arXiv:2601.17086v1 Announce Type: new Abstract: Neural text-to-speech (TTS) systems systematically mispronounce low-resource proper nouns, particularly non-English names, brands, and geographic locations, due to their underrepresentation in predominantly English training corpora. Existing solutions typically rely on expensive multilingual data collection, supervised finetuning, or manual phonetic annotation, which limits the deployment of TTS systems in linguistically diverse settings. We introduce SonoEdit, a model editing technique that surgically corrects pronunciation errors in pre-trained TTS models without retraining. Instead of costly finetuning […]

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arXiv:2602.16895v1 Announce Type: new Abstract: Understanding information-dense documents like recipes and scientific papers requires readers to find, interpret, and connect details scattered across text, figures, tables, and other visual elements. These documents are often long and filled with specialized terminology, hindering the ability to locate relevant information or piece together related ideas. Existing tools offer limited support for synthesizing information across media types. As a result, understanding complex material remains cognitively demanding. This paper presents a framework for […]

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arXiv:2603.08806v1 Announce Type: new Abstract: We present Test-Driven AI Agent Definition (TDAD), a methodology that treats agent prompts as compiled artifacts: engineers provide behavioral specifications, a coding agent converts them into executable tests, and a second coding agent iteratively refines the prompt until tests pass. Deploying tool-using LLM agents in production requires measurable behavioral compliance that current development practices cannot provide. Small prompt changes cause silent regressions, tool misuse goes undetected, and policy violations emerge only after deployment. […]

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Key Takeaways Digital PR delivers compounding returns: a single feature in a respected publication can drive traffic, backlinks, and brand authority for years Privacy regulations and Apple’s ATT framework have eroded paid ad attribution, making it harder to prove what your spend actually generates AI-driven search results favour brands that already appear in trusted editorial sources, giving digital PR a direct line to long-term discoverability Editorial coverage in respected publications carries social proof that converts both customers and […]

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arXiv:2601.00227v1 Announce Type: new Abstract: Recent advances show that large language models (LLMs) can act as autonomous agents capable of generating GPU kernels, but integrating these AI-generated kernels into real-world inference systems remains challenging. FlashInfer-Bench addresses this gap by establishing a standardized, closed-loop framework that connects kernel generation, benchmarking, and deployment. At its core, FlashInfer Trace provides a unified schema describing kernel definitions, workloads, implementations, and evaluations, enabling consistent communication between agents and systems. Built on real serving […]

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Gratitude and ambition aren’t opposites. They’re the only two things that keep each other from becoming dangerous. Ed Catmull spent twenty years trying to make a computer-animated film. He’d wanted it since he was a kid watching Disney movies in the 1950s, realized he couldn’t draw, and decided to find another way. He got a PhD in computer science. He worked at Lucasfilm. He co-founded Pixar with Steve Jobs in 1986 and spent the next nine years building […]

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On-policy distillation (OPD), which aligns the student with the teacher’s logit distribution on student-generated trajectories, has demonstrated strong empirical gains in improving student performance and often outperforms off-policy distillation and reinforcement learning (RL) paradigms. In this work, we first theoretically show that OPD is a special case of dense KL-constrained RL where the reward function and the KL regularization are always weighted equally and the reference model can by any model. Then, we propose the Generalized On-Policy Distillation […]

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The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory’s Legacy Survey of Space and Time comes online, overwhelming the traditional physics-based inference pipelines. A continuous-time forecasting AI model is of interest because it can deliver millisecond-scale inference for thousands of objects per day, whereas legacy MCMC codes need hours per object. In this paper, we propose SELDON, a new continuous-time variational autoencoder for panels of sparse […]

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