Tootfinder

Kremlin: Russian-American dialogue to continue despite diplomatic tensions: https://benborges.xyz/2025/07/14/kremlin-russianamerican-dialogue-to-continue.html

A look at the Catholic Church's decade-long dialogue with Silicon Valley on AI, as Pope Leo prepares to make AI's potential threat to humanity a signature issue (Wall Street Journal)
https://www.wsj.com/tech/ai/pope-leo-ai-te

What Can Robots Teach Us About Trust and Reliance? An interdisciplinary dialogue between Social Sciences and Social Robotics
Julien Wacquez (ETIS, CNRS), Elisabetta Zibetti (CHART), Joffrey Becker (ENSEA, ETIS), Lorenzo Aloe (ETIS, CHART), Fabio Amadio (LARSEN), Salvatore Anzalone (CHART), Lola Ca\~namero (ETIS, CY, CNRS, ENSEA), Serena Ivaldi (LARSEN, LORIA - AIS)

What Can Robots Teach Us About Trust and Reliance? An interdisciplinary dialogue between Social Sciences and Social Robotics
As robots find their way into more and more aspects of everyday life, questions around trust are becoming increasingly important. What does it mean to trust a robot? And how should we think about trust in relationships that involve both humans and non-human agents? While the field of Human-Robot Interaction (HRI) has made trust a central topic, the concept is often approached in fragmented ways. At the same time, established work in sociology, where trust has long been a key theme, is rarely br…

Those of you who frequently watch TV shows or movies in languages you don’t understand (e.g. with subtitles):
When you recall dialogue in the show/film, do you remember it in the actual language you heard, or in your native language?

No War With Iran Will Erase Israel's Crimes in Gaza. Don't Get Distracted.
Israel has adopted a worldview in which every problem should be solved through force, and the people of Israel have become a collective that admires force and brutality while scorning dialogue and compromise.

OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue
Xuelong Geng, Qijie Shao, Hongfei Xue, Shuiyuan Wang, Hanke Xie, Zhao Guo, Yi Zhao, Guojian Li, Wenjie Tian, Chengyou Wang, Zhixian Zhao, Kangxiang Xia, Ziyu Zhang, Zhennan Lin, Tianlun Zuo, Mingchen Shao, Yuang Cao, Guobin Ma, Longhao Li, Yuhang Dai, Dehui Gao, Dake Guo, Lei Xie

OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue
Empathy is crucial in enabling natural interactions within spoken dialogue systems, allowing machines to recognize and respond appropriately to paralinguistic cues such as age, gender, and emotion. Recent advancements in end-to-end speech language models, which unify speech understanding and generation, provide promising solutions. However, several challenges persist, including an over-reliance on large-scale dialogue datasets, insufficient extraction of paralinguistic cues vital for conveying …

Putin's negotiator calls for 'constructive dialogue' as Trump shifts stance on Ukraine: https://benborges.xyz/2025/07/14/putins-negotiator-calls-for-constructive.html

STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Zhenye Yang, Jinpeng Chen, Huan Li, Xiongnan Jin, Xuanyang Li, Junwei Zhang, Hongbo Gao, Kaimin Wei, Senzhang Wang
https://arxiv.org/abs/2508.10669

STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences via a dialog module and builds user profiles through a recommendation module to generate appropriate recommendations. However, existing CRS faces challenges in capturing the deep semantics of user preferences and dialogue context. In particular, the efficient integration of external knowledge graph…

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization
Xinge Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li
https://arxiv.org/abs/2508.09074

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization
Reinforcement Learning Fine-Tuning (RLFT) has achieved notable success in tasks with objectively verifiable answers (e.g., code generation, mathematical reasoning), yet struggles with open-ended subjective tasks like role-playing dialogue. Traditional reward modeling approaches, which rely on independent sample-wise scoring, face dual challenges: subjective evaluation criteria and unstable reward signals.Motivated by the insight that human evaluation inherently combines explicit criteria with i…

Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
Baihan Lin
https://arxiv.org/abs/2508.07520 https://arxiv.or…

Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue -- whether between humans, between human and AI, or among groups -- as a living system with interpretable structure that can be visualized, compared, and understood. Unlike traditional conversation analysis that reduces rich interaction to statistical summaries, our approach reveals the temporal architecture of d…

Today I had cause to get out my old #Acorn #RISCOS 3 Style Guide to demonstrate that the fundamentals of good #UX can date back well over 30 years - same with the

La Caisse de dépôt finance-t-elle un génocide Š Gaza ?
https://www.lapresse.ca/dialogue/chroniques/2025-07-10/on-a-pose-la-question-pour-vous/la-caisse-finance-t-elle-un-genocide-a-gaza.php

AC/DC: LLM-based Audio Comprehension via Dialogue Continuation
Yusuke Fujita, Tomoya Mizumoto, Atsushi Kojima, Lianbo Liu, Yui Sudo
https://arxiv.org/abs/2506.10312

AC/DC: LLM-based Audio Comprehension via Dialogue Continuation
We propose an instruction-following audio comprehension model that leverages the dialogue continuation ability of large language models (LLMs). Instead of directly generating target captions in training data, the proposed method trains a model to produce responses as if the input caption triggered a dialogue. This dialogue continuation training mitigates the caption variation problem. Learning to continue a dialogue effectively captures the caption's meaning beyond its surface-level words. As a…

Dialogue Systems Engineering: A Survey and Future Directions
Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa, Yoichi Matsuyama, Kazunori Komatani
https://arxiv.org/abs/2508.02279

Dialogue Systems Engineering: A Survey and Future Directions
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is impo…

The local indie theater was showing Terminator 2 tonight as part of its summer blockbuster series. The boy is off to college next week, but wanted to see it with me. ❤️
Mostly still holds up well. Some of the dialogue is pretty over the top, but he enjoyed it. I would have enjoyed it no matter what, having him along for it.
https://ca…

Terminator 2: Judgment Day
It's nothing personal.

And that's how you do it 🎤🗑️ #AIChatBot

Video-Guided Text-to-Music Generation Using Public Domain Movie Collections
Haven Kim, Zachary Novack, Weihan Xu, Julian McAuley, Hao-Wen Dong
https://arxiv.org/abs/2506.12573

Video-Guided Text-to-Music Generation Using Public Domain Movie Collections
Despite recent advancements in music generation systems, their application in film production remains limited, as they struggle to capture the nuances of real-world filmmaking, where filmmakers consider multiple factors-such as visual content, dialogue, and emotional tone-when selecting or composing music for a scene. This limitation primarily stems from the absence of comprehensive datasets that integrate these elements. To address this gap, we introduce Open Screen Sound Library (OSSL), a dat…

AI Tutors vs. Tenacious Myths: Evidence from Personalised Dialogue Interventions in Education
Brooklyn J. Corbett, Jason M. Tangen
https://arxiv.org/abs/2506.09292

AI Tutors vs. Tenacious Myths: Evidence from Personalised Dialogue Interventions in Education
Misconceptions in psychology and education persist despite clear contradictory evidence, resisting traditional correction methods. This study investigated whether personalised AI dialogue could effectively correct these stubborn beliefs. In a preregistered experiment (N = 375), participants holding strong psychology misconceptions engaged in one of three interventions: (1) personalised AI dialogue targeting their specific misconception, (2) generic textbook-style refutation, or (3) neutral AI d…

The (Re)generation of the Nonhuman: Nature and Text in Dialogue
https://ift.tt/dix6KqF
updated: Monday, June 16, 2025 - 9:57amfull name / name of organization: Israel Eweka/NeMLA…
via Input 4 RELCFP

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
David Schlangen, Sherzod Hakimov, Jonathan Jordan, Philipp Sadler
https://arxiv.org/abs/2507.08491

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation. The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available. The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose r…

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction
Syeda Kisaa Fatima, Tehreem Zubair, Noman Ahmed, Asifullah Khan
https://arxiv.org/abs/2506.11475

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction
This paper introduces LUCID-MA (Learning and Understanding Crime through Dialogue of Multiple Agents), an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data. Our system that consists of three core components: an analysis assistant that highlights spatiotemporal crime patterns, a feedback component that reviews and refines analytical results and a prediction component that forecasts future crime trends. With a well-designed prompt and the L…

Can Large Language Models Improve Phishing Defense? A Large-Scale Controlled Experiment on Warning Dialogue Explanations
Federico Maria Cau, Giuseppe Desolda, Francesco Greco, Lucio Davide Spano, Luca Vigan\`o
https://arxiv.org/abs/2507.07916

Can Large Language Models Improve Phishing Defense? A Large-Scale Controlled Experiment on Warning Dialogue Explanations
Phishing has become a prominent risk in modern cybersecurity, often used to bypass technological defences by exploiting predictable human behaviour. Warning dialogues are a standard mitigation measure, but the lack of explanatory clarity and static content limits their effectiveness. In this paper, we report on our research to assess the capacity of Large Language Models (LLMs) to generate clear, concise, and scalable explanations for phishing warnings. We carried out a large-scale between-subj…

First Ask Then Answer: A Framework Design for AI Dialogue Based on Supplementary Questioning with Large Language Models
Chuanruo Fu, Yuncheng Du
https://arxiv.org/abs/2508.08308

First Ask Then Answer: A Framework Design for AI Dialogue Based on Supplementary Questioning with Large Language Models
Large Language Models (LLMs) often struggle to deliver accurate and actionable answers when user-provided information is incomplete or ill-specified. We propose a new interaction paradigm, First Ask Then Answer (FATA), in which, through prompt words, LLMs are guided to proactively generate multidimensional supplementary questions for users prior to response generation. Subsequently, by integrating user-provided supplementary information with the original query through sophisticated prompting te…

I kinda hate marketing . . . but I enjoy supporting talented artists. I worked with two artists to get some promotional art for .Liar, Cheater, Sinner, Saint.' Here's the art from @gargoyle.pastures that inspired me to throw away the cover I had already bought and use one of these panels instead. Links to the full panels are up at https://www.

Apple adds a 3x playback option for podcasts in iOS26, an Enhance Dialogue tool to hear speech more clearly, and a puzzle game for Apple News Plus subscribers (Jay Peters/The Verge)
https://www.theverge.com/news/684708/apple-podcasts-speed-news-emoji-game…

Apple turns up the speed on Podcasts and adds a new emoji game to News
Apple will let users listen to podcasts at up to 3x speed in the Podcast app, and Apple News Plus subscribers will be able to play a new Emoji Game.

A Topological Semantics of Dialogue: Nerve Structures and Logical Extraction
Andreu Ballus Santacana
https://arxiv.org/abs/2506.00615 https://

A Topological Semantics of Dialogue: Nerve Structures and Logical Extraction
We introduce a concise, topologically-motivated semantics for finite dialogues by mapping each utterance to an open set in a fixed semantic space, building the corresponding nerve complex of joint satisfiability, and extracting fundamental combinatorial invariants: 1. The negative nerve, which enumerates all finite collections of utterances whose opens have empty intersection, providing a straightforward criterion for merging separate transcripts without contradiction. 2. The global int…

Data-Aware Socratic Query Refinement in Database Systems
Ruiyuan Zhang, Chrysanthi Kosyfaki, Xiaofang Zhou
https://arxiv.org/abs/2508.05061 https://arxiv.o…

Data-Aware Socratic Query Refinement in Database Systems
In this paper, we propose Data-Aware Socratic Guidance (DASG), a dialogue-based query enhancement framework that embeds \linebreak interactive clarification as a first-class operator within database systems to resolve ambiguity in natural language queries. DASG treats dialogue as an optimization decision, asking clarifying questions only when the expected execution cost reduction exceeds the interaction overhead. The system quantifies ambiguity through linguistic fuzziness, schema grounding con…

Conversational Search: From Fundamentals to Frontiers in the LLM Era
Fengran Mo, Chuan Meng, Mohammad Aliannejadi, Jian-Yun Nie
https://arxiv.org/abs/2506.10635

Conversational Search: From Fundamentals to Frontiers in the LLM Era
Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand the users' search intent within the conversational context and then return the relevant information through a flexible, dialogue-based interface. The recent powerful large language models (LLMs) with capacities of instruction following, content generation, and reasoning, attract significant attention and advancements, …

Well - now I'm kinda stumped.
(no - the password is not Schrödinger)
(from the folks at emailmeform[.]com)
#stupidpasswordrules

"here's a dialogue I had with Claude" is always half-way between
- man, I was so stoned last night, and, you know, have you wondered about [the bleeding obvious considered profound]..."
and
- "I had the weirdest dream; I was [and then, and then, and, I can't quite remember, but then]..."

