Pith. sign in

REVIEW 4 major objections 6 minor 3 cited by

LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read LLMs understand scene graphs well but fail to generate them from text, a new benchmark of 120 scenarios finds.

desk verdict Solid benchmark, likely-true central finding, but the MA-SGG scoring is underspecified and the headline generation gap is probably overstated. read the letter →

arxiv 2505.19510 v2 pith:DY5UXYEG submitted 2025-05-26 cs.CL

Dongil Yang , Minjin Kim , Sunghwan Kim , Beong-woo Kwak , Minjun Park , Jinseok Hong , Woontack Woo , Jinyoung Yeo This is my paper
classification cs.CL
keywords scenegraphslargelanguagemodelsbenchmarkactiondecompositiongraphgenerationunderstandingin-contextlearningembodiedAI
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces TSG Bench, a benchmark of 120 real-world scenarios pairing narrative text with sequences of action-centric scene graphs, and uses it to test eleven LLMs on two understanding tasks and two generation tasks. Its central finding is a split: models nearly reach human accuracy when reading and reasoning over scene graphs, but fall far short when asked to generate graphs from narratives, especially when one sentence implies several actions. The authors identify action decomposition as the bottleneck: models have difficulty splitting a complex or implicit description into the discrete, ordered scenes that the reference format requires. The paper also shows that few-shot prompting helps generation substantially, while chain-of-thought mainly helps reasoning, and that models can repair faulty graphs better when told what type of error they contain.

What carries the argument

The central object is the action-centric scene graph: a graph whose nodes come from four categories (person, action, object, hand1/hand2) and whose edges come from three categories (verb, dobj, preposition), so that every scene is a set of triplets. The benchmark's design choices carry the argument: each narrative description is aligned with one or more such graphs, a predefined vocabulary of valid nodes and edges per scenario forces generation to be a closed-vocabulary task, and hand1/hand2 tracking encodes temporal state. The load-bearing mechanism is the decomposition of a description into an ordered sequence of scene graphs, which is what the multiple-action generation task tests and what the paper finds LLMs fail at.

What would settle it

Have a new set of annotators independently convert the same narratives into scene graphs without being shown the reference decomposition or the hand1/hand2 conventions. If their graphs agree with each other and with the benchmark reference on a random sample, the generation gap is a real model failure; if annotators produce different but plausible decompositions, the multiple-action generation scores are inflated and should be re-measured against a distribution of references.

Watch

Extended reading notes

Core claim

The paper claims, stated for a fair reader, that current large language models are strong at scene graph understanding but weak at scene graph generation, and that the main cause is a failure to decompose complex narratives into distinct scenes. On TSG Bench, the best model reaches 98.40% accuracy on description selection and 90.60% exact match on graph question answering, yet only 68.43 F1 on single-action generation and 58.80 F1 on multiple-action generation, compared with human scores of 82.50 and 75.60. When generation is split into node prediction, edge prediction, and action decomposition, most models lose the most at action decomposition; error cases concentrate on actions that are implicit or repeated. Higher descriptiveness in the input, ten-shot in-context learning, and being told the error type when refining a graph all raise performance, but none closes the generation gap.

Load-bearing premise

The results assume the reference scene graphs are the single correct reading of each narrative; if a different decomposition is also valid, low generation scores would partly reflect annotation choices rather than model failure.

