Thinking effort aligns between humans and reasoning models in abductive reasoning

arXiv:2609.01867 · cs.CL, cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Comments: 14 pages, 5 figures. To appear in Findings of EMNLP 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 48× in this paper · explained in 1 episode
• Accuracy — 26× in this paper · explained in 1 episode
• Trace — 12× in this paper · explained in 2 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Reasoning Traces — 3× in this paper · explained in 3 episodes
• Verifiable Rewards — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy Alignment — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aphasia — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Abductive Reasoning with Syllogistic Forms in Large Language Models
• Abductive Commonsense Reasoning
• Language Models are Few-Shot Learners
• Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• DeepSeek-V3 Technical Report
• Training Large Language Models to Reason in a Continuous Latent Space
• Distilling the Knowledge in a Neural Network
• The Curious Case of Neural Text Degeneration
• Large Language Models are Zero-Shot Reasoners
• Dissociating language and thought in large language models
• Frontier LLMs Still Struggle with Simple Reasoning Tasks
• s1: Simple test-time scaling
• gpt-oss-120b & gpt-oss-20b Model Card
• Let's Think Dot by Dot: Hidden Computation in Transformer Language Models
• Wait, Wait, Wait... Why Do Reasoning Models Loop?
• Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs
• Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

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