Tagged articles

multi-agent LLM

3 articles · Page 1 of 1
DeepHub IMBA
DeepHub IMBA
Jul 23, 2026 · Artificial Intelligence

DecentMem Dual-Pool Memory Halves Token Use and Boosts Collaboration

DecentMem replaces the shared memory of large‑language‑model multi‑agent systems with a decentralized dual‑pool design—an exploitation pool for proven strategies and an exploration pool for novel ideas—driven by an online router, achieving up to 49% token savings, 23.8% accuracy gains and faster self‑evolution.

DecentMemLLM-as-judgedual-pool memory
0 likes · 12 min read
DecentMem Dual-Pool Memory Halves Token Use and Boosts Collaboration
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 3, 2026 · Artificial Intelligence

How 8 Agents Can Converge Stably: Trust‑Region Constraints Reshape Multi‑Agent LLM Workflows

The paper introduces TeamTR, a trust‑region fine‑tuning framework that mitigates compounding occupancy shift in multi‑agent LLM workflows by fresh rollout sampling and token‑level KL constraints, achieving stable performance gains of up to 7.1% overall and dramatic improvements on large‑scale tasks such as AIME24.

AI CoordinationFine-tuningTeamTR
0 likes · 9 min read
How 8 Agents Can Converge Stably: Trust‑Region Constraints Reshape Multi‑Agent LLM Workflows
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 26, 2026 · Artificial Intelligence

Grok 4.20 Returns: Inside Its Multi‑Agent Design and Real‑World Benchmarks

The article examines the surprise launch of Grok 4.20, detailing its four‑agent architecture, how it cuts hallucinations by about 65%, and presents third‑party benchmark rankings that place it first in Search Arena and fourth in Text Arena, while also showcasing user‑tested code‑generation and creative capabilities.

AI benchmarksGrok 4.20code generation
0 likes · 7 min read
Grok 4.20 Returns: Inside Its Multi‑Agent Design and Real‑World Benchmarks