arXiv subjects

Hanjing Shi

Publications and source records attributed to Hanjing Shi.

9 recordsLinked to original sources

arXiv2026

How Far Do Capability Cues Travel? Anthropomorphism and Differentiated Trust in a Platform-Embedded AI Assistant

Visible AI capabilities need not translate into broader judgments of trustworthiness. In a randomized 2 x 2 experiment with 270 U.S.-based Reddit users, an embedded assistant displayed one or three functions, with or without a brief rationale. Displaying three functions increased perceived multifunctionality; no other randomized main effect survived correction across the six outcomes. Rationale availability did not reliably increase perceived intelligence. Exploratory analysis indicated stronger uptake of the functional display at higher objective AI literacy. Among concurrently measured judgments, perceived multifunctionality was associated with perceived intelligence, which was associated with anthropomorphism and all three trust dimensions. After accounting for perceived intelligence, anthropomorphism was positively associated with benevolence, but not reliably with integrity or ability. These findings separate interface effects from relationships among users' perceptions and show why ability, integrity, and benevolence should be evaluated separately.

cs.HC↗

arXiv2026

When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation

Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves. We investigated how source-text condition and output rendering relate to perceived translation quality, and how output and system appraisals relate to trust and stated disclosure willingness in a plain-text interface. A focal 2 * 2 comparison (N=306) using TransLingo examined simple generated narratives and complex literary-philosophical prose alongside LLM-generated readability-oriented outputs and researcher-revised fidelity-oriented outputs. A descriptive stimulus audit indicated greater source retention in fidelity-oriented outputs in both source-text conditions. Factorial analyses showed a significant rendering-by-source-text-condition interaction in perceived quality. Participants rated fidelity-oriented outputs higher than readability-oriented outputs for the simple narratives, whereas no reliable rendering difference emerged for the complex prose. A corresponding source-condition-dependent pattern was observed for perceived intelligence, agency-oriented anthropomorphic attribution, and task-performance trust. A separate theory-ordered appraisal-structure SEM characterized concurrent associations among perceived quality, perceived intelligence, agency-oriented anthropomorphic attribution, task-performance trust, and stated disclosure willingness across six domains, with task-performance trust as the proximal correlate of stated willingness. The observed rating pattern distinguishes source access from source evaluability: for the complex stimuli, displaying the source did not ensure that one overall-quality rating reflected differences in retained content. It also separates support for evaluating translation output from data-handling support for decisions about what personal text to entrust to a system.

cs.HC↗

arXiv2026

Outer Limits: An Experimental Approach to Controlled Content Manipulation within the Reddit Interface

Independent researchers often lack access to intervention capabilities for controlled experiments on live social media platforms. We present Outer Limits, a browser-based system for controlled content experiments within the existing Old Reddit interface, rather than in a reconstructed simulation. The system renders content locally, records study events, and contains configured voting and commenting actions so that neither constructed content nor experimental write interactions reach Reddit. In a 219-participant perceptual-fidelity study, ART ANOVAs found no significant Post Type, Participant Awareness, or interaction effects. Exploratory TOSTs met the d = plus-minus 0.50 equivalence criterion for the marginal contrasts and for Post Type within the forewarned subgroup. We also illustrate the system with a factorial study varying post frame, comment frame, and comment stance. Outer Limits combines three properties that the approaches considered here provide separately: precise control over experimental content, an existing platform interface, and containment of experimental content and interactions from the host community.

cs.HC↗

arXiv2026

Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems

Traditional AI alignment primarily focuses on individual model outputs; however, autonomous agents in long-horizon workflows require sustained reliability across entire interaction trajectories. We introduce APEMO (Affect-aware Peak-End Modulation for Orchestration), a runtime scheduling layer that optimizes computational allocation under fixed budgets by operationalizing temporal-affective signals. Instead of modifying model weights, APEMO detects trajectory instability through behavioral proxies and targets repairs at critical segments, such as peak moments and endings. Evaluation across multi-agent simulations and LLM-based planner--executor flows demonstrates that APEMO consistently enhances trajectory-level quality and reuse probability over structural orchestrators. Our results reframe alignment as a temporal control problem, offering a resilient engineering pathway for the development of long-horizon agentic systems.

cs.AI↗

arXiv2026

When Visibility Outpaces Verification: Delayed Verification and Narrative Lock-in in Agentic AI Discourse

Agentic AI systems-autonomous entities capable of independent planning and execution-reshape the landscape of human-AI trust. Long before direct system exposure, user expectations are mediated through high-stakes public discourse on social platforms. However, platform-mediated engagement signals (e.g., upvotes) may inadvertently function as a ``credibility proxy,'' potentially stifling critical evaluation. This paper investigates the interplay between social proof and verification timing in online discussions of agentic AI. Analyzing a longitudinal dataset from two distinct Reddit communities with contrasting interaction cultures-r/OpenClaw and r/Moltbook-we operationalize verification cues via reproducible lexical rules and model the ``time-to-first-verification'' using a right-censored survival analysis framework. Our findings reveal a systemic ``Popularity Paradox'': high-visibility discussions in both subreddits experience significantly delayed or entirely absent verification cues compared to low-visibility threads. This temporal lag creates a critical window for ``Narrative Lock-in,'' where early, unverified claims crystallize into collective cognitive biases before evidence-seeking behaviors emerge. We discuss the implications of this ``credibility-by-visibility'' effect for AI safety and propose ``epistemic friction'' as a design intervention to rebalance engagement-driven platforms.

