arXiv subjects

Zihong He

Publications and source records attributed to Zihong He.

5 recordsLinked to original sources

arXiv2026

AppLooper: An Agentic Application Engineering Loop for Accountable Release with Virtual-User Feedback

Much existing research on coding agents organizes application development as an iterative loop of requirement interpretation, implementation, tool execution, evaluation, and repair. As these loops run longer, requirements may drift; users may lose awareness of the current state and rationale for changes; and generated applications may remain insufficiently grounded in target users' contexts and needs. Application engineering therefore requires a mechanism connecting owner intent, target-user experience, development changes, and responsibility for release. We present AppLooper, a human--coding-agent--virtual-user application engineering loop for accountable release. An application owner confirms frozen requirements, supplies feedback, inspects candidates, and retains final release authority. A development agent produces and revises versioned candidates. A virtual-user agent cohort executes interface scenarios grounded in target users and contexts of use. Besides, an owner-intent simulation agent retests only requirements, constraints, and feedback explicitly confirmed by the owner, abstaining when evidence is insufficient. A testing agent performs read-only developmental checks by reproducing reported failures, running existing regression tests, and exercising the current candidate through its browser interface. The orchestration layer groups the resulting findings and routes them into development revision, targeted retesting, and owner inspection. AppLooper binds requirements, feedback sources, interface targets, development changes, retesting outcomes, owner interactions, and release decisions to specific versions. It thereby extends sustained coding-agent iteration into a traceable and reviewable lifecycle in which humans retain final responsibility for release. Source code is available at https://github.com/ZihongHe/applooper.

cs.HC↗

arXiv2026

Observable Social Life Spaces: Exploring User Interpretations of agent-side life context in human-agent interaction

Many AI agents are organized around instrumental "command-execution" interactions, where users primarily encounter agents through task requests and responses. Recent work on generative agents and agent life worlds has drawn attention to agents that maintain social contexts beyond direct user commands. In this paper, we study how observable social life spaces shape users' subjective experience, relational interpretations, and perceived equality during human-agent interaction. We introduce the \textit{Observable Social Life Spaces} paradigm, where agents inhabit a continuous virtual environment, engage in daily activities, and form social relationships that users can directly observe. Through an exploratory mixed-methods study ($N=24$), we found that the Observable condition yielded higher perceived-equality ratings and more frequent equality-related role descriptions than the Baseline and Unobservable conditions, but participant-level analysis suggests that the quantitative effect should be interpreted cautiously. We discuss perceived equality as a user-perception signal shaped by this design, with attention to boundary conditions including visual richness, novelty, and person-like attribution from visible agent cues.

cs.HC↗

arXiv2026

Tap-to-Adapt: Learning User-Aligned Response Timing for Speech Agents

Response timing judgment is a critical component of interactive speech agents. Although there exists substantial prior work on turn modeling and voice wake-up, there is a lack of research on response timing judgments continuously aligned with user intent. To address this, we propose the Tap-to-Adapt framework, which enables users to naturally activate or interrupt the agent via tap interactions to construct online learning labels for response timing models. Under this framework, Dilated TCN and a sequential replay strategy play significant roles, as demonstrated through data-driven experiments and user studies. Additionally, we develop an evaluation and continuous data mining system tailored for the Tap-to-Adapt framework, through which we have collected approximately 20,000 samples from the user studies involving 20 participants.

cs.HC↗

arXiv2024

Human-inspired Perspectives: A Survey on AI Long-term Memory

With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant. These capabilities are crucial for enhancing the performance of AI systems across a wide range of tasks. However, there is currently no comprehensive survey that systematically investigates AI's long-term memory capabilities, formulates a theoretical framework, and inspires the development of next-generation AI long-term memory systems. This paper begins by introducing the mechanisms of human long-term memory, then explores AI long-term memory mechanisms, establishing a mapping between the two. Based on the mapping relationships identified, we extend the current cognitive architectures and propose the Cognitive Architecture of Self-Adaptive Long-term Memory (SALM). SALM provides a theoretical framework for the practice of AI long-term memory and holds potential for guiding the creation of next-generation long-term memory driven AI systems. Finally, we delve into the future directions and application prospects of AI long-term memory.

cs.AI↗

arXiv2024

AFSPP: Agent Framework for Shaping Preference and Personality with Large Language Models

The evolution of Large Language Models (LLMs) has introduced a new paradigm for investigating human behavior emulation. Recent research has employed LLM-based Agents to create a sociological research environment, in which agents exhibit behavior based on the unfiltered characteristics of large language models. However, these studies overlook the iterative development within a human-like setting - Human preferences and personalities are complex, shaped by various factors and subject to ongoing change as a result of environmental and subjective influences. In light of this observation, we propose Agent Framework for Shaping Preference and Personality (AFSPP), exploring the multifaceted impact of social networks and subjective consciousness on LLM-based Agents' preference and personality formation. With AFSPP, we have, for the first time, successfully replicated several key findings from human personality experiments. And other AFSPP-based experimental results indicate that plan making, sensory perceptions and social networking with subjective information, wield the most pronounced influence on preference shaping. AFSPP can significantly enhance the efficiency and scope of psychological experiments, while yielding valuable insights for Trustworthy Artificial Intelligence research for strategies to prevent undesirable preference and personality development.

cs.MA↗