Pre-generation tool-call gating via linear probes on LLM hidden states. F1 ≈ 0.91–0.94 on BFCL v4, 14–22× faster than full generation. Cross-architecture transfer across Llama / Qwen / Phi / Mistral (3B–7B) with ≥96% retention.

A bf16 LoRA fine-tune of Qwen 3.5 4B for function calling on xLAM. v1.0 ships below the BFCL gate with full per-category failure analysis.

Specification for a reproducible, provenance-bound multi-lane LLM benchmark suite.

Open LLM leaderboard featuring Xiaomi MiMo v2.5 & MiMo 100T head-to-head with GPT-5, Claude, Gemini, DeepSeek, Llama 4. ARC-AGI · SWE-Bench · MMLU-Pro · GPQA · HumanEval · BFCL.

Independent audit of a fine-tuned LLM tool-calling PoC — BFCL regression decomposition, inference stack risk assessment, and production recommendation for a FinTech client. Qwen-2.5, LoRA, SGLang, H100.

AAFV: Agent Architecture Feedback Validator

OpenEuroLLM snapshot of the Berkeley Function Calling Leaderboard evaluation harness and OLMo evaluation orchestration.

A compact, model-first notation for LLM tool definitions. Re-encodes JSON Schema with a median ~30% input-token reduction and behavior-preserving fallback. Includes the specification, a reference converter, a deployment protocol, and the full evaluation.

Post-train a small instruction-tuned LLM for reliable multi-step tool use

Tool-call reliability fine-tuning lab with open-weight model training, benchmark evaluation, and serving notes.