The paper introduces a cause-aware error recovery framework for cascaded Automatic Speech Recognition – Large Language Model (ASR‑LLM) pipelines in Spoken Dialogue Systems. It replaces simple ASR confidence filtering with precision‑focused detectors that use deep ASR latent representations to classify token‑level errors into perception, comprehension, and deletion failures. This fine‑grained diagnosis enables the LLM to execute targeted, multi‑turn clarification strategies, leading to a more than two‑fold increase in recall on domain‑shift errors and significant reductions in word error rate and downstream task errors across varied accents, distortions, and domains.
By Yizhou Peng, Ziyang Ma, Changsong Liu, Yi-Wen Chao, Xie Chen, Eng Siong Chng
arXiv:2606. 17904v1 Announce Type: new Abstract: Language models increasingly serve as advisory systems in maintenance operations.
By Guillermo Gil de Avalle, Laura Maruster, Shaina Raza, Christos Emmanouilidis
IRWOZ 2.0 is a refined dialogue dataset for industrial human‑robot interaction, expanding to 390 dialogues across four domains—Assembly, Delivery, Position, and Relocation. The dataset was improved using large language models (Mistral and Claude‑3.5) for generation and quality refinement, including manual corrections and automated typo removal. Benchmark tests show a substantial boost in dialogue state‑tracking performance, with GPT‑2’s BLEU‑4 score rising from 0.1651 to 0.5604 compared to the original IRWOZ.
By Chen Li, Dimitrios Chrysostomou
arXiv:2609.35812v1 Announce Type: new
Abstract: In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. E...
By Vaishnav Negi, Lev Sorokin, Soroosh Tayebi Arasteh, Andrea Stocco
arXiv:2608.27750v1 Announce Type: new
Abstract: The hidden states of large language models (LLMs) are known to capture rich information relating to model knowledge and behavior that can be hard to ex...
By Eric Yeats, Brendan Kennedy, Loc Truong, John Buckheit, Jung Lee, Jesse Friedbaum, John Emanuello, Henry Kvinge
Evaluation of Computer-Use Agents (CUAs) is often limited to the final deliverables they create (at the end of hundreds of steps) and assessed with functional verifiers, as seen in OSWorld. However, s...
arXiv:2606. 14119v1 Announce Type: new Abstract: Fault diagnostics and recovery in smart factories is challenging because critical information is dispersed across manuals of multiple machines which are interconnected through the manufacturing process.
By Yash Pulse, Yong-Bin Kang, Abhik Banerjee, Abdur Forkan, Prem Prakash Jayaraman
arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.
By Xinmei Huang, Jie Song, Peng Li, Fuxin Jiang, Jing Zhang, Tieying Zhang, Jianjun Chen, Chenming Liu, Tao Yang, Maoyin Liu, Wenda Li, Hong Chen, Cuiping Li
arXiv:2608.29286v1 Announce Type: new
Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructi...
By Jinzhe Li, Gengxu Li, Jinnan Li, Yuan Wu, Yi Chang
arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.
By Niranjan Kumar M, Balaji Nagarajan, Karthik Nair, Faysal Satter, Nithin Surendran
arXiv:2609.36700v1 Announce Type: cross
Abstract: When conversing with large language models (LLMs), users often begin with a simple question and build towards a multi-hop question through follow-up...
By Pranav Handa, Ariful Azad
arXiv:2602. 14643v4 Announce Type: replace Abstract: Large language models struggle to maintain strict adherence to structured workflows in high-stakes domains such as healthcare triage.
By Lu\'is Silva, Diogo Gon\c{c}alves, Catarina Farinha, Clara Matos, Lu\'is Ungaro