Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents
Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents explores Ares optimizes LLM agents' reasoning efforts to reduce costs and maintain task success rates.. Commercial viability score: 4/10 in Agents.
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 $10K - $14K 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 ThisCalibrate-Then-Act: Cost-Aware Exploration in LLM AgentsViability: 3.0
- Higher ViabilityWhen Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-MakingViability: 7.0
- Higher ViabilityThink Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM AgentsViability: 5.0
- Higher ViabilityLearning to Configure Agentic AI SystemsViability: 7.0
- Higher ViabilityAgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents
Related Resources
- AgentSpeak(glossary)
- Mixture-of-Agents(glossary)
- Agents(glossary)
- What is the future of AI agents according to Nothing's CEO?(question)
- How do LLM efficiency advancements impact the development of AI agents?(question)
- How does AgentXRay contribute to the explainability of AI agents in complex decision-making processes?(question)
- Agents – Use Cases(use_case)