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REVIEW 5 major objections 6 minor 2 cited by

A Gold Standard Dataset and Evaluation Framework for Depression Detection and Explanation in Social Media using LLMs

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper introduces a gold-standard dataset of 1,017 expert-annotated social media posts pairing depressive text spans with DSM-5 symptom labels, and uses it to score how faithfully LLM explanations of depression align with clinical…

desk verdict A genuinely new span-plus-symptom dataset idea, but the empirical evaluation is currently uninterpretable due to an internal few-shot provenance contradiction and missing reliability evidence. read the letter →

arxiv 2507.19899 v1 pith:AUXFSRED submitted 2025-07-26 cs.CL

Prajval Bolegave , Pushpak Bhattacharya This is my paper
classification cs.CL
keywords depressiondetectionsocialmediaLLMexplanationgold-standarddatasetDSM-5symptomsspanannotationevaluationframeworkfew-shotprompting
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

This paper sets out to build a gold-standard benchmark for depression detection and explanation in social media. It contributes 1,017 public posts annotated by licensed mental health professionals at two levels: spans of text that express depressive symptoms, and the corresponding one or more of 12 DSM-5 symptom categories. Because each post carries both span-level and symptom-level labels, the authors argue, the dataset supports fine-grained evaluation of LLM-generated explanations rather than only post-level classification. The companion evaluation framework scores explanations by weighted span recall, symptom F1, and LLM-judged coherence. Empirically, the paper finds that few-shot prompting does not consistently improve model faithfulness: GPT-4.1 scores best zero-shot (combined 0.6997), while Gemini 2.5 Pro reaches the top combined score (0.7171) only in the few-shot setting.

What carries the argument

The load-bearing object is the dual-layer expert annotation: each post carries highlighted depressive spans (minimal self-contained text units) and labels drawn from 12 DSM-5-derived symptom categories, stored in JSON. On top of this sits the Explanation Quality Score, defined as $0.3 \cdot \text{Span-Recall} + 0.4 \cdot \text{Symptom-F1} + 0.3 \cdot (\text{Coherence}/5)$, where Span-Recall checks whether expert spans appear in the model's explanation via exact and fuzzy text matching, Symptom-F1 matches extracted symptom mentions to the 12 categories via keyword mapping, and Coherence is a 1–5 rating by a fixed LLM judge. The weights were chosen with clinicians, giving symptom attribution the largest share. This machinery is what lets the paper turn faithful explanation into a measurable quantity and compare models.

What would settle it

Re-annotate the same 1,017 posts with a second independent team of licensed clinicians and compare the two annotation sets: if span-level agreement (e.g., token-level F1 between the two sets of highlights) or symptom-level agreement (e.g., Cohen's kappa) is low, then the span-recall and symptom-F1 scores reported here are measuring annotation noise as much as model quality.

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Extended reading notes

Core claim

The central claim is that a gold-standard resource for explainable depression detection must annotate both the evidence and the interpretation: which exact phrases in a post are depressive indicators, and which DSM-5 symptom categories they instantiate. The paper asserts that it provides the first dataset combining these two layers, with each of 1,017 social media posts manually labeled by licensed clinicians, yielding an average of 2.4 symptoms per post and coverage of all 12 categories. On this basis it proposes the Explanation Quality Score, a weighted combination of Span-Recall (0.3), Symptom-F1 (0.4), and a normalized LLM-based Coherence Score (0.3), meant to operationalize faithfulness as alignment with expert-annotated evidence plus diagnostic relevance. The empirical companion claim is that current proprietary LLMs differ substantially on these axes, and that few-shot prompting does not yield consistent gains: GPT-4.1's span recall drops from 0.618 zero-shot to 0.578 few-shot, while Gemini 2.5 Pro, unrunnable zero-shot due to formatting failures, posts the best combined score (0.7171) when given domain-adapted examples.

