View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (22)

[1]
Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models
2025Qiguang Chen, Dengyun Peng et al.
[2]
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
2025Linan Yue, Yichao Du et al.
[3]
RLPR: Extrapolating RLVR to General Domains without Verifiers
2025Tianyu Yu, Bo Ji et al.
[4]
Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning
2025Yangfan He, Xiao Ding et al.
[5]
ThinkSwitcher: When to Think Hard, When to Think Fast
2025Guosheng Liang, Longguang Zhong et al.
[6]
Think Only When You Need with Large Hybrid-Reasoning Models
2025Lingjie Jiang, Xun Wu et al.
[7]
Thinkless: LLM Learns When to Think
2025Gongfan Fang, Xinyin Ma et al.
[8]
AdaptThink: Reasoning Models Can Learn When to Think
2025Jiajie Zhang, Nianyi Lin et al.
[9]
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning
2025Chenwei Lou, Zewei Sun et al.
[10]
Efficient Inference for Large Reasoning Models: A Survey
2025Yue Liu, Jiaying Wu et al.
[11]
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
2025Yang Sui, Yu-Neng Chuang et al.
[12]
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
2025Qiying Yu, Zheng Zhang et al.
[13]
TokenSkip: Controllable Chain-of-Thought Compression in LLMs
2025Heming Xia, Yongqi Li et al.
[14]
CoT-Valve: Length-Compressible Chain-of-Thought Tuning
2025Xinyin Ma, Guangnian Wan et al.
[15]
O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning
2025Haotian Luo, Li Shen et al.
[16]
The Lessons of Developing Process Reward Models in Mathematical Reasoning
2025Zhenru Zhang, Chujie Zheng et al.
[17]
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
2024Xingyu Chen, Jiahao Xu et al.
[18]
HybridFlow: A Flexible and Efficient RLHF Framework
2024Guangming Sheng, Chi Zhang et al.
[19]
RouteLLM: Learning to Route LLMs with Preference Data
2024Isaac Ong, Amjad Almahairi et al.
[20]
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
2024Yaowei Zheng, Richong Zhang et al.

Showing 20 of 22 references

Founder's Pitch

"Innovative framework enhances large reasoning models to reduce overthinking and improve efficiency."

Reasoning ModelsScore: 3View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

0/4 signals

0

Quick Build

0/4 signals

0

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/26/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.