TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models
Authors: Zhenkun Gao
Xuhong Wang
Xin Tan
Yuan Xie
Venue: ICLR 2026 Poster (2026)
Keywords: MLLMs
Primary area: datasets and benchmarks
TL;DR: We introduce TPRU, a large-scale dataset for training and evaluating sequential image understanding in MLLMs, and use it to train a model that outperforms GPT-4o on temporal reasoning tasks.

Abstract: Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural visual data, a bottleneck hindering their application in real-world embodied AI. This gap is largely caused by a systemic failure in training paradigms, which lack large-scale, procedurally coherent data. To address this problem, we introduce TPRU, a large-scale dataset sourced from diverse embodied scenarios such as robotic manipulation and GUI navigation. TPRU is systematically designed to cultivate temporal reasoning through three complementary tasks: Temporal Reordering, Next-Frame Prediction, and Previous-Frame Review. A key feature is the inclusion of challenging negative samples, compelling models to transition from passive observation to active, cross-modal validation. We leverage TPRU with a reinforcement learning (RL) fine-tuning methodology, specifically targeting the enhancement of resource-efficient models. Experiments show our approach yields dramatic gains: on our manually curated TPRU-Test, the accuracy of TPRU-7B soars from 50.33\% to 75.70\%, a state-of-the-art result that significantly outperforms vastly larger baselines, including GPT-4o. Crucially, these capabilities generalize effectively, demonstrating substantial improvements on established benchmarks. The codebase is available at \url{https://github.com/Stephen-gzk/TPRU/}.

Published as a conference paper at ICLR 2026
TPRU: ADVANCING TEMPORAL AND PROCEDURAL UNDERSTANDING IN LARGE MULTIMODAL MODELS
Zhenkun Gao!.2 Xuhong Wang?: Xin Tan 1:; Yuan Xie
East China Normal University, Shanghai, China
Shanggaai Artificial Intelligence Laboratory, Shanghai, China Shanghai Innovation Institute, Shanghai, China
512759011498stu. ecnu. menu.edue edu. cn, wangxuhongg@pjlab orr..........oo.o.oo.o.......... cn, (xtan, yxie wrie)Ocs... @cs. ecnu. edu.
ABSTRACT
Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural vi- sual data, bottleneck hindering their application in real-world embodied AI. This gap is largely caused by a systemic failure in training paradigms, which lack large- scale, procedurally coherent data. To address this problem, we introduce TPRU, a large-scale dataset sourced from diverse embodied scenarios such as robotic ma- nipulation and GUI navigation. TPRU is systematically designed to cultivate tem- poral reasoning through three complementary tasks: Temporal Reordering, Next- Frame Prediction, and Previous-Frame Review. A key feature is the inclusion of challenging negative samples, compelling models to transition from passive obser- vation to active, cross mooss-modal validation. We leverage TPRU with reinforcement learning (RL) fine-tuning methodology, specifically targeting the enhancement of sssource-efficien models. Experiments show our approach yields dramatic gains: on our manually curated TPRU-Test, the accuracy of TPRU-7B soars from 50.33% 75.70%, a state-of-the-an result that significantly outperforms vastly larger baselines, including GPT-4o. Crucially, these capabilities generalize effec- tively, demonstrating substantial improvements on established benchmarks. The TPRU/
codebase is available at https: //t::://gtttt com/
INTRODUCTION
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities (Jin al. 2025), with leading large-scale open-source (Bai et al. 2025 Zhu et al. (025) and proprietary mod- wide range of vision-language tasks. However, this progress masks a critical and widening gap: while massive, expensive models show emerging competence, their smaller and more efficient counterparts struggle profoundly with com- plex reasoning. Especially when they try to understand temporal and procedural image sequences (Song al., 2025) Tang et al.) 2025 Zhang et al. 2025). This capability gap not merely an academic concern but primary obstacle hindering the deployment of MLLMs in real-world and in- teractive applications. Downstream tasks like robotic manipulation, embodied navigation (Li et al.) 2025b Gu al.) 2025), ) and instruction following often operate vices where deploying dozens or hundreds billion parameter models is infeasible (Ji et al., 2025 Lu et al. 2024: (Savaa al. 2019). Consequently, the inability of small models to grasp state changes and procedural logic represents a fundamental bottleneck for the entire field of embodied AI.
els (Hurst et al. 2024) achieving remarkable performance
constrae- edge de-
The root of this deficiency lies not in model scale alone, but in systemic failure of the prevailing training paradigm. Existing paradigms predominantly focus on aligning text with a single image (L) al. 2023) treating multiple images as an unordered collection (Jiang et al.) (2024). Although datasets like -LaVA-NeeT--nterleee
(Li et al.) (2024) incorporate multi-frame inputs derived from videos, they primarily emphasize general sequential content comprehension rather than the fine- grained temporal and procedural understanding. This approach overlooks the critical distinction
"Corresponding authors.

---

Published as a conference paper at ICLR 2026
(a) Previous Data
Ability Display
Simpson dlpppparss bushes.
What cornect order mages occording
givan
Mechwerrh
Lago puzzies
Question: This the tanget assembly image: <inage Whihch oetion or D)
lage
correctly shows The conrect assembly step?
Lack of real scenes
dreanding
Brewe of Caut 6a
Ceepues Unientaar
1997-77
(b) TPRU
Question: These photos show.. Below are 4 images rondom order, labeled es 1, and 4, What the correct sequence of these images? vimagel vimage2) rinoge3> cmageds
Ordering Task
A. 4 2: B, 4.3 2 2 3
None the choice provided Andwer:
Average
vinag
pictures:
Question: These photos show.. The step is rimagels, followed by stop Zomagee> Select the correct option of step which con reach the findl stop 4rmnoge3> A. -imagedo; None of the choice provided: sinaget::
Next Frome Predict Task
b cinegedv <mage?>
Answer: C
cingg to
imagg
show.. 2eimagel,, followed 3-image2), 4s
Question:
stop
Previous Frome Review Task
stop
option step
A. .imagedy simagets: vimogedi; D, vingge:; Answer: provided:
4inege
VInGaR 2:
<ragge
iimage
vimage
Figure I: An overview of our TPRU dataset. Unlike prior synthetic datasets (a), TPRU built from real-world scenarios and structured into temporal tasks (b). As shown in the ability display (c), TPRU-7B achieves significant performance gains in temporal understanding.
between understanding set of images and comprehending a sequence of images. As shown in Fig- ure [a). the community's response has been to create evaluation-only benchmarks that repeatedly confirm this failure (Wang et al., 2024 Tang et al.. 2025), rather than addressing the root cause, which is the absence large-scale, high-quality real-world sequential data for training. This over- sight stems from the inherent difficulty of capturing the complex, continuous transformations of real-world actions. As result, smaller models are evaluated on a sophisticated skill they were never systematically taught, leading to poor performance in detecting procedural errors and leaving gen- uine procedural understanding an unsolved problem for deployable Al systems (Song et al. 2025
Fu et al. 2024a Tan et al. 2025 Wang et al. 2026).
To bridge this critical capability gap, particularly for resource-consterr constrained models, we introduce TPRU TTemporal-Procedural Understanding dataset), novel dataset designed to bridge the gap be- tween evaluation and training. First, to address data scarcity and authenticity, TPRU provides a large-scale training set (24,750 QA pairs, 126,000 images) sourced from four diverse and authen- tic embodied scenarios: robotic manipulation, embodied navigation, mobile GUI interaction, and LEGO assembly. More importantly, as depicted in Figure (b)) TPRU is just data collection but systematically structured dataset designed to cultivate a deep procedural understanding through three complementary task formats: Temporal Reordering, Next-Frame Prediction and Previous- Frame Review. To ensure models develop true comprehension beyond superficial heuristics, TPRU incorporates a significant number challenging negative samples with deliberate inconsistencies. forcing models to transition from passive "secing" to active validation. To benchmark this capabil- ity, we also present the TeRU-Test, a manually curated set of 461 challenging instances for rigorous evaluation.
Leveraging our TPRU dataset, we employed a reinforcement learning (RL) strategy to fine-tune suite of Qwen2.5-VL models, focusing smaller parameter counts. The results striking. Our RL-finetuned models not only show massive improvements over their base versions but also significantly outperform existing state-of-the-art (SOTA) MILMS proposed TPRU test set.
2