Load-bearing premise

The annotations by licensed mental health professionals are treated as a stable gold standard, but the paper reports no inter-annotator agreement, no number of annotators, and no adjudication procedure; if different clinicians highlight different spans or pick different symptom categories, the benchmark's model rankings lose their meaning.

Editorial extensions

If this is right

  • Few-shot prompting with domain-adapted examples can improve symptom identification and coherence while simultaneously lowering span recall, so benchmark users should report all three sub-metrics rather than only a combined score.
  • Because no model dominates, the paper's hybrid suggestion—use one model for span detection and another for symptom interpretation—becomes a concrete design option for clinical-support tools.
  • The dataset's span-level labels enable fine-tuning open-source LLMs for symptom-aware explanation generation, which the paper identifies as a direct application.
  • The evaluation framework gives a way to audit LLM explanations for clinical alignment before deployment, supporting responsible-disclosure protocols for automated mental health screening.

Reading between the lines

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

  • If the dataset is reused, the missing inter-annotator agreement figures will be the first thing to check: without them, span-recall differences between models (e.g., GPT-4.1's 0.618 vs. Claude's 0.464) may partly reflect which expert's spans a model happens to match.
  • The coherence metric appears near-saturated across models (4.93–4.99 out of 5), so the combined score is effectively dominated by span recall and symptom F1; a leaner framework could drop or reweight coherence.
  • The finding that in-context examples can narrow attention away from comprehensive span extraction likely transfers to other clinical explanation tasks, where recall of all relevant evidence matters more than polished formatting.
  • The evaluation framework generalizes naturally to fine-tuned open-source models, which the paper leaves out of scope; a natural next study would compare prompting-only against tuned models on the same 1,017 posts.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The manuscript introduces a dataset of 1,017 social media posts manually annotated by mental health professionals with depressive text spans mapped to 12 DSM-5 symptoms, and an evaluation framework that combines span recall, symptom F1, and an LLM-based coherence score into a weighted quality metric. Using this framework, the authors compare GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro under zero-shot and few-shot prompting and report that few-shot prompting does not consistently improve performance, with Gemini 2.5 Pro attaining the highest combined score (0.7171) in the few-shot setting. The paper claims this is the first dataset to combine span-level depressive indicators with DSM-5-aligned symptom labels.

Significance. The dataset addresses a real gap: existing resources provide post-level labels, while the proposed span-plus-symptom annotation enables more fine-grained evaluation of explanation faithfulness. If the annotations are reliable, the resource is reusable and the evaluation framework is a reasonable step toward clinically grounded benchmarking. Strengths include the use of licensed professionals, the explicit DSM-5 mapping, the independent reporting of component metrics, and the stated intention in Section 5.3 to keep few-shot examples disjoint from evaluation instances. However, the absence of inter-annotator agreement, the underspecified evaluation pipeline, and the internal contradiction about few-shot example provenance currently prevent the gold-standard and model-ranking claims from being accepted as stated.

