SMITH Framework: Jointly Optimizing Tool Creation and Execution for LLM Agents

The approach decomposes agent failures into schema, code, and outcome categories and optimizes each independently to improve procedural reasoning. No quantitative benchmark figures, model sizes, or dataset specifications appear in the available snippet.

Framework Design

SMITH treats tool fabrication and tool invocation as coupled objectives rather than sequential stages. Conventional agent pipelines generate a library of tools under one supervision signal and invoke them under another; SMITH collapses both into one policy and applies RL updates across both behaviors simultaneously. The joint optimization is the central architectural departure from prior tool-use frameworks and removes the boundary between the synthesizer module and the executor module.

Failure Decomposition

The paper partitions failure modes into three orthogonal categories, each receiving an independent optimization signal rather than a single trajectory-level reward:

• Schema failures — incorrect argument signatures, type mismatches, malformed tool descriptions.
• Code failures — runtime exceptions, syntax errors, failed compilation of generated functions.
• Outcome failures — correct execution producing an incorrect task result.

The decomposition implies that the policy gradient is computed over category-tagged transitions, not over full episodes as a monolithic success-or-failure signal. The ablation value of each category should in principle be testable independently.

Reimplementation Checklist

The available arXiv record carries no benchmark numbers. Before committing to a reproduction, the full PDF should be inspected for the following:

• Ablation isolating the contribution of joint training versus a sequential generate-then-invoke pipeline.
• Parameter efficiency comparison against agent frameworks that externalize tool creation to a separate module.
• Specification of the RL formulation: sparse trajectory-level reward versus dense step-level reward; on-policy versus off-policy updates.
• Evaluation harness that separates tool-correctness from task-correctness — a distinction frequently collapsed in existing agent benchmarks.

Implementation requires instrumentation that tags each trajectory segment with its failure category before gradient computation, so that the three independent optimization signals can be applied without interference. Until the paper's experimental section is reviewed, no claim of empirical superiority over existing tool-use frameworks can be sustained.