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PIVOTS benchmark evaluates how well multimodal models predict bidirectional interpersonal relationship dimensions from video and text.

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2026-06-26 08:40 UTC pith:5S4OPME3

load-bearing objection PIVOTSBench is a new benchmark for fine-grained bidirectional interpersonal relationship prediction in MLLMs, but the abstract supplies almost no information on annotation process or validation. the 2 major comments →

arxiv 2606.23092 v1 pith:5S4OPME3 submitted 2026-06-22 cs.CL

PIVOTSBench: Evaluating Fine-Grained Interpersonal Relationship Reasoning in Multimodal Large Language Models

Shuxiang Zhang , Yiting Yin , Wenxuan Song , Yuhang Wu , Miao Liu This is my paper
classification cs.CL
keywords interpersonal relationshipsmultimodal large language modelsbenchmark evaluationsocial reasoningvisual cuesbidirectional predictionpsychology dimensions
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper presents PIVOTS as the first benchmark designed to test multimodal large language models on predicting fine-grained, bidirectional interpersonal relationship dimensions drawn from psychology research. It draws on Social-IQ 2.0 and YouTube data while adding auxiliary tasks that require models to identify the visual cues needed for those predictions. A sympathetic reader would care because everyday social interactions depend on exactly this kind of multimodal reasoning, yet current models have no standardized way to measure progress on it. The work evaluates both proprietary and open-source models, runs ablation studies on visual input and social-role information, and compares joint versus pairwise prediction settings for the relationship dimensions.

Core claim

Existing multimodal large language models have not been tested on the ability to predict bidirectional interpersonal relationship dimensions grounded in established psychology; PIVOTS supplies the first benchmark built from Social-IQ 2.0 and YouTube data together with auxiliary visual-cue identification tasks to close that gap and to examine how visual modalities and explicit role information affect performance.

What carries the argument

The PIVOTS benchmark, which measures prediction of bidirectional relationship dimensions plus identification of supporting visual cues from multimodal conversational data.

Load-bearing premise

The selected data sources, psychology-derived dimensions, and auxiliary visual-cue tasks together form a valid measure of the interpersonal reasoning capability being tested.

What would settle it

A finding that model scores on PIVOTS show no correlation with human judgments of the same relationship dimensions, or that performance remains unchanged when visual cues are removed, would indicate the benchmark does not isolate the intended capability.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Models can now be compared directly on their ability to score bidirectional relationship dimensions.
  • Ablation results show how much visual input and explicit social-role information contribute to those scores.
  • Joint prediction settings improve performance over independent pairwise scoring for the same dimensions.
  • Auxiliary visual-cue tasks provide a separate signal of whether models use the right evidence for their relationship predictions.

Where Pith is reading between the lines

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

  • Low scores across current models would imply that scaling alone has not produced reliable social reasoning and that targeted training on relationship dimensions may be needed.
  • If the benchmark generalizes beyond the chosen video sources, it could serve as a testbed for checking whether improvements on PIVOTS transfer to real-world conversational agents.
  • Pairwise versus joint prediction differences suggest that relationship dimensions are interdependent, which future model architectures might exploit explicitly.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper introduces PIVOTS, the first benchmark constructed from Social-IQ 2.0 and YouTube data to evaluate MLLMs on predicting bidirectional interpersonal relationship dimensions grounded in psychology research. It additionally provides auxiliary tasks for identifying critical visual cues and reports evaluations of both proprietary and open-source MLLMs together with ablation studies on the effects of visual modalities, explicit social role information, and joint versus pairwise prediction settings.

