3rd Workshop on Test-Time Updates (TTU): Putting Updates to the Test!#

Our third workshop on test-time updates will be held at ICLR 2026!

When and Where. The workshop will be held on Apr. 27 in Room 206 of the conference in Rio de Janeiro.

Scope. Note the scope encompasses test-time updates broadly. This includes test-time adaptation, test-time training, post-training updates, and model editing. As a workshop at ICLR, it is important to host and cross-pollinate work across different learning settings and domains.

Consider joining us to discover and contribute to the latest on updates after training: the test begins now!

Call for Papers#

Topics We will welcome and highlight content on test-time and post-training updates:

Foundations & Objectives: Unsupervised/self-supervised losses at test time; implicit/explicit regularization; stability–plasticity trade-offs; theory of adaptation and generalization under shift.
Parameterizations & Interfaces: Input-space updates (learnable augmentations, prompts), feature-space adapters (BN/affine, LoRA adapters), head-level edits, retrieval-augmented updates, black-box query strategies for closed foundation models.
Shift, Attacks, & Tasks: Coping with domain and style shift, distribution drift, adversarial perturbations, label shift, online continual learning and task switches, model availability attacks.
Adaptation of Foundational Models (FM): Adapting LLMs/VLMs and domain FMs to specialized/personalized settings via in-context learning, adapters/LoRA, TTU-RL, and model editing and unlearning.
Safety, Reliability, & Alignment: Uncertainty, conformal prediction at test time, fallback/abstention, guardrails and risk monitors, privacy-preserving updates, auditability, and roll-back.
Dynamic Architectures: Recurrent depth models, looped transformers, dynamically allocating compute (early-exit networks, mixture-of-depth), and iterative test-time optimization (deep equilibrium networks, implicit computation).
Metrics, Datasets, & Benchmarks: End-to-end metrics that couple utility (accuracy, calibration) with costs (compute, memory, wall-clock, energy); realistic streams and recurrences; reproducible TTU pipelines.
Cost-Aware & Green TTU: Methods and evaluations under compute/energy budgets, latency/throughput targets, edge constraints, carbon accounting, and cost–quality frontiers; any improvement must justify its operational footprint.

Keywords Adaptation, Continual Learning, Robustness, Personalization, Model Editing, Foundation Models, Reliability, Green AI.

Format We will welcome the submission of short papers (= 4 pages content, not including the references, as well as an (optional) appendix with an unlimited number of pages). We will also welcome the submission of tiny papers (= 2 pages content, not including the references, without an appendix). Accepted submissions will be selected for poster and oral presentation at the workshop. The workshop will not include proceedings.

Invited Speakers#

Amrith Setlur

Peter Baile Chen

Jonas Geiping

Eleni Triantafillou

Guillaume Vray

Paper Submission (Done)#

Please see the system on OpenReview:

Main track (4 pages): https://openreview.net/group?id=ICLR.cc/2026/Workshop/TTU_Main_Track

Tiny paper track (2 pages): https://openreview.net/group?id=ICLR.cc/2026/Workshop/TTU_Tiny_Track

Please use the ICLR 2026 paper kit for preparing your submission: ICLR/Master-Template

Submission deadline: Feb. 6th 2026 (AoE)
Decisions to authors: Mar. 1st, 2026
Camera ready: TBD

Call for Reviewers (Done)#

We are thankful for all of our volunteer reviewers for completing their assignments! All reviewers will be credited here for their academic service once the paper process is complete.