Editorial extensions

If this is right

  • If the finding holds, LLM-based systems for embodied AI or robotics cannot yet rely on direct text-to-scene-graph generation; they need an explicit decomposition step before graph construction.
  • Scene graph understanding and generation should be treated as distinct capabilities in future benchmarks, since high understanding scores do not predict generation scores.
  • Action decomposition is a concrete target for improvement: methods that segment narratives into elementary actions before producing graphs should raise multiple-action generation F1.
  • Providing the number of actions as a clue is not enough; models still miss implicit and repeated actions, so counting and repetition handling need separate attention.
  • Error-type information is a practical lever for graph repair systems, since refinement improves markedly when the model knows whether the error is redundant, missing, mismatched, or reversed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the multiple-action generation gap may be partly an artifact of the reference format, because the benchmark assumes a single canonical decomposition with fixed hand1/hand2 assignments; measuring inter-annotator agreement on the reference graphs would reveal how much of the deficit is genuine model failure.
  • Editorial inference: because attributes and multiple actors are excluded from the scene graphs, the benchmark may overstate generation ability for richer scenes; adding attributes would stress node prediction in ways the current closed vocabulary avoids.
  • Editorial inference: the descriptiveness result suggests a testable extension: intentionally rewrite narratives to be more explicit (adding action counts and temporal connectives) and measure whether multiple-action generation F1 rises monotonically, which would isolate decomposition from format adherence.
  • Editorial inference: the hallucination counts imply that constraint decoding against the allowed vocabulary would likely narrow the small-model performance gap regardless of reasoning improvements.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. TSG Bench is a benchmark for evaluating LLMs on scene graph understanding (SGQA and SGDS) and generation (SA-SGG and MA-SGG), built by adapting the EASG video-derived scene graphs into text narratives and reference graphs through a human-in-the-loop pipeline. The paper evaluates 11 LLMs and reports strong performance on understanding tasks but substantially lower F1 on generation tasks, with MA-SGG being the hardest and action decomposition identified as the main bottleneck. Additional analyses examine node/edge/action subtasks, the effect of implicit and repetitive actions, prompting methods, error refinement, and hallucination in generation.

Significance. If the MA-SGG scoring is validated, this is a useful benchmark: it is one of the first systematic evaluations of LLMs on dynamic, action-centric scene graphs derived from text; the data and code are released; and the decomposition analysis into node/edge/action-subtasks is a practical diagnostic. The qualitative trend is consistent across 11 models, which lends some confidence to the central claim. However, the main quantitative conclusion rests on an underspecified multi-graph matching protocol and on reference graphs that are not uniquely entailed by the text; the single-run evaluation with no variance and the small human sample further limit the precision of the headline numbers.

major comments (4)
  1. [§4.2] The MA-SGG evaluation protocol is underspecified. The statement that "evaluation is conducted separately for each generated graph" does not define how the k generated graphs are aligned to the k reference graphs. If matching is positional, any reordering of valid graphs is scored as incorrect, which would inflate the measured generation gap; if it is greedy or set-level, the paper should say so and provide the algorithm. Please specify the exact alignment procedure (e.g., Hungarian matching over graph-level F1, or scoring over all permutations) and report the sensitivity of the main MA-SGG F1 scores to this choice.
  2. [Appendix A and Table 7] The reference graphs for MA-SGG are not uniquely entailed by the narrative. The Step 3 checklist explicitly permits actions that are only "logically inferred" from the text, and the hand1/hand2 tracking convention plus the disclosed count k impose a strong annotation prior. Table 7 Case 2 is a concrete instance: the reference assumes three paint graphs, while the model's pick-up/dip/paint decomposition is a defensible reading of the same sentence. Under strict single-reference scoring, such legitimate alternative decompositions are counted as errors, so the reported human-model gap (75.6 vs 58.8 in Table 2) and the claim that LLMs fail at action decomposition may be inflated. Please report inter-annotator agreement on a sample of MA-SGG references and either allow multiple reference graphs or introduce a soft matching metric that accepts segmentation variants.
  3. [§4.1 and Appendix B] The experimental protocol has no variance estimate. All model numbers come from a single inference run at temperature 0.1, so differences such as GPT-4o vs DeepSeek-V3 (SA-SGG F1 59.23 vs 54.45; MA-SGG F1 43.99 vs 39.34) may be within run-to-run noise. Report multiple runs with confidence intervals or, at minimum, a stability analysis. In addition, human performance is computed on only 30 examples (Appendix B) while models are evaluated on the full 1,188/853 samples; to make the human-model comparison meaningful, either evaluate humans on a larger random subset or provide interval estimates for the human scores.
  4. [§4.3, Tables 2 and 9] There are internal numerical inconsistencies that need correction. The text states "Qwen-2.5-72B (66.15, 43.73) and Mistral-large (69.76, 37.76)" for SA/MA F1, but Table 2 reports 54.42/36.78 and 58.15/35.13 for these models. Additionally, Table 9 reports F1 values inconsistent with the given precision/recall pairs (e.g., Qwen-2.5-72B Node Single: P=80.39, R=81.56, F1 should be about 80.97, not 71.18; Edge Multiple: P=84.53, R=82.19, F1 should be about 83.35, not 79.74). Correct these numbers and re-verify all tables; if the subtask scores change, the analysis in §5.1 must be updated accordingly.
minor comments (6)
  1. [§3.5 and Table 1] The text reports 4,298 scene graphs while Table 1 and the dataset construction description report 4,289; please unify the counts.
  2. [Table 8] The LCS-based action segmentation metric should define how precision and recall are computed over predicted and reference action sequences, and how the LCS is applied to graph-level actions.
  3. [Figure 7 caption] The caption contains a typo: "GPT-40" should be "GPT-4o."
  4. [§5.2 and Figure 5] The repetition subset is curated by filtering for words such as "times", "repeat", and "repeated", but Figure 5 shows only GPT-4o and Claude-3.5-Sonnet; either extend this analysis to all 11 models or explicitly justify why only two are reported.
  5. [Appendix A] No inter-annotator agreement is reported for the human-refined scene graphs, SGQA questions, or SGDS distractors; reporting IAA (e.g., Cohen's kappa or percentage agreement) would strengthen the reliability claim of the benchmark.
  6. [References] The Anthropic reference contains a duplicated year: "2024. 2024." and should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark grounded in external EASG graphs with human refinement; no fitted input or self-citation chain is load-bearing.

