pentagonalize.bsky.social @pentagonalize.bsky.social · 12/02/2026Call for Submissions: flann.cs.yale.edu/cfp.html Registration: flann.cs.yale.edu/registration... Contact: flann@cs.yale.eduflann.cs.yale.eduFLaNN Workshop 2026 000
pentagonalize.bsky.social @pentagonalize.bsky.social · 12/02/2026We also have (limited) financial support available on a need basis for graduate student who are not able to attend otherwise. 🙂 (Only for students with an accepted abstract, please see website and register before the abstract submission deadline for it) 100
pentagonalize.bsky.social @pentagonalize.bsky.social · 12/02/2026The FLaNN Workshop submission deadline has been extended to Feb 19! Invited talks + posters (non-archival): expressivity, computation, and learning in neural nets/LLMs. Previous work welcome. Graduate students encouraged to submit! 📍 Yale University 🗓️ May 11-13, 2026 100
pentagonalize.bsky.social @pentagonalize.bsky.social · 04/02/2026We welcome posters on the formal expressivity, computational properties, and learning behavior of neural nets (incl. LLMs). Graduate students are especially encouraged to submit! Contact: flann@cs.yale.edu 000
pentagonalize.bsky.social @pentagonalize.bsky.social · 04/02/2026📣 FLaNN 2026 at Yale 🍮 Invited talks+posters (non-archival): expressivity, computation, and learning in neural nets/LLMs Speakers: Pablo Barceló, David Chiang, Will Merrill, Naomi Saphra, Gail Weiss Abstracts due Feb 12, 2026 Details: flann.cs.yale.edu 232
pentagonalize.bsky.social @pentagonalize.bsky.social · 31/01/2026Deadline in just under two weeks! 011
pentagonalize.bsky.social @pentagonalize.bsky.social · 19/12/2025Thank you on behalf of the organizing committee: Robert Frank, Lena Strobl, Dana Angluin, Timos Antonopoulos, Arman Cohan, Tom McCoy, Ruzica Piskac, Andy Yang 000
pentagonalize.bsky.social @pentagonalize.bsky.social · 19/12/2025Location: Yale University, New Haven, Connecticut, USA Workshop date: May 11-13, 2026 Abstract submissions due: February 12, 2026 Website: flann.cs.yale.edu Contact: flann@cs.yale.edu More information to come!flann.cs.yale.eduFLaNN Workshop 2026 100
pentagonalize.bsky.social @pentagonalize.bsky.social · 19/12/2025Announcing the first Workshop on Formal Languages and Neural Networks (FLaNN)! We invite the submission of abstracts for posters that discuss the formal expressivity, computational properties, and learning behavior of neural network models, including large language models (LLMs). 1105
pentagonalize.bsky.social @pentagonalize.bsky.social · 03/10/2025Read the cookbook: arxiv.org/abs/2510.00368 Join us for weekly seminars on formal language theory, ML, NLP, and more: flannseminars.github.io 012
pentagonalize.bsky.social @pentagonalize.bsky.social · 03/10/2025Thanks to all the chefs: @ccwatson.bsky.social, @antonxue.bsky.social, @satwik77.bsky.social, @ll4r3n4.bsky.social, @lambdaviking.bsky.social, Emile Dos Santos Ferreira, @anejsvete.bsky.social, @dchiang.bsky.social 122
pentagonalize.bsky.social @pentagonalize.bsky.social · 03/10/2025There is no better way to understand what transformers can do than to get your hands dirty and construct them, weight-by-weight. The Transformer Cookbook provides a guide for anyone aiming to understand the expressive power of transformers on such a formal level. 112
pentagonalize.bsky.social @pentagonalize.bsky.social · 03/10/2025We present The Transformer Cookbook: a collection of recipes for programming algorithms directly into transformers! Hungry for an induction head? Craving a Dyck language recognizer? We show you step-by-step how to cook up transformers for these algorithms and many more!arxiv.orgThe Transformer CookbookWe present the transformer cookbook: a collection of techniques for directly encoding algorithms into a transformer's parameters. This work addresses the steep learning curve of such endeavors, a prob... 155
Reposted by @pentagonalize.bsky.socialdchiang.bsky.social @dchiang.bsky.social · 23/12/2024New paper and two not-so-new papers on arXiv about transformer expressivity: (1) With @pentagonalize and Dana Angluin, "Simulating Hard Attention Using Soft Attention" arxiv.org/abs/2412.09925arxiv.orgSimulating Hard Attention Using Soft AttentionWe study conditions under which transformers using soft attention can simulate hard attention, that is, effectively focus all attention on a subset of positions. First, we examine several variants of ... 231
