DTDR dynamically retrieves relevant tools by modeling dependencies from demonstrations and conditioning on the evolving agent plan, improving function calling success rates by 23-104% over static retrievers across benchmarks.
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UNVERDICTED 4representative citing papers
ToolPRM provides fine-grained intra-call process supervision via a new dataset and reward model, outperforming outcome and coarse-grained alternatives on function-calling benchmarks.
Speculative Interaction Agents achieve 1.3-2.2x speedups for real-time tool-calling agents via async I/O decoupling and speculative calls, with clock-based training for small edge models.
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
citing papers explorer
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Dynamic Tool Dependency Retrieval for Lightweight Function Calling
DTDR dynamically retrieves relevant tools by modeling dependencies from demonstrations and conditioning on the evolving agent plan, improving function calling success rates by 23-104% over static retrievers across benchmarks.
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ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling
ToolPRM provides fine-grained intra-call process supervision via a new dataset and reward model, outperforming outcome and coarse-grained alternatives on function-calling benchmarks.
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Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
Speculative Interaction Agents achieve 1.3-2.2x speedups for real-time tool-calling agents via async I/O decoupling and speculative calls, with clock-based training for small edge models.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.