arXiv subjects

Jinheng Li

Publications and source records attributed to Jinheng Li.

3 recordsLinked to original sources

arXiv2026

ClawMobile: Rethinking Smartphone-Native Agentic Systems

Smartphones represent a uniquely challenging environment for agentic systems. Unlike cloud or desktop settings, mobile devices combine constrained execution contexts, fragmented control interfaces, and rapidly changing application states. As large language models (LLMs) evolve from conversational assistants to action-oriented agents, achieving reliable smartphone-native autonomy requires rethinking how reasoning and control are composed. We introduce ClawMobile as a concrete exploration of this design space. ClawMobile adopts a hierarchical architecture that separates high-level language reasoning from structured, deterministic control pathways, improving execution stability and reproducibility on real devices. Using ClawMobile as a case study, we distill the design principles for mobile LLM runtimes and identify key challenges in efficiency, adaptability, and stability. We argue that building robust smartphone-native agentic systems demands principled coordination between probabilistic planning and deterministic system interfaces. The implementation is open-sourced~\footnote{https://github.com/ClawMobile/ClawMobile} to facilitate future exploration.

cs.MA↗

arXiv2025

Event Extraction in Large Language Model

Large language models (LLMs) and multimodal LLMs are changing event extraction (EE): prompting and generation can often produce structured outputs in zero shot or few shot settings. Yet LLM based pipelines face deployment gaps, including hallucinations under weak constraints, fragile temporal and causal linking over long contexts and across documents, and limited long horizon knowledge management within a bounded context window. We argue that EE should be viewed as a system component that provides a cognitive scaffold for LLM centered solutions. Event schemas and slot constraints create interfaces for grounding and verification; event centric structures act as controlled intermediate representations for stepwise reasoning; event links support relation aware retrieval with graph based RAG; and event stores offer updatable episodic and agent memory beyond the context window. This survey covers EE in text and multimodal settings, organizing tasks and taxonomy, tracing method evolution from rule based and neural models to instruction driven and generative frameworks, and summarizing formulations, decoding strategies, architectures, representations, datasets, and evaluation. We also review cross lingual, low resource, and domain specific settings, and highlight open challenges and future directions for reliable event centric systems. Finally, we outline open challenges and future directions that are central to the LLM era, aiming to evolve EE from static extraction into a structurally reliable, agent ready perception and memory layer for open world systems.

cs.CL↗

arXiv2018

Anomaly Analysis for Co-located Datacenter Workloads in the Alibaba Cluster

In warehouse-scale cloud datacenters, co-locating online services and offline batch jobs is an efficient approach to improving datacenter utilization. To better facilitate the understanding of interactions among the co-located workloads and their real-world operational demands, Alibaba recently released a cluster usage and co-located workload dataset, which is the first publicly dataset with precise information about the category of each job. In this paper, we perform a deep analysis on the released Alibaba workload dataset, from the perspective of anomaly analysis and diagnosis. Through data preprocessing, node similarity analysis based on Dynamic Time Warping (DTW), co-located workloads characteristics analysis and anomaly analysis based on iForest, we reveals several insights including: (1) The performance discrepancy of machines in Alibaba's production cluster is relatively large, for the distribution and resource utilization of co-located workloads is not balanced. For instance, the resource utilization (especially memory utilization) of batch jobs is fluctuating and not as stable as that of online containers, and the reason is that online containers are long-running jobs with more memory-demanding and most batch jobs are short jobs, (2) Based on the distribution of co-located workload instance numbers, the machines can be classified into 8 workload distribution categories1. And most patterns of machine resource utilization curves are similar in the same workload distribution category. (3) In addition to the system failures, unreasonable scheduling and workload imbalance are the main causes of anomalies in Alibaba's cluster.

cs.DC↗