major comments (5)
  1. [Section 3.1, 5.2, 5.3, and Appendix A.1.2] The paper contradicts itself about the source of the few-shot examples. Section 3.1 says they are 'curated from our expert-annotated dataset', while Section 5.2 says they are 'synthetically constructed examples' and Section 5.3 says they are 'not drawn from this dataset'. Appendix A.1.2 also states there are 'Four exemplar posts', which matches neither the one-per-symptom description in Section 5.2 nor the synthetic-construction claim, and the one example shown reads like a genuine social-media post. Because the central empirical comparison depends on few-shot test items being disjoint from the evaluation posts, this contradiction must be resolved by specifying the exact examples, their provenance, and their overlap with the 1,017 posts; otherwise the few-shot results are uninterpretable.
  2. [Section 4.2] No inter-annotator agreement is reported, and the annotation protocol does not state how many annotators labeled each post or how disagreements were adjudicated. Since every span-recall and symptom-F1 score in Table 1 is computed against these labels as ground truth, the gold-standard claim is load-bearing. The authors should report token- or span-level agreement (for example, boundary-aware F1, or Cohen's or Fleiss' kappa per symptom) and describe the adjudication procedure.
  3. [Section 3.3] The evaluation pipeline is underspecified: the exact and fuzzy matching thresholds, the predefined keyword list for the 12 symptoms, and the handling of non-conforming model outputs are not given. Moreover, the coherence judge is GPT-4.1, which is also an evaluated model, and the judge's ratings are not validated against human raters. Please provide the matching implementation, release the keyword or symptom lexicon, report a human-correlation check for the coherence rubric, and analyze sensitivity to using a different judge.
  4. [Section 6.1 and Table 1] The model comparisons are reported as point estimates from single runs, with no error bars, repetitions, or significance tests. Several differences are numerically tiny (Claude 3.7 zero-shot 0.6705 versus few-shot 0.6701, and GPT-4.1 few-shot 0.7074 versus Gemini 2.5 Pro few-shot 0.7171), and Gemini 2.5 Pro is excluded from zero-shot without quantitative support. The authors should report variance across repeated sampling, run significance tests or provide confidence intervals, and supply the excluded Gemini zero-shot outputs or a systematic criterion for the exclusion.
  5. [Section 3.2, Eq. (1)] The combined quality score's weights (0.3, 0.4, 0.3) are chosen 'in consultation with clinical experts', but no elicitation method or robustness analysis is reported. Since the headline ranking (for example, Gemini 0.7171 versus GPT-4.1 0.7074) is driven by this weighted sum, the authors should show that the ranking is stable across a reasonable range of weights, or present the component scores as the primary comparison rather than relying on a single combined number.
minor comments (6)
  1. [Throughout] The text contains many typos and missing words (for example, 'explaination', 'imput', 'standared', 'Gemi', 'Gemni', and 'wa'); a careful proofreading pass is needed.
  2. [Table 1] The table omits the Gemini 2.5 Pro zero-shot row even though the text discusses it; add an explicit 'not evaluated' row to make the design transparent.
  3. [Appendix A.1.2] The appendix says 'Four exemplar posts' but only one is shown and the rest are summarized as '[3 additional example post-explanation pairs]'; provide the full few-shot set to support reproducibility.
  4. [Section 4] The manuscript does not include a data availability statement or a link to the dataset; since the dataset is a central contribution, a release mechanism with appropriate ethical and legal safeguards is needed.
  5. [Related Work] The claim of being 'the first dataset to combine span-level depressive indicators with DSM-5-aligned symptom labels' needs a more systematic comparison with existing resources; the related-work section covers only SMHD, EmoMent, IMHI, and Mental-LLM.
  6. [Appendix C] The code snippet does not specify the exact API endpoint or all parameters used across models; consider including a complete implementation note for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the evaluation is anchored to external expert span and symptom labels, and the composite weights are expert-chosen constants rather than fitted outputs.

full rationale

The paper's central empirical claims are evaluated against expert-annotated spans and DSM-5-derived symptom categories, which are external to the LLMs being tested. Span-Recall checks whether model explanations contain expert-annotated depressive spans, and Symptom-F1 compares predicted symptoms to expert labels; neither quantity is fitted from model outputs, so the evaluation does not reduce to the model predictions by construction. The Explanation Quality Score weights (0.3/0.4/0.3) are reported as expert-consultation choices rather than fitted parameters, and the same metric values are disclosed component-wise, so the weighted aggregate is transparent rather than circular. There are no load-bearing self-citations, imported uniqueness theorems, or ansatz smuggled via citations. Two caveats are worth stating, but neither is circularity under the definitions used here: the coherence sub-score is produced by GPT-4.1, which is also one of the evaluated models, introducing a possible self-preference bias in a 30% component; and Sections 3.1, 5.2, and 5.3 contradict each other about whether few-shot examples were drawn from the evaluation dataset, which is a data-leakage and validity concern for the few-shot comparison rather than a reduction of a result to its own input. Because the core gold-standard evaluation is anchored to external human labels, the paper's main derivation chain is self-contained.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper does not introduce new physical or formal entities. Its assumptions are domain assumptions about annotation reliability, the completeness of the DSM-5 category set, and the validity of an LLM-based coherence judge. The only hand-set numeric parameters are the composite weights and the generation temperature, both of which can shift the reported rankings.