full rationale

TSG Bench's ground-truth scene graphs are inherited from the external EASG dataset (Rodin et al., 2024) and then human-refined, as stated in Section 3.4: 'We derive TSG Bench from the Ego-centric Action Scene Graphs (EASG) dataset...' and 'human workers meticulously inspect and refine the graph elements one by one.' The benchmark does not fit any parameter to model outputs and then re-predict it; precision/recall/F1 are computed against fixed reference graphs. The inclusion of GPT-4o/GPT-4o-mini in data construction is a potential source of bias but not an instance of circularity: the reference graphs are not defined as the evaluated models' outputs, and the human refinement makes the ground truth independent of any single model's prediction. The MA-SGG matching ambiguity and the possibility of multiple valid decompositions are validity/fairness concerns about the metric, not circular reductions; the paper neither defines the reference in terms of model outputs nor imports a load-bearing result from the authors' own prior work. Therefore no circular step can be exhibited, and the score is 0.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the validity of the EASG-derived reference graphs, the chosen scene graph schema, the hand-tracking conventions, the single-run evaluation protocol, and the human annotation quality. None of these are tested within the paper, and they are load-bearing for the measured performance gap.

free parameters (1)
  • sampling temperature = 0.1
    Set after a pilot study; affects generation variability and could shift F1 scores, but not fitted to the benchmark data.
assumptions (5)
  • domain assumption EASG dataset provides valid action-centric scene graphs.
    The benchmark derives all reference graphs from EASG (Rodin et al., 2024); errors in EASG propagate to TSG Bench.
  • domain assumption The action-centric scene graph schema (person/action/object/hand nodes; verb/dobj/preposition edges) is an appropriate target representation.
    This schema is adopted from prior work and constrains what counts as correct; other schemas might yield different results.
  • domain assumption The hand1/hand2 assignment rules are well-defined and inferable from text.
    MA-SGG and SA-SGG require tracking which hand holds which object; if the narrative does not determine this, the reference is underdetermined.
  • ad hoc to paper Single-run inference at temperature 0.1 yields representative model performance.
    The paper runs each model once; no repeated sampling or variance reporting, so conclusions rest on one sample per model.
  • domain assumption Human annotations are accurate and consistent.
    Three annotators refined LLM-generated graphs and narratives; the paper reports an alignment process but no inter-annotator agreement measure.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study." pith.science (2026). https://pith.science/paper/DY5UXYEG

@misc{pith2026250519510,
  author       = {Pith},
  title        = {Pith review of: LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DY5UXYEG}},
  note         = {Machine review of arXiv:2505.19510}
}
read the original abstract

