Profile-Then-Reason: Bounded Semantic Complexity for Tool-Augmented Language Agents
📰 ArXiv cs.AI
Profile-Then-Reason framework reduces latency and error propagation in tool-augmented language agents
Action Steps
- Synthesize an explicit workflow using a language model
- Apply deterministic or guarded operators to execute the workflow
- Bound the execution to reduce latency and error propagation
- Evaluate the framework's performance on various tasks and datasets
Who Needs to Know This
AI engineers and researchers working on language model agents can benefit from this framework to improve the efficiency and accuracy of their models, while product managers can apply this technology to develop more robust language-based products
Key Insight
💡 Bounded execution framework can reduce latency and error propagation in language model agents
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🤖 Profile-Then-Reason framework improves tool-augmented language agents! 📈
Key Takeaways
Profile-Then-Reason framework reduces latency and error propagation in tool-augmented language agents
Full Article
Title: Profile-Then-Reason: Bounded Semantic Complexity for Tool-Augmented Language Agents
Abstract:
arXiv:2604.04131v1 Announce Type: new Abstract: Large language model agents that use external tools are often implemented through reactive execution, in which reasoning is repeatedly recomputed after each observation, increasing latency and sensitivity to error propagation. This work introduces Profile--Then--Reason (PTR), a bounded execution framework for structured tool-augmented reasoning, in which a language model first synthesizes an explicit workflow, deterministic or guarded operators exe
Abstract:
arXiv:2604.04131v1 Announce Type: new Abstract: Large language model agents that use external tools are often implemented through reactive execution, in which reasoning is repeatedly recomputed after each observation, increasing latency and sensitivity to error propagation. This work introduces Profile--Then--Reason (PTR), a bounded execution framework for structured tool-augmented reasoning, in which a language model first synthesizes an explicit workflow, deterministic or guarded operators exe
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