---

Published as a conference paper at ICLR 2026
Remarkably, our TPRU-7B and TPRU-32B surpass the performance of the much larger proprietary model GPT-4o and large-scale sourcs models. Furthermore, as shown in Figure Tc). TPRU- 7B exhibits substantial gains on established multi- benchmarks like MuirBench (Wang al. 2024) and LEGOOPPuzles (ang et al. 2025). These findings demonstrate that the temporal rea- soning gap in small models not inherent limitation of their scale but solvable challenge of targeted data and training. We have unlocked SOTA-level procedural understanding in models small enough for practical, real-world deployment. Our contributions are summarized as below:
We construct TPRU, new, large-scale, high-quality multi-image dataset focused on fine- grained temporal and procedural understanding, designed to empower smaller models for embodied contexts. The dataset and its creation methodology will be publicly released.
We present challenging held-out test set, TPRU-Test, manually curated and verified to rigorously evaluate temporal understanding in MILMs.
We propose and validate an effective reinforcement learning- training methodology that enables small-to-medium-ssieed MLLMs to achieve and even surpass the temporal un- derstanding capabilittis of vastly larger models. Extensive experiments demonstrate this superiority and strong generalization on both our TPRU-test and existing public bench- marks.
RELATED WORK
While Multimodal Large Language Models excel single-image comprehension (Fu et al. 2024a Li et al. 2025a Zhang et al., 2024a), multi-image reasoning remains a challenge. Recent work has thus expanded beyond the single-frame paradigm to tackle complex real-world tasks (Wang et al., 20256), necessitating specialized instruction-tung -utting datasets and evaluation benchmarks.
2.1 MULTI-IMAGE TRAINING DATA
To enhance the multi-image capabilities of Multimodal Large Language Models (MLLMs), re- searchers have constructed large-scale instruction-tune tuning datasets. For instance, Mantis- (Jiang et al. 2024) adopts skill-oriented strategy, efficiently imbuing models with four core abili- ties via meticulously constructed 72 K-sample dataset. LLa VA-NeXT--nnterlavve (Li et 2024) advances this direction by leveraging its 1. 1.8mll M4-Instruct dataset and a unified interleaved data format seamlessly handle multi-image multi-image. video, and 3D scenarios. Addressing conversational depth, MMDU (Liu et al.) (2024) and MMCR (Yan et al. 2025) provide large-scale datasets specifi- cally for training models on coherent reasoning multi-ture turti-turn dialogues. Furthermore, datasets have expanded into specific domains, such as RoboBrain's 's ShareRobot (Ji et al.) 2025). which provides planning and affordance annotations for roboties tasks, and GUI Odyssey (Lu et al. 2024), which focuses cross-application mobile device navigation (Luo al. 2025). In parallel, innovative data generation paradigms are emerging that move beyond reliance on manual annotation. Jigsaw-RI (Wang et al. 2025c), for example, enhances models' spatial awareness by programmatically gen- erating jigsaw puzzle tasks for rule-based visual reinforcement learning. Taking this further, MiCo (Chen al. 2025) proposes fully self-supervised reinforcement learning framework, enabling models to learn complex reasoning from programmatically constructed contrastive image triplets without any instruction data.
While these datasets have significantly advanced multi-image instruction tuning, they often treat im- ages an unordered collection. Even sequential datasets lack a systematic framework for teaching procedural flow principles. Our work addresses this gap by introducing TPRU. Through its comple- mentary three temporal tasks, TPRU is specifically engineered instill a foundational understanding of procedural dynamics.
2.2 BENCHMARKS FOR MULTI-IMAGE EVALUATION
Concurrently with the development of training data, a suite of rigorous benchmarks has been es- tablished to evaluate these emerging capabilities. For comprehensive and robust evaluation, Muir- Bench (Wang et al. (024)) presents a 2,600-question test whose key innovation a pairwise de- sign-matching each answerable question with minimally different, unanswerable variant rig-
3

---

Published as a conference paper at ICLR 2026
orously test against hallucination. Targeting specific reasoning domains, STRIPCIPHER (Wang jet al. 2025b) leverages wordless comic strips to assess narrative and temporal logic, while Te (Song et al. 2025) focuses event ordering with a design that resists single-modality shorteuts. For spatial and physical reasoning, LEGO- Puzzles (Tang et al. 2025) creates a challenging testbed for multi-step planning based LEGO instructions, and MV-Math (Wang al., (025a) fills critical gap in multi-visual context mathematical reasoning using real K-12 educational materials. Other benchmarks probe more fundamental visual abilities; BLINK (Fu et al., 2024b) aims decouple core visual perception from linguistic reasoning, and MMRA (Wu et al. (022)) evaluates the ability to identify cross-image relations at multiple granularities. Finally, to systematize the application of these benchmarks, toolkits like VLMEEalKit (Duan et al. 2024) provide standardized evaluation framework, greatly facilitating reproducible and comprehensive assessment across the community.
A critical limitation of existing work is their evaluation-only nature, creating a gap between training and testing. TPRU bridges this gap by providing a large-scale structured training set alongside a challenging test set, unifying the development and evaluation of procedural understanding.
TPRU
While current Multi-modal Large Language Models excel static single-image tasks, their perfor- mance degrades sharply as the number of input images increases (Li et al., 2024). This deficiency is exacerbated when processing image sequences that represent coherent process event. Recent work shows existing MLLMs largely fail to comprehend temporal dynamics and sequential relation- ships between visual frames. This limitation is starkly revealed on benchmarks for narrative comics, procedural instructions, and event ordering (Song et al. 20255] Tang et al. 2025 Wang et al. 2025b 2024). Consequently, the inability to grasp temporal dynamics severely hinders their applicability in weal-world scenarios that demand comprehension of procedural activities and evolving states (Tang
ct al. 2025 Ji al. 2025).
To systematically enhance and evaluate the capability of MILMS in comprehending image se- quences with temporal and procedural order, we propose the TPRU dataset. The dataset consists of two components. TPRU-25K is fine-tuning set with 24.750 samples across four procedural sce- narios, designed to enhance the model's temporal and procedural understanding. TPRU-test is an evaluation benchmark comprising 461 manually annotated samples across five application scenar- ios. The detailed data sourcing and construction methodologies for TPRU-25k and TPRU-test are elaborated in Sections 3.1 and 3.2, respectively.
3.1
TPRU-25K
Data Sources. TPRU aims to provide MLLMs with high-quality, multi-image sequential data char- acterized by clear procedural and temporal structures. To construct a dataset with coherent proce- dural logic and ensure that the image sequences represent meaningful, ordered events, sourced data from four diverse and complementary real-world scenarios: (1) Robotic Manipulation. Data is primarily derived from the "planning" tasks in the ShareRobot dataset (Ji et al., 2025), where we sample video frames to create discrete action sequences. (2) LEGO Assembly. Data is curated from 36 high-quality stop-motion videos from the YouTube creator Arvin Bricks, providing blur- free, state-distinet images ideal for part-to-whole reasoning,
(3) GUI Operation. A novel dataset
constructed from four-step screenshot sequences from GUI Odyssey (Lu et al., [024) capture goal-driven digital workflows. (4) Embodied Navigation. This category consists of ordered visual observations from agents navigating in simulated environments like Habitat (Savva et al.) 2019). The diversity of these sources ensures the data is not confined to single domain, fostering the development of generalizable sequential understanding.
Generation Pipeline Our data generation pipeline systematically transforms raw sequential data from diverse sources into structured training instances. The process involves three main stages: sequence filtering, text description generation, and task formulation, illustrated in Figure 2
(a) Filtering and Quality Control. Our dataset constructed from heterogeneous sources, in- cluding temporally sampled video frames and ordered screenshots from embodied agent tasks and mobile UI interactions. We process these diverse inputs into a canonical format of coherent im- age sequences, each containing three four images. To ensure high data quality and procedural
4

