{"id":"6c4f8233-1ba0-4d07-b4ac-b9961cf31be1","arxiv_id":"2606.21930","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"MindTailor constructs case formulations from seekers' post histories and uses multi-agent collaborative refinement to generate personalized emotional support, outperforming baselines on empathy and preference in evaluations on the new ReddiSupp dataset.","lead":"The paper presents MindTailor, a system that builds a case formulation from a user's past social media posts and refines emotional support responses through iterative critique by multiple AI counselor agents using different strategies. A smart generalist might read it because mental health support via AI is expanding rapidly and history-aware personalization could address a clear limitation in current chatbots.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Evaluation validity may be sensitive to prompt design or rater expectations rather than measuring true support quality","rationale":"The reader's weakest_assumption correctly isolates the evaluation validity issue as the load-bearing point for an empirical outperformance claim. No more granular technical flaw (e.g., dataset construction details or algorithmic inconsistency) can be diagnosed from the supplied abstract, and the full text would be required to check for additional issues such as data leakage or implementation specifics. The concern is therefore unchanged from the reader's assessment.","tokens_in":1686,"tokens_out":349,"duration_ms":12889,"concrete_test":"Re-run the LLM-as-Judge evaluation on the same response pairs using a neutral prompt template that omits any reference to 'case formulation', 'post history', or 'MindTailor' and uses only generic quality criteria; if the preference margin for MindTailor shrinks below statistical significance or reverses, the headline claim is sensitive to evaluation design.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that MindTailor outperforms baselines on empathy, personalization, understanding, and overall preference—rests entirely on three subjective evaluations (LLM-as-Judge, expert human raters, seeker user study). LLM judges are known to be sensitive to prompt phrasing and can favor responses that match certain stylistic cues (e.g., explicit history references). Expert ratings and seeker preferences can similarly reflect demand characteristics or expectations if raters are not fully blinded or if prompts highlight the history-grounded case formulation. The abstract provides no information on prompt templates, blinding procedures, inter-rater reliability, or controls for these artifacts, making it possible that reported gains are evaluation artifacts rather than genuine improvements.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes MindTailor, a framework for generating personalized emotional support responses on social media by constructing case formulations from seekers' post histories and iteratively refining outputs via collaborative critique among counselor agents using distinct strategies. It introduces the ReddiSupp dataset (798 Reddit posts with prior histories) and claims that MindTailor outperforms baselines in empathy, personalization, understanding, and overall preference, as shown by LLM-as-Judge evaluation, expert human evaluation, and a seeker user study.","tokens_in":1815,"tokens_out":448,"duration_ms":17584,"significance":"If the central performance claims hold under rigorous validation, the work would advance history-grounded personalization in emotional support systems, addressing a gap beyond current-state features like emotional state or persona. The ReddiSupp dataset construction is a clear strength that enables future research on this task. However, the significance is tempered by the reliance on subjective evaluations whose validity is not yet demonstrated.","major_comments":[{"comment":"Abstract: The central claim of outperformance across empathy, personalization, understanding, and preference rests on three subjective evaluations but provides no information on statistical tests, baseline implementation details, inter-rater agreement, effect sizes, blinding procedures, or prompt templates. This is load-bearing because LLM-as-Judge and human ratings in subjective domains are known to be sensitive to prompt phrasing and demand characteristics; without these controls the reported gains cannot be distinguished from evaluation artifacts.","section":"Abstract"},{"comment":"Evaluation sections (implied by abstract description of LLM-as-Judge, expert, and user study): No evidence is presented that the history component causally drives gains (e.g., via ablation removing history grounding) or that raters were blinded to system identity, making it impossible to rule out that preferences reflect stylistic cues rather than support quality.","section":"Evaluation Methodology"}],"minor_comments":[{"comment":"The abstract and introduction would benefit from explicit comparison to prior work on history-aware personalization to clarify the precise novelty of the case-formulation step.","section":"Introduction"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the thoughtful and constructive feedback. We address each major comment below and commit to revisions that strengthen the reporting and validation of our evaluation results.","responses":[{"response":"We agree that additional methodological details are required to support the central claims. The full manuscript describes baseline implementations in Section 4, but we acknowledge the lack of explicit statistical tests, inter-rater agreement, effect sizes, blinding procedures, and prompt templates. In the revised manuscript we will add: (1) results of statistical significance tests (e.g., paired t-tests or Wilcoxon tests with p-values) comparing MindTailor to baselines; (2) inter-rater agreement metrics such as Cohen’s kappa for the expert evaluation; (3) effect sizes (Cohen’s d); (4) a clear statement on blinding (raters received anonymized responses without system labels); and (5) the exact prompt templates used for LLM-as-Judge. These additions will be placed in a new subsection of the evaluation methodology.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The central claim of outperformance across empathy, personalization, understanding, and preference rests on three subjective evaluations but provides no information on statistical tests, baseline implementation details, inter-rater agreement, effect sizes, blinding procedures, or prompt templates. This is load-bearing because LLM-as-Judge and human ratings in subjective domains are known to be sensitive to prompt phrasing and demand characteristics; without these controls the reported gains cannot be distinguished from evaluation artifacts."