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ConnectomeBench: Can LLMs proofread the connectome?

Neural Information Processing Systems

Connectomics--the mapping of neural connections in an organism's brain--currently requires extraordinary human effort to proofread the data collected from imaging and machine-learning assisted segmentation. With the growing excitement around using AI agents to automate important scientific tasks, we explore whether current AI systems can perform multiple tasks necessary for data proofreading. We introduce ConnectomeBench, a multimodal benchmark evaluating large language model (LLM) capabilities in three critical proofreading tasks: segment type identification, split error correction, and merge error detection. Using expert annotated data from two large open-source datasets--a cubic millimeter of mouse visual cortex and the complete Drosophila brain--we evaluate proprietary multimodal LLMs including Claude 3.7/4 Sonnet, o4-mini, GPT-4.1,





comparisons, (II) explain more intuition behind various design, (III) do our best to proofread our paper in revision

Neural Information Processing Systems

We thank all four reviewers' time, effort, and valuable suggestions. Is the applied sampling strategy the best? Results of different configurations when prune ResNet-32 on CIFAR-10 with one V100 GPU. "#SC" indicates the number of selected channels. Does different channel-wise interpolation (CWI) affect the perfor-17 CWI is a general operation to align feature maps with different sizes.




What is Apple Intelligence? Tech giant's AI platform for the new iPhone 16 is coming to the US next month - but UK users will have to wait

Daily Mail - Science & tech

As Apple launched the new iPhone 16 at its'Glowtime' event last night, it was the company's latest AI features which took centre stage once again. Now, Apple has finally revealed that its highly anticipated Apple Intelligence will begin to roll out in the US next month. As part of the iOS 18.1 update, iPhone 16 users will get access to AI features including rewriting tools, summarised notifications, and big improvements to Siri. However, UK tech fans will need to wait a little while longer as the California-based tech giant says that Apple Intelligence won't arrive there until December. So, with the rollout of Apple's first-ever AI tools just around the corner, MailOnline breaks down what is coming and when you can expect to try it out.


iOS 18: MailOnline's guide to the most exciting features coming in Apple's huge iPhone update - including AI-generated 'genmoji', hidden apps, and the ability to pay someone by touching your phones together

Daily Mail - Science & tech

No matter how you use your iPhone, Apple's huge iOS 18 update is certain to bring some big changes to your favourite apps. Revealed at the company's annual Worldwide Developers Conference (WWDC) this week, Apple's latest update will hit devices in autumn this year. From the controversial ability to hide apps to the ability to generate new emojis with AI, iOS 18 is set to roll out with a truly bewildering amount of changes. But the software update won't just give you new features, - it will also bring the new'Apple Intelligence' AI integration to compatible phones. If you're feeling overwhelmed by the absolute mountain of new content in this huge update, here's MailOnline's guide to the biggest features to look out for.


Proofread: Fixes All Errors with One Tap

arXiv.org Artificial Intelligence

The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users' typing experience. This paper demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in Gboard, enabling seamless sentence-level and paragraph-level corrections with a single tap. We describe the complete system in this paper, from data generation, metrics design to model tuning and deployment. To obtain models with sufficient quality, we implement a careful data synthetic pipeline tailored to online use cases, design multifaceted metrics, employ a two-stage tuning approach to acquire the dedicated LLM for the feature: the Supervised Fine Tuning (SFT) for foundational quality, followed by the Reinforcement Learning (RL) tuning approach for targeted refinement. Specifically, we find sequential tuning on Rewrite and proofread tasks yields the best quality in SFT stage, and propose global and direct rewards in the RL tuning stage to seek further improvement. Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56\% good ratio. We launched the feature to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with thousands of daily active users. Serving latency was significantly reduced by quantization, bucket inference, text segmentation, and speculative decoding. Our demo could be seen in \href{https://youtu.be/4ZdcuiwFU7I}{Youtube}.