---

Published as a conference paper at ICLR 2026
Data Preporation
Data Traihing
Dota Sources
Oxoeenn
Embodledd videos
Screershots
Navigation
Qwen2 5-VL-7B- Instruct
Next Frone
TPRU-78
Frame extraction
Trrstus Frons Revina Task
Row multiple pictures
Data Generation
Qaven2 5 -L-728
Filter with prompt
Ordering
Generation method: Shuffle the original order.
TTxt Description- .Ordering Questionn
Multiple pictures chronolegicel
Text Description
Generation
Next Frome Predict Tock
<Positive description:
image, Predict 3rd picture that moxed with other pictures.
Description Frome Fredict Quest lore:
These photos show the process of (The correct imge sequences)
>>
<Negative text descriptian:
destription dxstt nench moge sequnce debberstdy) These photos show process (The incornect oction about image sequences)
Generation method: 2nd, 3rd, 4th image, Review the picture that mixed with other pictures.
<Text Description- d'revoous Frame Review Questions
Multiple pictures text description
The TPRU dataset construction and training pipeline. Chronological image sequences from embodied sources curated with both positive and negative text descriptions. These image sequences are then formulated into three tasks (Ordering, Next Frame Prediction, and Previous Frame Review) fine-tune MLLMs for enhanced temporal and procedural understanding.
Figure 2:
integrity, every image sequence subjected to a rigorous filtering pipeline. We employ Qwen2.5- VL-72B (Bai et al., 2025) as an automated quality assessor discard sequences exhibiting visual blurriness, abrupt scene transitions, or lack of discernible temporal progression. This stringent protocol guarantees that only high-fidelity, logically coherent sequences are used for subsequent processing.
Description Generation and Robustness Enhancement. For each filtered image sequence, we generate a corresponding textual description using Qwen2.5-VIL-72B to reinforce the model's core vision-language alignment. To specifically bolster robustness and mitigate hallucination, we also introduce negative sampling strategy. This involves creating subset of instances where the textual description deliberately mismatched with the visual content (e.g., pairing the instruction "pick up the fork" with images of "putting down knife"). For these challenging cases, the target output is formulated as "None of the choices provided" (Wang et al., 2024). This forces the model to perform explicit cross-modal verification rather than relying solely on textual priors.
(c) Task Formulation. Based on the curated image sequences and their corresponding textual de- scriptions, formulate three distinct yet complementary tasks designed to comprehensively hance the model's temporal and procedural understanding ability.
Temporal Ordering. The primary objective of this task is evaluate and enhance the model's comprehension of an entire procedural timeline. We formulate this as reordering problem. For a given image group, we shuffle the temporal order of the frames and provide the corresponding textual description. The model is then required to output the correct permutation that restores the reasonable sequence of the event.
Next Frame Prediction. This task improves the model's grasp of temporal coherence and proce- dural flow. The model presented with the initial, second, and terminal frames of a four- four-frame procedural sequence and must select the correct intervening third frame from set of candidates. These candidates include distractors from other similar scenarios. This task directly simulates the immediate planning required for robotic agents anticipate the direct consequences of action.
5

---

Published as a conference paper at ICLR 2026
Previous Frame Review. This task enhances the model's ability to reconstruct the historical con- text of a procedural segment. The model is given the final three frames a four-frame sequence and is tasked with identifying the correct initial frame from a set of candidates. This capability improves the model's 's understanding of procedural prerequisites and its ability to trace an observed event back to its origin, fundamental aspect of comprehensive temporal understanding.
Collectively, these three complementary tasks engineered to advance MLLMs beyond static image analysis. By jointly addressing temporal ordering, forward prediction, and backward review, approach endows the model with more profound and structured comprehension of procedural dynamics, significantly enhancing its ability to interpret the temporal evolution of events depicted in image sequences.
3.2 TPRU-TEST
To rigorously evaluate the temporal and procedural understanding capabilities of MILLMs, we in- troduce TPRU-Test, dedicated, held-out evaluation set. TPRU-Test is meticulously curated and verified by human experts to ensure high quality and present novel generalization challenges. Its composition is deliberately diverse, incorporating the most demanding instances from four core domains (Robotic Manipulation, LEGO Assembly, GUI Operation, and Embodied Naviga- tion) alongside complex, human activities from the EPIC- HITCHIIS (Damen et al. 2020) dataset to probe model robustness. TPRU-Test inherits three complementary tasks of our training set, as- sessing temporal ordering. next-frame prediction, and previous-frane frame review. The curation protocol was exceptionally stringent. Each instance underwent a multi-stage human review where annotators picked and verified the ground-truth image sequence, authored plausible yet incorrect distractors, and ensured question clarity. Subsequently, every instance was cross-verified by at least one other expert to eliminate errors and subjective judgments. This process yields a high-quality benchmark of 461 instances across distinct scenarios, engineered to provide robust measure of genuine progress in temporal and procedural understanding for MLLMs.
4 EXPERIMENTS
In this section, we conduct comprehensive experiments validate the effectiveness of our proposed TPRU dataset. We fine-tuned Qwen2.5-VI. on TPRU-25k and evaluated on TPRU-test as well as on established public benchmarks. In Section 4.1, we assess the model's performance on MuirBench (Wang et al. 2024) and LEGO-Puzzles (Tan et al. 2025) benchmarks which include tasks relevant to temporal and procedural understanding. And we present the primary results on our TPRU-test set in Section 4.2. To ensure our method does not degrade performance on broader tasks, we evaluate on ceneral-purpose benchmarks in Section 4.3. Finally, in Section 4.4, we report series of abla- tion studies to investigate the contribution of key components of our dataset. The hyperparameter Settings and reward designs the experiment can be obtained respectively in Appendix A and
4.1 EVALUATION OF TEMPORAL RELATED BENCHMARKS
Performance on MuirBench. As presented in Table I our TPRU-finetuned models demonstrate significant improvements over their Qwen2.5-VL base models across all scales on the MuirBench (Wang et al. 2024). Notably, our TPRU-32B model achieves an overall accuracy of 68.42%, out- performing the powerful proprietary model GPT-4o (68.00%) and closely matching the much larger Qwen2.5-V1L-72B.
The most substantial gains are observed in the Ordering sub-task, which directly aligns with the temporal reasoning skills targeted by our TPRU dataset. TPRU-32B achieves remarkable 45.31 in this category, drastically surpassing both its base model and GPT-4o. This trend is consistent across scales, with the score of TPRU-7B also more than doubling from 14.06% to 34.38%. Beyond temporal tasks, our training methodology enhances broader relational reasoning abilities, evidenced by significant improvements in tasks such as Difference Spotting and Visual Retrieval. These results confirm that our approach not only instills specialized temporal skills but also strengthens general cross-image reasoning capabilities.

---

Published as a conference paper at ICLR 2026
Table 1: Performance on MuirBench. The light gray rows show the absolute improvement percentage points) of our models over their corresponding Qwen2.5-VV VL base models. Gains are shown in red, and losses in blue.
Model
Action
Anribute
Canoon Undeest.
Counting
Diggamm Undkest
Defference
Geographic Undertt
Image-Test Matcing
Ordering
Sorne Undeest.
Visual
Visul Revioval
Overall
Unles.
Similarity
Spinting
Grousling
Open-ooncce
61.75
55.59
79.11
31.25
66.
66.33
36.90
Owen2.5 VL 720
50,00
99.18
42.31
19.57
89:45
60.59
50.00
87.93
40.63
76.34
46.43
78.42
69.35
Owen25
51.02
58.53
26.56
41.67
67.12
Qwen2.5 VL-70
40.85
58.67
46.15
34.19
73.89
56.41
49.00
72.65
14,06
61.83
33:33
63.70
56.35
Quen2. V-V1-3
36.99
44.3 39
46
32.91
$9.04
46.47
49.00
$4.09
9.38
40.48
38.
46.62
Proprictrry
0emini-2,5-Flash
50,00
49.99
60.26
$2.05
92.71
70.00
47.00
86.64 64
46.85
63.87
59.52
70.89
73.73
GPT-4
44.51
56, 12
51
49.15
85.69
60.29
56.00
86.85
23.44
71.51
36.90
$0.14
68:00
Cluck-3.
Ours
YPRU-I28
40.24
51.02
51.28
47.44
85.68
63.24
60.00
87.28
45.31
75.81
44.05
50.14
68.42
TPRU 7B
41,46
57.65
47.44
34.62
82.91
63.82
63.00
82.11
34.35
67.74
38.00
75.68
65.04
Improvement
+0.01
1.02
+1.29
10.43
+502
19.41
134.00
+9.45
-20.32
-5.91
+4.77
+11.9
-6.65
YPRO
51.02
55.88
84.00
23.44
53.57
Improvenent
+3.0
+0.03
+1.29
+7.26
+9.30
+9.41
+35.00
+34.33
-14.6
+12.90
+13.09
+27 44
12.69
Performance on LEGO-Puzzles. The efficacy of our approach is further validated on the LEGO- Puzzles benchmark, which directly evaluates procedural assembly reasoning detailed in Table Our models consistently outperform their base versions, with TPRU-7B achieving an overall score of 42.8%. This result confirms that the skills learned from our TPRU-25K dataset exhibit strong positive generalization to complex, structured assembly tasks. The most pronounced improvements are concentrated in the Multi-Step Reasoning category, confirming the targeted impact of our train- ing methodology. For the TPRU-7B, performance on the Ordering task quadruples, soaring from 8.0% to 32.0%. Similarly, its capability in Backwards reasoning more than doubles, jumping from 22.0% to 49.0%, and significant gain is also observed in Next-Step sub-task. These enhancements strongly indicate that the procedural and causal reasoning abilities cultivated by the TPRU dataset effectively transfer to the logical, sequential challenges inherent in the LEGO-PPuzlle
Table 2: Performance points) models over their corresponding Qwen2.5-VL base models, Gains shown red, and losses bluc.
The light gray show the absolute improvement (in percentage
Model
Adjacney
Rotation
Mullivics
Next-Stop
Dependncy
Rotatin
Posttio
Backwands
Outlier
Open-sonre
ImemW1.3-788
63.0
80.0
29.0
IdenVL3-38B
60.0
Qwen? 5-V1-728
43.0
58.0
18.0
39.0
$7.0
76.0
53.0
52.0
74.0
43.0
430
53.7
Qwen2.5-V1 5-V1-78
30.0
57.0
3200
47.0
38.0
67.0
56.0
29.0
22.0
8.0
25.0
36.5
Qwen2 5-VL-38
29.0
55.0
30.0
36.0
32.0
65.0
48.0
19.0
4.0
32.6
Proprictory
Gemini 2.5 Flash
$2.0
58.0
37.0
55.0
58.0
74,0
53.0
49.0
40.0
46.0
29.0
50,1
4PT-4
51.0
51.0
72.0
Clande33 3.5 Sunnet
3.mnet
39,0
60.0
420
48.0
61.0
78.0
58.0
37.0
49.0
54.0
$3.6
Ours
TPRU-228
$2.0
Inprevement
-10
+1.0
-3.
-5.0
+10.0
-3.0
+10
43.0
44.0
45.
45.0
+1.7
TPRU-78
56.0
37.0
55.0
TPRU-3B
30,0
54.0
32.0
40.0
37.0
69.0
49,0
14.0
30.0
5.0
24.0
35.2
ieprovvnnn
4.2 EVALUATION OF GENERAL BENCHMARRSS
To ensure our specialized training does not degrade general capabilities, we evaluated our model on a range of broad multi -mage benchmarks, shown in Table The results confirm that finetuned models maintain or slightly improves performance across these diverse tasks. This pattern of stable positive gains, such MMMU (+2.6) and MMCR (+1.08) ) for TPRU-7B, demonstrates that

