tool-calling

Phil Schmid explains how AI agents are collapsing their tool catalogs into a single `exec` tool — Code Mode — where models write TypeScript/Python scripts in a sandbox to chain actions, and argues MCP will return strong in 2027, with a minimal example for Gemini 4 Argon.

The paper presents CGAP, a neuro-symbolic framework that compiles tool catalogs into a PDDL model and uses a classical planner with LLM-based goal parsing to guarantee executable multi-tool plans, outperforming standard tool-calling agents by up to 28.9 points on four benchmarks.

The paper presents Saidzoda-Tajik-LFM-2.6B, a Tajik adaptation of Liquid AI's LFM2.5-2.6B-Base for tool calling on modest hardware, using LoRA continued pretraining followed by supervised fine-tuning. Tajik tool-call accuracy jumps from 20.69% to 64.14%, but the authors show CPT wipes out native tool-call formats and harms English MMLU (60.8 to 52.5).

The user recommends Factory’s technical article “Droid 1.0: Building a Frontier Harness,” highlighting engineering optimizations such as Programmatic Tool Calling, on-demand tool loading, long-running background tasks, and Agent Factory, and sharing a case in which the same optimization had opposite effects on costs for different models (Opus vs. Kimi).

A user asks whether Gemma 4 E4B remains the best small multimodal model (under 8B/6GB) for running a local, tool-calling personal assistant on a Mac via MLX, noting failures with Ling 3.0 tiny, LFM models, and Qwen 3.5 4B/9B.

A practitioner shares lessons from a keynote Q&A on why Strands agents pick the wrong tools or call them in the wrong order, and writes up a practical guide with links and code snippets on how to fix tool selection and ordering issues.

Gemma-4-31B-Humanlike-Chat 是基于 Google Gemma 4 31B 的微调模型，通过 SFT 与 on-policy 蒸馏训练，实现了无需提示的自然人类化文本风格，同时在 When2Call、BFCL 等工具调用基准上超越原版，且知识能力与基座持平。

A long-form engineering article discussing harness design for vertical-domain Agents: how to integrate project knowledge, workspaces, identity authentication, and long-task recovery on top of a Deep Agents foundation, and how to iterate on adding or removing capabilities based on evaluation results.

Sente Labs open-sourced extensible-mcp, an MCP proxy that dynamically loads MCP servers without polluting the LLM context while enforcing zero-trust policy controls (Rego, CEL, and now Cedar) over tool calls. Tool definitions live in a queryable vector store, and policies and credentials are kept outside the LLM's reach, distinguishing user intent from granted rights.

有人在Qwen模型权重中直接构建了一台32位RISC-V CPU，使其能够在模型内部执行指令，这可能对工具调用和计算机操作能力产生影响。

Liquid AI released LFM3-Audio-2B, a compact 2B-parameter audio model that runs locally on laptops and edge devices, supporting voice chat, multilingual speech across 18 languages, audio understanding, and tool calling with an in-house audio encoder and LiquidCodec tokenizer.

Ubicloud explains how vLLM's new constrained decoding support (server-side flag in vLLM 0.31) fixes tool-call schema accuracy, boosting it to 100% for newer Kimi and DeepSeek models after only 92% compliance with vLLM 0.28.

SPECMEM提出了一种在部署期间持续维护的工具调用记忆系统，通过在线写回已验证的调用记录并配合LRU淘汰和副作用门控机制，在多会话多用户场景下实现最高1.74倍的墙钟加速，同时避免了不可逆操作的错误。

A practitioner ran 10 agent harnesses on Qwen3.5-4B across 5 tasks and found that failures came from exact-match edits, literal instruction-following, miscounting, and API mismatches — not malformed tool calls. Six of ten agents passed all tasks, with an 11x spread in run time on identical hardware.

A dev shares results from building a voice booking agent on ElevenLabs with AWS Lambda tools, achieving accurate pricing/availability but struggling with 5s P95 latency on tool turns and keeping booking steps ordered. The post asks the community how to reduce tool-turn latency, enforce step ordering, and make the post-call LLM judge reliable.

The author introduces Helix-AGI, an open-source agent harness that replaces JSON tool-calling with a dual-RWKV bicameral architecture pairing a conversational Speaking Head with a non-language Sensory-Actualizer Head, coupled in latent space and driven by a Habitus graph of learned habitual behaviors with real-time visualization.

LoopX addresses the hard engineering problem of whether a remote side has executed after a tool call timeout in long-horizon agents. It achieves idempotent recovery through stable operation/Job identities, reading back external facts before deciding how to recover, and compositional verification — and has open-sourced this local-first control plane for long-horizon agents.

作者对比了手写 agent loop 和使用 LangGraph create_agent 构建购物助手，说明框架本质上是帮你运行同样的循环，并额外提供 checkpointer 记忆、流式输出、重试和追踪。建议先手写循环理解原理，再用框架落地。

本文提出 Tracer，一种面向 LLM 智能体的轨迹感知工具检索前端，用 trie 索引历史工具调用序列并结合经验与上下文先验对下一步调用进行排序。在六个环境、四个模型上，它在 24 组对比中 21 组提升了工具选择准确率，同时每步输入 token 减少约 61%–80%。

PRISM is a framework for evaluating how activation interventions transfer across precision, languages, and model scales in tool-calling language models, showing that quantized NF4 labels and cross-lingual supervision can expose and mitigate unsafe tool-call behaviors even in large models up to 70B parameters.