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New benchmark HiMA-Ecom targets long-horizon memory in multi-agent AI

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced HiMA-Ecom, a new benchmark designed to evaluate hierarchical multi-agent systems in e-commerce scenarios. This benchmark includes over 22,000 instances, with a significant portion focusing on memory entries for agents. The study also proposes Variance-Reduction Group Relative Policy Optimization (VR-GRPO) to manage context budgets and improve agent training efficiency. Agents utilizing VR-GRPO with 3B and 7B models demonstrated performance improvements over existing models like DeepSeek-R1 and DeepSeek-V3, with memory mechanisms reducing convergence time. AI

IMPACT Introduces a new benchmark and optimization technique for long-horizon memory in multi-agent AI systems, potentially improving training efficiency and performance.

RANK_REASON The cluster describes a new academic paper introducing a novel benchmark and optimization method for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark HiMA-Ecom targets long-horizon memory in multi-agent AI

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The cluster describes a new academic paper introducing a novel benchmark and optimization method for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Junxing Hu, Ai Han, Haolan Zhan, Pu Wei, Zhiqian Zhang, Yuhang Guo, Jiawei Lu, Zhen Chen, Haoran Li, Zicheng Zhang, Ling Li ·

    HiMA-Ecom: A Long-Horizon Memory Benchmark for Hierarchical Multi-Agent E-commerce Assistants

    arXiv:2506.19846v3 Announce Type: replace Abstract: Long-horizon agentic systems become long-context systems by accumulation rather than through a single long document, since every step reassembles a prompt from role instructions, tool and sub-agent descriptions, recalled memorie…

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