---

Published as a conference paper at ICLR 2026
method for enhancing temporal reasoning successfully avoids catastrophic forgetting and preserves the model's foundational abilities. The relevant content of these benchmarks can be obtained from the appendix.
Table 3: Evaluation on general multi-image benchmarks. Results show that our TPRU models maintain comparable performance to their base models, indicating that our specialized training does not degrade general capabilities.
MME-
RealWorld QA
Model
BLINK
MMCR
MMTBench
MMStar
MMMU-Dev
Overall
RealWorl-LLiie
Open-source
Intemm113-788
65.40
66.30
78.00
20.29
73.20
72.50
64.20
62.84
Intern 7nterVLL3-38H
67.30
64.49
75.60
21.38
71.80
71,50)
62.00
62.01
Dwen2.5-VI.-38.
45.96
59.34
68.89
37.32
58.89
54,93
34.67
51.43
Qwen2.5-VL-7B
44.55
54.76
68.10
22.83
61.46
61.67
44.40
51.11
Qwen2.5-VLL.3 LLBB3B
41.94
48.97
65.36
19.20
60.47
54.40
44.67
47.86
Ours
TPRU-32B
69.80
39.86
56.25
52.26
50.84
TPRU-7B
45.34
55.86
69.54
23.91
61.85
61.13
47.00
52.09
TPRU-3B
39.19
48. 13
66.54
21.01
60.89
54.93
44.67
47.91
4.3 MAIN RESULTS ON TPRU-TEST
We evaluate the core efficacy of our approach on the proposed TPRU-fest, benchmark specifi- cally designed to assess fine-grained temporal ordering, causal prediction, and procedural consis- tency. The results, presented Figure g demonstrate that fine-tuning with our TPRU dataset with RL methodology yields substantial and consistent performance improvements across various model scales. Notably, the accuracy TPRU-7B soars from 50.33% 75.70% while the TPRU-3B shows similarly strong improvement from 37.96% to 60.95% This level of performance is highly com- petitive: TPRU-7B surpasses the powerful proprietary model GPT-4o by significant margin. These results unequivocally validate the effectivvneess of our training paradigm. The dramatic performance gains underscore that targeted training on procedurally-growe negative samples and optimized via reinforcement learning, is potent strategy for instilling robust sequential reasoning capabilities in MLLMs.
data, enriched with challenging
Geries
Clarcie Sorio
Cunes Sorto
Iniamit Turko
67.585
70.6%
A0.155%
40
57.70%
55.40C
Apouroccy
40:02E
40
oPP41
GPPdl
Conded Somel
Gembn 2 B-aiin
Inteni Aug
iimi up
Gvanth $-1-5
Qwent 548-78
Goerd Svi
melka
TPRU-7
Owertz
Figure 3: Performance of different models on TPRU-te
4.4 ABLATION STUDIES
To systematically investigate the contributions of the components of our methodology, conducted a series of ablation studies. We evaluate the impact of task composition, negative samples and data volume. All experiments conducted by training variants of the Qwen2.5-VI-7B model,
8

---

Published as a conference paper at ICLR 2026
with performance evaluated on MuirBench and Lego-Puzzles. The detailed results are presented in Appendix.
Table 4: Ablation study on the effect of different TPRU data components for the Qwen2.5-VL-77 model on MuirBench and LEGO-Puzzles benchmarks. The inclusion of a component marked with V. All scores are Overall Accuracy (%). The volume of data remains consistent.
TPRU Data Components
Benchmark Accuracy (%)
Ordering
Previous Frame Review
Next Frame Predict
MuirBench
LEGO-Puzzles
60.8
39.0
61.7
38.0
61.6
41.1
62.5
40.3
62.2
40.3
62.2
39.
63.8
42.3
Impact of Task Composition. We conducted ablation study on small-scale dataset of 8,250 samples to analyze the individual and combined contributions of our core training tasks, with results presented in Table The findings reveal clear synergistic effect. While each task component individually improves baseline performance, their pairwise combinations yield further gains. A model trained on the complete dataset integrating all three tasks ultimately achieves the highest performance. This confirms that the diversity of these complementary reasoning skills is crucial for fostering comprehensive and robust understanding of temporal and procedural logic.
Efficacy of Negative Samples. A core design choice in TPRU is the inclusion of negative sam- ples, which are instances with deliberate procedural inconsistencies that force the model to reject all options. An ablation study confirms their impact. As illustrated in Figure 4a training without these negative samples significantly degrades performance on temporal reasoning benchmarks like LEGO-Puzzles and MuirBench. This result validates hypothesis that teaching model to plicitly reject invalid logic is critical for advancing from pattern recognition to robust procedural understanding.
05.0%
64,00
64.%%
(3-45%
13.00%
40.0%
58.02%
40 10
32%
+2.0%
33.0%
50.%
350
36.0%
MLIRBerch
58.0%
5,000
20100 35,00
38.0
15:00
Den V. TIRLONI regale
Number Training
(b) Muirbench
(c) Lego-Puzzles
Figure 4: Ablation analysis. (a) Ablation on negative samples. (b) and (c) show the performance with different training samples.
Impact of Data Volume. We evaluated training on data subsets up to the full 24,750 samples. As shown in [igurre/4blandee performance on MuirBench and LEGO-Puzzle improves with data size but begins to plateau near the full set. This pattern of diminishing returns indicates our dataset is sufficiently comprchensive to instill robust temporal understanding without requiring further scaling.
Impact of Training Strategy. To validate the necessity of our Reinforcement Learning framework, conducted a direct comparison between the proposed GRPO approach and standard Supervised Fine-Tuning (SFT). Both experiments were performed using the Qwen2.5. VL-7B backbone under consistent hyperparameters via the ALaMA-Factor framework Sareeworr/Zhnnggg et al. (024).
As shown in Table while SFT provides a solid performance baseline, GRPO consistently achieves superior results across all benchmarks. These results indicate that GRPO is more effective than

---

Published as a conference paper at ICLR 2026
Table Ablation study on training paradigms, We compare the performance of standard Supervised Fine- Tuning (SFT) against our GRPO approach using the Qw-n2.5-VL-7B backbone.
Training Paradigm
TPRU-Test
MuirBench
LEGO-Puzzles
Supervised Fine- Muninn (SFT)
72.88
63.03
40.60
TPRU-7B (GRPO)
75.70
65.04
42.82
Table 6: Performance comparison with state-of-the-ary Video LLMs on MuirBench, LEGO- Puzzlesuzzes, and TPRU-Test datasets. The "Ordering" columns denote specific subtasks requiring precise se- quential reasoning. Best results are highlighted in bold.
MuirBench
LEGO-Puzzles
Model
TPRU-Test
Overall
Ordering
Overall
Ordering
LLaVA-Videe-OU Sidde--wee2--7
37.88
15.63
31.91
1.0
38.61
LLaVA-Video Video-iQeeo2222 Qwen2-72B
41.81
10.94
41.64
5.0
44.03
SmolVLM2-2555 21122-2566----6 Video
27.92
21.88
28.18
0.0
17.79
SmolVLMM-500M Video
25.92
27.27
0.0
16.92
V1TA-16K
53.07
17.19
34.45
2.0
39.26
Qwen2.5-Ommi-78
59.11
18.75
36.45
12.0
46.85
TPRU-7B (Ours)
65.04
34.38
42.82
32.0
75.70
SFT for this domain, as moves beyond simple pattern imitation to better cultivate the advanced reasoning capabilities required for complex procedural multi-modal tasks.
4.5 COMPARISON WITH
BASELINES
To further elucidate the distinction between procedural temporal understanding and general video understanding, conducted comprehensive evaluation using several state-of-the-art Video Large Language Models. We posit that while general video understanding typically focuses on recognizing what events occur, procedural multi-image understanding places greater emphasis on inferring the precise sequence and consequential relationships between discrete actions.
We compared our TPRU-7B model against strong open-source video baselines, including LLaVA- Video (Zhang et al. 2024b), SmolVLM2 (Marafioti et al. 2025), Long-VITA Shen et al. (2025). and 2025), the MuirBench, LEGO-Puzzles, and our TPRU-Test datasets.
Qwen2.5-Ommi (Xu al.
As presented in Table TPRU-7B significantly outperforms these powerful video models across all
three benchmarks.
Results reveal a critical gap in current video models regarding fine-grained temporal ordering. For instance, on the MuirBench Ordering subtask, the por-ferfonnnng baseline Qwen2.5-0mn) scores only 18.8, whereas TPRU-7B achieves 34.38.
These findings support our hypothesis: while general video pre-training provides fundamental tem- poral awareness, it is insufficient for precise procedural understanding. Our approach bridges this gap through targeted data synthesis and training designed for discrete state changes.
CONCLUSION
In this work, we addressed the critical failure of MILMS in comprehending visual sequences by introducing TPRU, a large-scale dataset designed to systematically teach temporal and procedural logic. Our experiments show that fine-tuning with a reinforcement learning strategy on TPRU yields dramatic performance gains, with TPRU-7B not only dominating its baseline but also outperform- ing the much larger GPT-4o and generalizing strongly to benchmarks like MuirBench and LEGO- Puzzles. The primary contribution of this work is the demonstration that targeted, procedurally- grounded data can effectively close the reasoning gap for smaller, more efficient models, moving the frontier of capable AI from massive systems towards practical, deployable agents.
10

---

Published as a conference paper at ICLR 2026
ETHICAL CONDUCT AND SOCIETAL IMPACT STATEMENT
This research was conducted with a steadfast commitment to ethical integrity, in strict accordance with the ICLR Code of Ethics. All experimental procedures and data handling protocols comply with applicable national and international laws, institutional regulations, and established ethical standards.
The data utilized in this study were sourced exclusively from publicly available, open-access datasets, or were obtained with explicit and appropriate authorization from the data providers. To safeguard individual privacy and ensure data security, all datasets underwent rigorous preprocess- ing, including anonymization and the removal of personally identifiable information (PII) where applicable.
Specifically regarding the video content sourced from the "Arvin Bricks" YouTube channel, these materials are utilized strictly for non-commercial, transformative academic research. Consistent with practices in recent literature (Ju et al., (025), we operate under the principle of fair use for publicly available creative works governed by the Standard YouTube License. Our processing and analysis of this data are conducted solely to advance scientific understanding, with no commercial intent or redistribution of the original assets.
The primary objective of this work is to contribute to the advancement of scientific knowledge. We have carefully considered the potential societal impacts of our research and have found foresee- able risks of direct harm or misuse. The authors explicitly disavow any application of this work for malicious or unethical purposes. Furthermore, the authors declare that there are no competing financial or personal interests that could have influenced the outcomes of this research.
REPRODUCIBILITY STATEMENT
To ensure the transparency and reproducibility of our research, we provide a detailed description of our methodology, datasets, and experimental setup within the main body and appendix of this paper. To facilitate verification and extension of our work by the research community, all associated source code, data preprocessing scripts, and necessary model files are publicly available. We have released the complete set of materials foster collective progress in the academic community.
ACKNOWLEDGEMENTS
This work was supported by the Science and Technology Commission of Shanghai Municipality (Grant Nos. 2551 I 102700, 25511 103300, 25511 104302), the National Natural Science Foundation of China (Grant Nos. 62302167, U23A20343, W2521 174), and the Young Elite Scientists Sponsor- ship Program by CAST (Grant No. YESS20240780).
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.1300 13923, 2025.
Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are on the right way for evaluating large vision-language models? Advances in Newral Information Processing Systems, 37:27056-27087, 2024.
Xi Chen, Mingkang Zhu, Shaoteng Liu, Xiaoyang Wu, Xiaogang Xu, Yu Liu, Xiang Bai, and Heng- shuang Zhao. Mico: Multi-image contrast for reinforcement visual reasoning. arXiv preprint arXiv:2506.2244 2025.
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, al. The spic-kitchens dataset: Collection, challenges and baselines. IEÉE Transactions on Pattern Analysis and Ma- chine Intelligence, 43(1 41411 2020.

---

Published as a conference paper at ICLR 2026
Haodong Duan, Jumming Yang, Yuxuan Qiao, Xinyu Fang, Lin Chen, Yuan Liu, Xiaoyi Dong, Yuhang Zang, Pan Zhang, Jiaqi Wang, et al. Vlmevalkit: An open-source toolkit for evaluat- ing large multi-modalig models. In Proceedings of the 32nd ACM international conference multimedia, pp. 11198-1 2001, 2024.
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin. Bo Li, Xinyu Fang, Sirui Zhao, Haodong Duan, Xing Sun, Ziwei Liu, Liang Wang, et al. Mme-survey: A comprehensive survey on evaluation of multimodal llms. arXiv preprint arXiv:241 15296,, 2024a.
Xingyu Fu, Yushi Hu, Bangzheng Li. Yu Feng. Haoyu Wang, Xudong Lin, Dan Roth, Noah A Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive. In European Conference Computer Vision, pp.
Springer, 2024b.
Tianjun Gu, Linfeng Li, Xuhong Wang, Chenghua Gong, Jingyu Gong, Zhizhong Zhang, Yuan Xie, Lizhuang Ma, and Xin Tan. Doraemon: Decentralized ontology-aware reliable agent with enhanced memory oriented navigation. arXiv preprint arXiv:2505.21990 2025.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21277 2024.
Yuheng Ji, Huajie Tan, Jiayu Shi, Xiaoshuai Hao, Yuan Zhang, Hengyuan Zhang, Pengwei Wang, Mengdi Zhao, Yao Mu, Pengju An, et al. Robobrain: A unified brain model for robotic manipu- lation from abstract to concrete. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 1724-1734, 2025.
Dongfu Jiang, Xuan He, Huaye Zeng, Cong Wei, Max Ku, Qian Liu, and Wenhu Chen. Mantis: Interleaved multi-image instruction tuning. arXiv preprint arXiv:2405.0148. 2024.
Yizhang Jin, Jian Li, Tianjun Gu, Yexin Liu, Bo Zhao, Jinxiang Lai, Zhenye Gan, Yabiao Wang, Chengjie Wang, Xin Tan, al. Efficient multimodal large language models: A survey. Visual Intelligence, 3(1):77, 2025.
Yiming Ju, Jijin Hu, Zhengxiong Luo, Haoge Deng, Li Du, Chengwei Wu, Donglin Hao, Xinlong Wang, Tengfei Pan, et al. Ci-vid: A coherent interleaved text-video dataset. arXiv preprint vrXiv:2507.01938 2025.
Bangyan Li, Wenxuan Huang, Zhenkun Gao. Yeqiang Wang, Yunhang Shen, Jingzhong Lin, Ling You, Yuxiang Shen, Shaohui Lin, Wanli Ouyang, et al. Llava-radz: Can multimodal large language models effectively tackle zero-shot radiology recognition? arXiv preprint arXiv:2503.07487. 2025a.
Feng Li, Renrui Zhang. Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleavc: Tackling multi-image, video, and 3d in large multimodal models. arXiv preprint arXiv:2407.07800 2024.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference machine learning, pp. 19730-19742. PMLR, 2023.
LinFeng Li, Jian Zhao, Yuan Xie, Xin Tan, and Xuelong Li. Compassnav: Steering from path imitation decision understanding in navigation. arXiv preprint arXiv:2510.10154 2025b.
Ziyu Liu, Tao Chu, Yuhang Zang, Xilin Wei, Xiaoyi Dong, Pan Zhang, Zijian Liang, Yuanjun Xiong, Yu Qiao, Dahua Lin, al. Mmdu: A multi-turn multi-image dialog understanding benchmark and instruction-tuning dataset for Ivlms. Advances in Neural Information Processing Systems, 37: 8698-8733, 2024.
Quanfeng Lu, Wenqi Shao, Zitao Liu, Fanqing Meng, Boxuan Li. Botong Chen, Siyuan Huang, Kaipeng Zhang, Yu Qiao, and Ping Luo. Gui odyssey: A comprehensive dataset for cross-app gui navigation mobile devices. arXiv preprint arXiv:2406 08451, 2024.
12

