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This tutorial from freeCodeCamp explains how to design schemas, validate outputs, and handle failures to reliably extract structured data from LLMs, covering techniques like constrained outputs, retry loops, and streaming.
This article describes an experiment showing that GPT-5.6 Sol can consistently stop before making a tool call by setting a numeric threshold just above a boundary, with all 25 test pairs demonstrating the expected behavior.
A user shares a positive review of the Ornith 1.5 model, highlighting its high inference speed and strong tool-calling capabilities, comparing it favorably to Qwen 3.8 for daily testing use.
PROOF-Gen presents a per-scenario reflective optimization method to recover failed trajectories for distilling tool-calling capabilities, enhancing data quality and model performance in distillation pipelines.
Describes a speculative programmatic tool calling method for agent harnesses, where LLMs queue up tool calls during token streaming to act as futures in code execution.
The article benchmarks fine-tuned Qwen3.6-35B-A3B models for tool calling capabilities, showing Ornith 1.5 and Tiel-Coder perform best, approaching scores of larger Qwen models. The study uses extensive GPU time and the tool-eval-bench utility for evaluation.
Granite 4.2 is IBM's new family of reasoning LLMs available in 3B, 8B, and 30B sizes, featuring thinking modes, tool calling, and trained with a multi-stage reinforcement learning pipeline under the Apache 2.0 license.
The article proposes speculative programmatic tool calling (sPTC), a technique to optimize tool calls in AI harnesses by overlapping execution with token generation, reducing latency inspired by speculative execution in CPUs and LLMs.
The article describes the development of a 48M parameter model specialized for tool calling in AI agents, which uses grammar to ensure valid JSON outputs and is open-source for customization on specific API catalogs.
Introducing Speculative Programmatic Tool Calling (sPTC), a technique to speculate on tool calls during code generation to overlap with token generation and execution time, improving efficiency in AI harnesses.
MobilePA-Bench is an interactive benchmark designed to evaluate mobile planning agents on complex real-world tasks, focusing on tool-calling, sub-agent collaboration, memory usage, and skill invocation under runtime constraints.
A compilation of community feedback on Qwen3.8-27B, highlighting its strong performance in agentic coding and tool-calling, while noting issues with knowledge recall and thinking-level settings.
A fine-tuned version of Gemma 4 12B that improves tool-calling reliability by 2.7x, optimized for consumer GPUs with 16GB VRAM using QLoRA training.
GLM 5.3 AI model is currently available for free through ZenMux and Z.ai, featuring a 1M context window, up to 128K output, and tool calling with MCP support.
The author shares their hands-on experience setting up the OC agentic framework, troubleshooting voice model and local LLM issues, and building a secure, sandboxed workspace for AI agents.
DART-SD proposes a topology-aware retrieval and tuning framework for self-distillation of LLM-based tool-calling agents, improving policy diversity by correcting only critical topological breakpoints while preserving valid reasoning.
The paper explores how looped language models, which use iterative latent computation, improve compositional tool calling in agentic systems, showing benefits for multi-step API interactions.
@mtasic85 demonstrates that with prompt programming alone, without fine-tuning, LFM2.5 2.6B can behave close to Qwen3.8 27B in tool calling and skill system applications.
Startups are learning to build cost-effective AI agents with GPT-5.6 by leveraging smarter model selection, reasoning, and tool calling for complex work.
The author built a web-searching AI agent from scratch using JavaScript and LLM tool-calling, demonstrating agent construction without frameworks and sharing it on GitHub for feedback.