# GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents

arXiv: https://arxiv.org/abs/2606.16813 · HTML: https://mlsanity.com/paper/2606.16813/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 8 of 8)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents](https://mlsanity.com/paper/2606.06284/) (2026)
- [Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems](https://mlsanity.com/paper/2606.01416/) (2026)
- [Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents](https://mlsanity.com/paper/2606.13884/) (2026)

## Built on by (most cited first, 2 of 2)

- [Entity Binding Failures in Tool-Augmented Agents](https://mlsanity.com/paper/2606.30531/) (2026)
- [Don't Offer What Can't Be Done: Deterministic Executability Gating for LLM Skill Selection at Scale](https://mlsanity.com/paper/2608.01050/) (2026)

## Abstract (arXiv)

Tool-augmented LLM agents rely on runtime filtering to decide which tools should be visible at each step. Causal Minimal Tool Filtering (CMTF) reduces tool-choice confusion by exposing only the next causally necessary tool frontier, but it assumes that the user request has already been mapped to a symbolic goal state. In practice, requests such as "handle my appointment" or "take care of this email" may correspond to multiple possible goals. This creates wrong-goal execution, where an agent follows a valid causal tool path for an unintended objective. We introduce GIST-CMTF, a goal-state inference layer that predicts candidate symbolic goals over the same state-transition vocabulary used by CMTF, estimates ambiguity, and either applies CMTF or exposes clarification as a causal action that produces missing goal or state variables. We evaluate GIST-CMTF across seven model backends, six filtering methods, and 120 controlled tool-use tasks. GIST-CMTF achieves 97.0% task success, compared with 80.1% for top-goal CMTF and 82.9% for semantic-goal CMTF. It reduces wrong-goal execution from 19.4% under top-goal CMTF to 2.5%, while preserving the one-tool exposure of causal filtering and using substantially fewer tokens than all-tools exposure. These results suggest that reliable tool-augmented agents should validate goal state, not only tool relevance, before exposing external actions.