free parameters (2)
  • Composite metric weights (w1, w2, w3) = 0.3, 0.4, 0.3
    Chosen in consultation with clinical experts (Section 3.2). These weights determine the combined rating and final model ranking, but no sensitivity analysis is presented.
  • Generation temperature = 0.7
    Set by the authors for all API calls (Section 5.4). It affects output variability and therefore the span and symptom metrics, but no ablation is reported.
assumptions (3)
  • domain assumption The 12 DSM-5-derived symptom categories fully cover the depressive content in the annotated posts.
    Symptom-F1 and the few-shot examples are built around these categories (Section 4.2). If a post expresses a depressive symptom not in the list, the measured alignment will be incomplete.
  • domain assumption Expert annotations are reliable enough to be treated as a gold standard.
    The paper labels the dataset gold standard but reports no inter-annotator agreement (Section 4.2). This assumption is load-bearing because all evaluation metrics use these labels as ground truth.
  • domain assumption GPT-4.1 can score coherence with a fixed prompt without systematic bias toward its own outputs.
    The coherence score is produced by GPT-4.1 (Section 3.2), which is also one of the evaluated models. No human-rater calibration is provided.

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Cite this review

Pith. "Pith review of A Gold Standard Dataset and Evaluation Framework for Depression Detection and Explanation in Social Media using LLMs." pith.science (2026). https://pith.science/paper/AUXFSRED

@misc{pith2026250719899,
  author       = {Pith},
  title        = {Pith review of: A Gold Standard Dataset and Evaluation Framework for Depression Detection and Explanation in Social Media using LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AUXFSRED}},
  note         = {Machine review of arXiv:2507.19899}
}
read the original abstract

Early detection of depression from online social media posts holds promise for providing timely mental health interventions. In this work, we present a high-quality, expert-annotated dataset of 1,017 social media posts labeled with depressive spans and mapped to 12 depression symptom categories. Unlike prior datasets that primarily offer coarse post-level labels \cite{cohan-etal-2018-smhd}, our dataset enables fine-grained evaluation of both model predictions and generated explanations. We develop an evaluation framework that leverages this clinically grounded dataset to assess the faithfulness and quality of natural language explanations generated by large language models (LLMs). Through carefully designed prompting strategies, including zero-shot and few-shot approaches with domain-adapted examples, we evaluate state-of-the-art proprietary LLMs including GPT-4.1, Gemini 2.5 Pro, and Claude 3.7 Sonnet. Our comprehensive empirical analysis reveals significant differences in how these models perform on clinical explanation tasks, with zero-shot and few-shot prompting. Our findings underscore the value of human expertise in guiding LLM behavior and offer a step toward safer, more transparent AI systems for psychological well-being.

Figures

Figures reproduced from arXiv: 2507.19899 by the authors.

Figure 1
Figure 1. Annotation interface used by certified annotators to highlight depressive spans and select DSM-5-aligned symptom categories. { "14": { "post": [ "i hate myself so much for being ................." ], "words": [ "i hate myself so much", "i just feel so bitter", "they don’t deserve me", "i’m garbage" ], "symptoms": [ 0, // Depressed Mood 5 // Feelings of Worthlessness or Excessive Guilt ] } } Listing 1: Example of ann… view at source ↗

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Forward citations

Cited by 2 Pith papers

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

  1. WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A dense mixture-of-experts model guided by training-only weak evidence-layout priors outperforms single-detector baselines on Chinese and English user-level depression detection.

  2. Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

    cs.AI 2026-07 conditional novelty 5.0 of 10

    An LLM-assisted, expert-verified annotation pipeline produces DSM-5-TR-aligned depression labels with evidence traces, showing high agreement and reduced effort in a 10-case pilot, while its self-evolving memory remai...

Reference graph

Works this paper leans on

31 extracted references · 29 canonical work pages · cited by 2 Pith papers

  1. [1]

    Detailed definitions with examples for each symptom category

    work page

  2. [2]

    Explanation: This post indicates several de- pressive symptoms: - **Anhedonia**:

    Four exemplar posts with expert-crafted ex- planations The few-shot prompt structure was: You are a mental health assistant. Determine whether the following post shows signs of depression. If it does, generate explaination by considering phrases from post indicative of depression and depression symptoms present in post from the following categories of sta...

    work page

  3. [3]

    there is something very wrong with my brain

    **Feelings of Worthlessness or Excessive Guilt:** - "there is something very wrong with my brain" This phrase reflects self-blame and a sense of personal defectiveness

    work page

  4. [4]

    how am i ever going to get work done

    **Hopelessness About the Future:** - "how am i ever going to get work done" - "how do you keep on living like this forever" These statements suggest hopelessness and despair about improvement or the future

    work page

  5. [5]

    sometime i think i wa not meant for this world

    **Suicidal Thoughts or Self-Harm:** - "sometime i think i wa not meant for this world" This phrase indicates passive suicidal ideation or thoughts of not wanting to exist

    work page

  6. [6]

    I feel empty inside,

    Depressed Mood (Sadness, Hopelessness, Empti- ness) Persistent feelings of sadness, despair, or emotional numbness. The individual may feel overwhelmed by negative emotions, leading to thoughts of hopeless- ness about the future. Example Phrases: "I feel empty inside," "Nothing makes sense anymore," "I just want to disappear."

    work page

  7. [7]

    I don’t enjoy anything anymore,

    Anhedonia (Loss of Interest or Pleasure) A marked reduction in interest or enjoyment in activ- ities that were once pleasurable, including hobbies, social interactions, or daily tasks. This can lead to withdrawal from personal and professional commit- ments. Example Phrases: "I don’t enjoy anything anymore," "Even my favorite things feel pointless." [defi...

    work page

  8. [8]

    i can barely function at all

    **Fatigue or Loss of Energy:** - "i can barely function at all" - "i take everything just to keep myself alive" These phrases suggest significant fatigue and a struggle to maintain basic functioning

    work page

Show all 31 references
  1. [9]

    i go day without eating

    **Changes in Appetite (Weight Loss or Gain):** - "i go day without eating" Indicates a loss of appetite and possibly significant weight loss

  2. [13]

    i used to be highly functional before but it now i can barely function at all

    **Anhedonia (Loss of Interest or Pleasure):** - "i used to be highly functional before but it now i can barely function at all" Implies a loss of previous abilities and interests. **Summary:** The post contains clear expressions of fatigue, appetite disturbance, hopelessness, ...

  3. [14]

    I used to be highly functional before but now I can barely function at all

    "I used to be highly functional before but now I can barely function at all"

  4. [15]

    I take everything just to keep myself alive

    "I take everything just to keep myself alive"

  5. [16]

    I go day without eating

    "I go day without eating"

  6. [17]

    Sometimes I think I was not meant for this world

    "Sometimes I think I was not meant for this world"

  7. [18]

    There is something very wrong with my brain

    "There is something very wrong with my brain"

  8. [19]

    How am I supposed to fix it

    "How am I supposed to fix it"

  9. [20]

    How do you keep on living like this forever

    "How do you keep on living like this forever" ## Depression Symptoms Present

  10. [21]

    highly functional before

    **Fatigue or Loss of Energy**: The statement about being "highly functional before" but now "can barely function at all" indicates severe energy depletion and difficulty performing daily tasks

