DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models
cs.AI, cs.CL
Submitted: 2026-06-22
Updated: 2026-10-03
Terminology
Sources
- Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
- Program Synthesis with Large Language Models
- Language Models (Mostly) Know What They Know
- Evaluating Large Language Models Trained on Code
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
- SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
- Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
- Adaptive Computation Time for Recurrent Neural Networks
- Hierarchical Budget Policy Optimization for Adaptive Reasoning
- Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
- Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
- MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
- Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection
- Qwen3 Technical Report
- Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
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