Tootfinder

NoticeLight: Embracing Socio-Technical Asymmetry through Tangible Peripheral Robotic Embodiment in Hybrid Collaboration
Marie Altmann, Kimberly Hegemann, Ali Askari, Vineetha Rallabandi, Max Pascher, Jens Gerken
https://arxiv.org/abs/2506.22125

NoticeLight: Embracing Socio-Technical Asymmetry through Tangible Peripheral Robotic Embodiment in Hybrid Collaboration
Hybrid collaboration has become a fixture in modern workplaces, yet it introduces persistent socio-technical asymmetries-especially disadvantaging remote participants, who struggle with presence disparity, reduced visibility, and limited non-verbal communication. Traditional solutions often seek to erase these asymmetries, but recent research suggests embracing them as productive design constraints. In this context, we introduce NoticeLight: a tangible, peripheral robotic embodiment designed to…

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Zhou Chen, Sanjoy Kundu, Harsimran S. Baweja, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2506.17516

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Active event perception, the ability to dynamically detect, track, and summarize events in real time, is essential for embodied intelligence in tasks such as human-AI collaboration, assistive robotics, and autonomous navigation. However, existing approaches often depend on predefined action spaces, annotated datasets, and extrinsic rewards, limiting their adaptability and scalability in dynamic, real-world scenarios. Inspired by cognitive theories of event perception and predictive coding, we p…

Mitigating Non-Target Speaker Bias in Guided Speaker Embedding
Shota Horiguchi, Takanori Ashihara, Marc Delcroix, Atsushi Ando, Naohiro Tawara
https://arxiv.org/abs/2506.12500

Mitigating Non-Target Speaker Bias in Guided Speaker Embedding
Obtaining high-quality speaker embeddings in multi-speaker conditions is crucial for many applications. A recently proposed guided speaker embedding framework, which utilizes speech activities of target and non-target speakers as clues, drastically improved embeddings under severe overlap with small degradation in low-overlap cases. However, since extreme overlaps are rare in natural conversations, this degradation cannot be overlooked. This paper first reveals that the degradation is caused by…

LeanExplore: A search engine for Lean 4 declarations
Justin Asher (Independent Researcher)
https://arxiv.org/abs/2506.11085 https://a…

LeanExplore: A search engine for Lean 4 declarations
The expanding Lean 4 ecosystem poses challenges for navigating its vast libraries. This paper introduces LeanExplore, a search engine for Lean 4 declarations. LeanExplore enables users to semantically search for statements, both formally and informally, across select Lean 4 packages (including Batteries, Init, Lean, Mathlib, PhysLean, and Std). This search capability is powered by a hybrid ranking strategy, integrating scores from a multi-source semantic embedding model (capturing conceptual me…

NAVER LABS Europe Submission to the Instruction-following Track
Beomseok Lee, Marcely Zanon Boito, Laurent Besacier, Ioan Calapodescu
https://arxiv.org/abs/2506.01808

NAVER LABS Europe Submission to the Instruction-following Track
In this paper we describe NAVER LABS Europe submission to the instruction-following speech processing short track at IWSLT 2025. We participate in the constrained settings, developing systems that can simultaneously perform ASR, ST, and SQA tasks from English speech input into the following target languages: Chinese, Italian, and German. Our solution leverages two pretrained modules: (1) a speech-to-LLM embedding projector trained using representations from the SeamlessM4T-v2-large speech encod…

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting
Sankalan Pal Chowdhury, Terry Jingchen Zhang, Donya Rooein, Dirk Hovy, Tanja K\"aser, Mrinmaya Sachan
https://arxiv.org/abs/2506.08702

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting
The rapid development of Large Language Models (LLMs) opens up the possibility of using them as personal tutors. This has led to the development of several intelligent tutoring systems and learning assistants that use LLMs as back-ends with various degrees of engineering. In this study, we seek to compare human tutors with LLM tutors in terms of engagement, empathy, scaffolding, and conciseness. We ask human tutors to annotate and compare the performance of an LLM tutor with that of a human tut…

Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition
Mu Yang, Szu-Jui Chen, Jiamin Xie, John Hansen
https://arxiv.org/abs/2506.05706

Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition
One challenge of integrating speech input with large language models (LLMs) stems from the discrepancy between the continuous nature of audio data and the discrete token-based paradigm of LLMs. To mitigate this gap, we propose a method for integrating vector quantization (VQ) into LLM-based automatic speech recognition (ASR). Using the LLM embedding table as the VQ codebook, the VQ module aligns the continuous representations from the audio encoder with the discrete LLM inputs, enabling the LLM…