Significance. If the benchmark construction and validation are sound, the work would fill a notable gap by providing a multimodal, psychology-grounded testbed for fine-grained social reasoning that current MLLMs largely lack. The inclusion of bidirectional dimensions, visual-cue auxiliaries, and systematic ablations on modality and role information could yield actionable insights into model limitations and the value of explicit social context.

major comments (2)
  1. [§3] §3 (PIVOTS construction): the manuscript supplies no information on how the psychology-derived relationship dimensions were operationalized into concrete tasks or items, how inter-annotator agreement was measured during annotation, or whether the benchmark was validated against human performance baselines. These omissions prevent assessment of whether PIVOTS actually measures the intended interpersonal-reasoning capability.
  2. [§4] §4 (data curation and auxiliary tasks): details are absent on the precise curation pipeline from Social-IQ 2.0 and YouTube sources, the criteria used to select or filter clips, and the design of the auxiliary visual-cue tasks. Without these, the representativeness and internal validity of the benchmark cannot be evaluated.
minor comments (2)
  1. [Abstract] The abstract states that 'detailed ablation studies' were conducted yet provides no quantitative summary of the main ablation outcomes; adding one or two key numerical findings would improve readability.
  2. [Experiments] Figure and table captions could more explicitly link each result to the corresponding research question (e.g., modality effect, role information effect).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The two major comments highlight areas where additional methodological detail will improve the manuscript's clarity and allow better evaluation of the benchmark. We address each point below and will revise the paper accordingly.

read point-by-point responses

  1. Referee: [§3] §3 (PIVOTS construction): the manuscript supplies no information on how the psychology-derived relationship dimensions were operationalized into concrete tasks or items, how inter-annotator agreement was measured during annotation, or whether the benchmark was validated against human performance baselines. These omissions prevent assessment of whether PIVOTS actually measures the intended interpersonal-reasoning capability.

    Authors: We agree that §3 would benefit from expanded description of the operationalization process. In the revision we will add: (i) explicit mapping from each psychology-derived dimension to the concrete task format (including example items and response options), (ii) the annotation guidelines and inter-annotator agreement statistics (e.g., percentage agreement and Cohen’s κ) obtained during labeling, and (iii) human performance baselines collected on a held-out subset of the benchmark. These additions will directly address the concern about validity. revision: yes

  2. Referee: [§4] §4 (data curation and auxiliary tasks): details are absent on the precise curation pipeline from Social-IQ 2.0 and YouTube sources, the criteria used to select or filter clips, and the design of the auxiliary visual-cue tasks. Without these, the representativeness and internal validity of the benchmark cannot be evaluated.

    Authors: We concur that the curation pipeline and auxiliary-task design require more explicit documentation. The revised manuscript will include: a step-by-step description of clip extraction and filtering from Social-IQ 2.0 (e.g., speaker count, interaction length, quality thresholds) and from YouTube (search strategy, manual review criteria); and a dedicated subsection detailing how the auxiliary visual-cue tasks were constructed, including the annotation protocol for identifying critical visual elements and example task instances. These details will allow readers to assess representativeness and internal validity. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces an evaluation benchmark (PIVOTS) constructed from existing datasets (Social-IQ 2.0, YouTube) and psychology-derived dimensions, with auxiliary visual tasks. No equations, parameter fitting, predictions of fitted quantities, or derivation chains appear in the provided text. The central claim is the creation and use of this benchmark for MLLM evaluation, which does not reduce to self-definition or self-citation load-bearing steps. Self-citations, if present in the full text, are not load-bearing for any mathematical result here. This is a standard benchmark paper with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations or fitted parameters appear in the abstract; the contribution is the construction of an evaluation benchmark.

pith-pipeline@v0.9.1-grok · 5706 in / 1075 out tokens · 21387 ms · 2026-06-26T08:40:32.027556+00:00 · methodology

0 comments
read the original abstract

Humans possess an innate ability to understand fine-grained interpersonal relationships, which is central to everyday social interactions. Although such reasoning is inherently multimodal, it remains largely unexplored by existing multimodal large language models (MLLMs). To address this gap, we introduce PIVOTS, the first benchmark built from Social-IQ 2.0 and YouTube data to evaluate MLLMs' ability to predict bidirectional interpersonal relationship dimensions grounded in established psychology research. In addition, PIVOTS includes auxiliary tasks that assess models' ability to identify and leverage the critical visual cues underlying such predictions. We evaluate both proprietary and open-source MLLMs and conduct detailed ablation studies to analyze the effects of visual modalities and explicit social role information in conversational utterances. We further examine how joint and pairwise prediction settings benefit MLLMs in scoring bidirectional PIVOTS dimensions. Project page and resources: https://flynnzhangsx.github.io/PIVOTSBench/ .

Figures

Figures reproduced from arXiv: 2606.23092 by Miao Liu, Shuxiang Zhang, Wenxuan Song, Yiting Yin, Yuhang Wu.

Figure 1
Figure 1. Figure 1: Understanding complex social dynamics requires joint reasoning over language and visual cues. Although both examples involve parent–child interactions, they exhibit drastically different configurations across multiple interpersonal dimensions. Moreover, conversational utterances alone are often insufficient to reason about these dimensions, whereas visual cues provide complementary signals. where two parti… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the three hierarchical tasks defined in the PIVOTS benchmark. The scoring task asks models to assign six-dimensional PIVOTS relational scores from the video and conversation. The key-frame identification task requires selecting the frame that best reflects a specified relational dimension, while the causality analysis task reasons about the visual elements (e.g., facial expressions or gestures)… view at source ↗
Figure 3
Figure 3. Figure 3: Label distributions across emotion and tone [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visual scoring examples of the PIVOTS benchmark across various social dimensions. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example on Comparisons between Youtube and Social-IQ 2.0 videos. Both datasets have a video duration of approxi￾mately 30 seconds [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of Distributions between Social-IQ 2.0 (Top Row) and YouTube (Bottom Row) across Three [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Task 1 Prompt with separate prediction We use OpenAI’s ChatCompletion multimodal prompt format as the base format. This prompt consists of two parts: one is a multimodal upload link; the other is a "user prompt" that includes a PIVOTS instance and a PIVOTS question. The prompt template for separate prediction in Task 1 is shown in [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Task 1 Prompt with joint prediction Prompt Example [{ "role": "user", "content": [ { "type": "text", "text": "You need to answer the question based on the provided information. Input Information: 1. Video 2. Transcribed Text 3. the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note: Note that all the above scores are unidirectional… view at source ↗
Figure 10
Figure 10. Figure 10: Task 2 Prompt 21 [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 9
Figure 9. Figure 9: Task 1 Prompt with pairwise prediction Prompt Example [ { "role": "user", "content": [ { "type": "text", "text": "You need to answer the question based on the provided information. Input Information: 1. Video 2. the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note that all the above scores are unidirectional; for scores on the sa… view at source ↗
Figure 11
Figure 11. Figure 11: Task 3 Prompt this prompt, we first explicitly define the model’s role as a "research psychologist with 30 years of experience in social psychology and interpersonal dynamics," and mandate that the model must ex￾tract specific quotes or behavioral evidence from the input video or transcribed text as support before assigning any score. This ensures the model con￾ducts evidence-based scoring. It is worth no… view at source ↗
Figure 12
Figure 12. Figure 12: Multi-Stage Prompt 25 [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: In-Context Prompt 26 [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: MLLM’s Response Example 27 [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: MLLM’s Response Example 28 [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Annotation Interface. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_16.png] view at source ↗

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Reference graph

Works this paper leans on

16 extracted references · 1 canonical work pages · cited by 1 Pith paper

  1. [1]

    in charge

    Perceived dimensions of interpersonal rela- tions.Journal of Personality and social Psychology, 33(4):409. Eric Zhongcong Xu, Zeyang Song, Satoshi Tsutsui, Chao Feng, Mang Ye, and Mike Zheng Shou. 2022. Ava-avd: Audio-visual speaker diarization in the wild. InProceedings of the 30th ACM International Con- ference on Multimedia, pages 3838–3847. Zixiang Xu...

    work page arXiv 2022

  2. [2]

    ###" before and after. If your score is -2, then you should output ###-2### Transcribed Text [data1] Question [data2]

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note that all the above scores are unidirectional; for scores on the same dimension, A's score for B is not necessarily the same as B's score for A. Directly output your score. And wrap your score with "###" before and after. If yo...

