The Scaling Properties of Implicit Deductive Reasoning in Transformers
cs.AI, cs.CC, cs.LO, cs.SC
Submitted: 2026-05-05
Updated: 2026-09-25
Terminology
Sources
- Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
- Understanding intermediate layers using linear classifier probes
- Lower Bounds for Sparse Recovery
- Eliciting Latent Predictions from Transformers with the Tuned Lens
- Decoding by Linear Programming
- Premise Order Matters in Reasoning with Large Language Models
- Faithful Reasoning Using Large Language Models
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- How Many Features Can a Language Model Store Under the Linear Representation Hypothesis?
- Looped Transformers as Programmable Computers
- ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs
- SGD on Neural Networks Learns Functions of Increasing Complexity
- Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
- When Does LeJEPA Learn a World Model?
- Locating and Editing Factual Associations in GPT
- The Illusion of State in State-Space Models
- The Llama 3 Herd of Models
- Magistral
- WT5?! Training Text-to-Text Models to Explain their Predictions
- How Transformers Learn Causal Structure with Gradient Descent
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