mlsanityData to 7 Oct 2026

Tool Forge: A Validation-Carrying Toolchain for Governed Agentic Execution

arXiv 2605.28000 ↗First posted 27 May 2026cs.SE cs.AIcited by 1

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

Large language model agents are increasingly expected to perform operational work: calling APIs, manipulating files, assembling workflows, and acting inside enterprise systems. Yet the tool layer on which this execution depends is still commonly treated as either a hand-written integration artifact or a static list of schemas exposed to a model. This paper introduces Tool Forge, a validation-carrying toolchain for converting natural-language capability intent into governed, sandbox-verified, cataloged tool artifacts and exposing those artifacts to agents through a token-efficient routing layer. Tool Forge treats a tool as a capsule containing intent, capability contract, implementation, dependency policy, tests, documentation, runtime validation evidence, lifecycle state, credential bindings, and routing metadata. It also introduces a Router that exposes intent-scoped tool sessions instead of loading full catalog schemas into the model context. We describe the system architecture, validation pipeline, MCP-facing routing model, governance controls, and initial reproducible benchmarks from the open-source implementation. Across 83 Router benchmark cases, Tool Forge Router achieves aggregate micro-F1 of 0.901 while reducing estimated task-flow tool context by 99.2% relative to naive full-catalog schema exposure. In a 25-case end-to-end generation probe over local-tool tasks, Tool Forge generates 25 of 25 tool bundles, reaches micro-F1 of 0.940 against deterministic acceptance checks, and passes 23 of 25 live sandbox validations. These results are presented as an initial systems benchmark, not as a state-of-the-art claim. The paper identifies remaining challenges in adversarial routing, broader API grounding, sandbox isolation, and cross-system evaluation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 6 of its 30 references that are in the map. First posted on arXiv 27 May 2026.

Builds on

6 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Gorilla: Large Language Model Connected with Massive APIs24 May
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 4

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  4. 2026Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agentscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

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  2. 2026ChromaFlow: A Negative Ablation Study of Orchestration Overhead in Tool-Augmented Agent Evaluation
  3. 2026Formal Verification of Agentic Systems over Operational Data
  4. 2025ToolFuzz -- Automated Agent Tool Testing
  5. 2026MANTRA: Synthesizing SMT-Validated Compliance Benchmarks for Tool-Using LLM Agents
  6. 2026AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
  7. 2026Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction
  8. 2026E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios