Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by...
Simon Willison experimented with Claude Opus 5.5 to compose computer game music, specifically aiming for a style reminiscent of the original *Secret of Monkey Island*. He designed a simple text-based format for the music and built an artifact capable of playing it, including example tracks. The results were surprisingly good, prompting questions about whether this compositional ability is a new capability emerging in recent text models.
Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether a single model-inter...
Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physical environment remains difficult. Furniture asse...
We adapt Stevens's power law to measure the innate ability of AI models to read visualizations, which can reveal the built-in perceptual mechanisms of algorithmic models. In our pilot study, models se...
Jump Trading is leveraging OpenAI’s technology to broaden its quantitative research capabilities. The company employs extended AI workflows that integrate multiple data sources and incorporate human review. This approach enables more comprehensive and scalable analysis for its trading strategies.
Low-rank quantization error compensation (LQEC) recovers the accuracy lost under aggressive weight quantization by attaching a closed-form rank-$r$ adapter beside each frozen quantized weight, without...
The article describes a controlled experiment in which four AI assistants—Gemini, DeepSeek, ChatGPT, and Claude—were tested on a forecasting task that included four hidden traps: leakage, reporting delays, promotion effects, and structural breaks. The study examines how each assistant handled these challenges and compares their performance. The post was published on Towards Data Science.
Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders and parallel decoding. Recent dLLMs are often in...
Shared memory coordinates agents' actions, but correct records do not establish that those actions satisfy task requirements. Memory governance and failure diagnosis regulate or inspect recorded infor...
LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors manifest. Existing pre-emptive approaches either...
AI agents are increasingly being programmed to automate knowledge work over large collections of unstructured data. Such automation requires repeatability: when the underlying evidence is unchanged, t...
Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework for policy-aware decision-rule discovery, which...
As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcomes, poses a significant risk of skewed decision-m...
Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question answering. However, existing methods for automatin...
This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (theorem 2) giving a closed-form expression for t...
In clinical practice, agreement among independent experts is treated as evidence of reliability, and multi-round consensus has become a core mechanism of agentic medical question-answering systems. Wh...
The article explains the transition from the original Cowork system, which ran model inference and tool calls in a local Anthropic VM, to a new cloud‑based version that hosts both inference and the VM. The new design isolates each session in its own sandbox, eliminates local resource drain, and allows file access through the desktop app, addressing concerns about battery usage, performance, and continuity when a laptop is closed.