Open library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
563 papers · 561 with a Meow summary · 333 posts
Every paper prepared on Reading + AI, newest first.
Yuyao Ge, Shenghua Liu, Peizhong Gao et al.
Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving harne…
Hoang Pham, Cong-Duy Nguyen, Dong Le et al.
Persistent textual memory lets language-model agents reuse lessons from past tasks to improve on future multi-step tasks without retraining. Systems such as ACE and Dynamic Cheatsheet keep it as a playbook: lessons the agent writes after each task and reads in its prompt. A key challenge is to decide which lessons imp…
Edward J. Hu, Moksh Jain, Eric ElmozninoMila – Quebec AI Institute, Université de Montréal{edward.hu,moksh.jain,eric.elmoznino,…, Younesse KaddarUniversity of Oxford, Guillaume Lajoie et al.
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest—including sequence continuation, infilling, and other forms of…