MaDS: Long-Horizon GUI Automation via Synergizing Dual-Layer Memory and Multi-Round Debate (2026.acl-long)
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| Challenge: | Current methods struggle to distinguish targets in low Signal-to-Noise Ratio environments and lack sufficient pre-execution verification to prevent error accumulation. |
| Approach: | They propose a Memory-augmented Debate System to ensure precise grounding across diverse interfaces and handle irreversible errors in extended workflows. |
| Outcome: | The proposed system achieves a 90.23% task success rate on MaDS-Benchmark and strong performance on public benchmarks including AITW, AITZ, CAGUI, and GUIOdyssey. |
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| Challenge: | Existing multi-agent debate methods rely on multiple rounds of interaction among agents to reach consensus, and the final output is decided by majority voting in the last round. |
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S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
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Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
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| Challenge: | Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. |
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M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have shown their potential to deliver human-like judgments. |
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| Challenge: | Multi-Agent Debate systems leverage multiple LLMs to improve task performance. |
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MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning (2026.findings-acl)
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| Challenge: | Existing safety alignment methods, such as RLHF, fall into a Safety-Utility Trade-off, resulting in severe over-rejection of benign household instructions. |
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MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation (2025.naacl-long)
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| Challenge: | Existing evaluation metrics for memory-augmented dialogue systems lack practical value . current evaluation methods only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors. |
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Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)
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Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong
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UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)
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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
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SELENE: Selective and Evidence-Weighted LLM Debating for Efficient and Reliable Reasoning (2026.eacl-industry)
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| Challenge: | Existing multi-agent debate frameworks are computationally expensive and prone to degradation under pro-longed debates due to redundant exchanges and unstable judging. |
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