---

Published as a conference paper at ICLR 2026
Zhihao Luo, Wentao Yan, Jingyu Gong, Min Wang. Zhizhong Zhang, Xuhong Wang, Yuan Xie, and Xin Tan. Navimaster: Learning unified policy for gui and embodied navigation tasks. arXiv preprint 0tt.::250800204446 2025.
Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Za- kka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, et al. Smolvlm: Redefining small and efficient multimodal models. arXiv preprint urXiv:2504.05299 2025.
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al. Habitat: A platform for embodied ai research. In Proceedings of the IEEE/CVF international conference on computer vision, pp- 2019.
Zhihong Shao, Peiyi Wang. Qihao Zhu, Runxin Xu, Junxiao Song. Xiao Bi. Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathemati- cal reasoning in open language models. arXiv preprint 33Xiv:240..03300, 2024.
Yunhang Shen, Chaoyou Fu, Shaoqi Dong, Xiong Wang, Yi-Fan Zhang. Peixian Chen, Mengdan Zhang, Haoyu Cao, Ke Li, Shaohui Lin, al. Long-vita: Scaling large multi-modal models to million tokens with leading short-context accuracy. arXiv preprint arXiv:2502.05177, 2025.
Yingjin Song, Yupei Du, Denis Papern, and Albert Gatt. Burn after reading: Do multimodal large language models truly capture order of events image sequences? arXiv preprint arXiv:2506.
10415, 100445,506..0455, 2025.
Xin Tan, Bangwei Liu, Yicheng Bao, Qijian Tian, Zhenkun Gao, Xiongbin Wu, Zhihao Luo, Sen Wang, Yuqi Zhang. Xuhong Wang, al. Towards safe and trustworthy embodied ai: foundations, status, and prospects. 2025.
Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kaifeng Lyu, and Kai Chen. Lego-puzzles How good are milms at multi-step spatial reasoning? arXiv preprini ar5iv 19990, 2025.
Fei Wang, Xingyu Fu, James Y Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, et al. Muirbench: A comprehensive benchmark for robust multi-image understanding. arXiv preprint arXiv:2406.09411 2024.
Peijie Wang, Zhong- Li, Fei Yin, Dekang Ran, and Cheng-Lin Liu. Mv-math: Evaluating mul- timodal math reasoning in multi-visual contexts. In Proceedings of the Computer Vision and Patlern Recognition Conference, pp. 19541-19551, 2025a.
Sen Wang. Bangwei Liu, Zhenkun Gao, Lizhuang Ma, Xuhong Wang. Yuan Xie, and Xin Tan. Explore with long-term memory: A benchmark and multimodal llm-based reinforcement learning framework for embodied exploration. arXiv preprint arXiv:2601. 10744, 2026.
Xiaochen Wang, Heming Xia, Jialin Song. Longyu Guan, Yixin Yang, Qingxiu Dong, Weiyao Luo, Yifan Pu, Yiru Wang, Xiangdi Meng, et al. Beyond single frames: Can Imms comprehend tem- poral and contextual narratives in image sequences? arXiv preprint 2r22 2025b.
Zifu Wang, Junyi Zhu, Bo Tang, Zhiyu Li, Feiyu Xiong, Jiaqian Yu, and Mathew Blaschko. Jigsaw-rl: A study of rule-based visual reinforcement learning with jigsaw puzzles. arXiv 2025c.
preprint arXiv:2505.235000
Siwei Wu, Kang Zhu, Yu Bai, Yiming Liang. Yizhi Li, Haoning Wu, JH Liu, Ruibo Liu, Xing- wei Qu, Xuxin Cheng, et al. Mmra: A benchmark for evaluating multi-granularity and multi-image relational association capabilities in large visual language models. arXiv preprint arXiv:2407.1737, 2024.
XAI team. Grok-1.5 vision preview, April 2024. URL wwtph:ttp///// //x, http://xx..///e////////g///
Jin Xu, Zhifang Guo, Jinzheng He. Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang. Yang Fan, Kai Dang, al. Qwen2. 5-omni technical report. arXiv preprint arXiv:2503.20211 2025.
13

---

Published as a conference paper at ICLR 2026
Dawei Yan, Yang Li, Qing- Guo Chen, Weihua Luo, Peng Wang, Haokui Zhang, and Chunhua Shen. Mmer: Advancing visual language model in multimodal multi-turn contextual reasoning. arXiv preprint arXiv:2503.18533, 2025.
Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, et al. Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi. arXiv preprint arXiv:2404.16000 2024.
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang. Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, al. Mmmu: A massive multi-discipline multi- modal understanding and reasoning benchmark for expert agi. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 9556-9567, 2024.
Yi-Fan Zhang, Huanyu Zhang. Haochen Tian, Chaoyou Fu, Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wang, Qingsong Wen, Zhang Zhang, et al. Mme-realworld:: Could your multimodal IIm challenge high-resolution real-world scenarios that are difficult for humans? arXiv preprint arXiv:2408. 13257. 2024a.
Yizhen Zhang, Yang Ding, Shuoshuo Zhang, Xinchen Zhang, Haoling Li, Zhong-zhi Li, Peijie Wang, Jie Wu, Lei Ji, Yelong Shen, et al. Perl: Permutation-echanceed reinforcement learning for interleaved vision-language reasoning. arXiv preprint arXiv:2506.1440 2025.
Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713 2024b.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye. Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. arXiv preprint ar.iv:2403.13333 13372, 2024.
Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangehi Feng, Dongdong Kuang, and Yuwen Xiong. Easyrl: An efficient, scalable, multi-modality training framework. hiyouga/EasyRl 2025.
com/
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXhv:250..10479 2025.
14

---

Published as a conference paper at ICLR 2026
A EXPERIMENTAL SETUP
Hyperparameters. We fine-tuned the Qwen2.5-VL model (Bai et al. 2025 on our TPRU dataset. Our reinforcement learning methodology was implemented using the Easy-R1 framework (Zheng et al. 2025), employing the Group-wise Preference Optimization (GRPO) algorithm (Shao et al. 2024). Key settings included KL regularization with a coefficient of 0.01 and the generation rollouts per training sample. We used the AdamW optimizer with learning rate of le-6 and trained for 2 epochs on a cluster of 8 NVIDIA A800 GPUs.
Evaluation Benchmarks.
To comprehensively evaluate the performance of our model, we assessed its capabilities on wide variety of benchmarks. To test its generalized multi-image and multimodal reasoning abilities, we selected several established public benchmarks, including MMMERRalWWrldd-L
(Zhang et al.
2024a) and RealWorldQA (xAI team, 2024), BLINK (Fu et al.
2024b), MMCR Yann et al. 2025) MMMU (Yue et al. 2024), Muir- Bench (Wang et al. 2024). and LEGO-PuzZles (Tang et al. 2025). Furthermore, specifically mea- sure the improvements in temporal and procedural understanding, the core focus of our work. We evaluated our model on our proposed TPRU-test. To ensure standardized and reproducible eval- uation process across all benchmarks, we utilized the open-source VLMEvalKit framework (Duan) al., 2024). We also integrated our TPRU-test into this framework, allowing for consistent and streamlined evaluation methodology for both existing and our newly proposed tasks.
and MMStar (Chen et al. 2024). MMTBench (Ying et al., 2024),
B PROMPTS FOR MICRO IMAGE SEQUENCE FILTERING
System Prompt
You professional visual data analyst responsible for filtering multi-image sequences for a machine learning dataset. Your task is to ensure that each sequence meets the following strict quality and coherence standards:
1. Continuity of Action:
The images must depict a single, continuous, and uninter- rupted action or process performed by the same subject or agent.
Temporal Coherence: The sequence must have clear and logical chronological order. The state change between consecutive frames must be discernible and sensi- ble.
3. Visual Quality: All images in the sequence be clear and free of significant blur, corruption, or distracting artifacts. The main subject and object must be clearly visible.
4. Scene Consistency:
The background and core environment must remain consistent throughout the sequence. Changes should be cuts significant camera movement.
due to the action itself, not abrupt scene
User Prompt
Strietly analyze the provided image sequence based on the quality standards. Determine represents high-quality, coherent, and temporally logical process.
A sequence is considered unqualified any of the following are true:
The images are from different, unrelated scenes or actions.
The chronological order is illogical indiscernible.
The images severely blurry, low-resolution. contain duplicate frames.
The change in the scene is solely due to camera panning/zooming without mean- ingful action occurring.
The main object of the action is swapped, disappears, or is completely occluded.
Does this image sequence meet all the required standards? Please provide your answer single word: 'Yes' or 'No'.
15

