Text-to-speech (TTS) is usually evaluated by transcribing its output with an automatic speech recognition (ASR) system, so the word error rate also counts recognizer errors. We measure these errors on human recordings of the test sentences. On LibriSpeech and FLEURS, the evaluation recognizer misses 33-67% of very rare...
Kirill Borodin, Vasilii Kudryavtsev, Nikita Vasiliev et al.· 0 citations
Systems that aim to help a user engage with viewpoints other than their own have to decide how different those viewpoints should be. Viewpoints are mainly represented on a scalar scale, too coarse to distinguish viewpoints that disagree with the user about different things. We introduce a selection policy in which a ba...
Visual document understanding is essential for acquiring knowledge from visually rich documents and is attracting growing research interest. Existing surveys emphasize architectures or technical approaches, with limited attention to how task bottlenecks shape technical choices and research priorities. We present a task...
Yu-Fei Tong, Guan-Jie Cheng, Tian-Le Wu et al.· 0 citations
Memory enables language agents to carry useful distinctions from past experience into future decisions. Its value depends on which distinctions survive, whether a bounded agent can use them, and what can be recovered or revised as demands evolve. We develop a decision-theoretic framework that follows this value from ra...
Zehao Lin, Erenkai Zhu, Helan Hu et al.· 0 citations
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This work investigates the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction and suggests that training-free ICL offers an effective inference-time strategy for current language models to predict continuous music emotion.
Deep research agents synthesize open-web evidence into long reports, but current benchmarks do not evaluate research centered on a source video. We introduce Video-Centered Deep Research Report Generation. Given a video and user request, an agent identifies research questions, retrieves external multimodal evidence, an...
Jia-Hao Meng, Qi-Ze Yang, Yuxuan Wang et al.· 0 citations
Procedural mistake recognition requires identifying deviations from valid multi-step activities. We study post-completion recognition from videos with benchmark-provided step boundaries. We introduce PRISM (Procedural mistake Recognition via Interpretable Semantic Modeling), which grounds video segments in language and...
Olga Loginova, Oleksandr Bezrukov, Frank Keller· 0 citations
Policy enforcement for LLM agents has shifted from single-turn content moderation to compound, application-specific policies evaluated over multi-turn transcripts and tool-call trajectories. Fine-tuned guardrail models are efficient but degrade on compound policies, and while LLM-as-a-judge approaches achieve higher ac...
Personal agents need to draw on long-term interaction history to retrieve and apply external knowledge suited to a user's current request. Even an answer grounded in relevant documents can be unsuitable when it overlooks the user's circumstances. This creates two connected challenges: using historical conditions to ide...
Wenjie Zhang, Zhichao Shi, Xuhui Jiang et al.· 0 citations
Large Language Models (LLMs) can generate UML class diagrams from natural language requirements, but hallucinations may compromise the correctness and completeness of the resulting diagrams. Existing evaluations mostly assess the final diagram, without examining the intermediate traces of reasoning-capable LLMs, which...
Mojdeh Rahmanian, Foutse Khomh, Ashkan Sami et al.· 0 citations
Fine-grained image-text retrieval requires a model not only to assess global semantic consistency between an image and a text, but also to model visual evidence for local textual semantics, including objects, attributes, quantities, actions, and relations, so as to distinguish semantically similar candidates. Existing...
A curriculum of 11 vocal-mix issues at multiple severity levels is constructed and used to train MixJudge, a language-queryable audio critic that shows its ability to retrieve mixing issues, distinguish opposite issues, and order the severity of an issue.
Kuan-Hsun Ho, Yuan-Chiao Cheng, Yen-Tung Yeh et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
