# AWARE-US: Preference-Aware Infeasibility Resolution in Tool-Calling Agents

arXiv: https://arxiv.org/abs/2601.02643 · HTML: https://mlsanity.com/paper/2601.02643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)
- LLM alignment / RLHF / preference optimisation / [Preference alignment for recommenders and RAG](https://mlsanity.com/alignment/preference-alignment-for-recommenders-and-rag/)

## Builds on (most cited first, 28 of 28)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://mlsanity.com/paper/1909.05378/) (2019)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [SParC: Cross-Domain Semantic Parsing in Context](https://mlsanity.com/paper/1906.02285/) (2019)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [STaR-GATE: Teaching Language Models to Ask Clarifying Questions](https://mlsanity.com/paper/2403.19154/) (2024)
- [Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs](https://mlsanity.com/paper/2311.09469/) (2023)
- [Ask-before-Plan: Proactive Language Agents for Real-World Planning](https://mlsanity.com/paper/2406.12639/) (2024)
- [Faithful Persona-based Conversational Dataset Generation with Large Language Models](https://mlsanity.com/paper/2312.10007/) (2023)
- [Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools](https://mlsanity.com/paper/2404.11891/) (2024)
- [ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models](https://mlsanity.com/paper/2503.00564/) (2025)
- [A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models](https://mlsanity.com/paper/2311.16789/) (2023)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Task-Oriented Dialogue System as Natural Language Generation](https://mlsanity.com/paper/2108.13679/) (2021)

## Built on by (most cited first, 1 of 1)

- [Don't Start What You Can't Finish: A Counterfactual Audit of Support-State Triage in LLM Agents](https://mlsanity.com/paper/2604.16752/) (2026)

## Abstract (arXiv)

Tool-calling conversational agents querying structured databases often face two linked failures: underspecification (missing constraints needed for a precise query) andinfeasibility (a fully specified query returns anemptyset). Prior systems often respond with "no results" or apply ad hoc relaxations, which can violate user intent by discarding highly valued requirements. Wecast infeasibility handling as preference-aware query repair: when a query is unsatisfiable, the agent should relax the least important constraints. We propose three LLM-based methods to infer relative constraint importance from dialogue: (1) local weighting, (2) global one-shot weighting, and (3) pairwise ranking. Across extensive experiments in car recommendation, the local-weighting method trained with supervised fine-tuning and direct preference optimization best aligns with user preferences (48%), while global weighting achieves the highest correct-relaxation accuracy (56%); all three outperform prior infeasibility-resolution basel. We also introduce AWARE-US, a benchmark of 120+ persona-grounded queries requiring agents to (i) disambiguate a base request via conversa tion and (ii) resolve infeasibility in a way consistent with persona-implied preferences. For code refer to Github: https://github.com/mhtkrmz/Infeasible-task and the dataset is available on Hugging Face