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arxiv: 2604.24002 · v1 · submitted 2026-04-27 · 💻 cs.HC · cs.AI· cs.MM

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

Hamed Rahimi , Clemence Grislain , Adrien Jacquet Cretides , Olivier Sigaud , Mohamed Chetouani This is my paper

Pith reviewed 2026-05-08 02:28 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.MM
keywords intention recognitionvideo-language modelsopen-vocabularyforward-inverse modelinghuman-robot interactionmultimodal reasoninggoal inferencestructured selection
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The pith

A two-stage forward-inverse framework enables robust open-vocabulary intention recognition in video-language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to establish that decomposing intention understanding into an initial stage of generating goal candidates from video and text, followed by a selection stage of structured inference, allows video-language models to infer human goals more accurately. This matters because effective human-robot interaction depends on correctly integrating multimodal signals into coherent interpretations of user intent. A sympathetic reader would see value in the claim that this cognitive-science-inspired split cuts down on reasoning errors like hallucinations while preserving the model's existing capabilities. If the decomposition works as described, it supplies a practical way to handle open-vocabulary cases without the model losing prior knowledge when adapting to new intention tasks.

Core claim

The central claim is that intention understanding can be decomposed into goal candidate generation followed by structured inference through selection, following forward-inverse modeling. This structured process reduces hallucinations in latent reasoning and supports open-vocabulary human intention recognition. On the IntentQA and Inst-IT Bench datasets the resulting model reaches up to 80 percent accuracy, exceeds baseline performance by 30 percent, and matches human performance while avoiding catastrophic forgetting.

What carries the argument

The forward-inverse modeling decomposition, in which forward modeling generates goal candidates from multimodal inputs and inverse modeling performs structured selection to identify the intended goal.

If this is right

  • Open-vocabulary intention recognition improves in settings that combine video and language inputs.
  • Hallucinations during the model's internal reasoning about goals are reduced.
  • The model adapts to new intention tasks without losing performance on previous ones.
  • Multimodal signals are integrated into more coherent goal interpretations.
  • The approach supplies a foundation for intention-aware human-robot interaction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The explicit selection step could make model decisions more inspectable than direct end-to-end prediction.
  • Similar decomposition might help other multimodal prediction tasks such as forecasting pedestrian actions in video.
  • The method could be tested on longer video sequences to check whether the candidate-generation stage scales without added error.
  • Pairing the two-stage structure with real-time streaming video might enable live intention monitoring in collaborative settings.

Load-bearing premise

That separating goal candidate generation from the later selection step is what reduces hallucinations and prevents loss of prior knowledge.

What would settle it

Direct comparison on the IntentQA and Inst-IT Bench datasets showing that a single-stage video-language model without the selection step achieves equal or higher accuracy and equal or lower hallucination rates.

Figures

Figures reproduced from arXiv: 2604.24002 by Adrien Jacquet Cretides, Clemence Grislain, Hamed Rahimi, Mohamed Chetouani, Olivier Sigaud.

Figure 1
Figure 1. Figure 1: IntentVLM for open-vocabulary human intention recognition from video streams. Given a continuous video stream of view at source ↗
Figure 2
Figure 2. Figure 2: Closed-vocabulary solutions vs our approach. Conventional approaches formulate intention recognition as closed-set view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of IntentVLM. We cast the model as a view at source ↗
Figure 4
Figure 4. Figure 4: IntentQA Benchmark [60]. Illustrative examples of the four QA types in our training dataset. CW: a man points at a screen to guide a child’s attention. CH: a pointing gesture expresses the intent to drink. TN: a boy’s pointing causes the baby to look in that direction. TP: a girl’s pointing is triggered by the baby’s action. The red box indicates the correct answer view at source ↗
Figure 5
Figure 5. Figure 5: Inst-IT Benchmark [61]. Each sample consists of temporally grounded frame-level annotations with instance IDs, a coherent video-level description, and instance-focused QA pairs. The annotations capture fine-grained attributes, in￾teractions, and temporal changes of instances across frames, enabling explicit instance-level reasoning. and reasoning. Instance detection identifies what objects are present and … view at source ↗
Figure 4
Figure 4. Figure 4: Inst-IT Bench. The second dataset, Inst-IT Bench [61], is a fine￾grained multimodal benchmark designed to evaluate instance-level comprehension in both images and videos. It provides a more de￾tailed assessment of a model’s ability to reason about specific enti￾ties and their interactions within a scene. Formally, each sample in Inst-IT Bench can be represented as: 𝑥 = (𝑀, 𝑄, A, 𝑎∗ ), (6) where 𝑀 denotes t… view at source ↗
Figure 7
Figure 7. Figure 7: Performance on the Inst-IT benchmark. (a) shows that our model remains highly competitive with baseline methods, view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes IntentVLM, a two-stage video-language framework for open-vocabulary human intention recognition. Drawing on forward-inverse modeling from cognitive science, it decomposes the task into goal-candidate generation followed by structured selection-based inference, with the goal of reducing hallucinations in VLM latent reasoning. Evaluated on IntentQA and Inst-IT Bench, the method is reported to reach up to 80% accuracy, exceed baselines by 30%, match human performance, and avoid catastrophic forgetting.

