An observatory detected the first radio signal from the interstellar object 3I/Atlas. An image of the interstellar comet 3I/Atlas, captured by the Hubble telescope on July 21, 2025. More evidence has emerged to support the natural origin of comet 3I/Atlas . After several weeks of conspiracy theories, social media debates, and speculation on popular podcasts such as Joe Rogan's, this interstellar object is still a comet . The most recent confirmation came from an observatory in South Africa that detected the first radio signal from 3I/Atlas.

Federated Learning enables collaborative fine-tuning of Large Language Models (LLMs) across decentralized Non-Independent and Identically Distributed (Non-IID) clients, but such models' massive parameter sizes lead to significant memory and communication challenges. This work introduces Meerkat, a sparse zeroth-order optimization (ZO) method designed for federated LLM fine-tuning. By limiting fine-tuning to a transferable, static, extremely sparse subset of parameters, Meerkat achieves remarkable communication efficiency, enabling cost-effective high-frequency synchronization. With theoretical analysis and experiments, we show that this high-frequency communication effectively mitigates Non-IID data challenges and leads to superior performance compared to full-parameter ZO. Furthermore, experiment results show that Meerkat outperforms existing sparsity baselines with better performance at the same communication frequency. To further handle Non-IID drift, Meerkat leverages traceable local updates and forms a virtual path for each client. This virtual path mechanism reveals the GradIP phenomenon: the inner products between LLM pre-training gradients maintained by server and client gradients estimated via ZO converges for extreme Non-IID clients but oscillates for IID ones. This distinct behavior provides a signal for identifying clients with extreme data heterogeneity. Using this signal, Meerkat-vp is proposed to analyze GradIP trajectories to identify extreme Non-IID clients and applies early stopping to enhance aggregated model quality. Experiments confirm that Meerkat and Meerkat-vp significantly improve the efficiency and effectiveness of ZO federated LLM fine-tuning.

Leveraging Large Language Models' remarkable proficiency in text-based tasks, recent works on Multi-modal LLMs (MLLMs) extend them to other modalities like vision and audio. However, the progress in these directions has been mostly focused on tasks that only require a coarse-grained understanding of the audio-visual semantics. We present Meerkat, an audio-visual LLM equipped with a fine-grained understanding of image and audio both spatially and temporally. With a new modality alignment module based on optimal transport and a cross-attention module that enforces audio-visual consistency, Meerkat can tackle challenging tasks such as audio referred image grounding, image guided audio temporal localization, and audio-visual fact-checking. Moreover, we carefully curate a large dataset AVFIT that comprises 3M instruction tuning samples collected from open-source datasets, and introduce MeerkatBench that unifies five challenging audio-visual tasks. We achieve state-of-the-art performance on all these downstream tasks with a relative improvement of up to 37.12%.

Large Language Models (LLMs) have emerged as a potential solution to assist digital health development with patient education, commonly medication-related enquires. We trained and validated Med-Pal, a medication domain-specific LLM-chatbot fine-tuned with a fine-grained and expert curated dataset from a selection of five light-weighted open-source LLMs of smaller parameter size (7 billion or less) regarding computational constraints and prioritizing operational efficiency. A multi-disciplinary team performed a clinical evaluation of LLMs responses using the SCORE criteria, focusing on safety, accuracy, bias, reproducibility, and ease of understanding. Best performing light-weighted LLM was chosen as Med-Pal for further engineering with guard-railing using adversarial prompting. Med-Pal and existing light-weighted LLMs, including pretrained Biomistral and finetuned Meerkat, were validated on an independent dataset on a broad range of medication-related questions (231 in total), 12 different question types across 14 different medication classes. Mistral-7b emerged as the top performer among selected lightweight LLMs, achieving the highest median score of 14 and 71.9% high-quality responses in accuracy and safety domains, hence chosen as the backbone LLM for Med-Pal. When compared against Biomistral, Med-pal outperformed in generating responses appropriate for patient communication, with significant reductions bias and errors typical of general LLMs. Comparable performance was observed when comparing Med-Pal with Meerkat. Med-Pal showcases the feasibility of developing and employing fine-tuned light-weighted LLMs to enhance digital health communications.

