Tool Use
Research papers, repositories, and articles about tool use
Showing 4 of 4 items
Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It
Shows that in tool-use RL, models often "forget" how to call tools because specific control tokens spike in probability, breaking format while the underlying skill stays. Interleaving supervised updates with RL and adding richer hints stabilizes training across formats and tasks. If your agent RL runs keep collapsing, this paper is a playbook. ([huggingface.co](https://huggingface.co/papers/2606.26027))
Heterogeneous Scientific Foundation Model Collaboration
Introduces Eywa, a framework that lets language models coordinate with domain‑specific scientific models across non-text data. Treats those models as tools inside an agent system and studies planning strategies across them. If you’re building AI for science, this shows how to wire specialized models into one reasoning loop. ([huggingface.co](https://huggingface.co/papers/2604.27351))
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
Adds a separate “ledger” of task state so customer-service agents stop forgetting rules and context between tool calls. If you deploy tool-calling agents in regulated domains, this is a ready-made design pattern.
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
Tracks customer data and policy state in a separate ledger so agents stop making forbidden tool calls. If you run support bots, this is directly actionable.