# jaccard Published articles for jaccard. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts DevFeed: [Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts]() Original publisher: [Read original article]() Author: Marcin Moskalewicz, Anna Sterna, Karolina Dro\\.zd\\.z, Kacper Dudzic, Marek Pokropski, Paula Flores Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Artificial Intelligence](), [Large Language Model](), [LLM evaluation / benchmarking](), [Embeddings](), [human review](), [human feedback]() Tags: [ai](), [ai-generated-content](), [analysis](), [anthropic](), [bias](), [claude](), [comparison](), [cs-ai](), [cs-cl](), [embeddings](), [evaluation](), [evaluations](), [experts](), [gemini](), [google](), [gpt](), [grounding](), [interviews](), [jaccard](), [language-models](), [large-language-models](), [llm]() ### AI overview The study evaluates GPT, Gemini, and Claude as aids to qualitative analysis of more than 150,000 words of life-story interviews with inpatients with Borderline Personality Disorder. Expert review, semantic comparisons, and evaluations by 115 non-experts found that AI themes overlapped with human interpretations by a variable 0-58%; all three models also identified themes omitted by the human researchers. Non-experts could not reliably distinguish AI-written from human-written themes. In blinded expert assessment against the raw data, Gemini 2.5 Pro was rated indistinguishable from human work for content validity and had the closest semantic style to humans. These abstract-level findings describe potential support for analysis, not proof that model output can replace clinical expertise. ### Source excerpt arXiv:2508.19008v4 Announce Type: replace Abstract: Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis. The models' interpretative potential was evaluated using a mixed-methods approach. Study A involved blinded and non-blinded judges in phenomenology and clinical psychology. The experts assessed the validity of AI-generated content using semantic congruence, Jaccard coefficients, and multidimensional validity ratings, including credibility, coherence, the substantiveness of results, and grounding in qualitative data. In Study B, neural methods were used to embed human- and model-generated theme descriptions in a multidimensional vector space. This approach provided an objectified computational measure of the difference between human and model semantics and linguistic style. In Study C, complementary non-expert evaluations were conducted (N=115) to examine the influence of thematic verbosity on the perception of human authorship and content validity. Overall, the results of AI analysis showed a highly variable overlap (0-58%) with the human interpretation, while all models identified themes originally omitted by human researchers, proving their capacity to mitigate human bias. At the thematic level, external evaluators were unable to reliably distinguish human-authored themes from those generated by AI. In terms of content validity assessed against raw data by high-level experts in the blinded mode, the performance of Gemini 2.5 Pro was indistinguishable from that of humans. The comparison of semantic vector embeddings showed that its style was also the closest to humans. ## The Optimal Sampler for Diverse TSP Solutions: Characterization, Limits, and Learning DevFeed: [The Optimal Sampler for Diverse TSP Solutions: Characterization, Limits, and Learning]() Original publisher: [Read original article]() Author: Ssu-Yuan Lo, Ching-Kai Wang, Kuan-Lun Chen, Hao-Tsung Yang Published: 2026-10-22T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Combinatorial optimization](), [Machine learning](), [computational complexity]() Tags: [approximation](), [cs-cg](), [cs-ds](), [cs-lg](), [dispersion](), [distribution](), [fault-tolerance](), [guarantees](), [hybrid](), [jaccard](), [learning](), [limits](), [logistics](), [robotics](), [sampling](), [selection](), [theoretical](), [tsp]() ### AI overview The paper's hybrid approach to the Diverse Traveling Salesman Problem samples a pool of tours, then selects a diverse set whose tour lengths stay within a prescribed multiple of the optimum. Since improving on a factor-two approximation for the selection stage is NP-hard, the authors focus on the sampling distribution. They give guarantees for either including an optimal set in the pool or obtaining at least k mutually distant tours; the latter uses linearly many samples in k but compares against the best βk tours, for β > 1. They also show that sampling spanning trees and converting them to tours does not reduce pool quality in principle. Experiments report that both objectives are learnable and that one model, without fine-tuning, outperforms baselines on in-distribution and out-of-distribution instances. ### Source excerpt arXiv:2610.28994v1 Announce Type: cross Abstract: We address the Diverse Traveling Salesman Problem (D-TSP) that seeks a set of $k$ distinct TSP tours. Each selected