# Just-In-Time Objectives: A General Approach for Specialized AI Interactions

arXiv: https://arxiv.org/abs/2510.14591 · HTML: https://mlsanity.com/paper/2510.14591/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences](https://mlsanity.com/paper/2404.12272/) (2024)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration](https://mlsanity.com/paper/2406.15951/) (2024)
- [Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties](https://mlsanity.com/paper/2309.00779/) (2023)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria](https://mlsanity.com/paper/2309.13633/) (2023)
- [Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces](https://mlsanity.com/paper/2309.14459/) (2023)
- [Grounding Gaps in Language Model Generations](https://mlsanity.com/paper/2311.09144/) (2023)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [Writer-Defined AI Personas for On-Demand Feedback Generation](https://mlsanity.com/paper/2309.10433/) (2023)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Generative and Malleable User Interfaces with Generative and Evolving Task-Driven Data Model](https://mlsanity.com/paper/2503.04084/) (2025)
- [Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models](https://mlsanity.com/paper/2310.13127/) (2023)
- [UIClip: A Data-driven Model for Assessing User Interface Design](https://mlsanity.com/paper/2404.12500/) (2024)
- [Malleable Overview-Detail Interfaces](https://mlsanity.com/paper/2503.07782/) (2025)

## Built on by (most cited first, 7 of 7)

- ["When to Hand Off, When to Work Together": Understanding Concurrent Human-Agent Interaction in Shared Co-Creative Workspaces](https://mlsanity.com/paper/2603.02050/) (2026)
- [Designing Proactive Thought Partners for Writing](https://mlsanity.com/paper/2609.01588/) (2026)
- [Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation](https://mlsanity.com/paper/2604.14668/) (2026)
- [MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems](https://mlsanity.com/paper/2608.16181/) (2026)
- [LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions](https://mlsanity.com/paper/2609.14849/) (2026)
- [PeerPen: AI-Assisted Writing for Online Mental Health Peer Support](https://mlsanity.com/paper/2609.14886/) (2026)
- [DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documents](https://mlsanity.com/paper/2609.29309/) (2026)

## Abstract (arXiv)

Large language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user's in-the-moment objective, then rapidly optimizing for that singular objective, enables LLMs to produce specialized tools, interfaces, and responses. Our work introduces just-in-time objectives, which model a user's goals to specialize LLM systems on the fly. We contribute an architecture for automatically inducing such objectives by passively observing user behavior, then steering downstream AI systems through generation and evaluation against this objective. Inducing just-in-time objectives (e.g., "Clarify the abstract's research contribution") enables automatic generation of tools, e.g., those that critique a draft based on relevant HCI methodologies, anticipate related researchers' reactions, or surface ambiguous terminology. In a series of experiments on participants' own tasks, JIT objectives enable LLM outputs that achieve 66-86% win rates over typical LLMs. In-person use sessions confirm that JIT objectives produce specialized tools that are unique to each participant and are rated as significantly higher quality than a standard LLM chat tool.