  11. [22]

    I go day without eating

    **Changes in Appetite**: "I go day without eating" directly references disturbed eating patterns, suggesting significant appetite changes

  12. [23]

    I was not meant for this world

    **Feelings of Worthlessness**: The belief that "I was not meant for this world" and that something is "very wrong with my brain" suggests deep feelings of being fundamentally flawed

  13. [24]

    how do you keep on living like this forever

    **Hopelessness About the Future**: The question "how do you keep on living like this forever" demonstrates a bleak outlook and inability to see improvement in their condition

  14. [25]

    how am I ever going to get work done

    **Difficulty Functioning**: The concern about "how am I ever going to get work done" shows impairment in daily responsibilities

  15. [26]

    not meant for this world

    **Possible Suicidal Ideation**: While not explicitly stating suicidal intent, phrases like "not meant for this world" and questioning how to "keep on living like this forever" suggest passive suicidal ideation or existential despair. The post presents multiple core symptoms of...

  16. [27]

    However, few-shot prompting gen- erally led to more comprehensive and symptomati- cally accurate explanations

    Symptom Coverage and Specificity: All models identified core depressive symptoms such as appetite changes, fatigue, hopelessness, and sui- cidal ideation. However, few-shot prompting gen- erally led to more comprehensive and symptomati- cally accurate explanations. Models unde...

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    GPT-4.1 and Gemini 2.5 Pro, under few-shot prompting, provided similarly well-organized outputs but with deeper clinical reasoning and contextual interpreta- tion

    Language and Coherence: Claude 3.7 Son- net produced notably structured and readable expla- nations even under zero-shot prompting, organizing symptoms into clearly labeled sections. GPT-4.1 and Gemini 2.5 Pro, under few-shot prompting, provided similarly well-organized output...

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    They not only matched key phrases but also interpreted their clin- ical implications with high fidelity

    Clinical Alignment with Expert Annota- tions: Few-shot GPT-4.1 and Gemini 2.5 Pro ex- hibited the highest alignment with expert-annotated spans and symptom categories. They not only matched key phrases but also interpreted their clin- ical implications with high fidelity. In c...

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    Zero-shot explanations tended to be more checklist-like or declarative, lacking explana- tory depth

    Interpretability and Justification Style: Few-shot prompted explanations were more re- flective and interpretive, often providing psycho- logical context (e.g., linking functional decline to anhedonia or existential despair to passive suici- dal ideation). Zero-shot explanatio...

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    not meant for this world

    Sensitivity to Passive Suicidal Ideation: Most models, especially under few-shot prompting, identified subtle cues such as "not meant for this world" as indicative of suicidal ideation. Claude and Gemini were particularly careful in framing such phrases within a clinical safet...

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    In Proceedings of the CHI Workshop on Computational Linguistics and Clinical Psychology

    Detecting depression and mental illness on social media: an integrative review. In Proceedings of the CHI Workshop on Computational Linguistics and Clinical Psychology. Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Explain your- self! leveraging...

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    In Proceedings of the 29th International Conference on Computational Linguistics , pages 6991–7001, Gyeongju, Republic of Korea

    EmoMent: An emotion annotated mental health corpus from two South Asian countries. In Proceedings of the 29th International Conference on Computational Linguistics , pages 6991–7001, Gyeongju, Republic of Korea. International Com- mittee on Computational Linguistics. Rafael A ...

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    medRxiv preprint medRxiv:2024.12.29.24319755

    Mentalqlm: A lightweight large language model for mental healthcare based on instruction tuning and dual lora modules. medRxiv preprint medRxiv:2024.12.29.24319755. Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel, Hong Yu, James Hendler, Marzyeh Ghas- semi, Anind K Dey, ...

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Reviewed August 6, 2026 · model on record in the stance chip above.