Robust Function Calling for LLM Agents Under Interface Drift
Tool-augmented language-model agents rely on interface text: tool descriptions, argument schemas, and the candidate tool list shown at inference time.
ProofPaper ↗
Key points
- These interfaces often change after deployment even when the underlying tool semantics remain fixed, creating a practical reliability problem for function-calling systems.
- We study this failure mode as interface drift and evaluate whether a lightweight, training-free repair layer can recover robustness without retraining the base model.
- Our method, SchemaShield-Lite, combines schema validation, candidate-set retry for missing tool names, and one-shot constrained repair.
- The results position validation-and-repair as a practical robustness layer for deployed LLM-agent tool use.
Sources (1)
- [1]Robust Function Calling for LLM Agents Under Interface DriftarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:49 AM
Tool-augmented language-model agents rely on interface text: tool descriptions, argument schemas, and the candidate tool list shown at inference time.
These interfaces often change after deployment even when the underlying tool semantics remain fixed, creating a practical reliability problem for function-calling systems.
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