  3. [3]

    image_url

    The prompt template for pairwise prediction in Task 1 is shown in Figure 9. We replace [def- inition] with the detailed definition and scoring criteria of the PIVOTS model, [data1] with the tran- scribed text corresponding to the video, [data2] with a specific question corresponding to the video. We uniformly sample 8 frames from each video as the visual ...

  4. [4]

    []", with each score separated by a comma—for example: [- 2,0,1,2,0,1] (each number corresponds sequentially to the PIVOTS dimensions). Wrap your score with

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note that all the above scores are unidirectional; for scores on the same dimension, A's score for B is not necessarily the same as B's score for A. Directly output your score for each dimension in the order of PIVOTS, enclosed in ...

  5. [5]

    []", with each score separated by a comma—for example: [-2,0,1,2,0,1] (each number corresponds sequentially to the PIVOTS dimensions). Wrap your score with

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note: Note that all the above scores are unidirectional; for scores on the same dimension, A's score for B is not necessarily the same as B's score for A. For each unidirectional score, directly output your score for each dimension...

  6. [6]

    type": "image_url

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note that all the above scores are unidirectional; for scores on the same dimension, A's score for B is not necessarily the same as B's score for A. Directly output your answer. And wrap your answer with "###" before and after. If ...

  7. [7]

    ###" before and after. If your answer is A, then you should output ###A### Question: [data1] Options :[data2]}, {

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Note that all the above scores are unidirectional; for scores on the same dimension, A's score for B is not necessarily the same as B's score for A. Directly output your answer. And wrap your score with "###" before and after. If y...

  8. [8]

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Evaluation Protocol:

  9. [9]

    semantic distance

    Phase 1: Affective Calibration (V & I): Analyze the input specifically to evaluate Valence (V) and Intensity (I). You must maintain a constant "semantic distance" between -2 and +2 across all evaluations. If this is the first case, establish it as the Anchor Standard; for subsequent cases, calibrate your scores against this baseline to prevent scale drift

  10. [10]

    Select the values that align most precisely with the behavioral evidence in the scene, ensuring strict adherence to the provided definitions

    Phase 2: Structural Dimensions (P, O, T, S): Conduct a targeted re-reading to evaluate Power, Objective, Time,and Stance. Select the values that align most precisely with the behavioral evidence in the scene, ensuring strict adherence to the provided definitions

  11. [11]

    Independently determine a complete set of PIVOTS values without referring to your findings from Phase 1 and 2

    Phase 3: Independent Synthesis (Blind Review): Approach the input again with a fresh perspective. Independently determine a complete set of PIVOTS values without referring to your findings from Phase 1 and 2

  12. [12]

    ###" before and after. If your score is -2, then you should output ###-2### Transcribed Text [data1] Question [data2]

    Phase 4: Reconciliation & Validation: Compare the scores from the previous phases. If identical: Proceed to the final output. If discrepancies exist: Verbalize a root-cause analysis, re-examine the input evidence, and undergo a final determination to reach a reconciled, high-confidence score. Note that all the above scores are unidirectional; for scores o...

  13. [13]

    Refer the definition of PIVOTS Model carefully, you will see the model in the following

  14. [14]

    closed) • Physical Distance (e.g., close vs

    Pay carefully attention to those details and use those specific signals to answer the questions : Facial Expressions (e.g., mouth corners, eyebrows) • Body Posture (e.g., open vs. closed) • Physical Distance (e.g., close vs. distant) • Body Height (e.g., standing vs. sitting) • Hand Gestures/Fidgeting (e.g., finger picking, pen spinning, clutching clothes...

  15. [15]

    You need to reason their perspectives separately." Input Information:

    Remember that the PIVOTS scores from A towards B may be significantly different from those from B towards A. You need to reason their perspectives separately." Input Information:

  16. [16]

    Unsolicited Advice

    the definition of PIVOTS (a six-dimensional interpersonal relationship framework) PIVOTS - Six-Dimensional Model [definition] Example you can refer: Example: The "Unsolicited Advice" (Focus: Valence) Scenario: An experienced Senior Colleague (A) is giving detailed career advice to a Junior Employee (B) during a coffee break. A thinks they are being a ment...