Bioacoustic research provides invaluable insights into the behavior, ecology, and conservation of animals. Most bioacoustic datasets consist of long recordings where events of interest, such as vocalizations, are exceedingly rare. Analyzing these datasets poses a monumental challenge to researchers, where deep learning techniques have emerged as a standard method. Their adaptation remains challenging, focusing on models conceived for computer vision, where the audio waveforms are engineered into spectrographic representations for training and inference. We improve the current state of deep learning in bioacoustics in two ways: First, we present the animal2vec framework: a fully interpretable transformer model and self-supervised training scheme tailored for sparse and unbalanced bioacoustic data. Second, we openly publish MeerKAT: Meerkat Kalahari Audio Transcripts, a large-scale dataset containing audio collected via biologgers deployed on free-ranging meerkats with a length of over 1068h, of which 184h have twelve time-resolved vocalization-type classes, each with ms-resolution, making it the largest publicly-available labeled dataset on terrestrial mammals. Further, we benchmark animal2vec against the NIPS4Bplus birdsong dataset. We report new state-of-the-art results on both datasets and evaluate the few-shot capabilities of animal2vec of labeled training data. Finally, we perform ablation studies to highlight the differences between our architecture and a vanilla transformer baseline for human-produced sounds. animal2vec allows researchers to classify massive amounts of sparse bioacoustic data even with little ground truth information available. In addition, the MeerKAT dataset is the first large-scale, millisecond-resolution corpus for benchmarking bioacoustic models in the pretrain/finetune paradigm. We believe this sets the stage for a new reference point for bioacoustics.

Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is essential to better understand the causes behind animal behaviour and what interventions are responsible for changes in their behaviours. This can help to predict unusual behaviours, mitigate detrimental effects and increase the well-being of animals. There has been work on modelling the dynamics behind swarms of birds and insects but the complex social behaviours of mammalian groups remain less explored. In this work, we propose a method to build behavioural models using causal structure discovery and graph neural networks for time series. We apply this method to a mob of meerkats in a zoo environment and study its ability to predict future actions and model the behaviour distribution at an individual-level and at a group level. We show that our method can match and outperform standard deep learning architectures and generate more realistic data, while using fewer parameters and providing increased interpretability.

Let's talk about how Synapse AI was made. Synapse actually started out -- let's not talk about confirmation bias of being a programmer, growing up in the hacking and phreaking scene, learning networking, network security, understanding systems, having a background in electrical engineering and computation chemistry -- let's start out with: We first threw a hackathon called "Hackendo," and I was running Techendo at the time, and the San Francisco Hacker News Meetup. Techendo was our news outlet. We wanted Techendo to capture entrepreneurs and tech developers blogging about what was happening. We did that because there was a real monopoly on the pipeline of launching a product and getting media engaged to talk about a product. That was mainly owned by incubators and accelerators, and it still is -- so there still is opportunity there.

The BBC's innovative'Spy in the Wild' series ends tonight with a special episode on how the show's realistic animal cameras were made. Spy in the Wild deployed over 30 ultra-realistic animatronic Spy Creatures across 31 locations over three years of filming. Over the course of the series the undercover cameras got up close and personal with some unique animal behaviours. The BBC's innovative'Spy in the Wild' series ends tonight with a special episode on how they made the show's realistic animal cameras. In tonight's behind the scenes programme viewers see the extraordinary effort that goes into getting the Spy Creatures to become part of so many different animal families.

The Orangutan looked quite magnificent. From her inquisitive eyes to her distinctive orange fur, she was just the sort of creature nature lovers adore watching on TV. But a closer inspection revealed something a little different about her. That's because she is actually an undercover robot, fitted with high-definition cameras behind her glass eyes and used to infiltrate the animal kingdom. The orangutan, as well as an adorable wolf-cub, an utterly convincing meerkat and an incredible floating otter are among 34 animatronic beasts created for the BBC's new series, Spy In The Wild.

In an incredible display of friendship, a meerkat has been captured roping a robot into babysitting duty. In the clip, which will be aired on the BBC One series'Spy in the Wild' this week, a mechanical meerkat is taken in and accepted into a colony of the animals. The incredible footage shows how meerkat mothers band together to raise their young, which can number as many as 18 a year. In an incredible display of friendship, a meerkat has been captured roping a robot into babysitting duty. In the clip, which will be aired on the BBC One series'Spy in the Wild' this week, a mechanical meerkat is taken in and accepted into a colony of the animals At first sight they look exactly like the real thing – cute, cuddly and in some cases terrifying creatures of the wild.