arXiv subjects

Ananya Drishti

Publications and source records attributed to Ananya Drishti.

3 recordsLinked to original sources

arXiv2026

Cogniscope: A Synthetic Longitudinal Benchmark and Browser-Based Evaluation Framework for Early-Risk Cognitive AI Systems

We present Cogniscope, an open evaluation framework for studying longitudinal early-risk AI systems under controlled behavioral drift, sparse observations, delayed evidence, and heterogeneous progression patterns. Cogniscope combines two complementary components: a synthetic simulation engine that generates privacy-preserving longitudinal behavioral traces aligned with configurable latent risk trajectories, and a browser-based data-collection instrument implemented as a Chrome extension for capturing naturalistic video interaction telemetry and micro-question responses during YouTube playback. The released benchmark includes 200,000 simulated video-interaction records from 200 users over 200 days, a 504-session schema-aligned synthetic deployment dataset across nine behavioral profiles, an 18-table relational schema, baseline evaluation scripts, and time-aware metrics including Early Risk Detection Error (ERDE) and time-to-detection (TTD). We emphasize that Cogniscope is not a diagnostic system and does not claim clinical validity. Instead, it provides a reusable testbed for evaluating how sequential models behave under known longitudinal challenges before deployment with real human-subject data. Experiments show that simple behavioral coherence signals separate simulated risk states under controlled priors, while rule-based deployment-profile classification remains challenging, motivating learned temporal models and robust evaluation protocols.

cs.HC↗

arXiv2025

Cogniscope: Modeling Social Media Interactions as Digital Biomarkers for Early Detection of Cognitive Decline

Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), are associated with subtle declines in memory, attention, and language that often go undetected until late in progression. Traditional diagnostic tools such as MRI and neuropsychological testing are invasive, costly, and poorly suited for population-scale monitoring. Social platforms, by contrast, produce continuous multimodal traces that can serve as ecologically valid indicators of cognition. In this paper, we introduce Cogniscope, a simulation framework that generates social-media-style interaction data for studying digital biomarkers of cognitive health. The framework models synthetic users with heterogeneous trajectories, embedding micro-tasks such as video summarization and lightweight question answering into content consumption streams. These interactions yield linguistic markers (semantic drift, disfluency) and behavioral signals (watch time, pausing, sharing), which can be fused to evaluate early detection models. We demonstrate the framework's use through ablation and sensitivity analyses, showing how detection performance varies across modalities, noise levels, and temporal windows. To support reproducibility, we release the generator code, parameter configurations, and synthetic datasets. By providing a controllable and ethically safe testbed, Cogniscope enables systematic investigation of multimodal cognitive markers and offers the community a benchmark resource that complements real-world validation studies.

cs.HC↗

arXiv2025

Causally-Informed Reinforcement Learning for Adaptive Emotion-Aware Social Media Recommendation

Social media recommendation systems play a central role in shaping users' emotional experiences. However, most systems are optimized solely for engagement metrics, such as click rate, viewing time, or scrolling, without accounting for users' emotional states. Repeated exposure to emotionally charged content has been shown to negatively affect users' emotional well-being over time. We propose an Emotion-aware Social Media Recommendation (ESMR) framework that personalizes content based on users' evolving emotional trajectories. ESMR integrates a Transformer-based emotion predictor with a hybrid recommendation policy: a LightGBM model for engagement during stable periods and a reinforcement learning agent with causally informed rewards when negative emotional states persist. Through behaviorally grounded evaluation over 30-day interaction traces, ESMR demonstrates improved emotional recovery, reduced volatility, and strong engagement retention. ESMR offers a path toward emotionally aware recommendations without compromising engagement performance.

cs.IR↗