from episode text on a benchmark where task-relevant information is stated in text; directly observed perceptual grounding remains untested.The authors identify larger backbones, alternative fusion strategies, and perceptually grounded ambiguity as future directions.Source: Conclusion · p. 17

Funding

The authors report that the work received no specific funding.

Funding

Author contributions (CRediT)

The section records author responsibilities across the team and states that the study involved no human participants, animals, or personally identifiable data.

Author contributions (CRediT)

  • Ziya Hosseynzadeh contributed conceptualization, curation, analysis, investigation, methodology, administration, validation, visualization, and writing; Farjad Fallah contributed curation, analysis, investigation, methodology, software, validation, and visualization.Source: Author contributions (CRediT) · p. 18
  • Farnoosh Fallah contributed curation, analysis, investigation, methodology, software, validation, and visualization; Soroush Gooran and Hossein Sameti contributed conceptualization, analysis, administration, and supervision.Gooran also contributed validation and review; Sameti also contributed investigation. Mohammad Amanloo contributed curation, analysis, and software.Source: Author contributions (CRediT) · p. 18

Ethics statement

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