mlbwhf@yahoo.com A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
Source: MIT News – Artificial intelligence
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A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.
Source: OpenAI News
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Europe is in the middle of a record-breaking heat wave, and the grid is being pushed to its limits as people turn to fans and air-conditioning to try to stay cool. Some power plants won’t be online to help handle the load. On June 23,…
Source: MIT Technology Review
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SummaryRFIC design is a complex “dark art” that limits progress in wireless technologies like 5G, autonomous vehicles, and satellite communications.Princeton researchers use reinforcement learning and inverse design to rapidly create RFICs from scratch.Diffusion models rapidly generate novel or human-interpretable RF layouts, achieving record performance and…
Source: IEEE Spectrum
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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Introducing: the Engineering issue We can’t fix everything, but we can be ambitious. We can take on the challenge of making…
Source: MIT Technology Review
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The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles. Now the payment company Stripe, founded by brothers…
Source: MIT Technology Review
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OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.
Source: OpenAI News
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arXiv:2412.08147v2 Announce Type: replace-cross Abstract: Pareto fronts are useful to find good task-mixing strategies for multitask finetuning, but they are also costly to compute. To reduce costs, recent works have used existing model merging methods to help train cheap surrogate models to estimate the Pareto…
Source: cs.AI updates on arXiv.org
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arXiv:2509.03647v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from "self-preference bias": a tendency to favor their own outputs over those of other models. This bias undermines fairness and reliability in evaluation pipelines, particularly for tasks like…
Source: cs.AI updates on arXiv.org
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arXiv:2606.24797v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are predominantly evaluated through answer correctness, while the grounding of predictions in relevant video evidence remains largely unexamined. This disconnect…
Source: cs.AI updates on arXiv.org
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