3 papers
cs.MA2026
Scaling Inference-Time Compute with Teams of Coding Agents
Keenan Samway, Daniel D'souza, Omar Darwiche Domingues +3
Multi-agent systems promise a new axis for scaling inference: rather than having one agent think longer or sample more attempts, a task is split across many agents that work in par…
cs.LG2026
Learning is Forgetting: LLM Training As Lossy Compression
Henry C. Conklin, Tom Hosking, Tan Yi-Chern +5
Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are structured. This limits our abilit…
cs.CL2025
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…