Orchid: Orchestrating Context Across Creative Workflows with Generative AI
- URL: http://arxiv.org/abs/2508.19517v1
- Date: Wed, 27 Aug 2025 02:12:37 GMT
- Title: Orchid: Orchestrating Context Across Creative Workflows with Generative AI
- Abstract summary: We present Orchid, a system that gives its users affordances to specify, reference, and monitor context.<n>Specifically, Orchid enables users to specify context related to the project, themselves, and different styles.<n>In a within-subjects study, participants using Orchid to execute creative tasks produced more novel and feasible outcomes.
- Score: 4.6965273212575545
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context is critical for meaningful interactions between people and Generative AI (GenAI). Yet mainstream tools offer limited means to orchestrate it, particularly across workflows that span multiple interactions, sessions, and models, as often occurs in creative projects. Re specifying prior details, juggling diverse artifacts, and dealing with context drift overwhelm users, obscure intent, and curtail creativity. To address these challenges, we present Orchid, a system that gives its users affordances to specify, reference, and monitor context throughout evolving workflows. Specifically, Orchid enables users to (1) specify context related to the project, themselves, and different styles, (2) reference these via explicit mentions, inline selection, or implicit grounding, and (3) monitor context assigned to different interactions across the workflow. In a within-subjects study (n=12), participants using Orchid to execute creative tasks (compared to a baseline toolkit of web search, LLM-based chat, and digital notebooks) produced more novel and feasible outcomes, reporting greater alignment between their intent and the AI's responses, higher perceived control, and increased transparency. By prioritizing context orchestration, Orchid offers an actionable step toward next generation GenAI tools that support complex, iterative workflows - enabling creators and AI to stay aligned and augment their creative potential.
Related papers
- IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inference [22.214499732123173]
IntPro is a proxy agent that learns to adapt to individual users via retrieval-conditioned intent inference.<n>We show that IntPro achieves strong intent understanding performance with effective context-aware reasoning capabilities.
arXiv Detail & Related papers (2026-02-10T07:20:58Z) - FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback [92.67587639164908]
We present FronTalk, a benchmark for front-end code generation with multi-modal feedback.<n>We focus on the front-end development task and curate FronTalk, a collection of 100 multi-turn dialogues.<n> Evaluation of 20 models reveals two key challenges that are under-explored systematically in the literature.
arXiv Detail & Related papers (2025-12-05T23:28:09Z) - UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist [107.04196084992907]
We introduce UniVA, an omni-capable multi-agent framework for next-generation video generalists.<n>UniVA employs a Plan-and-Act dual-agent architecture that drives a highly automated and proactive workflow.<n>We also introduce UniVA-Bench, a benchmark suite of multi-step video tasks spanning understanding, editing, segmentation, and generation.
arXiv Detail & Related papers (2025-11-11T17:58:13Z) - DyFlow: Dynamic Workflow Framework for Agentic Reasoning [79.19799197382478]
DyFlow is a dynamic workflow generation framework that adaptively constructs and adjusts reasoning procedures based on task requirements and real-time intermediate feedback.<n>We systematically evaluate DyFlow across diverse domains, including social reasoning, biomedical tasks, mathematical problem solving, and code generation.<n>Results demonstrate that DyFlow significantly outperforms existing baselines, achieving substantial Pass@k improvements and exhibiting robust generalization across diverse domains.
arXiv Detail & Related papers (2025-09-30T10:36:23Z) - ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation [49.805992099208595]
We introduce ThematicPlane, a system that enables users to navigate and manipulate high-level semantic concepts.<n>This interface bridges the gap between tacit creative intent and system control.
arXiv Detail & Related papers (2025-08-08T06:57:14Z) - Is Visual in-Context Learning for Compositional Medical Tasks within Reach? [68.56630652862293]
In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks.<n>We introduce a novel method for training in-context learners using a synthetic compositional task generation engine.
arXiv Detail & Related papers (2025-07-01T15:32:23Z) - Expanding the Generative AI Design Space through Structured Prompting and Multimodal Interfaces [1.051328497890725]
ACAI (AI Co-Creation for Advertising and Inspiration) is a multimodal generative AI tool designed to support novice designers by moving beyond traditional prompt interfaces.<n>This work contributes to HCI research on generative systems by showing how structured interfaces can foreground user-defined context, improve alignment, and enhance co-creative control in novice creative.
arXiv Detail & Related papers (2025-04-19T14:57:32Z) - Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows [28.404085240725717]
Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to an AI system, and insufficient flexibility of AI systems to support diverse creative (workflow flexibility)<n>Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags.<n>Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven intent.
arXiv Detail & Related papers (2025-02-26T01:13:47Z) - Asynchronous Tool Usage for Real-Time Agents [61.3041983544042]
We introduce asynchronous AI agents capable of parallel processing and real-time tool-use.
Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting.
This work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.
arXiv Detail & Related papers (2024-10-28T23:57:19Z) - ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation [87.39861573270173]
We introduce the novel task of prompt-adaptive workflow generation, where the goal is to automatically tailor a workflow to each user prompt.
We propose two LLM-based approaches to tackle this task: a tuning-based method that learns from user-preference data, and a training-free method that uses the LLM to select existing flows.
Our work shows that prompt-dependent flow prediction offers a new pathway to improving text-to-image generation quality, complementing existing research directions in the field.
arXiv Detail & Related papers (2024-10-02T16:43:24Z) - Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding [4.531390381842394]
In this work, we focus on relation extraction in a transfer learning set-up.<n>We adapt Chain-of-Thought decoding recently developed for reasoning problems, to generative relation extraction.
arXiv Detail & Related papers (2024-08-05T10:10:01Z) - Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow [18.15979295351043]
Generative AI brings novel and impressive abilities to help people in everyday tasks.
It is uncertain how useful generative AI are after the novelty wears off.
We conducted a three-week longitudinal study with 12 users to understand the familiarization and customization of generative AI tools for science communication.
arXiv Detail & Related papers (2024-02-15T11:39:11Z) - Towards End-to-End Open Conversational Machine Reading [57.18251784418258]
In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base.
We model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework.
arXiv Detail & Related papers (2022-10-13T15:50:44Z) - Exploring Relational Context for Multi-Task Dense Prediction [76.86090370115]
We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads.
We explore various attention-based contexts, such as global and local, in the multi-task setting.
We propose an Adaptive Task-Relational Context module, which samples the pool of all available contexts for each task pair.
arXiv Detail & Related papers (2021-04-28T16:45:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.