The remarkable reasoning and generalization capabilities of Large Language Models (LLMs) have paved the way for their expanding applications in embodied AI, robotics, and other real-world tasks. To effectively support these applications, grounding in spatial and temporal understanding in multimodal environments is essential. To this end, recent works have leveraged scene graphs, a structured representation that encodes entities, attributes, and their relationships in a scene. However, a comprehensive evaluation of LLMs' ability to utilize scene graphs remains limited. In this work, we introduce Text-Scene Graph (TSG) Bench, a benchmark designed to systematically assess LLMs' ability to (1) understand scene graphs and (2) generate them from textual narratives. With TSG Bench we evaluate 11 LLMs and reveal that, while models perform well on scene graph understanding, they struggle with scene graph generation, particularly for complex narratives. Our analysis indicates that these models fail to effectively decompose discrete scenes from a complex narrative, leading to a bottleneck when generating scene graphs. These findings underscore the need for improved methodologies in scene graph generation and provide valuable insights for future research. The demonstration of our benchmark is available at https://tsg-bench.netlify.app. Additionally, our code and evaluation data are publicly available at https://github.com/docworlds/tsg-bench.

Figures

Figures reproduced from arXiv: 2505.19510 by the authors.

Figure 1
Figure 1. An illustration showing how a scene graph [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of TSG Bench. Scene graph question answering aims to answer a question by reasoning over scene graphs. Scene graph description selection is a multiple-choice task of selecting the correct description of a given scene graph. Single action scene graph generation focuses on generating a scene graph based on a description representing a single action. Multiple action scene graph generation aims to generate mult… view at source ↗
Figure 3
Figure 3. Domain distribution across scenarios. 3.5 Benchmark Statistics [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: F1-score results on decomposed scene graph generation tasks, distinguishing between single-action and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: The results of the comparative evaluation un [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Example interface of the TSG Editor, designed to facilitate the creation of scene graph datasets from [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: The line graph shows F1 scores of GPT-40 and Claude across varying levels of descriptiveness in the MA-SGG data. The histogram represents sample frequencies within each descriptiveness ratio range. C Additional Analysis C.1 Analysis of the Impact of Description Length …
Figure 8
Figure 8. Figure 8: The distribution of relationships in the scene graph dataset, with “with” appearing most frequently, [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: The frequency distribution of scene graph objects on a logarithmic scale, with “hand1” and “others” [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: The frequency distribution of verbs in the scene graph, with “others” as the dominant category, followed [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: The zero-shot prompt for scene graph description selection tasks. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: The zero-shot prompt for scene graph question answering tasks. [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: The zero-shot prompt for single action scene graph generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: The zero-shot prompt for multiple action scene graph generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: CoT prompt for scene graph description selection tasks. [PITH_FULL_IMAGE:figures/full_fig_p020_15.png]
Figure 16
Figure 16. Figure 16: CoT prompt for scene graph question answering tasks. [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]
Figure 17
Figure 17. Figure 17: CoT prompt for single action scene graph generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p021_17.png]
Figure 18
Figure 18. Figure 18: CoT prompt for multiple action scene graph generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p022_18.png]
Figure 19
Figure 19. Figure 19: Few-shot prompt for scene graph description selection tasks. [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]
Figure 20
Figure 20. Figure 20: Few-shot prompt for scene graph qusetion answering tasks. [PITH_FULL_IMAGE:figures/full_fig_p024_20.png]
Figure 21
Figure 21. Figure 21: Few-shot prompt for single action scene graph generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]
Figure 22
Figure 22. Figure 22: Few-shot prompt for multiple action scene graph generation tasks. [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering

    cs.CV 2026-01 unverdicted novelty 6.0 of 10

    KG-ViP fuses scene graphs and commonsense graphs via a query-based retrieval-and-fusion pipeline to improve multi-modal LLM performance on visual question answering.

  2. Designing Memory-Augmented AR Agents for Spatiotemporal Reasoning in Personalized Task Assistance

    cs.AI 2025-08 conditional novelty 5.0 of 10

    A position paper proposing a four-module memory-augmented AR agent framework that uses stored scene graphs of past user experiences to personalize task guidance.

  3. Pluri-perspectivism in Human-robot Co-creativity with Older Adults

    cs.HC 2025-07 conditional novelty 5.0 of 10

    A five-dimensional pluri-perspectivist model is introduced to guide context-sensitive, co-creative human-robot interaction, grounded in theory and interviews with artists and art teachers.

Reference graph

Works this paper leans on

46 extracted references · 44 canonical work pages · cited by 3 Pith papers

  1. [1]

    - Each graph is composed of a set of triplets ‘[node1, edge, node2]‘

    Input: - Context: A list of scene graphs representing the preceding context. - Each graph is composed of a set of triplets ‘[node1, edge, node2]‘. ‘node1‘ and ‘node2‘ are one of person, action, object and hand. ‘edge‘ represents the relationship between them (e.g., ‘verb‘, ‘dobj‘, ‘from‘, ‘with‘). - Target Scene Graph: A set of triplets that should be des...

    work page

  2. [3]

    - Do not output any additional text or explanation

    Output: - Be sure to choose only one letter of the matching description. - Do not output any additional text or explanation. Only the letter in [ ] (e.g., [A]). Key rules of edges in a triplet: - ‘verb‘ describes the action performed by ‘person‘. - ‘dobj‘ links the action to its direct object (‘node2‘). - Other edges like ‘from‘ and ‘with‘ describe spatia...

    work page

  3. [22]

    Available edges

    Map synonyms or semantically similar words to edges in the "Available edges" list

    work page

  4. [23]

    (e.g., person -> verb -> add)

    verb: can only connect person and an action node. (e.g., person -> verb -> add)

    work page

  5. [24]

    dobj: connects an action and an object node, only when it is the direct object of the action (e.g., add -> dobj -> flour)

    work page

  6. [25]

    Available nodes

    preposition: connects one of the four types of node pairs: action & object / action & hand / object & object / hand & object (e.g., take -> from -> table) Output Format: The output must consist of triplets (one per line) in the format below. node -> edge -> node node -> edge -> node ... Use only the "Available nodes" and "Available edges" provided. No add...

    work page

  7. [29]

    If there are fewer visible actions than {num_scene_graphs}, **additional relevant actions may be inferred** to reach the required count

    work page

  8. [30]

    Available Nodes

    However, use only the "Available Nodes" and "Available Edges" provided. **If a necessary node is missing, use the closest semantically matching node from the available list**

    work page

Show all 46 references
  1. [50]

    Available Nodes

    Here are the explanations for each edge. - verb: can only connect person and an action node. (e.g., person -> verb -> add) - dobj: connects an action and an object node, only when it is the direct object of the action (e.g., add -> dobj -> flour) - preposition: connects one of...

  2. [52]

    Task: - Think step-by-step and determine which description best matches the Target Scene Graph

  3. [53]

    Think:" - Then, output your final answer under

    Output: - Output your rationale under "Think:" - Then, output your final answer under "Final Answer:" - For the final answer, be sure to choose only one letter of the matching description and write it in the format of (e.g., [X]), where "X" represents a single alphabet letter....

  4. [57]

    There must be a triplet that starts with a person node

  5. [60]

    Consider the context to identify the node

    A node may appear explicitly or be hidden implicitly in the given sentence. Consider the context to identify the node

  6. [71]

    Available nodes

    Here are the explanations for each edge. - verb: can only connect person and an action node. (e.g., person -> verb -> add) - dobj: connects an action and an object node, only when it is the direct object of the action (e.g., add -> dobj -> flour) - preposition: connects one of...

  7. [96]

    Available Nodes

    Here are the explanations for each edge. - verb: can only connect person and an action node. (e.g., person -> verb -> add) - dobj: connects an action and an object node, only when it is the direct object of the action (e.g., add -> dobj -> flour) - preposition: connects one of...

  8. [99]

    person",

    Output: - Be sure to choose only one letter of the matching description. - Do not output any additional text or explanation. Only the letter in [ ] (e.g., [A]). Key rules of edges in a triplet: - ‘verb‘ describes the action performed by ‘person‘. - ‘dobj‘ links the action to i...

