Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
📰 ArXiv cs.AI
Learn how Evoflux enables inference-time evolution of executable tool workflows for compact agents, improving their efficiency and effectiveness
Action Steps
- Apply Evoflux to existing compact language models to enhance their tool workflow generation capabilities
- Use Evoflux to discover tools from live catalogs and satisfy schemas
- Configure Evoflux to preserve dependencies across intermediate outputs and ground final responses in executed evidence
- Test Evoflux with small planners to generate plausible workflow graphs
- Evaluate the performance of Evoflux in reducing cost, latency, and deployment risk for tool agents
Who Needs to Know This
AI researchers and developers working on compact language models and tool agents can benefit from Evoflux to improve their models' performance and deployment
Key Insight
💡 Evoflux enables compact agents to efficiently generate and execute tool workflows at inference time, reducing deployment risk and improving performance
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🤖 Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents 🚀
Key Takeaways
Learn how Evoflux enables inference-time evolution of executable tool workflows for compact agents, improving their efficiency and effectiveness
Full Article
Title: Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Abstract:
arXiv:2606.12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracki
Abstract:
arXiv:2606.12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracki
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