---

Published as a conference paper at ICLR 2026
C PROMPTS FOR MODEL TRAINING
Reasoning and Output Instruction Template
You will presented with a task involving a set images. The specific task is described in the content below. Carefully analyze the images and the specific task description provided above. Your response must strictly follow the format rules below.
ANSWER FORMAT RULES
Your response format depends on the specific task presented in the content above:
For an Ordering Task: the question asks for the correct sequence of images (labeled A, B, C. D), your answer must be single string representing the correct order of the labels. Do not include spaces or other characters.
2. For a Multiple-Choice Question (MCQ): the question provides options (c.g., A, B, C, D, E), your answer must be the single letter corresponding to the correct option. If you believe none of the options are correct, choose the letter for the "None of the choices provided" option if available.
EXAMPLES
Example for Ordering Task:
<think>The process starts with image C, which shows the initial state. Image A adds the first component. Image B continues the process, and image D shows the final, completed assembly. Therefore, the correct sequence C, then A, then B, then D.</think> Cansvr>CABB</aaaaa.e
Example for a Multiple- Choice Question (MCQ):
<think>The question asks to predict the state of the phone screen after tapping the 'Set- tings' icon. The first image shows the home screen. Option C correctly displays the main settings menu, which is the expected outcome. Options A, B, and D show irrelevant scrcens.</thin
<answer>C<<ansveer
You must enclose your reasoning process in <think> tags and your final answer in <answer> tags. Output only the content within these tags, with no additional text or ex- planation.
D ABLATION STUDY ON DATA FROM DIFFERENT TRAINING STAGES.
Table 7: Ablation study on the training stage order for the Qwen2.5-VIL-7B model. All scores are Overall Accuracy (%).
Training Strategy
Benchmark Accuracy (%)
Stage
Stage
Stage
MuirBench
LEGO-Puzzles
Ordering
Next Frame Prediction
Previous Frame Review
61.31
39,91
Ordering
Previous Frame Review
Next Frame Prediction
61.58
40.45
Previous Frame Review
Ordering
Next Frame Prediction
60.38
42.64
Next Frame Prediction
Ordering
Previous Frame Review
64.23
Next Frame Prediction
Previous Frame Review
Ondering Ordering
63.62
Previous Frame Review
Next Frame Prediction
63.62
42.09
All tasks combined
65.03
42.82
E THE USE OF LLM
During the writing and editing of this paper, the author(s) utilized Large Language Models (such ChatGPT) for text refinement to improve the clarity and accuracy of the language. These tools
16

---

Published as a conference paper at ICLR 2026
primarily used for grammar checking, optimizing phrasing, and enhancing readability. All core ideas, the research design, data analysis, and conclusions are the original work of the author(s). The author(s) take full responsibility for the final content of the manuseript and have carefully reviewed all Al-assisted modifications.
F TRAINING STABILITY AND REWARD ANALYSIS
F.1 TRAINING STABILITY
To demonstrate the stability of our Reinforcement Learning (RL) training process, visualize the reward curves in Figure S As illustrated, the model's average reward exhibits a rapid and smooth ascent during the initial training stages. Subsequently, the curve successfully converges to a high- score plateau and maintains stability throughout the remaining steps, showing no signs of collapse or drastic oscillations. This convergence trajectory provides strong empirical evidence our GRPO training configuration is both stable and efficient.
orttic ctttc//ccor//mn meeon
pp My Ma
po Mar
alwae
0.6
355
1/ / y
0.5
My Laph Laplahee
63
Stop
022
Stop
130
100
(a) TPRU-3B
(b) TPRU-7B
(c) TPRU-32B
The plots (a), (b) and (c) display the reward score across training steps. The curves demonstrate rapid initial convergence followed by stable high-score plateau, indicating a stable optimization process without collapse or drastic oscillations.
Figure 5: RL Training Reward Curves.
F.2 ABLATION ON FORMAT REWARDS
To investigate whether the inclusion of format-specific reward induces overfitting to the prompt structure, we conducted a dedicated ablation study. We finetuned the Qwen2.5-VIL777 without the format reward, forcing the model to learn solely from the core task accuracy reward.
As presented in Table 8 while removing the format reward results in marginal performance de- crease, the model retains robust capabilities across all benchmarks. For instance, on the TPRU-test, the performance only drops slightly from 75.70% to 74.40%. These results substantiate that the vast majority of our model's performance stems from the acquisition of core temporal reasoning skills rather than superficial mimiery of the output format. The format reward serves beneficial auxil- iary mechanism that enhances training stability and provides minor performance gains, but is not the primary driver of the model's reasoning ability.
Table Ablation study on the impact of format rewards. "TPRU-7B (No-Format Rewardy" denotes the model trained solely with task-accuracy rewards, excluding format-specific constraints.
MuirBench
LEGO-Puzzles
Model
TPRU-Test
Overall Ordering
Overall Ordering
TPRU-7B (No-Format Reward)
63.96
29.69
41.64
28.0
74.40
TPRU-7B (Ours)
65.04
34.4
42.82
32.0
75.70
17


