Transformers Stop Thinking Too Early, and a Tiny LoRA Fixes It
cs.AI, cs.CL, cs.LG
Submitted: 2026-09-29
Updated: 2026-09-29
Project page: https://lunamos.github.io/stop-thinking-too-early
Terminology
Sources
- Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
- Lessons from Studying Two-Hop Latent Reasoning
- A Mechanistic Analysis of Looped Reasoning Language Models
- How do Language Models Bind Entities in Context?
- Gemma 3 Technical Report
- The Llama 3 Herd of Models
- How Do LLMs Perform Two-Hop Reasoning in Context?
- RULER: What's the Real Context Size of Your Long-Context Language Models?
- Beyond Steering Vector: Flow-based Activation Steering for Inference-Time Intervention
- Entity Tracking in Language Models
- Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts
- Racing Thoughts: Explaining Contextualization Errors in Large Language Models
- The Power of Scale for Parameter-Efficient Prompt Tuning
- Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
- Pointer Sentinel Mixture Models
- A retrieval conditioned rebinding circuit for dynamic entity tracking in large language models
- In-context Learning and Induction Heads
- Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
- Language Models use Lookbacks to Track Beliefs
- Measuring and Narrowing the Compositionality Gap in Language Models
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