cs.CY↗

arXiv2026

Human Control Is the Anchor, Not the Answer: Early Divergence of Oversight in Agentic AI Communities

Oversight for agentic AI is often discussed as a single goal ("human control"), yet early adoption may produce role-specific expectations. We present a comparative analysis of two newly active Reddit communities in Jan--Feb 2026 that reflect different socio-technical roles: r/OpenClaw (deployment and operations) and r/Moltbook (agent-centered social interaction). We conceptualize this period as an early-stage crystallization phase, where oversight expectations form before norms reach equilibrium. Using topic modeling in a shared comparison space, a coarse-grained oversight-theme abstraction, engagement-weighted salience, and divergence tests, we show the communities are strongly separable (JSD =0.418, cosine =0.372, permutation $p=0.0005$). Across both communities, "human control" is an anchor term, but its operational meaning diverges: r/OpenClaw} emphasizes execution guardrails and recovery (action-risk), while r/Moltbook} emphasizes identity, legitimacy, and accountability in public interaction (meaning-risk). The resulting distinction offers a portable lens for designing and evaluating oversight mechanisms that match agent role, rather than applying one-size-fits-all control policies.

cs.AI↗

arXiv2026

Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design

Multilingual large language models (MLLMs) are increasingly deployed across cultural, linguistic, and political contexts, yet existing governance frameworks largely assume English-centric data, homogeneous user populations, and abstract notions of fairness. This creates systematic risks for low-resource languages and culturally marginalized communities, where data practices, model behavior, and accountability mechanisms often fail to align with local norms, rights, and expectations. Drawing on cross-cultural perspectives in human-centered computing and AI governance, this paper synthesizes existing evidence on multilingual model behavior, data asymmetries, and sociotechnical harm, and articulates a culturally grounded governance framework for MLLMs. We identify three interrelated governance challenges: cultural and linguistic inequities in training data and evaluation practices, misalignment between global deployment and locally situated norms, values, and power structures, and limited accountability mechanisms for addressing harms experienced by marginalized language communities. Rather than proposing new technical benchmarks, we contribute a conceptual agenda that reframes multilingual AI governance as a sociocultural and rights based problem. We outline design and policy implications for data stewardship, transparency, and participatory accountability, and argue that culturally grounded governance is essential for ensuring that multilingual language models do not reproduce existing global inequalities under the guise of scale and neutrality.

cs.CL↗

arXiv2025

Distinguishing the formation paths of massive compact ETGs through their internal dynamical structures

Massive early-type galaxies (ETGs) are thought to form in two phases: an initial phase of rapid star formation and a later phase of mergers. A small fraction of these galaxies, called red nuggets, formed during the first phase may have survived frozen until today, experiencing no massive mergers since z~2. Nearby massive compact ETGs are considered candidates for such relic galaxies. We study the internal dynamical structures of 15 compact ETGs with existing integral field unit (IFU) observations and 79 compact ETGs from the TNG50 simulation. We dynamically decompose each galaxy into a disk, bulge, and hot inner stellar halo, for both observations and simulations. In TNG50, the luminosity fraction of the hot inner stellar halo (or the size of the spheroid, which includes the bulge and halo) strongly correlates with the galaxy merger history. The true merger-free galaxies show an extremely low fraction of a hot inner stellar halo (or an extremely compact spheroid). Although such compactness could result from the tidal stripping of satellites, tidal forces would also destroy the dynamically cold disk (if one exists) when the halo is removed. Thus, a galaxy is guaranteed to be merger-free if it has a very low fraction of the hot inner stellar halo and retains a dynamically cold disk. Comparing observed galaxies with TNG50, we identify 7 of the 15 compact ETGs, PGC 11179, UGC 3816, NGC 2767, NGC 1277, PGC 32873, PGC 12562, and PGC 70520, as true merger-free galaxies. These galaxies have compact, massive bulges, likely formed through secular heating, as supported by their TNG50 analogues.

astro-ph.GA↗

arXiv2023

A Critique of Human-Autonomous Team Dynamics: Contrasting Qualitative and Quantitative Perspectives

The critique paper provides an in-depth analysis of two influential studies in the field of Human-Autonomous Teams (HATs). Musick et al. explored qualitative dimensions of HAT dynamics, examining the influence of team composition on emotions, cognitive processes, and the development of team cognition. Their research revealed that teams with a majority of human members, known as Multi-Human HATs, generally surpass Multi-Agent HATs in performance, highlighting the critical influence of human perception on team dynamics. Employing qualitative interview analysis anchored in theoretical frameworks, Musick et al. captured the detailed subtleties of participants' experiences. In contrast, Schelble et al. utilized a quantitative methodology to provide data-driven insights into how the perception of AI teammates affects team performance. Despite the rich insights from Musick et al.'s qualitative research, their findings face limitations in terms of broader applicability. Both Musick et al. and Schelble et al. agree in their conclusions that Multi-Human HATs typically outperform their Multi-Agent counterparts, again emphasizing the crucial role of human perception in team dynamics. The critique paper suggests that future research should focus on understanding perceptions of teams heavily reliant on AI. Such investigations could illuminate how trust and skepticism are shaped in teams where AI plays a dominant role.

cs.HC↗