tour must not exceed a prescribed multiple of the optimal tour length, while the maximum pairwise Jaccard similarity within the selected set is minimized. This formulation is important for applications requiring both solution quality and fault tolerance, such as logistics planning, robotics, and strategic patrolling. We propose a novel hybrid framework that decomposes D-TSP into a sampling stage and a selection stage. The selection stage is a max-min dispersion problem, for which any approximation better than a factor of two is NP-hard; we therefore focus on the sampling stage. Our contributions are both theoretical and empirical. Theoretically, we give two end-to-end guarantees for the optimal sampling distribution, from two perspectives: including an optimal set in the sampled pool, or having at least $k$ mutually distant tours in the pool. The second reduces the number of samples from exponential to linear in $k$, at the cost of weakening the benchmark from the best $k$ tours to the best $\beta k$ for some $\beta > 1$. For practical use, we prove that a pipeline that samples spanning trees and then converts to tours does not hurt the quality of the pool in principle. Empirically, both objectives are learnable, and our experiments not only validate the analysis but also outperform the baselines on both in-distribution and out-of-distribution instances, by a single trained model with no fine-tuning. ## Conditional Average Treatment Effect Distillation for Hypothetical State-Intervention Targeting Under Budget Constraints DevFeed: [Conditional Average Treatment Effect Distillation for Hypothetical State-Intervention Targeting Under Budget Constraints]() Original publisher: [Read original article]() Author: Vasundhara Acharya, B\\"ulent Yener Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Post-training optimization]() Tags: [bayesian](), [bootstrap](), [capacity](), [cs-lg](), [distillation](), [effect](), [jaccard](), [machine-learning](), [models](), [policies](), [policy](), [public-health](), [robust](), [rules](), [stable](), [state](), [student](), [study](), [uncertainty](), [validation]() ### AI overview The study tests whether simpler models can reproduce budgeted targeting decisions from flexible causal-effect models when no historical decision policy is logged. Causal Forest and Bayesian Causal Forest teachers estimate treatment effects; decision tree and random forest students approximate them. At a 70% capacity cap, students came within 0.011 absolute utility of their teachers across PIMA obesity and glucose and NHANES smoking-history experiments, with allocation Jaccard similarities of 0.738-0.937. K-means subgroup and matched-size causal-tree policies were also competitive, and the highest point estimate varied by application. Paired comparisons were nonsignificant after Holm adjustment; agreement near the budget boundary was less stable for some teacher-student pairs. The observational estimates rely on causal-identification assumptions and need longitudinal or prospective validation. ### Source excerpt arXiv:2610.25627v1 Announce Type: new Abstract: Personalized public-health targeting must capture heterogeneous intervention benefits while remaining interpretable under resource constraints. Flexible causal-effect models can be hard to operationalize, while coarse subgroup policies may lose individualized effect information. We study budget-constrained hypothetical state interventions in observational data without a logged historical decision policy. For obesity, elevated glucose, and lifetime smoking-history states, causal-structure-informed pretreatment covariates are prespecified, with capacity capped at 70% of those observed in the adverse state. We evaluate causal distillation where Causal Forest and Bayesian Causal Forest teachers estimate conditional average treatment effects (CATEs), which decision tree and random forest students approximate. We compare individualized policies with unsupervised K-means subgroup policies and supervised causal-tree policies with leaf counts matched to K-means cluster counts. Observational policies are assessed on held-out data using a doubly robust estimator, bootstrap uncertainty, and decision-focused teacher-student fidelity measures. Across PIMA obesity and glucose and NHANES smoking-history experiments, each distilled student was within 0.011 absolute utility of its teacher, with allocation Jaccard similarities of 0.738-0.937. K-means and causal-tree policies were also competitive, with the highest point-estimate policy varying by application. Prespecified paired comparisons were nonsignificant after Holm adjustment, while near-budget-boundary agreement was less stable than global fidelity for some teacher-student pairs. Findings support causal distillation for translating flexible effect models into simpler budgeted targeting rules, although observational estimates depend on causal-identification assumptions and require longitudinal or prospective validation.