Reviews:
[{"review_id": "HfPlekEVA6", "reviewer": "Reviewer_2ZJj", "summary": "This paper introduces TPRU (Temporal-Procedural Understanding dataset): a large training and evaluation datasets for temporal coherence in embodied multimodal foundation models.\n\nThe paper notes that current training paradigms for embodied multimodal foundation models do not train explicitly for temporal understanding, which leads to fundamental limitations on their ability to perform in real world applications. In contrast, the TPRU dataset offers an alternative that bakes temporal understanding tasks into the model.\n\nTPRU dataset includes three temporal reasoning tasks:\n- Temporal Reordering: photos from a sequential stream are given in a random order and the MLLM is required to order it.\n- Next-Frame Prediction: photos from a sequential stream are given in order with one in the middle missing, the MLLM must choose which image is missing\n-  Previous-Frame Review: photos from a sequential stream are given in order with one in the beginning missing, the MLLM must choose which image starts the sequence\n\nThe dataset is collected from multiple sources with different embodiments (GUI, robotic manipulation, navigation) and base MLLMs are used to filter and label them. Furthermore, each task type has negative samples (i.e. samples where the correct answer is to reject all given options).\n\nA 7B model trained on TPRU shows significant uplift compared to the base model and other strong baselines, achieving SOTA results on the TPRU-test set.", "questions": "- How do you know that the TPRU-test set (461 examples) is sufficiently deduplicated from the training data (25k examples). How did you confirm there's no overlap or too much semantic similarity between held out set and training set to avoid information leakage?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The dataset is moderately large (~25k examples, ~126k images), which is useful for training.\n- The dataset covers a variety of embodiments (e.g. GUI, robotic manipulation, navigation...), which is useful for multiple applications.\n- The test set is manually curated/validated and held-out from training, which means it's likely to be high quality and a good measure of generalization.\n- The TPRU-trained models and the data generation pipeline have comprehensive evaluations and ablation studies, which show the importance of the different tasks and negative answers, as well as various model sizes.\n- The TPRU-trained models retain the same performance on general benchmarks like MMStar and MMMU-Dev.\n- I found the point about training the MLLMs to explicitly to reject invalid logic very interesting, and I think it can be useful in other applications with these models.", "weaknesses": "- It's unclear to me why only GRPO was used on the data as a learning algorithm/paradigm, but not simpler training regimes like simple SFT, which is usually the first thing people try.\n\n- The dataset poses all examples as multiple-choice-questions (MCQs) where one of the answer is \"reject all answers\", it's unclear why this format was chosen instead of free generation of answers.\n\n- It's unclear to me why the 7B model ends up performing better on TPRU-test than the 32B model. \n\n- Section 3 is called \"TRPU\" in the paper, it should be \"TPRU\" for consistency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0waPhPdMVR", "reviewer": "Reviewer_T1WF", "summary": "This paper introduces TPRU, a large-scale dataset designed to enhance MLLMs’ temporal and procedural understanding. The dataset includes three complementary tasks—Temporal Reordering, Next-Frame Prediction, and Previous-Frame Review—and a held-out evaluation set. The authors also finetune Qwen-VL using RL to demonstrate the dataset’s effectiveness.", "questions": "none", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Valuable dataset contribution. The creation of a large-scale dataset focused on temporal and procedural reasoning is a meaningful engineering effort. If released publicly, it could be beneficial for the community and future research.\n\nClear visual presentation. Figures are well-designed and make the method and dataset structure easy to understand.\n\nComprehensive related work and supplementary content. The paper provides detailed literature review and supplemental materials that help contextualize the contribution.", "weaknesses": "Motivation needs stronger justification. The core motivation—that existing datasets treat images as unordered—is not fully convincing. For example, LLaVA-Next-Interleave and other multimodal corpora already include temporal and sequential interactions (e.g., embodied tasks, spatial sequences). The paper should more clearly articulate what specific gaps remain and how TPRU uniquely addresses them.\n\nDataset source selection lacks coherence. The four data domains—robotic manipulation, LEGO assembly, GUI operation, and embodied navigation—appear loosely connected. It is unclear why these domains were chosen, whether they are complementary, or whether their combination leads to emergent abilities. As written, it feels as if these datasets were chosen opportunistically rather than driven by a principled rationale.\n\nModest performance gains. The improvements over the baseline are relatively small. More discussion is needed to interpret the results and understand the practical significance of the dataset.\n\nEvaluation scope is limited. Relying on a single self-introduced benchmark is not sufficiently convincing. Additional established benchmarks related to temporal or sequential understanding would provide more robust validation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M8yyCfESPF", "reviewer": "Reviewer_xbSM", "summary": "This paper introduces *TPRU* (Temporal and Procedural Understanding), a real-world, multi-image dataset and training recipe to improve temporal and procedural understanding in small-to-medium Large Multimodal Models (MLLMs). TPRU contains 24,750 QA pairs over 126k images sourced from four embodied scenarios (robotic manipulation, embodied navigation, mobile GUI interaction, LEGO assembly) and is organized into three complementary tasks: Temporal Reordering, Next-Frame Prediction, and Previous-Frame Review. The authors also curate TPRU-Test (461 expert-verified items) for evaluation. Using rule-based RL (GRPO) on Qwen2.5-VL backbones, the method's TPRU-7B improves from 50.33% to 75.70% on TPRU-Test and shows transfer to MuirBench and LEGO-Puzzles without degrading general multi-image performance.", "questions": "1. How accurate is the automated filtering? A human audit of a random sample (≥500 sequences) would quantify dataset reliability better.\n2. Have you tried >4-frame sequences or variable-length ordering?\n3. Have you tried free-form forecasting (describe the next state), counterfactuals (what step would prevent reaching frame 4?), or procedure repair (select the minimal fix to an erroneous sequence)? These would test deeper causal modeling than recognition-style choices.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well written and easy to follow.\n2. By pairing a training dataset explicitly structured for temporal reasoning with a matched held-out test set, this paper tries to address a known gap where multi-image sequences are often treated as unordered sets. The negative-sample design explicitly trains rejection of inconsistent options, pushing models toward cross-modal verification rather than text-prior heuristics.\n3. The ablations show all three tasks are synergistic, negative samples materially help, and scaling provides diminishing returns.\n4. The reported results demonstrate that small/efficient models can close much of the gap to very large proprietary systems on temporal, procedural understanding via data+RL, which is important for edge deployments.", "weaknesses": "1. Quality control and description generation rely on Qwen2.5-VL-72B. This can induce latent bias or style leakage into both data and targets. While pragmatic, the paper does not quantify inter-annotator agreement on machine-generated descriptions nor analyze failure modes from automated filtering. A small human-validated subset analysis for precision/recall of filter acceptance and error taxonomy would strengthen robustness claims.\n2. The three tasks are framed around 3–4-frame sequences with MCQ/permutation outputs. This is a strong first step but may not capture long-horizon dependencies, branching plans, open-ended multi-turn grounding, or action-conditioned predictions typical in robotics.\n3. Most comparisons are to multi-image MLLM benchmarks. Strong video-temporal baselines (e.g., models trained on video or long-context visual streams) are not reported. Even if adapters are needed, a discussion or a pilot comparison would clarify how much of the reported gains are specific to the quiz-style multi-image setup vs. general temporal understanding.\n4. The RL setup does not provide training stability metrics (reward curves, variance across seeds), nor does it probe whether the format reward induces prompt-format overfitting rather than genuine reasoning upgrades.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PQF3zrZXWx", "reviewer": "Reviewer_MMD1", "summary": "This paper addresses the critical failure of smaller, deployable MLLMs in understanding temporal and procedural visual data, a bottleneck for real-world embodied AI applications. To solve this, the authors introduce TPRU, a large-scale dataset sourced from diverse embodied scenarios like robotic manipulation and GUI navigation. The dataset is systematically structured into three complementary tasks: Temporal Reordering, Next-Frame Prediction, and Previous-Frame Review, and includes challenging negative samples to force models to perform active validation. Using a reinforcement learning (RL) fine-tuning strategy with TPRU, the authors achieved dramatic gains: the TPRU-7B model's accuracy soared from 50.33% to 75.70% on their manually curated TPRU-Test.", "questions": "1. How does your dataset, with its static or tabletop scenarios and GUI tasks, generalize to the dynamic, real-world challenges of embodied AI?\n2. Your data pipeline heavily relies on Qwen2.5-VL-72B. How did you prevent this single model's biases from being amplified in your dataset?\n3. Given the small 461-instance TPRU-Test mirrors your training tasks, how do you prove the \"significant gains\" are true generalization and not just overfitting?\n4. Could you please provide the confidence interval or significant results on the TPRU test set?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "+ The paper effectively addresses a novel and well-framed problem regarding the deficiency in temporal understanding in MLLMs, substantiated by the introduction of a large-scale and valuable dataset, TPRU.\n\n+ Despite being vague on the task scope, the methodology and data generation pipeline are presented with great clarity, supported by well-designed and informative figures that make the complex processes easy to understand.\n\n+ The application of an RL fine-tuning strategy provides a novel insight for this task.\n\n+ The benchmark evaluates a breadth of public benchmarks.", "weaknesses": "+ There is a notable discrepancy between the paper's stated motivation of advancing embodied AI and the composition of the TPRU dataset. A significant portion of the data, GUI navigation, has limited relevance to physical agent-environment interaction. Furthermore, the embodied scenarios included are largely constrained to static (pure navigation) or tabletop settings, which may not fully represent the complexity and dynamism of real-world embodied tasks.\n\n+ The data pipeline's heavy reliance on a single upstream model (Qwen2.5-VL-72B) for both quality filtering and text description generation risks inheriting and amplifying that model's intrinsic biases.\n\n+ The TPRU-Test is small, with only 461 instances, and its tasks directly mirror the training setup. While this design is effective for measuring direct improvement on the learned skills, it concludes \"significant gains\" more easily achievable within its own data distribution. The evidence for the generalizability of these improvements to a broader range of temporal understanding tasks remains somewhat limited.\n\n+ Although the paper claims the performance gain is notable, from Table 2, the fine-tuning process did not lead to universal improvements across all evaluated sub-tasks. Notably, there were performance regressions on certain benchmarks, such as a significant drop on the \"Multiview\" sub-task of LEGO-Puzzles for TPRU-7B and negative gains on other sub-tasks for TPRU-32B.\n\n+ The paper relies solely on point estimates for accuracy (e.g., in Tables 1-3)  and lacks statistical robustness checks like confidence intervals or significance testing, weakening the conclusions drawn from small test sets.\n\n+ A potential ethics statement concern is the authors' claim to use videos from YouTube (e.g., Arvin Bricks) without specifying the exact license used.\n\nI will consider raising my rating if the authors could address my concerns above.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)