Significance. If the reported gains can be isolated to the proposed decomposition via controlled experiments, the work could supply a practical structured-reasoning template for reliable multimodal intention inference in human-robot interaction.

major comments (2)
  1. [Abstract] Abstract: The headline performance claims (80% accuracy, +30% over baseline, human-level) are stated without any description of model architecture, training procedure, baseline definitions, statistical tests, or error analysis, rendering it impossible to assess whether the data support the claims.
  2. [Results] Results section: The central methodological claim—that the forward-inverse two-stage decomposition reduces hallucinations and produces the observed gains—is load-bearing yet unsupported by ablations (single-stage vs. two-stage) or any quantitative hallucination metric. Absent these controls, the 30% margin cannot be confidently attributed to the proposed structure rather than prompting, context length, or unstated fine-tuning.
minor comments (1)
  1. [Abstract] Abstract: The clause 'including text, visual cues to form' is grammatically incomplete and should read 'including text and visual cues to form'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on improving the clarity of our claims and the empirical support for our methodological contributions. We address each major comment below and have made targeted revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline performance claims (80% accuracy, +30% over baseline, human-level) are stated without any description of model architecture, training procedure, baseline definitions, statistical tests, or error analysis, rendering it impossible to assess whether the data support the claims.

    Authors: We agree that the abstract, due to its brevity, does not provide these supporting details, which limits immediate assessment of the claims. The full manuscript describes the two-stage video-language architecture and forward-inverse decomposition in Section 3, the training and inference procedure in Section 4, baseline definitions and comparisons in Section 5.1, and statistical tests plus error analysis in Section 5.3. To address the concern directly, we have revised the abstract to concisely reference the forward-inverse modeling approach, the video-language model backbone, the evaluation on IntentQA and Inst-IT Bench, and the use of accuracy with human-comparison baselines. We have also expanded the error analysis subsection in the results to better link the reported metrics to the data. revision: yes

  2. Referee: [Results] Results section: The central methodological claim—that the forward-inverse two-stage decomposition reduces hallucinations and produces the observed gains—is load-bearing yet unsupported by ablations (single-stage vs. two-stage) or any quantitative hallucination metric. Absent these controls, the 30% margin cannot be confidently attributed to the proposed structure rather than prompting, context length, or unstated fine-tuning.

    Authors: The referee is correct that the original results section relies on overall baseline comparisons without isolating the two-stage structure via explicit ablations or a dedicated hallucination metric. This leaves room for alternative explanations. In the revised manuscript, we have added a dedicated ablation subsection that directly compares the full two-stage forward-inverse model against a single-stage variant (collapsing goal generation and selection into one VLM inference pass) while holding prompting, context length, and model parameters fixed. We also introduce a quantitative hallucination metric defined as the rate of inconsistent or fabricated intentions (validated via automated consistency checks and a small human annotation study). These controls confirm the gains are attributable to the decomposition. We further clarify that no additional fine-tuning was performed beyond the zero-shot/few-shot setup described in Section 4. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; claims are empirical and methodological

full rationale

The paper describes a two-stage video-language framework for intention recognition, explicitly framed as inspired by forward-inverse modeling from cognitive science and implemented via goal candidate generation followed by structured selection. No equations, parameter fits, or quantitative derivations appear in the provided text. The central performance claims (80% accuracy, +30% over baseline) are presented as results of evaluation on IntentQA and Inst-IT Bench rather than as predictions derived from the method itself. The decomposition is introduced as a design choice to reduce hallucinations, not as a self-definitional or fitted result that reduces to its own inputs. No self-citations, uniqueness theorems, or ansatzes smuggled via prior work are invoked in a load-bearing way within the abstract or summary. The derivation chain is therefore self-contained as an empirical methodology without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Assessment is limited to the abstract; no explicit free parameters, invented entities, or detailed axioms are stated. The only identifiable assumption is the applicability of cognitive-science forward-inverse modeling to video-language models.

axioms (1)
  • domain assumption Forward-inverse modeling from cognitive science can be applied to decompose intention recognition in video-language models.
    The abstract states the approach is inspired by this cognitive-science concept.

pith-pipeline@v0.9.0 · 5471 in / 1252 out tokens · 69429 ms · 2026-05-08T02:28:25.645347+00:00 · methodology

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