Satwik Ram Kodandaram, Monalika Padma Reddy, Xiaojun Bi +3cs.HC cs.AI
Computer-use agents are emerging as a paradigm for agentic human-AI interaction, combining language reasoning with multi-modal interface grounding to operate GUIs. Yet their effectiveness for blind screen-reader users in real-world desktop workflows remains unclear. We present a three-week diary study with 8 blind users using OLLA, a screen-reader-accessible CUA prototype, collecting 1,258 commands across 12 applications with screenshots, UI trees, model responses, and action traces. We evaluate GPT-5 during deployment and re-execute the same commands with four additional models. GPT-5 achieved the highest success rate at 52.5%. Trace analysis reveals grounding, planning, constraint-tracking, and termination failures, while interviews reveal beyond-automation needs.
Large language model (LLM) agents that interact with graphical user interfaces increasingly rely on either raw screenshots or platform-specific accessibility application programming interfaces (APIs) to perceive interface state. Both approaches have limitations for assistive applications: screenshot-based perception lacks the semantic roles and relationships required by screen readers, while platform-specific APIs such as Windows UI Automation, macOS Accessibility, Android AccessibilityService, and web ARIA require separate integrations for each platform. This paper proposes an architecture that uses the Model Context Protocol (MCP) as a unified transport and schema layer between heterogeneous accessibility frameworks and LLM-based assistive agents. An MCP accessibility server exposes ARIA-aligned roles, labels, states, and focusable-element hierarchies through a platform-independent representation, enabling consistent interaction across operating systems and applications. The framework also introduces an MCP resource model for persisting user accessibility preferences across sessions. The architecture is analyzed with respect to three research questions: protocol extensibility for accessibility-tree representation, latency and semantic fidelity trade-offs between accessibility trees and screenshot-based perception, and support for persistent accessibility profiles through MCP resources. Rather than presenting an empirical implementation, this work contributes a conceptual framework supported by comparative analysis of accessibility APIs, GUI agent architectures, and the MCP specification. The analysis suggests that a standardized MCP accessibility layer can reduce platform-specific integration complexity while preserving the semantic information required for accessible AI agents, providing a foundation for future implementation and evaluation.
Modern web interfaces are increasingly difficult to use with screen readers, particularly when pages update dynamically or hide important structure behind visual layout. Recent UI agents can act on such interfaces; however, for assistive agents to be truly useful, they must behave as collaborators that keep users informed and in control, rather than as tools that simply take actions on users' behalf. Most existing benchmarks judge systems primarily by task completion, without assessing how well they explain their actions or support user oversight. We introduce NeXUI, a benchmark for assistive agents that must navigate interfaces while explaining each step in clear language for nonvisual use. NeXUI pairs realistic user goals with instrumented interface states, enabling agents to reason from both visual context and structural information. Its evaluation measures safety, efficiency, and task success, while also checking whether explanations are grounded in the interface state. In our experiments, we find that NeXUI remains challenging even for state-of-the-art foundation models, with % Gemini-3.5-Flash achieving only a 44\% success rate and poor explanation scores, making it a useful foundation for future research and development. By focusing on navigation, explanation, and user control, NeXUI provides a clearer way to study agents that can support blind and visually impaired users in modern computing environments.
Indoor navigation remains a critical accessibility challenge for the blind and low-vision (BLV) individuals, as existing solutions rely on costly per-building infrastructure. We present an agentic framework that converts a single floor plan image into a structured, retrievable knowledge base to generate safe, accessible navigation instructions with lightweight infrastructure. The system has two phases: a multi-agent module that parses the floor plan into a spatial knowledge graph through a self-correcting pipeline with iterative retry loops and corrective feedback; and a Path Planner that generates accessible navigation instructions, with a Safety Evaluator agent assessing potential hazards along each route. We evaluate the system on the real-world UMBC Math and Psychology building (floors MP-1 and MP-3) and on the CVC-FP benchmark. On MP-1, we achieve success rates of 92.31%, 76.92%, and 61.54% for short, medium, and long routes, outperforming the strongest single-call baseline (Claude 3.7 Sonnet) at 84.62%, 69.23%, and 53.85%. On MP-3, we reach 76.92%, 61.54%, and 38.46%, compared to the best baseline at 61.54%, 46.15%, and 23.08%. These results show consistent gains over single-call LLM baselines and demonstrate that our workflow is a scalable solution for accessible indoor navigation for BLV individuals.