Thorsten Ball's article argues that creating a functional code-editing agent is surprisingly straightforward. It posits that the core components are a large language model (LLM), a loop for interaction, and sufficient tokens. The author demonstrates building a basic agent in Go with fewer than 400 lines of code, showcasing its ability to converse and utilize tools. The article then introduces three key tools: read_file, list_files, and edit_file, illustrating how the agent can be instructed to interact with the file system and even modify code. By combining these tools and a simple prompting mechanism, the agent can perform complex tasks like solving riddles in files and creating or altering JavaScript code. The author concludes that the apparent complexity of such agents is largely due to the impressive capabilities of modern LLMs and practical engineering, rather than hidden secrets.

This research paper introduces a novel two-stage framework for more accurately identifying lung tumors in CT scans. The approach first performs a coarse, full-volume localization of potential tumors, followed by an anatomy-aware post-processing step to refine these regions. Subsequently, a second stage focuses on detailed segmentation of the identified regions of interest, utilizing uncertainty information to improve accuracy, especially in challenging areas. The study demonstrates that this uncertainty-guided, coarse-to-fine method, combined with anatomical knowledge, leads to better tumor delineation and reduces false positives compared to traditional methods.

This research introduces Complex-Edit, a new benchmark for evaluating how well image editing models follow instructions with varying levels of complexity. The benchmark was created using GPT-4o to generate atomic editing tasks, which were then simplified and combined into more intricate instructions. The authors also present a suite of metrics and a VLM-based evaluation system to assess instruction following, identity preservation, and perceptual quality of edited images. Experiments using Complex-Edit reveal that open-source models lag behind proprietary ones, especially with more complex instructions, and that increased complexity can negatively impact the retention of original image elements and overall aesthetic quality. The study further examines sequential editing and a Best-of-N strategy as potential methods for handling complex edits and notes a tendency for models trained on synthetic data, including advanced models, to produce increasingly synthetic-looking results with higher instruction complexity.

This research introduces "sleep-time compute," a novel method for enhancing large language model efficiency by allowing them to process contextual information offline, before user queries arrive. By anticipating potential questions and pre-computing relevant inferences, this approach significantly reduces the computational resources and latency needed at test time to achieve comparable or even better accuracy on reasoning tasks. The study demonstrates that sleep-time compute can lead to substantial savings in test-time compute and can be further amplified by scaling the offline processing or by applying it to multiple related queries sharing the same context. Moreover, the effectiveness of sleep-time compute is strongly correlated with how predictable the user's query is based on the available context, suggesting strategic application for maximum benefit.

This paper introduces Miras, a novel framework for designing sequence models by drawing parallels between neural architectures and associative memory. The authors reconceptualize models like Transformers and linear RNNs as associative memory modules guided by an "attentional bias" objective. Miras offers a unified perspective based on four key choices: memory architecture, attentional bias, retention gate, and memory learning algorithm. The work presents three new sequence models, Moneta, Yaad, and Memora, developed within this framework, demonstrating enhanced performance in tasks like language modeling and long-context recall compared to existing state-of-the-art models. The framework provides a foundation for understanding and developing future sequence architectures by exploring diverse attentional biases and retention mechanisms. The authors also address making the training of these models more efficient and parallelizable.

This paper introduces RUKA, a newly designed open-source, low-cost, and anthropomorphic robotic hand intended for research in dexterous manipulation. It details RUKA's hardware design, emphasizing its tendon-driven actuation, 3D-printed components, and off-the-shelf parts, achieving a balance of precision, durability, and affordability. The paper further presents a data-driven control approach utilizing motion capture gloves for teleoperation and policy learning, demonstrating RUKA's capabilities through various reachability, strength, and durability tests, outperforming existing robotic hands in several metrics. Finally, it explores applications of RUKA in teleoperation and imitation learning, highlighting its potential as an accessible platform for advancing robotic dexterity.

The provided paper introduces GenEAva, a novel framework for generating high-quality cartoon avatars with detailed facial expressions by fine-tuning a diffusion model on realistic faces and then applying stylization. To overcome limitations in existing datasets, the authors created GenEAva 1.0, the first expressive cartoon avatar dataset featuring 13,230 avatars across 135 distinct expressions with balanced demographics. Experiments demonstrate that GenEAvaproduces more expressive faces than current models like SDXL and ensures novel identities without memorizing training data, with user studies validating the preservation of identity and expression during the cartoon conversion. This work offers both a new method and a valuable resource for advancing research in expressive avatar creation.

The provided text introduces VCR-Bench, a novel benchmark designed to evaluate the Chain-of-Thought (CoT) reasoning capabilities of large vision-language models (LVLMs) in video understanding. Current benchmarks for video understanding often fall short by not thoroughly assessing the reasoning process, focusing mainly on final answer accuracy and struggling to differentiate between perception and reasoning abilities. To address these limitations, VCR-Bench offers a multi-dimensional evaluation framework with detailed annotations of reasoning steps across diverse video types and tasks. Evaluations using VCR-Bench reveal that current LVLMs still have significant shortcomings in video reasoning, particularly in extracting and understanding temporal-spatial information, despite a strong correlation between CoT quality and answer accuracy.

This Amazon Science blog post introduces HalluMeasure, a novel approach for automatically detecting hallucinationsin large language model outputs. The method employs a three-pronged strategy combining claim-level evaluations, chain-of-thought reasoning, and the classification of hallucination error types. By decomposing LLM responses into individual claims and comparing them to relevant context, HalluMeasure aims to improve the accuracy of identifying incorrect assertions. Furthermore, it categorizes these errors into specific linguistic types, offering valuable insights for developers to refine their models. The described research presented at EMNLP 2024 demonstrates enhanced performance in hallucination detection compared to existing solutions.

Meta AI has introduced its Llama 4 family of large language models, highlighting two new open-weight models: Llama 4 Scout and Llama 4 Maverick, along with a powerful, still-training model called Llama 4 Behemoth. These multimodal models feature innovations like Mixture-of-Experts architecture and significantly expanded context windows, outperforming previous Llama iterations and competing with or exceeding the capabilities of other leading models on various benchmarks. The release emphasizes open access, with the new models available for download and integration, aiming to foster innovation and the development of personalized AI experiences. Meta also details their approach to pre-training, post-training, and safety measures implemented in the Llama 4 series, underscoring their commitment to responsible AI development.