Meta Platforms Surges 4% as Smart Glasses Launch and AI Push Give Investors Reason to Buy the Dip - Yahoo Finance
<a href="https://news.google.com/rss/articles/CBMimgFBVV95cUxOQUJkWVdoQlcySFNMcFY0RTJGNGh5bWNzS2ZWUlRrNXZ2MmJ0OWJvM0ZSLXZKd0RYTXBXX1llY3JyMTAxd0RxMVplVVNicUZNNUJmdHhib0VBWmVQMmVVbXFZLUdTTkVkbWowczVJZUlMQ0ltMk9rUlNPQWZMLTFHWXo0V0l0a0s0cEI5Mk9weEc1R2NMYWROUm1B?oc=5" target="_blank">Meta Platforms Surges 4% as Smart Glasses Launch and AI Push Give Investors Reason to Buy the Dip</a> <font color="#6f6f6f">Yahoo Finance</font>
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The Thiomi Dataset: A Large-Scale Multimodal Corpus for Low-Resource African Languages
arXiv:2603.29244v1 Announce Type: new Abstract: We present the Thiomi Dataset, a large-scale multimodal corpus spanning ten African languages across four language families: Swahili, Kikuyu, Kamba, Kimeru, Luo, Maasai, Kipsigis, Somali (East Africa); Wolof (West Africa); and Fulani (West/Central Africa). The dataset contains over 601,000 approved sentence-level text annotations and over 385,000 audio recordings across nine languages, collected through a dedicated community data collection platform involving over 100 contributors. The Thiomi platform collected data for nine languages; Swahili data was supplemented with existing Common Voice recordings. A multi-tier quality assurance pipeline achieves 86-100% text approval rates for the six primary languages. To validate the dataset's utility
China’s DeepSeek Cuts U.S. Chipmakers Out of AI Model Launch - Sri Lanka Guardian
<a href="https://news.google.com/rss/articles/CBMiigFBVV95cUxQRFRJR0REVDdIUmxqMnNKZzRLLU1ETzVmV2FkakV6bkREdzJJbG1mNWpzMVdtLWNIYlBILUwyX3UtWEFOT09icjVzT1dvdTdjVzVtNDdJU28xME5STFprWHBUTmllb1FwcEV4VGtHZzNJMGUzUVAzMXFrUzBlQkczTHhfM09Cc0NFZmc?oc=5" target="_blank">China’s DeepSeek Cuts U.S. Chipmakers Out of AI Model Launch</a> <font color="#6f6f6f">Sri Lanka Guardian</font>
![[New Research] You need Slack to be an effective agent](https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=600&q=80)
[New Research] You need Slack to be an effective agent
Purchasesforce Superintelligence is excited to announce some new research. While we do not generally share research on LessWrong, this work was particularly influenced by prior work on LessWrong, so we found it appropriate to share back. As you know, Purchasesforce Superintelligence is a leading AI R&D laboratory. Recently, our research has focused on enhancing agentic capabilities. Here at Purchasesforce, we believe that autonomous AI agents, fully integrated into modern enterprise tools, will drive the future of enterprise operations. After reading the nascent literature on LessWrong describing the relationship between Slack and AI Agents, we were shocked by how closely it related with our own research directions. Of course, as the world's leading AI-first productivity platform, we have
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Dual-Imbalance Continual Learning for Real-World Food Recognition
arXiv:2603.29133v1 Announce Type: new Abstract: Visual food recognition in real-world dietary logging scenarios naturally exhibits severe data imbalance, where a small number of food categories appear frequently while many others occur rarely, resulting in long-tailed class distributions. In practice, food recognition systems often operate in a continual learning setting, where new categories are introduced sequentially over time. However, existing studies typically assume that each incremental step introduces a similar number of new food classes, which rarely happens in real world where the number of newly observed categories can vary significantly across steps, leading to highly uneven learning dynamics. As a result, continual food recognition exhibits a dual imbalance: imbalanced sample
Efficient Bilevel Optimization with KFAC-Based Hypergradients
arXiv:2603.29108v1 Announce Type: new Abstract: Bilevel optimization (BO) is widely applicable to many machine learning problems. Scaling BO, however, requires repeatedly computing hypergradients, which involves solving inverse Hessian-vector products (IHVPs). In practice, these operations are often approximated using crude surrogates such as one-step gradient unrolling or identity/short Neumann expansions, which discard curvature information. We build on implicit function theorem-based algorithms and propose to incorporate Kronecker-factored approximate curvature (KFAC), yielding curvature-aware hypergradients with a better performance efficiency trade-off than Conjugate Gradient (CG) or Neumann methods and consistently outperforming unrolling. We evaluate this approach across diverse tas
Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
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TrajectoryMover: Generative Movement of Object Trajectories in Videos
arXiv:2603.29092v1 Announce Type: new Abstract: Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve, especially for non-expert editors. Existing methods focus on prescribing an object's 3D or 2D motion trajectory in a video, or on altering the appearance of an object or a scene, while preserving both the video's plausibility and identity. Yet a method to move an object's 3D motion trajectory in a video, i.e., moving an object while preserving its relative 3D motion, is currently still missing. The main challenge lies in obtaining paired video data for this scenario. Previous methods typically rely on clever data generation approaches to construct plausible paired data from unpaired videos, but
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