ToolFormerMicro: Composable Tool Schema Compression via Gated Cross-Attention

ToolFormerMicro is a ~428M parameter encoder-decoder model that compresses verbose tool schemas into compact 8-token gist vectors via gated cross-attention. Initialized from Qwen2.5-0.5B and trained with a three-stage curriculum (schema auto-encoding, contrastive discrimination, end-to-end tool calling), the model achieves 0.818 Tool Selection Accuracy with zero false positives across seen, held-out, and unseen tool splits. Three composability experiments verify order independence (delta=0.000), sub-linear scaling to 200 tools, and bit-identical cache invalidation on hot-swap — properties that make gist representations a drop-in composable primitive for tool-augmented LLM serving.