Today, I’ll give a talk at the workshop “Re-Designing the Modern Religious Bookshelf – A Faculty-Library Dialogue on (Re-)Classifying Religious Literature,” organized by @… and Christian Meyer.

Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
\v{S}imon Sedl\'a\v{c}ek, Bolaji Yusuf, J\'an \v{S}vec, Pradyoth Hegde, Santosh Kesiraju, Old\v{r}ich Plchot, Jan \v{C}ernock\'y
https://arxiv.org/abs/2506.08633

Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
In this work, we approach spoken Dialogue State Tracking (DST) by bridging the representation spaces of speech encoders and LLMs via a small connector module, with a focus on fully open-sourced and open-data components (WavLM-large, OLMo). We focus on ablating different aspects of such systems including full/LoRA adapter fine-tuning, the effect of agent turns in the dialogue history, as well as fuzzy matching-based output post-processing, which greatly improves performance of our systems on nam…

HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots
Subasish Das
https://arxiv.org/abs/2507.04160 https://…

HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots
This paper introduces HyperSumm-RL, a hypertext-aware summarization and interaction analysis framework designed to investigate human perceptions of social robot leadership through long-form dialogue. The system utilizes a structured Natural Language Processing (NLP) workflow that combines transformer-based long dialogue summarization, leadership style modeling, and user response analysis, enabling scalable evaluation of social robots in complex human-robot interaction (HRI) settings. Unlike pri…

"it feels like the hope with generative AI is that no matter how much you may hate it, the goal is to bombard you with its horrors and its excesses so frequently that you become numb to it."
(Original title: I’m Getting Real Tired Of Not Being Able To Trust That A Video Game Doesn’t Have AI Crap In It)
https://

I'm Getting Real Tired Of Not Being Able To Trust That A Video Game Doesn't Have AI Crap In It - Aftermath
It sucks that players are having to scour every asset and line of dialogue

Dialogue-Based Multi-Dimensional Relationship Extraction from Novels
Yuchen Yan, Hanjie Zhao, Senbin Zhu, Hongde Liu, Zhihong Zhang, Yuxiang Jia
https://arxiv.org/abs/2507.04852

Dialogue-Based Multi-Dimensional Relationship Extraction from Novels
Relation extraction is a crucial task in natural language processing, with broad applications in knowledge graph construction and literary analysis. However, the complex context and implicit expressions in novel texts pose significant challenges for automatic character relationship extraction. This study focuses on relation extraction in the novel domain and proposes a method based on Large Language Models (LLMs). By incorporating relationship dimension separation, dialogue data construction, a…

Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship
Craig Steven Wright
https://arxiv.org/abs/2506.22497

Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship
This paper reconceptualises peer review as structured public commentary. Traditional academic validation is hindered by anonymity, latency, and gatekeeping. We propose a transparent, identity-linked, and reproducible system of scholarly evaluation anchored in open commentary. Leveraging blockchain for immutable audit trails and AI for iterative synthesis, we design a framework that incentivises intellectual contribution, captures epistemic evolution, and enables traceable reputational dynamics.…

Finished “You Are Here” by David Nicholls.
A mutual friend convinces Michael & Marnie to hike, at least partway, from coast to coast across the English countryside. Not intended for each other, initially, a sweet & genuine connection deepens with every step. So, too, does healing & growth in the aftermaths of failed marriages.
Great character development & fantastic dialogue. You LIKE them very much. Rushed ending, though.
4/5 stars ⭐️⭐️⭐️⭐️

You Are Here
Sometimes you need to get lost to find your way . . . …

Flint's lead pipe replacement marks the end of a 10-year crisis. #climatechange #climatesolutions #climate

Flint finally replaced its lead pipes
The contamination in Flint, Michigan, sparked a national dialogue about the harms of lead pipes and the country’s aging water infrastructure.

FreeBSD: icons are broken in Geany, GIMP, HandBrake, PDF Arranger, Remmina, and various other applications.
In addition to the four screenshots here in Mastodon:
― https://i.imgur.com/iJ2eKe6.png – the Save As dialogue in Firefox (the File Open dialogue is similarly broken)
―

My only real complaint about The Naked Gun is that I missed some of the dialogue because everybody around me was laughing so hard.

Rager:
https://github.com/w3c/wcag/issues/4460
Part of my frustration with this 1.2.5 discussion (no gaps means auto-pass a lack of AD) was the earlier assertion that videos always have gaps (ha ha, no:

1.2.3 / 1.2.5 Audio description - No pause · Issue #4460 · w3c/wcag
If a video has no pause in dialogue to provide audio descriptions, is the video considered to meet SC 1.2.3 / 1.2.5 Audio description? Even if the content author want to provide audio descriptions ...

Spec-TOD: A Specialized Instruction-Tuned LLM Framework for Efficient Task-Oriented Dialogue Systems
Quang-Vinh Nguyen, Quang-Chieu Nguyen, Hoang Pham, Khac-Hoai Nam Bui
https://arxiv.org/abs/2507.04841

Spec-TOD: A Specialized Instruction-Tuned LLM Framework for Efficient Task-Oriented Dialogue Systems
Task-oriented dialogue (TOD) systems facilitate goal-driven interactions between users and machines. While recent advances in deep learning have improved the performance, TOD systems often struggle in low-resource scenarios with limited labeled data. To address this challenge, we propose Spec-TOD, a novel framework designed to train an end-to-end TOD system with limited data. Spec-TOD introduces two main innovations: (i) a novel specialized end-to-end TOD framework that incorporates explicit ta…

“They’re sacrificing the moral center of Harvard Divinity School,” #Gaza #Palestine #Harvard

How a Harvard initiative on the Israeli-Palestinian conflict collapsed amid antisemitism claims
Former staff of Harvard Divinity School's Religion and Public Life program say it encouraged a healthy dialogue, but critics accused it of antisemitism.

WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Yaoyao Qian, Jindan Huang, Yuanli Wang, Simon Yu, Kyrie Zhixuan Zhou, Jiayuan Mao, Mingfu Liang, Hanhan Zhou
https://arxiv.org/abs/2506.01881

WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because users frequently do not fully understand their own needs, while systems require precise intent definitions. Current LLM-based agents cannot effectively distinguish between linguistically complete and contextually triggerable expressions, lacking frameworks for collaborative intent formation. We present…

Finished 1923 this week. Doesn't have the elŠn of Yellowstone/1883, esp when it comes to dialogue, but was a very solid ride nonetheless. My favorite thing is how they brought the era to life.
You don't have to have watched anything else to get it, but the other stories fill in context.
That said, I'd start with 1883, to this, to Yellowstone if I was new to it.
#film

1923
Follow a new generation of the Dutton family during the early twentieth century when pandemics, historic drought, the end of Prohibition and the Great Depression all plague the mountain west, and the Duttons who call it home.

Good evening, #pride !
Good evening, love!
I'm sorry but you don't look like you're banned!
***
These are the opening words of Budapest mayor, Gergely #Karácsony, from the green party "Dialogue", addressing the crowd in the city's Pride demonstrat…

CoVoMix2: Advancing Zero-Shot Dialogue Generation with Fully Non-Autoregressive Flow Matching
Leying Zhang, Yao Qian, Xiaofei Wang, Manthan Thakker, Dongmei Wang, Jianwei Yu, Haibin Wu, Yuxuan Hu, Jinyu Li, Yanmin Qian, Sheng Zhao
https://arxiv.org/abs/2506.00885

CoVoMix2: Advancing Zero-Shot Dialogue Generation with Fully Non-Autoregressive Flow Matching
Generating natural-sounding, multi-speaker dialogue is crucial for applications such as podcast creation, virtual agents, and multimedia content generation. However, existing systems struggle to maintain speaker consistency, model overlapping speech, and synthesize coherent conversations efficiently. In this paper, we introduce CoVoMix2, a fully non-autoregressive framework for zero-shot multi-talker dialogue generation. CoVoMix2 directly predicts mel-spectrograms from multi-stream transcriptio…

ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction
Jaros{\l}aw A. Chudziak, Micha{\l} Wawer
https://arxiv.org/abs/2507.03435

ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction
This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, …

Why Robots Are Bad at Detecting Their Mistakes: Limitations of Miscommunication Detection in Human-Robot Dialogue
Ruben Janssens, Jens De Bock, Sofie Labat, Eva Verhelst, Veronique Hoste, Tony Belpaeme
https://arxiv.org/abs/2506.20268

Why Robots Are Bad at Detecting Their Mistakes: Limitations of Miscommunication Detection in Human-Robot Dialogue
Detecting miscommunication in human-robot interaction is a critical function for maintaining user engagement and trust. While humans effortlessly detect communication errors in conversations through both verbal and non-verbal cues, robots face significant challenges in interpreting non-verbal feedback, despite advances in computer vision for recognizing affective expressions. This research evaluates the effectiveness of machine learning models in detecting miscommunications in robot dialogue. U…

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Xun Yang, Meng Wang
https://arxiv.org/abs/2508.10848 …

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in mathematics and programming, while research in the psychological domain has predominantly emphasized emotional support and empathetic dialogue, with limited attention to reasoning mechanisms that are bene…

ScamAgents: How AI Agents Can Simulate Human-Level Scam Calls
Sanket Badhe
https://arxiv.org/abs/2508.06457 https://arxiv.org/pdf/2508.06457

ScamAgents: How AI Agents Can Simulate Human-Level Scam Calls
Large Language Models (LLMs) have demonstrated impressive fluency and reasoning capabilities, but their potential for misuse has raised growing concern. In this paper, we present ScamAgent, an autonomous multi-turn agent built on top of LLMs, capable of generating highly realistic scam call scripts that simulate real-world fraud scenarios. Unlike prior work focused on single-shot prompt misuse, ScamAgent maintains dialogue memory, adapts dynamically to simulated user responses, and employs dece…

What I don't hear from the #establishment #Democrats is any discussion about how to actually reach out and #TalkWithVoters (They talk TO voters, but it's not a dialogue (they talk AT voters?)…

Learning Japanese with Jouzu: Interaction Outcomes with Stylized Dialogue Fictional Agents
Zackary Rackauckas, Julia Hirschberg
https://arxiv.org/abs/2507.06483

Learning Japanese with Jouzu: Interaction Outcomes with Stylized Dialogue Fictional Agents
This study investigates how stylized, voiced agents shape user interaction in a multimodal language learning environment. We conducted a mixed-methods evaluation of 54 participants interacting with anime-inspired characters powered by large language models and expressive text-to-speech synthesis. These agents responded in Japanese character language, offering users asynchronous, semi-structured conversation in varying speech styles and emotional tones. We analyzed user engagement patterns, perc…

MEET BIANCA-IOANA MARCU, FPF EUROPE MANAGING DIRECTOR
https://fpf.org/blog/meet-bianca-ioana-marcu-fpf-europe-managing-director/
@…

'There should have been a dialogue' — Zelensky on controversial Ukrainian law that sparked protests: https://benborges.xyz/2025/07/25/there-should-have-been-a.html

Chatbots? We solved the same problem tens of thousands years ago, with a simpler and more ecological solution. It is called "talking to oneself" (or "internal dialogue").
Well, unless you're looking for someone more intelligent to talk to. But if all the stupidity from the Internet collected into an #LLM really fits the bill…
#AI

ChatChecker: A Framework for Dialogue System Testing and Evaluation Through Non-cooperative User Simulation
Roman Mayr, Michel Schimpf, Thomas Bohn\'e
https://arxiv.org/abs/2507.16792

ChatChecker: A Framework for Dialogue System Testing and Evaluation Through Non-cooperative User Simulation
While modern dialogue systems heavily rely on large language models (LLMs), their implementation often goes beyond pure LLM interaction. Developers integrate multiple LLMs, external tools, and databases. Therefore, assessment of the underlying LLM alone does not suffice, and the dialogue systems must be tested and evaluated as a whole. However, this remains a major challenge. With most previous work focusing on turn-level analysis, less attention has been paid to integrated dialogue-level quali…

MDC-R: The Minecraft Dialogue Corpus with Reference
Chris Madge, Maris Camilleri, Paloma Carretero Garcia, Mladen Karan, Juexi Shao, Prashant Jayannavar, Julian Hough, Benjamin Roth, Massimo Poesio
https://arxiv.org/abs/2506.22062

