PrISM-Observer: Intervention Agent to Help Users Perform Everyday Procedures Sensed using a Smartwatch
- URL: http://arxiv.org/abs/2407.16785v1
- Date: Tue, 23 Jul 2024 18:38:07 GMT
- Title: PrISM-Observer: Intervention Agent to Help Users Perform Everyday Procedures Sensed using a Smartwatch
- Abstract summary: PrISM-Observer is a smartwatch-based, context-aware, real-time intervention system designed to support daily tasks by preventing errors.
Unlike traditional systems that require users to seek out information, the agent observes user actions and intervenes proactively.
- Score: 43.41204564594764
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We routinely perform procedures (such as cooking) that include a set of atomic steps. Often, inadvertent omission or misordering of a single step can lead to serious consequences, especially for those experiencing cognitive challenges such as dementia. This paper introduces PrISM-Observer, a smartwatch-based, context-aware, real-time intervention system designed to support daily tasks by preventing errors. Unlike traditional systems that require users to seek out information, the agent observes user actions and intervenes proactively. This capability is enabled by the agent's ability to continuously update its belief in the user's behavior in real-time through multimodal sensing and forecast optimal intervention moments and methods. We first validated the steps-tracking performance of our framework through evaluations across three datasets with different complexities. Then, we implemented a real-time agent system using a smartwatch and conducted a user study in a cooking task scenario. The system generated helpful interventions, and we gained positive feedback from the participants. The general applicability of PrISM-Observer to daily tasks promises broad applications, for instance, including support for users requiring more involved interventions, such as people with dementia or post-surgical patients.
Related papers
- From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
arXiv Detail & Related papers (2026-08-03T12:52:04Z) - ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop [1.4279471205248535]
ProcAgent is a vision-based procedural assistant for real-time adaptive guidances on a single NVIDIA Jetson AGX Orin.<n>We evaluate ProcAgent along four dimensions: perception accuracy, reasoning, task-level performance, and user experience.
arXiv Detail & Related papers (2026-06-09T17:56:28Z) - Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations [14.480725554900777]
We introduce Momento, a benchmark for persistent agentic task completion in multi-session service environments.<n> Experimental results reveal that current agents fail primarily through misestimation of user state.
arXiv Detail & Related papers (2026-05-30T18:08:51Z) - VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [63.13827503828231]
We introduce VitaBench 2.0, a benchmark for evaluating personalized and proactive agent behavior in long-term user interactions.<n>Results show that real-world personalization remains highly challenging even for state-of-the-art models.
arXiv Detail & Related papers (2026-05-26T15:07:38Z) - Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis [3.3237915628874632]
We argue that effective agent evaluation goes beyond correctness alone, incorporating conversation quality, efficiency and systematic diagnosis of agent errors.<n>We propose new metrics that capture both turn efficiency and intermediate progress of the agent.<n>We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels.
arXiv Detail & Related papers (2026-03-16T16:14:28Z) - Modeling Distinct Human Interaction in Web Agents [59.600507469754575]
We introduce the task of modeling human intervention to support collaborative web task execution.<n>We identify four distinct patterns of user interaction with agents -- hands-off supervision, hands-on oversight, collaborative task-solving, and full user takeover.<n>We deploy these intervention-aware models in live web navigation agents and evaluate them in a user study, finding a 26.5% increase in user-rated agent usefulness.
arXiv Detail & Related papers (2026-02-19T18:11:28Z) - AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios [49.90735676070039]
The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow.<n>We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks.<n>We propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks.
arXiv Detail & Related papers (2026-01-28T13:49:18Z) - Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems [0.0]
Recent advances in agentic AI have shifted the focus from standalone Large Language Models to integrated systems.<n>We propose an end-to-end Agent Assessment Framework with four evaluation pillars encompassing LLMs, Memory, Tools, and Environment.<n>We validate the framework on a representative Autonomous CloudOps use case, where experiments reveal behavioral deviations by conventional metrics.
arXiv Detail & Related papers (2025-12-14T18:17:40Z) - Breaking Single-Tester Limits: Multi-Agent LLMs for Multi-User Feature Testing [22.10696272175415]
We propose MAdroid, a novel multi-agent approach powered by the Large Language Models (LLMs) to automate the multi-user interactive task for app feature testing.<n>Specifically, MAdroid employs two functional types of multi-agents: user agents (Operator) and supervisor agents (Coordinator and Observer)<n>Our evaluation, which included 41 multi-user interactive tasks, demonstrates the effectiveness of our approach, achieving 82.9% of the tasks with 96.8% action similarity.
arXiv Detail & Related papers (2025-06-21T01:38:53Z) - Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism [48.41735416075536]
Interactive Imitation Learning (IIL) allows agents to acquire desired behaviors through human interventions.<n>We propose the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations.
