# Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate

arXiv: https://arxiv.org/abs/2603.08017 · HTML: https://mlsanity.com/paper/2603.08017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 20 of 20)

- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Towards a Science of Scaling Agent Systems](https://mlsanity.com/paper/2512.08296/) (2025)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [A Compact Embedding for Facial Expression Similarity](https://mlsanity.com/paper/1811.11283/) (2018)
- [Vibe coding: programming through conversation with artificial intelligence](https://mlsanity.com/paper/2506.23253/) (2025)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [Interactive AI Alignment: Specification, Process, and Evaluation Alignment](https://mlsanity.com/paper/2311.00710/) (2023)
- [Designing for Human-Agent Alignment: Understanding what humans want from their agents](https://mlsanity.com/paper/2404.04289/) (2024)
- [SIGGesture: Generalized Co-Speech Gesture Synthesis via Semantic Injection with Large-Scale Pre-Training Diffusion Models](https://mlsanity.com/paper/2405.13336/) (2024)
- [Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature](https://mlsanity.com/paper/2507.06000/) (2025)
- [Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction](https://mlsanity.com/paper/2402.09683/) (2024)
- [OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models](https://mlsanity.com/paper/2508.21061/) (2025)
- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- ["The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation](https://mlsanity.com/paper/2503.16791/) (2025)
- [A11yShape: AI-Assisted 3-D Modeling for Blind and Low-Vision Programmers](https://mlsanity.com/paper/2508.03852/) (2025)
- [Cognitive Biases in LLM-Assisted Software Development](https://mlsanity.com/paper/2601.08045/) (2026)
- [Designing Telepresence Robots to Support Place Attachment](https://mlsanity.com/paper/2501.03420/) (2025)
- [Trade-offs in Social-Norm Framings for Health Chatbots: Balancing Trust and Preference](https://mlsanity.com/paper/2509.15575/) (2025)

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

- [Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration](https://mlsanity.com/paper/2604.07121/) (2026)

## Abstract (arXiv)

In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings.