# DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment

arXiv: https://arxiv.org/abs/2511.15331 · HTML: https://mlsanity.com/paper/2511.15331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 8 of its 104 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

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

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Interactive AI Alignment: Specification, Process, and Evaluation Alignment](https://mlsanity.com/paper/2311.00710/) (2023)
- [Inkspire: Supporting Design Exploration with Generative AI through Analogical Sketching](https://mlsanity.com/paper/2501.18588/) (2025)
- [Brickify: Enabling Expressive Design Intent Specification through Direct Manipulation on Design Tokens](https://mlsanity.com/paper/2502.21219/) (2025)
- [Scratch Copilot Evaluation: Assessing AI-Assisted Creative Coding for Families](https://mlsanity.com/paper/2305.10417/) (2023)
- [Designing with Language: Wireframing UI Design Intent with Generative Large Language Models](https://mlsanity.com/paper/2312.07755/) (2023)
- [Pinning "Reflection" on the Agenda: Investigating Reflection in Human-LLM Co-Creation for Creative Coding](https://mlsanity.com/paper/2402.09750/) (2024)
- [NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modification](https://mlsanity.com/paper/2508.02823/) (2025)

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

- [CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Tasks](https://mlsanity.com/paper/2604.10587/) (2026)

## Abstract (arXiv)

Recent large language models (LLMs) show promise in design tasks, yet a fundamental misalignment persists: design thinking requires iterative intent formulation, while LLMs treat inputs as complete specifications. This challenges design intent formulation, where designers must progressively refine understanding through exploration. Existing tools either sacrifice exploratory flexibility for structural stability or leave reasoning implicit, failing to support human-LLM alignment. Through a formative study with eight designers, we introduce curated reasoning-enabling designers to explicitly inspect, reorganize, and selectively regenerate LLM reasoning structures. We present DesignerlyLoop, implementing this through a two-layer structure separating design intent from LLM reasoning. A study with 20 designers demonstrates that curated reasoning significantly improves design quality and creativity. Our work contributes a novel interaction paradigm for human-LLM alignment, transforming LLMs from content generators into structured reasoning partners in creative design.