会议信息

AGCS 2026: International Conference on Agentic, Generative, and Cognitive AI

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截稿日期:
2026-07-01
通知日期:
2026-09-10
会议日期:
2026-10-27
会议地点:
Paris, France
浏览: 855   关注: 0   参加: 0

会伴指数 (CP-I)

44.2 / 100
全站第 3,048 名 / 共 5,651 个会议 · 前 54%
证据有限：这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里，也没有录用率数据，因此分数的大部分回落到了中性基准。
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%) 无数据 —— 按中性基准 50 分计入 —
社区关注 (10%)
15
资料公开度 (15%)
35

用到的输入: 过去 24 个月打开过本页的研究者：6 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 25% —— 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 2026-09-05

征稿

AGCS 2026 (International Conference on Agentic, Generative, and Cognitive AI) is an academic conference held in Paris, France on 2026-10-27. The paper submission deadline is 2026-07-01. Acceptance notifications are sent on 2026-09-10.

The International Conference on Agentic, Generative, and Cognitive AI Systems (AGCS 2026)(AGCS 2026) invites the submission of high-quality, original research papers in all areas of Agentic, Generative, and Cognitive AI. We welcome both theoretical and applied work that advances the state of the art. Conference Tracks Submissions are invited to the following five main tracks: Track 1: Agentic AI Systems This track focuses on the design, implementation, and evaluation of autonomous agents and multi-agent systems. Topics of interest include autonomous reasoning, planning, and decision-making; tool-using and function-calling agents; memory and context management in agents; human-agent collaboration and interaction; and agent safety, alignment, and robustness. Track 2: Generative AI and Foundation Models This track covers the theory, development, and application of generative models and foundation models. Topics of interest include large language models (LLMs) and multimodal models; diffusion models, GANs, and other generative architectures; prompt engineering and in-context learning; retrieval-augmented generation (RAG); and creative AI and artistic applications. Track 2: Generative AI and Foundation Models This track covers the theory, development, and application of generative models and foundation models. Topics of interest include large language models (LLMs) and multimodal models; diffusion models, GANs, and other generative architectures; prompt engineering and in-context learning; retrieval-augmented generation (RAG); and creative AI and artistic applications. Track 3: Cognitive AI and Neural-Symbolic Systems This track explores AI systems inspired by human cognition and the integration of neural and symbolic approaches. Topics include cognitive architectures and frameworks; neural- symbolic reasoning; brain-inspired computing and neuromorphic systems; common sense reasoning and world models; and explainable and interpretable AI. Track 4: AI Ethics, Safety, and Alignment This track is dedicated to the responsible development and deployment of AI. Topics of interest include AI safety and robustness; value alignment and goal specification; fairness, accountability, and transparency; privacy-preserving AI; and AI governance and policy. Track 5: AI Applications and Impact This track showcases the real-world impact of AI across various domains. Topics include AI for Healthcare and Life Sciences; AI for Climate and Sustainability; AI for Education; AI for Social Good; and AI in Finance, Manufacturing, and other industries. Submission Guidelines Papers must be formatted according to the specified conference template (to be provided). Submissions can be either full papers (up to 8 pages) or short papers (up to 4 pages), excluding references. The review process is double-blind, and submissions must be anonymized with no author names or affiliations. Submissions must be original work and not be under review at any other conference or journal. Authors may submit supplementary materials, such as technical appendices, code, and data, though reviewers are not required to review this material. All submissions must be made through the official conference submission system (link to be provided).
由 Dou Sun 最后更新于

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