Tagged articles

multi‑agent LLM

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Aug 11, 2026 · Artificial Intelligence

DecentMem’s Dual‑Pool Memory Cuts Token Usage by Almost 50%

The article analyzes the limitations of a shared memory pool in large‑language‑model multi‑agent systems and presents DecentMem, a decentralized dual‑pool architecture with an online router that balances exploitation and exploration, achieving up to 23.8% higher accuracy, 49% token reduction, and 2.5× faster evolution across several benchmarks.

DecentMemdual‑pool memorymulti‑agent LLM

0 likes · 12 min read

DecentMem’s Dual‑Pool Memory Cuts Token Usage by Almost 50%

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

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 coordinationTeamTRfine-tuning

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

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