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M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
Arkadeep Acharya, Akash Ghosh, Pradeepika Verma, Kitsuchart Pasupa, Sriparna Saha, Priti Singh
https://arxiv.org/abs/2510.06888

M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medica…

Glitch analysis and asteroseismic modelling of subgiant $\mu$ Herculis: confirming and interpreting the $\Gamma_1$ peak as the helium glitch
Advik Gupta, Kuldeep Verma, Hans Kjeldsen, Frank Grundahl, J{\o}rgen Christensen-Dalsgaard, Mark L. Winther, Jakob L. R{\o}rsted, Amalie Stokholm, V\'ictor Aguirre B{\o}rsen-Koch
https://arxiv.org…

Glitch analysis and asteroseismic modelling of subgiant $μ$ Herculis: confirming and interpreting the $Γ_1$ peak as the helium glitch
The measurements of pressure-mode and mixed-mode oscillation frequencies in subgiant stars offer a unique opportunity to probe their internal structures -- from the surface to the deep interior -- and to precisely determine their global properties. We have conducted a detailed asteroseismic analysis of the benchmark subgiant $μ$ Herculis using eight seasons of radial velocity observations from the SONG telescope, and have determined its mass, radius, age, and surface helium abundance to be $1.…

Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation
Rishikesh Devanathan, Varun Nathan, Ayush Kumar
https://arxiv.org/abs/2508.18210 …

Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation
Synthetic transcript generation is critical in contact center domains, where privacy and data scarcity limit model training and evaluation. Unlike prior synthetic dialogue generation work on open-domain or medical dialogues, contact center conversations are goal-oriented, role-asymmetric, and behaviorally complex, featuring disfluencies, ASR noise, and compliance-driven agent actions. In deployments where transcripts are unavailable, standard pipelines still yield derived call attributes such a…

DRES: Benchmarking LLMs for Disfluency Removal
Maria Teleki, Sai Janjur, Haoran Liu, Oliver Grabner, Ketan Verma, Thomas Docog, Xiangjue Dong, Lingfeng Shi, Cong Wang, Stephanie Birkelbach, Jason Kim, Yin Zhang, James Caverlee
https://arxiv.org/abs/2509.20321

DRES: Benchmarking LLMs for Disfluency Removal
Disfluencies -- such as "um," "uh," interjections, parentheticals, and edited statements -- remain a persistent challenge for speech-driven systems, degrading accuracy in command interpretation, summarization, and conversational agents. We introduce DRES (Disfluency Removal Evaluation Suite), a controlled text-level benchmark that establishes a reproducible semantic upper bound for this task. DRES builds on human-annotated Switchboard transcripts, isolating disfluency removal from ASR errors an…

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