LLM-based conversational interface for Android reduces task time and mental effort for blind users versus traditional gesture-based screen readers like TalkBack.
Title resolution pending
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 4
citation-polarity summary
roles
background 4polarities
background 4representative citing papers
FOCAL cuts token use by 60% and VLM calls by 72% on desktop streams while raising key recall from 0.38 to 0.61 and staying robust to task switches that break baselines.
EdgeFlow reduces mobile LLM cold-start latency up to 4.07x versus llama.cpp, MNN, and llm.npu by NPU-aware adaptive quantization, SIMD-friendly packing, and synergistic granular CPU-NPU pipelining at comparable accuracy.
LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
AgentProg reframes interaction history as a program with variables and control flow, plus a belief state for partial observability, achieving SOTA success rates on long-horizon GUI benchmarks while baselines degrade.
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
ClawMobile proposes a hierarchical system separating probabilistic LLM planning from structured deterministic execution to improve stability and reproducibility of agentic systems on real smartphones.
citing papers explorer
-
Insight: Enhancing Mobile Accessibility for Blind and Visually Impaired Users with LLMs
LLM-based conversational interface for Android reduces task time and mental effort for blind users versus traditional gesture-based screen readers like TalkBack.
-
FOCAL: Filtered On-device Continuous Activity Logging for Efficient Personal Desktop Summarization
FOCAL cuts token use by 60% and VLM calls by 72% on desktop streams while raising key recall from 0.38 to 0.61 and staying robust to task switches that break baselines.
-
EdgeFlow: Fast Cold Starts for LLMs on Mobile Devices
EdgeFlow reduces mobile LLM cold-start latency up to 4.07x versus llama.cpp, MNN, and llm.npu by NPU-aware adaptive quantization, SIMD-friendly packing, and synergistic granular CPU-NPU pipelining at comparable accuracy.
-
LDMDroid: Leveraging LLMs for Detecting Data Manipulation Errors in Android Apps
LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
-
AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Management
AgentProg reframes interaction history as a program with variables and control flow, plus a belief state for partial observability, achieving SOTA success rates on long-horizon GUI benchmarks while baselines degrade.
-
MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
-
A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
-
ClawMobile: Rethinking Smartphone-Native Agentic Systems
ClawMobile proposes a hierarchical system separating probabilistic LLM planning from structured deterministic execution to improve stability and reproducibility of agentic systems on real smartphones.