MDC-R: The Minecraft Dialogue Corpus with Reference
We introduce the Minecraft Dialogue Corpus with Reference (MDC-R). MDC-R is a new language resource that supplements the original Minecraft Dialogue Corpus (MDC) with expert annotations of anaphoric and deictic reference. MDC's task-orientated, multi-turn, situated dialogue in a dynamic environment has motivated multiple annotation efforts, owing to the interesting linguistic phenomena that this setting gives rise to. We believe it can serve as a valuable resource when annotated with reference,…

On Mitigating Data Sparsity in Conversational Recommender Systems
Sixiao Zhang, Mingrui Liu, Cheng Long, Wei Yuan, Hongxu Chen, Xiangyu Zhao, Hongzhi Yin
https://arxiv.org/abs/2507.00479

On Mitigating Data Sparsity in Conversational Recommender Systems
Conversational recommender systems (CRSs) capture user preference through textual information in dialogues. However, they suffer from data sparsity on two fronts: the dialogue space is vast and linguistically diverse, while the item space exhibits long-tail and sparse distributions. Existing methods struggle with (1) generalizing to varied dialogue expressions due to underutilization of rich textual cues, and (2) learning informative item representations under severe sparsity. To address these …

This https://arxiv.org/abs/2502.13033 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Classical notions of computation and the Hasegawa-Thielecke theorem
In the spirit of the Curry-Howard correspondence between proofs and programs, we define and study a syntax and semantics for classical logic equipped with a computationally involutive negation, using a polarised effect calculus. A main challenge in designing a denotational semantics is to accommodate both call-by-value and call-by-name evaluation strategies, which leads to a failure of associativity of composition. Building on the work of the third author, we devise the notion of dialogue duplo…

Text-to-SQL Task-oriented Dialogue Ontology Construction
Renato Vukovic, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Hsien-Chin Lin, Shutong Feng, Nurul Lubis, Milica Gasic
https://arxiv.org/abs/2507.23358

Text-to-SQL Task-oriented Dialogue Ontology Construction
Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separation is explicit, using an external database structured by an explicit ontology to ensure explainability and controllability. However, building such ontologies requires manual labels or supervised training. We introduce TeQoDO: a Text-to-SQL task-oriented Dialogue Ontology constr…

This https://arxiv.org/abs/2504.13861 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark
Though Large Vision-Language Models (LVLMs) are being actively explored in medicine, their ability to conduct telemedicine consultations combining accurate diagnosis with professional dialogue remains underexplored. In this paper, we present 3MDBench (Medical Multimodal Multi-agent Dialogue Benchmark), an open-source framework for simulating and evaluating LVLM-driven telemedical consultations. 3MDBench simulates patient variability through four temperament-based Patient Agents and an Assessor …

Modeling and Visualization Reasoning for Stakeholders in Education and Industry Integration Systems: Research on Structured Synthetic Dialogue Data Generation Based on NIST Standards
Wei Meng
https://arxiv.org/abs/2506.16952

Modeling and Visualization Reasoning for Stakeholders in Education and Industry Integration Systems: Research on Structured Synthetic Dialogue Data Generation Based on NIST Standards
This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system. The scarcity of real interview data, absence of structured variable modeling, and lack of interpretability in inference mechanisms have limited the analytical accuracy and policy responsiveness of EII research. To resolve these challenges, we propose a structural modeling paradigm based on the National Institute of Standards and Technology (NI…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new/
[5/6]:
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Pr...

Personalized Socially Assistive Robots With End-to-End Speech-Language Models For Well-Being Support
Mengxue Fu, Zhonghao Shi, Minyu Huang, Siqi Liu, Mina Kian, Yirui Song, Maja J. Matari\'c
https://arxiv.org/abs/2507.14412

Personalized Socially Assistive Robots With End-to-End Speech-Language Models For Well-Being Support
Socially assistive robots (SARs) have shown great potential for supplementing well-being support. However, prior studies have found that existing dialogue pipelines for SARs remain limited in real-time latency, back-channeling, and personalized speech dialogue. Toward addressing these limitations, we propose using integrated end-to-end speech-language models (SLMs) with SARs. This work 1) evaluated the usability of an SLM-enabled SAR dialogue system through a small user study, and 2) identified…

Zelensky and Trump discuss air defense and joint drone production amid tensions with Russia: https://benborges.xyz/2025/07/04/zelensky-and-trump-discuss-air.html

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Selina Heller, Mohamed Ibrahim, David Antony Selby, Sebastian Vollmer
https://arxiv.org/abs/2507.08440

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or policies. These conferences often rely on facilitated discussions to ensure productive dialogue and collective agreement. Recently, Large Language Models (LLMs) have shown significant promise in simulating real-world scenarios, particularly through collaborative multi-agent systems that mimic group in…

This https://arxiv.org/abs/2505.22767 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge
Large Language Models (LLMs) are typically analysed through architectural, behavioural, or training-data lenses. This article offers a theoretical and experiential re-framing: LLMs as dynamic instantiations of Collective human Knowledge (CK), where intelligence is evoked through dialogue rather than stored statically. Drawing on concepts from neuroscience and AI, and grounded in sustained interaction with ChatGPT-4, I examine emergent dialogue patterns, the implications of fine-tuning, and the …

Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Keisuke Ueda, Wataru Hirota, Takuto Asakura, Takahiro Omi, Kosuke Takahashi, Kosuke Arima, Tatsuya Ishigaki
https://arxiv.org/abs/2507.08350

Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Large language models (LLMs) are increasingly used to support creative tasks such as research idea generation. While recent work has shown that structured dialogues between LLMs can improve the novelty and feasibility of generated ideas, the optimal design of such interactions remains unclear. In this study, we conduct a comprehensive analysis of multi-agent LLM dialogues for scientific ideation. We compare different configurations of agent roles, number of agents, and dialogue depth to underst…

SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models
Han Yin, Yafeng Chen, Chong Deng, Luyao Cheng, Hui Wang, Chao-Hong Tan, Qian Chen, Wen Wang, Xiangang Li
https://arxiv.org/abs/2508.06372

SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models
The Speaker Diarization and Recognition (SDR) task aims to predict "who spoke when and what" within an audio clip, which is a crucial task in various real-world multi-speaker scenarios such as meeting transcription and dialogue systems. Existing SDR systems typically adopt a cascaded framework, combining multiple modules such as speaker diarization (SD) and automatic speech recognition (ASR). The cascaded systems suffer from several limitations, such as error propagation, difficulty in handling…

State-Inference-Based Prompting for Natural Language Trading with Game NPCs
Minkyung Kim, Junsik Kim, Hwidong Bae, Woongcheol Yang, Sangdon Park, Sohee Bae
https://arxiv.org/abs/2507.07203

State-Inference-Based Prompting for Natural Language Trading with Game NPCs
Large Language Models enable dynamic game interactions but struggle with rule-governed trading systems. Current implementations suffer from rule violations, such as item hallucinations and calculation errors, that erode player trust. Here, State-Inference-Based Prompting (SIBP) enables reliable trading through autonomous dialogue state inference and context-specific rule adherence. The approach decomposes trading into six states within a unified prompt framework, implementing context-aware item…

FD-Bench: A Full-Duplex Benchmarking Pipeline Designed for Full Duplex Spoken Dialogue Systems
Yizhou Peng, Yi-Wen Chao, Dianwen Ng, Yukun Ma, Chongjia Ni, Bin Ma, Eng Siong Chng
https://arxiv.org/abs/2507.19040

FD-Bench: A Full-Duplex Benchmarking Pipeline Designed for Full Duplex Spoken Dialogue Systems
Full-duplex spoken dialogue systems (FDSDS) enable more natural human-machine interactions by allowing real-time user interruptions and backchanneling, compared to traditional SDS that rely on turn-taking. However, existing benchmarks lack metrics for FD scenes, e.g., evaluating model performance during user interruptions. In this paper, we present a comprehensive FD benchmarking pipeline utilizing LLMs, TTS, and ASR to address this gap. It assesses FDSDS's ability to handle user interruptions,…

Retrospective Sparse Attention for Efficient Long-Context Generation
Seonghwan Choi, Beomseok Kang, Dongwon Jo, Jae-Joon Kim
https://arxiv.org/abs/2508.09001 https://

Retrospective Sparse Attention for Efficient Long-Context Generation
Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, inference over extended contexts is bottlenecked by the Key-Value (KV) cache, whose memory footprint grows linearly with sequence length and dominates latency at each decoding step. While recent KV cache compression methods identify and load important tokens, they focus predominantly on input contexts and fail to address the cumulative attention erro…

Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Paulo Ricardo Knob, Leonardo Scholler, Juliano Rigatti, Soraia Raupp Musse
https://arxiv.org/abs/2507.02537

Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Conversational agents have made significant progress since ELIZA, expanding their role across various domains, including healthcare, education, and customer service. As these agents become increasingly integrated into daily human interactions, the need for emotional intelligence, particularly empathetic listening, becomes increasingly essential. In this study, we explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions. Starting from a small dat…

Enhancing User Engagement in Socially-Driven Dialogue through Interactive LLM Alignments
Jiashuo Wang, Kaitao Song, Chunpu Xu, Changhe Song, Yang Xiao, Dongsheng Li, Lili Qiu, Wenjie Li
https://arxiv.org/abs/2506.21497

Enhancing User Engagement in Socially-Driven Dialogue through Interactive LLM Alignments
Enhancing user engagement through interactions plays an essential role in socially-driven dialogues. While prior works have optimized models to reason over relevant knowledge or plan a dialogue act flow, the relationship between user engagement and knowledge or dialogue acts is subtle and does not guarantee user engagement in socially-driven dialogues. To this end, we enable interactive LLMs to learn user engagement by leveraging signals from the future development of conversations. Specificall…

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
Andrew Cornfeld, Ashley Miller, Mercedes Mora-Figueroa, Kurt Samuels, Anthony Palomba
https://arxiv.org/abs/2506.00076

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
Television networks face high financial risk when making programming decisions, often relying on limited historical data to forecast episodic viewership. This study introduces a machine learning framework that integrates natural language processing (NLP) features from over 25000 television episodes with traditional viewership data to enhance predictive accuracy. By extracting emotional tone, cognitive complexity, and narrative structure from episode dialogue, we evaluate forecasting performance…

MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing
Michael Clemens, Ana Marasovi\'c
https://arxiv.org/abs/2507.06329 ht…

MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing
While AI presents significant potential for enhancing music mixing and mastering workflows, current research predominantly emphasizes end-to-end automation or generation, often overlooking the collaborative and instructional dimensions vital for co-creative processes. This gap leaves artists, particularly amateurs seeking to develop expertise, underserved. To bridge this, we introduce MixAssist, a novel audio-language dataset capturing the situated, multi-turn dialogue between expert and amateu…

Optimizing Conversational Product Recommendation via Reinforcement Learning
Kang Liu
https://arxiv.org/abs/2507.01060 https://arxiv.o…

Optimizing Conversational Product Recommendation via Reinforcement Learning
We propose a reinforcement learning-based approach to optimize conversational strategies for product recommendation across diverse industries. As organizations increasingly adopt intelligent agents to support sales and service operations, the effectiveness of a conversation hinges not only on what is recommended but how and when recommendations are delivered. We explore a methodology where agentic systems learn optimal dialogue policies through feedback-driven reinforcement learning. By mining …

Real-time Generation of Various Types of Nodding for Avatar Attentive Listening System
Kazushi Kato, Koji Inoue, Divesh Lala, Keiko Ochi, Tatsuya Kawahara
https://arxiv.org/abs/2507.23298

Real-time Generation of Various Types of Nodding for Avatar Attentive Listening System
In human dialogue, nonverbal information such as nodding and facial expressions is as crucial as verbal information, and spoken dialogue systems are also expected to express such nonverbal behaviors. We focus on nodding, which is critical in an attentive listening system, and propose a model that predicts both its timing and type in real time. The proposed model builds on the voice activity projection (VAP) model, which predicts voice activity from both listener and speaker audio. We extend it …

Nations torn by infighting will lose, Ukraine's military intelligence chief warns amid controversial law: https://benborges.xyz/2025/07/23/nations-torn-by-infighting-will.html

FACE: A Fine-grained Reference Free Evaluator for Conversational Recommender Systems
Hideaki Joko, Faegheh Hasibi
https://arxiv.org/abs/2506.00314 https://…

