A
Ares
Adaptive reasoning framework that optimizes LLM inference costs by dynamically scaling effort
Open SourceFree
About
Research framework for building efficient LLM agents through adaptive reasoning effort selection. Dynamically allocates computational resources by identifying which subtasks require deep reasoning versus lighter processing, reducing inference costs up to 50% while maintaining accuracy. Addresses the problem of static reasoning strategies that waste compute on simple tasks while under-resourcing complex ones.
Details
| Language | |
| Patterns |
Tags
frameworkautonomousopen-sourcepythonevaluationresearch
Quick Info
- Organization
- Research Collaboration
- Pricing
- open-source
- Free Tier
- Yes
- Updated
- Mar 10, 2026
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