The results confirm the hypothesis that LLMs, when instructed through Prompt Engineering, can achieve performance comparable to or superior to manual extraction by health professionals, and demonstrate its potential as a complementary verification tool alongside specialists.
L. Farias, Hanna Reckziegel, Daniela Duarte da Silva Bagatini et al.· 0 citations
While modern text-to-speech (TTS) systems generate highly natural speech and support inline non-verbal vocalization (NVV) tags, accurate control over these events remains challenging. A key gap is the lack of established post-training methods for non-verbal control in continuous autoregressive flow-matching TTS. To thi...
Qiaolin Wang, Pedro Sandoval-Segura, Anunaya Joshi et al.· 0 citations
A training-free method that reformulates question--frame matching as comparing frames against a set of dynamically generated visual cues, showing that agentic cue refinement drives active re-exploration of the video, yielding relative similarity gains of up to 92% over the initial context.
W. Kang, Hanieh Deilamsalehy, Yumo Xu et al.· 0 citations
This work aims to promote broader adoption of OT in speech processing by reviewing OT foundations through intuitive physical interpretations and highlighting connections to modern generative models, and demonstrating OT applications in cross-domain and cross-modal speech tasks.
Xu-Gang Lu, Yu Tsao· 0 citations
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A practical way to build general-purpose manipulation experiments around GPT-6-Astra is suggested: supply machine-readable body descriptions and synchronized demonstrations, and turn useful agent-generated feedback routines into reusable skills, while the agent adapts actions from current images.
Si-Da He, Ling-Xi Xie, Yun-Ning Cao et al.· 0 citations
This work introduces MM-VeriRec, a verifiable multimodal recommendation protocol and failure-guided fusion method for hidden visual constraints, image-text mismatch, and impossible-task abstention in agentic multimodal recommendation.
A cross-domain learning model is proposed, using an original neural network architecture based on a Temporal Convolutional Network and multiple prediction heads, designed to improve handwriting trace reconstruction from sensor data, where each domain benefits from additional data provided by the other domain.
Florent Imbert, Yann Soullard, Éric Anquetil et al.· 0 citations
Recent work suggests that obscure language registers can weaken large language model refusal behavior, especially when paired with black-box prompt optimization. It remains unclear whether failures stem from non-standard surface form, culturally grounded framing, or optimization over an expressive prompt-strategy space...
It is found that no single retriever family dominates across documents; parser and chunker choice interact significantly; MPNet-base is a consistent underperformer with a severe failure mode on table-derived questions; and the corpus exhibits a near-saturated evidence-preservation ceiling above 98%, indicating that ret...
PalmLeaf-VQA provides a standardized benchmark for advancing culturally grounded and layout-aware multimodal document analysis and reveals substantial limitations in current MLLMs for rare-script, degraded-layout, and preservation-oriented document understanding.
Large language models (LLMs) are increasingly being employed as variation operators in metaheuristics, generating or modifying candidate solutions, heuristics, or programs inside iterative search loops. This shift reframes variation as a model call conditioned on different types of information. We introduce an operator...
Camilo Chacón Sartori, Guillem Rodríguez-Corominas, Christian Blum· 0 citations
CHESTPHENOT is a compact 0.5-3B language model that jointly extracts finding labels, three-class status (present/absent/uncertain), and verbatim supporting evidence spans and demonstrates that locally deployable models can provide competitive and directly auditable radiology-report extraction without relying on externa...
Kai Yu, Chen-Yu Zhu, Zai-Fu Zhan 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.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
