Tiny and Short Paper
3-5 pages in ICLR format + references and appendix; welcomes late-breaking results, replication studies, and conceptual explorations with emphasis on clarity and potential impact rather than extensive experiments.
Machine Learning/CORE Unranked
LIT Workshop @ ICLR 2026
Workshop on Latent & Implicit Thinking – Going Beyond CoT Reasoning
The LIT Workshop @ ICLR 2026 focuses on latent and implicit reasoning in vision and language models, exploring alternatives to explicit Chain-of-Thought (CoT) reasoning. It aims to unify research on non-autoregressive, continuous, and stateful reasoning mechanisms to advance more efficient and capable AI reasoning systems.
Key deadlines
Verified September 9, 2026Full paper
February 9, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
February 9, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Regular Paper
Up to 10 pages in ICLR format + references and appendix; for full-length research contributions.
Research areas in scope
01
Training Strategy for Implicit Reasoning
curriculum learningdistillationreinforcement learningpretraining from scratch
02
Looped Architectures
recurrence mechanisms (loop unrolling, dynamic halting)training curricula and stability for deep iterative models
03
Mechanical Interpretability of Implicit Reasoning
layer-wise specialization and attribution of reasoning functionscausal interventions on intermediate representations
04
Special Thinking Tokens
text CoT compressed to special tokens (e.g., continuous thought tokens, VQ-VAE codes, gist tokens)CoT augmentation via filler or planning tokens
05
KV-Cache and Hybrid Stateful Reasoning
leveraging key/value caches for multi-step latent inferencecomparisons between pure activation vs. cache-augmented loops
06
Inference Paradigms Beyond Autoregression
text diffusion for bidirectional, iterative denoising-based reasoningfractal generative frameworks and next-block prediction
07
Theoretical Results on Depth, Scaling, and Efficiency
theoretical bounds on reasoning depth vs. layer countparameter- and compute-efficient designs for implicit reasoning
08
Evaluation and Benchmarks
metrics and probes for implicit vs. explicit CoT capabilitiesdatasets and tasks that stress ultra-deep or multi-hop latent reasoning
09
Limitations and Safety
understanding pros and cons of implicit and explicit CoTinterpretability & faithfulness of reasoning from a safety and alignment perspective
Policies worth checking twice
- Submissions must be anonymized for double-blind review.
- AI-generated papers are prohibited in the tiny and short paper tracks; limited AI assistance (e.g., grammar correction) is allowed if human authorship of intellectual contribution is maintained.
- Submissions must not have been previously published at ICLR or other machine learning venues.
- Dual submissions are allowed if not violating the guidelines of the other venue.
- Authors must disclose any substantial use of AI tools in submission metadata or acknowledgments.
- LLMs are not permitted for automated reviewing or decision-making; all evaluations are conducted by human reviewers.
- Appendices may be submitted but are not required to be reviewed.
- Workshop is non-archival; accepted papers will be published on the workshop website but not in formal proceedings.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026