Connectionists: [CFP] NeurIPS 2026 Workshop: From Pretrained Representations to Acting Agents (Deadline: Sept 5, 2026 AoE)

[Ping-Chun Hsieh]

Dear All,


We are excited to announce the PTA: From Pretrained Representations to
Acting Agents workshop at NeurIPS 2026 in Sydney, Australia! 📋 We’re
calling for submissions!


⏰ Paper submission deadline: Sept 5, 2026 (AoE)

🌐 Website: https://ptaworkshop.github.io/

📋 Submission link:
https://openreview.net/group?id=NeurIPS.cc/2026/Workshop/PTA


Our goal is to explore how to obtain actionable pretrained representations
and align them with control agents at test time—bridging pretraining and
test-time decision making.


Call for Papers:

   -

   Specific topics include, but are not limited to:


   1.

   What makes a pretrained representation actionable?
   2.

   How to align with and use pretrained representations at test time?
   3.

   How to adapt pretrained knowledge to new tasks?
   4.

   How to evaluate actionable representations?
   5.

   What can go wrong when pretrained agents act?


   -

   We welcome both short papers (4 pages) and long papers (up to 9 pages),
   formatted using the NeurIPS template and submitted through OpenReview.
   -

   All accepted contributions will be presented during the poster sessions.
   -

   A select number of submissions will also be invited for contributed
   talks.
   -

   Submissions are non-archival and may be under review or concurrently
   submitted elsewhere. We especially encourage ongoing and unpublished work.


If you are excited about connecting representation learning, RL, planning,
and test-time decision making, we hope you’ll join us in Sydney! 🦘



Best regards,

The Organizing Team

Ping-Chun Hsieh (NYCU), Kuang-Huei Lee (Google DeepMind), Bo Dai (Georgia
Tech), Yen-Ling Kuo (UVA), Georgia Chalvatzaki (TU Darmstadt), Karen Leung
(UW / NVIDIA), Co Yong (NTU), Claas Voelcker (UT Austin)
-- 
Ping-Chun Hsieh (謝秉均)
Associate Professor
Department of Computer Science
National Yang Ming Chiao Tung University
https://pinghsieh.github.io/
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