FACE: A Fine-grained Reference Free Evaluator for Conversational Recommender Systems
A systematic, reliable, and low-cost evaluation of Conversational Recommender Systems (CRSs) remains an open challenge. Existing automatic CRS evaluation methods are proven insufficient for evaluating the dynamic nature of recommendation conversations. This work proposes FACE: a Fine-grained, Aspect-based Conversation Evaluation method that provides evaluation scores for diverse turn and dialogue level qualities of recommendation conversations. FACE is reference-free and shows strong correlatio…

This https://arxiv.org/abs/2506.00975 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Inspired by the impressive capabilities of GPT-4o, there is growing interest in enabling speech language models (SLMs) to engage in natural, fluid spoken interactions with humans. Recent advancements have led to the development of several SLMs that demonstrate promising results in this area. However, current approaches have yet to fully exploit dual-channel speech data, which inherently captures the structure and dynamics of human conversation. In this work, we systematically explore the use of…

This https://arxiv.org/abs/2506.00618 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
With the rapid development of multimodal large language models (MLLMs), they are increasingly deployed as autonomous computer-use agents capable of accomplishing complex computer tasks. However, a pressing issue arises: Can the safety risk principles designed and aligned for general MLLMs in dialogue scenarios be effectively transferred to real-world computer-use scenarios? Existing research on evaluating the safety risks of MLLM-based computer-use agents suffers from several limitations: it ei…

DocCHA: Towards LLM-Augmented Interactive Online diagnosis System
Xinyi Liu, Dachun Sun, Yi R. Fung, Dilek Hakkani-T\"ur, Tarek Abdelzaher
https://arxiv.org/abs/2507.07870

DocCHA: Towards LLM-Augmented Interactive Online diagnosis System
Despite the impressive capabilities of Large Language Models (LLMs), existing Conversational Health Agents (CHAs) remain static and brittle, incapable of adaptive multi-turn reasoning, symptom clarification, or transparent decision-making. This hinders their real-world applicability in clinical diagnosis, where iterative and structured dialogue is essential. We propose DocCHA, a confidence-aware, modular framework that emulates clinical reasoning by decomposing the diagnostic process into three…

Decoding Instructional Dialogue: Human-AI Collaborative Analysis of Teacher Use of AI Tool at Scale
Alex Liu, Lief Esbenshade, Shawon Sarkar, Victor Tian, Zachary Zhang, Kevin He, Min Sun
https://arxiv.org/abs/2507.17985

Decoding Instructional Dialogue: Human-AI Collaborative Analysis of Teacher Use of AI Tool at Scale
The integration of large language models (LLMs) into educational tools has the potential to substantially impact how teachers plan instruction, support diverse learners, and engage in professional reflection. Yet little is known about how educators actually use these tools in practice and how their interactions with AI can be meaningfully studied at scale. This paper presents a human-AI collaborative methodology for large-scale qualitative analysis of over 140,000 educator-AI messages drawn fro…

RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
Jingyi Yang, Shuai Shao, Dongrui Liu, Jing Shao
https://arxiv.org/abs/2506.00618 https://…

RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
With the rapid development of multimodal large language models (MLLMs), they are increasingly deployed as autonomous computer-use agents capable of accomplishing complex computer tasks. However, a pressing issue arises: Can the safety risk principles designed and aligned for general MLLMs in dialogue scenarios be effectively transferred to real-world computer-use scenarios? Existing research on evaluating the safety risks of MLLM-based computer-use agents suffers from several limitations: it ei…

Response Attack: Exploiting Contextual Priming to Jailbreak Large Language Models
Ziqi Miao, Lijun Li, Yuan Xiong, Zhenhua Liu, Pengyu Zhu, Jing Shao
https://arxiv.org/abs/2507.05248

Response Attack: Exploiting Contextual Priming to Jailbreak Large Language Models
Contextual priming, where earlier stimuli covertly bias later judgments, offers an unexplored attack surface for large language models (LLMs). We uncover a contextual priming vulnerability in which the previous response in the dialogue can steer its subsequent behavior toward policy-violating content. Building on this insight, we propose Response Attack, which uses an auxiliary LLM to generate a mildly harmful response to a paraphrased version of the original malicious query. They are then form…

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Bing Song, Jianing Liu, Sisi Jian, Chenyang Wu, Vinayak Dixit
https://arxiv.org/abs/2506.23107

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Large language models (LLMs) have made significant strides, extending their applications to dialogue systems, automated content creation, and domain-specific advisory tasks. However, as their use grows, concerns have emerged regarding their reliability in simulating complex decision-making behavior, such as risky decision-making, where a single choice can lead to multiple outcomes. This study investigates the ability of LLMs to simulate risky decision-making scenarios. We compare model-generate…

Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
Raj Mahmud, Shlomo Berkovsky, Mukesh Prasad, A. Baki Kocaballi
https://arxiv.org/abs/2508.02328

Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
Conversational Recommender Systems (CRSs) deliver personalised recommendations through multi-turn natural language dialogue and increasingly support both task-oriented and exploratory interactions. Yet, the factors shaping user interaction preferences remain underexplored. In this within-subjects study (\(N = 139\)), participants experienced two scripted CRS dialogues, rated their experiences, and indicated the importance of eight system qualities. Logistic regression revealed that preference f…

AURA: Agent for Understanding, Reasoning, and Automated Tool Use in Voice-Driven Tasks
Leander Melroy Maben, Gayathri Ganesh Lakshmy, Srijith Radhakrishnan, Siddhant Arora, Shinji Watanabe
https://arxiv.org/abs/2506.23049

AURA: Agent for Understanding, Reasoning, and Automated Tool Use in Voice-Driven Tasks
Despite advances in language and speech technologies, no open-source system enables full speech-to-speech, multi-turn dialogue with integrated tool use and agentic reasoning. We introduce AURA (Agent for Understanding, Reasoning, and Automated Tool Use), the first open-source, speech-native assistant capable of completing complex, goal-driven tasks through dynamic tool invocation and multi-turn conversation. AURA combines open-weight ASR, TTS, and LLMs in a cascaded pipeline and supports tools …

