Aviary
- Published
- Source
- arXiv
- Paper number
- 007
- Field
- Agents
- arXiv ID
- 2412.21154
Key points
- From a framework perspective, Aviary provides a gym-style environment for language agents that use reasoning, tools, code, and iterative action-observation loops.
- In formal terms, the agent is treated as a policy in a language decision process, which is a language-based partially observable Markov decision process formulation.
- The tasks include molecular cloning, literature-based research QA, and protein stability engineering, all of which require multi-step scientific reasoning.
- As a result, online learning and test-time scaling let open-source non-frontier LLM agents compete with frontier agents and human experts at much lower cost.
Paper links
External research summaries. These are not HDATF publications or measured product results.