  9. [100]

    - Each graph is composed of a set of triplets [node1, edge, node2]

    Input: - Context: A list of scene graphs representing the preceding context. - Each graph is composed of a set of triplets [node1, edge, node2]. ‘node1‘ and ‘node2‘ are one of person, action, object and hand. ‘edge‘ represents the relationship between them (e.g., ‘verb‘, ‘dobj...

  10. [101]

    Task: - Determine which description best matches the Target Scene Graph

  11. [102]

    - Do not output any additional text or explanation

    Output: - Be sure to choose only one letter of the matching description. - Do not output any additional text or explanation. Only the letter in [ ] (e.g., [A]). Key rules of edges in a triplet: - ‘verb‘ describes the action performed by ‘person‘. - ‘dobj‘ links the action to i...

  12. [103]

    Generate precisely {num_scene_graphs} scene graphs—no more, no less

  13. [104]

    Each scene graph must depict an action that would be explicitly visible in a scene

  14. [105]

    If the sentence contains multiple implicit actions, distribute them among the scene graphs while ensuring the total count matches {num_scene_graphs}

  15. [106]

    If there are fewer visible actions than {num_scene_graphs}, additional relevant actions may be inferred to reach the required count

  16. [107]

    Available Nodes

    However, use only the "Available Nodes" and "Available Edges" provided. If a necessary node is missing, use the closest semantically matching node from the available list

  17. [108]

    Rules for A Scene Graph Representation:

    Ensure each graph maintains logical coherence while including essential contextual elements. Rules for A Scene Graph Representation:

  18. [109]

    A graph is composed of one or more triplets of nodes and edges

  19. [110]

    (Format: node -> edge -> node)

    A triplet starts with a node and another node is connected by an edge. (Format: node -> edge -> node)

  20. [111]

    Each triplet is split with a new line

  21. [112]

    There must be exactly one triplet that starts with a person node in a graph

  22. [113]

    Available nodes

    All nodes and edges must be one of "Available nodes" or "Available edges" provided. Rules for Node:

  23. [114]

    A node can be person, any action, any object, or any hand

  24. [115]

    Available nodes

    A node may appear explicitly or be hidden implicitly in the given sentence. Consider the context to identify the node from the "Available nodes" list, but do not create a new one

  25. [116]

    Available nodes

    Map synonyms or semantically similar words to nodes in the "Available nodes" list

  26. [117]

    Use default tools or body parts for actions that imply them (e.g., hands for grasping)

  27. [118]

    Treat each action as a node

  28. [119]

    Special Rules for Hand Node:

    Include "person" as the starting node in the graph. Special Rules for Hand Node:

  29. [120]

    If both hands are empty and a node is grasped, represent it as "hand1."

  30. [121]

    If one hand holds a node and another node is grasped, represent it as "hand2."

  31. [122]

    If all hands release their objects, reset the next grasping hand to "hand1."

  32. [123]

    hand1" and

    Ensure "hand1" and "hand2" are used contextually to avoid overlap or ambiguity

  33. [124]

    Rules for Edge:

    If the sentence implies using both hands (e.g., lifting a large object), represent both hands explicitly (e.g., hand1, hand2). Rules for Edge:

  34. [125]

    An edge can be verb, dobj, or any preposition

  35. [126]

    Available edges

    Use only the edges listed under "Available edges."

  36. [127]

    Available Nodes

    Here are the explanations for each edge. - verb: can only connect person and an action node. (e.g., person -> verb -> add) - dobj: connects an action and an object node, only when it is the direct object of the action (e.g., add -> dobj -> flour) - preposition: connects one of...

  37. [2019]

    with” appearing most frequently, followed by “on

    3d scene graph: A structure for unified seman- tics, 3d space, and camera. 2019 IEEE/CVF Interna- tional Conference on Computer Vision (ICCV), pages 5663–5672. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav ...

    2019 arXiv

  38. [2020]

    ArXiv, abs/2001.08837

    Graph constrained reinforcement learn- ing for natural language action spaces. ArXiv, abs/2001.08837. Prithviraj Ammanabrolu and Mark O. Riedl. 2021. Learning knowledge graph-based world models of textual environments. In Neural Information Pro- cessing Systems. Iro Armeni, Zh...

    2001 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.