Feedstack: Layering Structured Representations over Unstructured Feedback to Scaffold Human AI Conversation
Hannah Vy Nguyen, Yu-Chun Grace Yen, Omar Shakir, Hang Huynh, Sebastian Gutierrez, June A. Smith, Sheila Jimenez, Salma Abdelgelil, Stephen MacNeil
https://arxiv.org/abs/2506.03052…

Feedstack: Layering Structured Representations over Unstructured Feedback to Scaffold Human AI Conversation
Many conversational user interfaces facilitate linear conversations with turn-based dialogue, similar to face-to-face conversations between people. However, digital conversations can afford more than simple back-and-forth; they can be layered with interaction techniques and structured representations that scaffold exploration, reflection, and shared understanding between users and AI systems. We introduce Feedstack, a speculative interface that augments feedback conversations with layered affor…

DialogueForge: LLM Simulation of Human-Chatbot Dialogue
Ruizhe Zhu, Hao Zhu, Yaxuan Li, Syang Zhou, Shijing Cai, Malgorzata Lazuka, Elliott Ash
https://arxiv.org/abs/2507.15752

DialogueForge: LLM Simulation of Human-Chatbot Dialogue
Collecting human-chatbot dialogues typically demands substantial manual effort and is time-consuming, which limits and poses challenges for research on conversational AI. In this work, we propose DialogueForge - a framework for generating AI-simulated conversations in human-chatbot style. To initialize each generated conversation, DialogueForge uses seed prompts extracted from real human-chatbot interactions. We test a variety of LLMs to simulate the human chatbot user, ranging from state-of-th…

Out-of-Context Abduction: LLMs Make Inferences About Procedural Data Leveraging Declarative Facts in Earlier Training Data
Sohaib Imran, Rob Lamb, Peter M. Atkinson
https://arxiv.org/abs/2508.00741

Out-of-Context Abduction: LLMs Make Inferences About Procedural Data Leveraging Declarative Facts in Earlier Training Data
Large language models (LLMs) are trained on large corpora, yet it is unclear whether they can reason about the information present within their training data. We design experiments to study out-of-context abduction in LLMs, the ability to infer the most plausible explanations for observations using relevant facts present in training data. We train treatment LLMs on names and behavior descriptions of fictitious chatbots, but not on examples of dialogue with the chatbots. We find that OpenAI's GP…

This https://arxiv.org/abs/2504.18410 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Can Code Outlove Blood? An LLM-based VR Experience to Prompt Reflection on Parental Verbal Abuse
Parental verbal abuse leaves lasting emotional impacts, yet current therapeutic approaches often lack immersive self-reflection opportunities. To address this, we developed a VR experience powered by LLMs to foster reflection on parental verbal abuse. Participants with relevant experiences engage in a dual-phase VR experience: first assuming the role of a verbally abusive parent, interacting with an LLM portraying a child, then observing the LLM reframing abusive dialogue into warm, supportive …

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
Yihong Tang, Kehai Chen, Muyun Yang, Zhengyu Niu, Jing Li, Tiejun Zhao, Min Zhang
https://arxiv.org/abs/2506.01748

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interaction. However, recent RPAs are often built on explicit dialogue data, lacking deep, human-like internal thought processes, resulting in superficial knowledge and style expression. While Large Reasoning Models (LRMs) can be employed to simulate character thought, their direct application is hindered by attention diversi…

The Thin Line Between Comprehension and Persuasion in LLMs
Adrian de Wynter, Tangming Yuan
https://arxiv.org/abs/2507.01936 https://a…

The Thin Line Between Comprehension and Persuasion in LLMs
Large language models (LLMs) are excellent at maintaining high-level, convincing dialogues. They are being fast deployed as chatbots and evaluators in sensitive areas, such as peer review and mental health applications. This, along with the disparate accounts on their reasoning capabilities, calls for a closer examination of LLMs and their comprehension of dialogue. In this work we begin by evaluating LLMs' ability to maintain a debate--one of the purest yet most complex forms of human communic…

Prompt-Guided Turn-Taking Prediction
Koji Inoue, Mikey Elmers, Yahui Fu, Zi Haur Pang, Divesh Lala, Keiko Ochi, Tatsuya Kawahara
https://arxiv.org/abs/2506.21191

Prompt-Guided Turn-Taking Prediction
Turn-taking prediction models are essential components in spoken dialogue systems and conversational robots. Recent approaches leverage transformer-based architectures to predict speech activity continuously and in real-time. In this study, we propose a novel model that enables turn-taking prediction to be dynamically controlled via textual prompts. This approach allows intuitive and explicit control through instructions such as "faster" or "calmer" adapting dynamically to conversational partne…

Modelling Adjectival Modification Effects on Semantic Plausibility
Anna Golub, Beate Zywietz, Annerose Eichel
https://arxiv.org/abs/2507.21828 https://arxi…

Modelling Adjectival Modification Effects on Semantic Plausibility
While the task of assessing the plausibility of events such as ''news is relevant'' has been addressed by a growing body of work, less attention has been paid to capturing changes in plausibility as triggered by event modification. Understanding changes in plausibility is relevant for tasks such as dialogue generation, commonsense reasoning, and hallucination detection as it allows to correctly model, for example, ''gentle sarcasm'' as a sign of closeness rather than unkindness among friends [9…

Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection
Ali \c{S}enol, Garima Agrawal, Huan Liu
https://arxiv.org/abs/2506.21443 https://arxiv.org/pdf/2506.21443 https://arxiv.org/html/2506.21443
arXiv:2506.21443v1 Announce Type: new
Abstract: Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)\-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk\-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)\-Enhanced LLM framework that integrates pretrained LLMs with structured, task\-specific insights to perform fraud and concept drift detection. The proposed architecture consists of three main components: (1) a DK\-LLM module to detect fake or deceptive conversations; (2) a drift detection unit (OCDD) to determine whether a semantic shift has occurred; and (3) a second DK\-LLM module to classify the drift as either benign or fraudulent. We first validate the value of domain knowledge using a fake review dataset and then apply our full framework to SEConvo, a multiturn dialogue dataset that includes various types of fraud and spam attacks. Results show that our system detects fake conversations with high accuracy and effectively classifies the nature of drift. Guided by structured prompts, the LLaMA\-based implementation achieves 98\% classification accuracy. Comparative studies against zero\-shot baselines demonstrate that incorporating domain knowledge and drift awareness significantly improves performance, interpretability, and robustness in high\-stakes NLP applications.
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