The paper investigates how retrieval-augmented generation (RAG) affects single‑turn mental‑health question answering. It finds that always using retrieval improves response specificity but can lower overall quality and increase safety‑related failures. A lightweight selective retrieval policy, guided by psychoeducational, coping, and safety needs, better balances these trade‑offs by activating retrieval only when necessary.
arXiv:2607. 06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance.
By Felix Feldman, Joshua Harris, Timothy Laurence, Leo Loman, Ollie Higgins, Fan Grayson, Poonam Soma, Bethany Pace-Bonello, Michael Borowitz, Toby Nonnenmacher
arXiv:2607. 24817v1 Announce Type: cross Abstract: Digital mental health interventions (DMHIs) offer scalable support, but ensuring they accurately detect users' intent during volatile situations can be challenging.
By Anand Gupta, Akshat Surolia, Shubham Mishra, Shakil Imtiaz, Chaitali Sinha
arXiv:2606. 28337v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are often evaluated using final answer accuracy, even though their failures can originate from preprocessing, retrieval, context packing, or generation.
By Bharath Simha Reddy Muthyam
arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.
By Chandan Kumar Sah, Xiaoli Lian, Li Zhang
Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.
The paper introduces matched trajectory replay, a protocol that fixes answer states, evidence points, budgets, and action costs to evaluate how confidence signals influence agent actions. Using this method, the authors compare raw verbalized confidence with post‑hoc isotonic calibration across six model‑dataset pairs, finding that calibration can significantly improve accuracy of committed answers but may reduce coverage and increase retrieval usage. The study concludes that calibration helps interpret commitment risk but does not predict the benefit of additional retrieval, indicating the need for separate value‑of‑information estimates.
By Prateek Chhikara
arXiv:2605. 18937v2 Announce Type: replace Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights.
By Rory Sayres, Kejia Chen, Ayush Jain, Matthew Thompson, Jonathan Richina, Xiang Yin, Jimmy Hu, Fan Zhang, Bob Lou, Mike Sanchez, Ines Mezerreg, Meredith Schreier, Hamsa Subramaniam, I-Ching Lee, Yugang Jia, Daniel Mcduff, Yossi Matias, Avinatan Hassidim, Dale Webster, Yun Liu, Jackie Barr, Quang Duong
The paper introduces the concept of LLM‑specific utility, defining it as the performance gain a target large language model (LLM) achieves when provided with a passage compared to answering without evidence. A benchmark of utilitarian passages is built for four LLMs (Qwen3‑8B/14B/32B and Llama 3.1‑8B) across three QA datasets, revealing that each model benefits most from its own tailored evidence and that evidence optimized for other models is consistently suboptimal. The authors also create SpecUBench, a benchmark for LLM‑specific utility judgment, and show that current utility‑aware retrieval methods largely capture model‑agnostic usefulness, struggling to estimate LLM‑specific utility.
"whyItMatters":"The study demonstrates that retrieval‑augmented generation must consider model‑specific evidence selection to truly improve LLM performance, highlighting a gap in existing utility‑aware methods."
By Hengran Zhang, Keping Bi, Jiafeng Guo, Jiaming Zhang, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng
ConfRAG introduces a confidence-guided approach to reduce hallucinations in large language models and selectively trigger Retrieval-Augmented Generation (RAG) only when the model is uncertain. The ConfQA fine‑tuning strategy trains the model to answer correctly or respond with "I am unsure," achieving a drop in hallucination rates from 20‑40% to below 5% across factuality benchmarks. Building on ConfQA, ConfRAG limits external retrievals by more than 30% while maintaining over 95% accuracy in ideal scenarios.
By Yin Huang, Yifan Ethan Xu, Kai Sun, Vera Yan, Alicia Sun, Haidar Khan, Jimmy Nguyen, Jingxiang Chen, Mohammad Kachuee, Zhaojiang Lin, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
arXiv:2607. 10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.
By Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang, Andrew Lan
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard ones, and gives no signal for when a generated answer can be trusted.