The paper introduces ContextXR, a benchmarking framework designed to evaluate context‑aware interfaces in Extended Reality (XR) systems. It models an XR application as a graph of functional facets—semantically coherent groups of capabilities that support a shared user intent—and uses this representation to build MineXR++, a dataset enriched with facet‑level annotations. The framework defines three core tasks—context factor analysis, initial facet suggestion, and next facet suggestion—and evaluates suggestion methods via a simulated interaction metric that measures navigation and search cost to reach desired functionality.
arXiv:2609.40118v1 Announce Type: new Abstract: As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compa...
arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model b...
arXiv:2511.03728v2 Announce Type: replace Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memo...
arXiv:2609.37743v1 Announce Type: new Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even...
The paper introduces GPlan, a generative framework for Generative Spatiotemporal Intent Sequence Recommendation (GSISR) that integrates large language model reasoning into lightweight models. It employs Progressive Implicit CoT Distillation to compress reasoning into latent tokens for low-latency inference, and Spatiotemporal Counterfactual DPO to align plans with real-world constraints, improving sequence coherence and context responsiveness. Experiments and online A/B tests confirm the effectiveness of these techniques, and the authors provide an implementation and dataset on GitHub.
arXiv:2605. 18271v2 Announce Type: replace-cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness.
arXiv:2603.04191v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...
arXiv:2607. 03089v1 Announce Type: cross Abstract: HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and privacy risk, and fine-tuned LLM pipelines turn general-purpose models into task-specific classifiers.
arXiv:2608. 13606v1 Announce Type: new Abstract: The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users' experiences.
arXiv:2606. 13192v1 Announce Type: new Abstract: User experience (UX) centered on usability, perceived consistency, and functional clarity is fundamental to real-world user interfaces (UI).
arXiv:2608.20801v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains chal...
arXiv:2609.22971v1 Announce Type: new Abstract: Evaluating user experience (UX) on live websites through user testing is expensive, subjective, and difficult to scale. LLM agents offer a promising ro...