会议信息

ACMLC 2026: Asia Conference on Machine Learning and Computing

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截稿日期:
2026-06-01 延期
通知日期:
2026-07-01
会议日期:
2026-07-10
会议地点:
Beijing, China
届数:
8
浏览: 3735   关注: 1   参加: 1
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ACMLC
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会伴指数 (CP-I)

47.3 / 100
全站第 2,239 名 / 共 5,693 个会议 · 前 40%

人工智能与机器学习 第 243 / 742

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
59
社区关注 (10%)
27
资料公开度 (15%)
35

用到的输入: 有据可查的届次：8 · 在会伴关注它的研究者：1 人 · 过去 24 个月打开过本页的研究者：14 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-05

征稿

ACMLC 2026 (Asia Conference on Machine Learning and Computing) is an academic conference held in Beijing, China on 2026-07-10. The paper submission deadline is 2026-06-01 (extended). Acceptance notifications are sent on 2026-07-01.

Authors are invited to submit full papers describing original research work in areas including, but not limited to: (Note: Since this is a computer-related conference, please submit papers that are computer-oriented.) TRACK 1: Large Language Model Agents Theory and Applications Multimodal LLM Agents: Vision-language-audio integrated agent systems Agent Planning and Reasoning: Task decomposition, path planning, and logical reasoning with large models Tool Use and API Integration: External tool invocation and system integration capabilities for agents Multi-Agent Collaboration: Large model-driven multi-agent coordination and cooperation mechanisms Agent Safety and Alignment: Safety assurance and value alignment for trustworthy AI agents Domain-Specific Agents: Specialized agents for vertical domains such as healthcare, finance, and education Agent Evaluation and Benchmarking: Capability assessment frameworks and standardized testing for intelligent agents Agent Memory and Learning: Long-term memory systems and continual learning for persistent agents Human-Agent Interaction: Natural language interfaces and interaction design for AI agents Agent Architecture and Infrastructure: Scalable frameworks and platforms for deploying LLM agents TRACK 2: Social Computing and Human-AI Collaboration Computational Social Science: Simulation, prediction, and modeling of social phenomena and communities Human-AI Collaboration Patterns: Workflow design for AI agent and human cooperation Social Network Dynamics: Behavioral pattern mining and analysis in large-scale social networks Collective Intelligence: Group decision-making, crowdsourcing, and distributed problem-solving systems Agent-Driven Social Modeling: Using AI agents for social behavior analysis and human preference learning Social Media and Cultural Computing: Content analysis, sentiment analysis, cross-cultural AI systems, and bias mitigation AI Ethics and Social Impact: Research on AI systems' effects on social structures, relationships, and equity Explainable and Socially-Aware AI: Interpretable AI systems that understand and adapt to social contexts Digital Governance and Policy: AI applications in public administration, policy-making, and social governance Social Robotics and Interaction: Human-robot interaction in social and collaborative contexts Digital Humanities: AI applications in humanities research and cultural heritage preservation
由 Dunn Carl 最后更新于

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