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Newer Claude models (Opus 4.8 and Sonnet 5) exhibit worse tool-calling behavior by inventing extra fields in tool invocation arguments, causing validation failures, a regression compared to older models.
MiniCPM5-1B-Claude-Opus-Fable5-Thinking is a compact 1B thinking language model fine-tuned from openbmb/MiniCPM5-1B on Fable 5 data, enhancing coding and instruction-following while retaining the native thinking chat template and tool-call format. It supports up to 128K context and is suitable for local deployment.
An independent benchmark of PrismML's 1-bit Bonsai-8B against IBM's Granite and other models on CPU tool calling shows that with grammar-constrained decoding, Bonsai-8B achieves a 92% pass rate, outperforming larger models, but fails without constraints. Granite is the best raw model at 72%.
LongCat-2.0 model released with trillion parameters and 1M ultra-long context, supporting native tool calling and multi-step reasoning, with outstanding coding capabilities. Also introduces Token resource packs and pay-as-you-go API billing service.
Agents-A1 is a 35B Mixture-of-Experts agentic model from InternScience that achieves competitive performance against frontier-scale systems like GPT-5.5 and DeepSeek-V4-pro using long-horizon trajectory scaling and multi-teacher multi-domain distillation.
Qwen 3.6 35B outperforms DeepSeek v4 Flash on tool-heavy and coding-adjacent workflows, according to benchmarks from MiaAI Lab.
Owl Alpha is a new model designed for agentic workloads including tool calling, multi-step reasoning, long-context execution, code generation, automated workflows, and DevOps tasks.
MiaAI Lab tested Qwopus 3.6-27b Coder and found it underperformed compared to Qwen 3.6 27b and 35b in tool-calling and code generation, with broken HTML demos.
Liquid AI's LFM2.5-230M model demonstrates multi-step tool-calling capabilities on a Unitree G1 robot, running entirely on-device on an NVIDIA Jetson Orin, acting as a skill-selection layer.
This article points out that 80% of AI Agent production crashes are not due to model intelligence, but are caused by context overflow, tool misconfiguration, and sub-agent runaway. The author emphasizes that the watershed in 2026 lies in Harness (office systems, security) and Loop (automatic cycling mechanism), not the model itself.
This paper identifies and analyzes 'tool suppression' in open-weight LLMs when both tool calling and JSON schema constraints are simultaneously enabled, proposing the Constraint Priority Inversion hypothesis and a mitigation strategy called Transparent Two-Pass Execution.
A clear definition of an AI agent as an LLM within an agentic loop, covering components like LLM backbone, instructions, tools, environment, and additional details like context management and memory.
A user reports that the Qwen3.6-27B model performs better and more reliably with llama.cpp than with vLLM, citing tool call errors and 'lobotomized' behavior in vLLM despite extensive configuration.
Jackrong releases Qwopus3.6-27B-Coder-Compat-MTP-GGUF, a GGUF quantization of the Qwopus3.6-27B-Coder model with an expanded chat template for better interoperability with tool-using runtimes and OpenAI-compatible agent frameworks.
GLM 5.2 is a frontier open-source coding model that performs near Claude Opus quality on coding tasks, with excellent tool calling, planning, and local deployment capabilities, at no cost.
Rapid-MLX is a local LLM inference tool optimized for Apple M-series chips. Built on the MLX framework, it achieves 2 to 4 times faster inference than Ollama, supports multiple models, tool calling, and an OpenAI API-compatible interface.
LedgerAgent is a method for customer service agents that maintains task states in a separate ledger to improve policy adherence and state management during tool calling. It improves average passk over standard approaches across four domains.
GLM 5.2 marks a significant milestone for open-weight models, demonstrating strong context retention across long multi-step tasks and more reliable tool calling.
Moonshot AI 发布了专注于编程的开放式权重模型 Kimi K2.7 Code，拥有1万亿参数和384个专家，性能在MCP工具调用上超越Opus 4.8，成本仅为十分之一。
This paper studies how routing accuracy degrades as the number of agents scales from 10 to 110 in an enterprise productivity assistant, finding F1 drops of 16–23 percentage points. It diagnoses retrieval and confusion gaps and shows that embedding-based shortlisting recovers 10–11pp F1.