United States

Beginning in the mid-1980s, a “statistical revolution” in our field redefined what it meant to work on language from a computational perspective. Steve Abney, in his essay opening The Balancing Act: Combining Symbolic and Statistical Approaches to Language (Klavans and Resnik, eds., MIT Press, 1996), wrote: “In the space of the last ten years, statistical methods have gone from being virtually unknown in computational linguistics to being a fundamental given”. We are again caught up in revolutionary times. Except this time around, the revolution is hurtling forward even faster, and it is redefining not only the research we do, but what it means to do research in the first place, the role of research in society, and society itself. What does this mean for ACL – what defines us as a community, what is our role, and where do we go from here?

ACL 2026

A New Balancing Act: Reflections on the Relationship between Computational Linguistics and AI

keynote

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where nodes execute agents/sub-workflows and edges encode dependencies and message passing.
However, implementing complex graph workflows in current frameworks still requires substantial manual effort, offers limited reuse, and makes it difficult to integrate heterogeneous external context sources.
To overcome these limitations, we present MASFactory, a graph-centric framework for orchestrating LLM-based MAS.
It introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph.
In addition, the framework provides reusable components and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human-in-the-loop interaction. 
We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing.
Our code (https://github.com/BUPT-GAMMA/MASFactory) and video (https://youtu.be/ANynzVfY32k) are publicly available.

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

ACL has become 30+ times larger than two decades ago, and we face issues such as overwhelming participants, outdated papers, and low quality review. The reason can largely be attributed to increasing research in LLM, and AI accelerating research. Can AI help us address challenges in academic publication and evaluation? How can we accommodate AI-driven research? Could the research and publication paradigm be improved? In this talk, I will try to give some thoughts, sharing some of our recent work on AI review automation, paper recommendation, and AI arXiv, before briefly discussing a bit of AI research automation.

How Can AI Help Us Manage AI Conferences (where Troubles Are Caused by AI)

What makes some explanations more satisfying than others? Why are humans so driven to explain? In this talk I’ll take a cognitive scientific approach to explanation and understanding, drawing on decades of work in psychology, education, and philosophy to argue for a function-first approach to explanations—one on which we begin by characterizing what it is that explaining achieves. I’ll suggest that the process of explaining plays a crucial role in discovery and generalization, and that we can understand the structure and phenomenology of explanations by first appreciating how explaining drives learning and helps construct our understanding of the world. I’ll also consider some potential implications for explanation and understanding in AI systems.

Next from ACL 2026

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

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