},{"response":"We accept that an explicit ablation isolating the contribution of post-history grounding is needed to establish causality. We will add this ablation (MindTailor without case formulation from history) to the experimental results and report the corresponding drops in empathy, personalization, and preference scores. On blinding, the expert evaluation and seeker user study were conducted with responses presented without system identifiers; however, we will expand the methodology section to document the exact blinding protocol, any residual risks, and steps taken to mitigate demand characteristics. These changes will directly address the concern that observed preferences could stem from stylistic artifacts rather than support quality.","revision_made":"yes","referee_comment":"[Evaluation Methodology] Evaluation sections (implied by abstract description of LLM-as-Judge, expert, and user study): No evidence is presented that the history component causally drives gains (e.g., via ablation removing history grounding) or that raters were blinded to system identity, making it impossible to rule out that preferences reflect stylistic cues rather than support quality."}],"tokens_in":1341,"tokens_out":546,"duration_ms":26996,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"MindTailor is a system that uses a seeker's past Reddit posts to build a case formulation and then has multiple agents critique the response. The main new thing is the ReddiSupp dataset of 798 posts with histories, which lets them test history-aware support.\n\nThey do a decent job of motivating why current state alone is not enough and why history matters for personalization. The collaborative refinement step is a concrete way to bring in different therapeutic angles without just prompting one model.\n\nWhere it gets thin is the evaluation section. They claim better empathy, personalization, understanding, and overall preference from three methods: LLM judge, expert raters, and a seeker study. But the abstract does not mention any statistical tests, effect sizes, or inter-rater reliability numbers. LLM-as-judge is known to be unreliable for subjective tasks like this, and there's no mention of whether raters saw the history or were blinded to which system produced the response. If the improvements disappear under stricter controls, the central claim does not hold.\n\nThe paper is aimed at researchers building emotional support systems, especially those who want to move beyond single-turn or current-state personalization. Someone looking for a new dataset or an example of multi-agent critique in this area could find it useful.\n\nIt is worth sending to peer review. The dataset construction and the overall framing are solid enough to merit feedback, even though the evaluation needs substantial strengthening before publication.","headline":"MindTailor adds a new Reddit dataset and history-based case formulation to emotional support work, but the evaluation claims rest on unverified subjective judgments without controls.","tokens_in":2334,"tokens_out":365,"would_cite":false,"duration_ms":20472,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"MindTailor creates personalized emotional support by formulating cases from seekers' post histories and refining them collaboratively with counselor agents.","keywords":["emotional support","case formulation","post history","multi-agent collaboration","personalization","LLM evaluation","Reddit dataset","mental health AI"],"falsifier":"A follow-up experiment where real seekers receive ongoing support from MindTailor versus baselines and show no measurable difference in reported emotional improvement or satisfaction.","tokens_in":2569,"feed_emoji":"🧠","tokens_out":525,"duration_ms":25148,"temperature":0.7,"pith_summary":"The paper proposes that emotional support responses can be made more effective by first summarizing a seeker's past experiences into a case formulation and then having multiple AI counselor agents critique and refine the response using different therapeutic strategies. This addresses the limitation of prior methods that only consider current posts or states, ignoring how history shapes emotional needs. If correct, this would mean AI systems on social media could provide more tailored help by drawing on full user timelines rather than isolated messages. The authors support this with a new dataset of Reddit posts and evaluations showing gains in empathy and user preference.","feed_headline":"Post history case formulation improves AI emotional support","feed_subtitle":"MindTailor draws on seekers' prior posts and refines via counselor agents for higher empathy and preference.","key_machinery":"The case formulation from post history combined with collaborative refinement by multiple counselor agents using distinct strategies.","core_discovery":"MindTailor generates personalized emotional support responses by constructing a case formulation from the seeker's post history and iteratively refining responses through collaborative critique among counselor agents grounded in distinct counseling strategies. This approach, evaluated on the ReddiSupp dataset, outperforms baselines in LLM-as-a-Judge, expert human, and user studies for empathy, personalization, understanding, and overall preference.","pith_inferences":["If past posts prove central to quality, support systems may need to retain and process longer user timelines by default.","The collaborative refinement step could transfer to other dialogue tasks that benefit from multiple strategy perspectives, such as education or advice bots.","Real deployment would require testing whether the gains hold when seekers know responses come from an AI system rather than in blinded studies."],"forward_implications":["Responses show improved empathy, personalization, and understanding.","The method achieves the highest overall preference in user studies with seekers.","It outperforms baselines across LLM judge, expert, and human evaluations.","The ReddiSupp dataset of 798 Reddit posts with prior histories enables further history-aware support research."],"fun_headline_variants":["Seeker post history informs MindTailor case formulation","Counselor agents collaborate on response refinement","ReddiSupp enables history-grounded emotional support studies","Iterative critique among agents personalizes AI counseling"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The three evaluation methods—LLM-as-a-judge, expert human raters, and seeker user studies—provide unbiased and valid measures of emotional support quality.","fun_headline_variants_meta":{"raw":{"variants":["Seeker post history informs MindTailor case formulation","Counselor agents collaborate on response refinement","ReddiSupp enables history-grounded emotional support studies","Iterative critique among agents personalizes AI counseling"]},"model":"grok-4.3","cost_usd":0.004967,"raw_usage":{"total_tokens":2396,"prompt_tokens":603,"num_sources_used":0,"completion_tokens":57,"cost_in_usd_ticks":49674500,"prompt_tokens_details":{"text_tokens":603,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1736,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":603,"tokens_out":57,"duration_ms":14634,"temperature":1.0,"reasoning_tokens":1736,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T12:13:48.246508+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A follow-up experiment where real seekers receive ongoing support from MindTailor versus baselines and show no measurable difference in reported emotional improvement or satisfaction.","supporting_citations":[],"review_version":1}