ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning
ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning explores ConMax optimizes large reasoning models by compressing reasoning paths, reducing computational costs while maintaining accuracy.. Commercial viability score: 7/10 in AI Efficiency.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
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.
Related Papers
- Builds On ThisSelf-Compression of Chain-of-Thought via Multi-Agent Reinforcement LearningViability: 6.0
- Builds On ThisChain Of Thought Compression: A Theoritical AnalysisViability: 4.0
- Builds On ThisTRiMS: Real-Time Tracking of Minimal Sufficient Length for Efficient Reasoning via RLViability: 5.0
- Builds On ThisStable Adaptive Thinking via Advantage Shaping and Length-Aware Gradient RegulationViability: 3.0
- Prior WorkSmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning