Don't Offer What Can't Be Done: Deterministic Executability Gating for LLM Skill Selection at Scale
Ortal Ashkenazi, Vitalii Kloz, Mykhailo Ulianchenko
cs.AI, cs.CL, cs.SE
Submitted: 2026-08-02
Comments: 7 pages, 3 figures. Preprint. Submitted to the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2027), Applied Data Science Track; currently under review
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
- Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
- ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents
- ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents
- GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents
- WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation
- RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines
- RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
- Are Your Agents Upward Deceivers?
- LongFuncEval: Measuring the effectiveness of long context models for function calling
- Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases
- When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
- Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
- Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
- Agentic Model Checking
- Prompt Injection as Role Confusion
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