LLM Digest

Story

arxiv_agent_systems_research · Nov 3, 2026 · paper

Source brief

Octopus: One Brain, Many Branches for Latent-KV Multi-Agent Reinforcement Learning

arxiv.orgNov 3, 2026
original source linked

In brief

Multi-agent LLM systems explore in parallel by spawning sub-agents with isolated context windows, whereas single-agent systems append every search and verification step into one growing trajectory. However, existing m...

Feed lens

agenteval

Read the original at arxiv.org →Open in live feed Read that day’s brief

Octopus: One Brain, Many Branches for Latent-KV Multi-Agent Reinforcement Learning

Earlier in this thread 4 items

JHiMA-WebRL: A Joint Hierarchical Multi-Agent Reinforcement Learning Framework for Long-Horizon Web Tasks

MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors

Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide

Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models