arXiv Detail & Related papers (2025-06-10T18:43:26Z) - PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
arXiv Detail & Related papers (2025-06-06T17:29:49Z) - Get Experience from Practice: LLM Agents with Record & Replay [16.179801770737892]
This paper proposes a new paradigm called AgentRR (Agent Record & Replay), which introduces the classical record-and-replay mechanism into AI agent frameworks.<n>We detail a multi-level experience abstraction method and a check function mechanism in AgentRR.<n>In addition, we explore multiple application modes of AgentRR, including user-recorded task demonstration, large-small model collaboration and privacy-aware agent execution.
arXiv Detail & Related papers (2025-05-23T10:33:14Z) - PIPA: A Unified Evaluation Protocol for Diagnosing Interactive Planning Agents [12.052972947563424]
Existing benchmarks predominantly evaluate agent performance based on task completion as a proxy for overall effectiveness.<n>We propose PIPA, a unified evaluation protocol that conceptualizes the behavioral process of interactive task planning agents.<n>Our analyses show that agents excel in different behavioral stages, with user satisfaction shaped by both outcomes and intermediate behaviors.
arXiv Detail & Related papers (2025-05-02T21:27:10Z) - Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.
Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.
We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
arXiv Detail & Related papers (2025-02-18T17:12:26Z) - YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks [16.443149180969776]
Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks.
Such AR capabilities can help AI Agents see and listen to actions that users take which can relate to multimodal capabilities of human users.
Proactivity of AI Agents on the other hand can help the human user detect and correct any mistakes in agent observed tasks.
arXiv Detail & Related papers (2025-01-16T08:06:02Z) - ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding [9.921932789361732]
We present a novel evaluation dataset, ProMQA, to measure system advancements in application-oriented scenarios.
ProMQA consists of 401 multimodal procedural QA pairs on user recording of procedural activities coupled with their corresponding instruction.
arXiv Detail & Related papers (2024-10-29T16:39:28Z) - SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation [89.24729958546168]
We present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents.
SPA-Bench offers three key contributions: A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines.
A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption.
arXiv Detail & Related papers (2024-10-19T17:28:48Z) - Active Fine-Tuning of Generalist Policies [54.65568433408307]
We propose AMF (Active Multi-task Fine-tuning) to maximize multi-task policy performance under a limited demonstration budget.
We derive performance guarantees for AMF under regularity assumptions and demonstrate its empirical effectiveness in complex and high-dimensional environments.
arXiv Detail & Related papers (2024-10-07T13:26:36Z) - Benchmarking Mobile Device Control Agents across Diverse Configurations [19.01954948183538]
B-MoCA is a benchmark for evaluating and developing mobile device control agents.
We benchmark diverse agents, including agents employing large language models (LLMs) or multi-modal LLMs.
While these agents demonstrate proficiency in executing straightforward tasks, their poor performance on complex tasks highlights significant opportunities for future research to improve effectiveness.
arXiv Detail & Related papers (2024-04-25T14:56:32Z) - Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention [0.6918368994425961]
Current content moderation follows a reactive, trial-and-error approach.
We introduce a proactive, predictive approach that enables moderators to anticipate the impact of their actions before implementation.
We study the reactions of 16,540 users to a massive ban of online communities on Reddit.
arXiv Detail & Related papers (2024-04-23T08:52:41Z) - WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? [83.19032025950986]
We study the use of large language model-based agents for interacting with software via web browsers.
WorkArena is a benchmark of 33 tasks based on the widely-used ServiceNow platform.
BrowserGym is an environment for the design and evaluation of such agents.
arXiv Detail & Related papers (2024-03-12T14:58:45Z) - Tell Me More! Towards Implicit User Intention Understanding of Language
Model Driven Agents [110.25679611755962]
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
We introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users' implicit intentions through explicit queries.
We empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals.
arXiv Detail & Related papers (2024-02-14T14:36:30Z) - MobileAgent: enhancing mobile control via human-machine interaction and
SOP integration [0.0]
Large Language Models (LLMs) are now capable of automating mobile device operations for users.
Privacy concerns related to personalized user data arise during mobile operations, requiring user confirmation.
We have designed interactive tasks between agents and humans to identify sensitive information and align with personalized user needs.
Our approach is evaluated on the new device control benchmark AitW, which encompasses 30K unique instructions across multi-step tasks.
arXiv Detail & Related papers (2024-01-04T03:44:42Z) - Conveying Autonomous Robot Capabilities through Contrasting Behaviour
Summaries [8.413049356622201]
We present an adaptive search method for efficiently generating contrasting behaviour summaries.
Our results indicate that adaptive search can efficiently identify informative contrasting scenarios that enable humans to accurately select the better performing agent.
arXiv Detail & Related papers (2023-04-01T18:20:59Z) - One-shot action recognition towards novel assistive therapies [63.23654147345168]
This work is motivated by the automated analysis of medical therapies that involve action imitation games.
The presented approach incorporates a pre-processing step that standardizes heterogeneous motion data conditions.
We evaluate the approach on a real use-case of automated video analysis for therapy support with autistic people.
arXiv Detail & Related papers (2021-02-17T19:41:37Z)
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.