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Researcher profile

Agam Goyal

Agam Goyal contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceHuman-Computer InteractionComputation and Languagecs.CYMachine LearningSocial and Information Networks

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Published work

3 published item(s)

preprint2026arXiv

Algorithmic Cultivation: How Social Media Feeds Shape User Language

Algorithmic feeds have become primary environments for encountering information online, yet while they shape what people see, less is known about how sustained feed exposure shapes how people write. Drawing on Cultivation Theory, we examine whether algorithmic feeds function as online environments that leave measurable traces in users' language. We leverage a large-scale longitudinal dataset of 235M posts by 4M users on Bluesky, and conduct a quasi-experimental study matching an initial pool of 368,513 users exposed to one of three feeds -- News, Science, and Blacksky -- with a pool of 2,001,915 active control users who did not engage with any of these feeds. We examine linguistic evolution across three dimensions: lexico-semantics, psycholinguistics, and topics. We find that users exposed to these feeds show significantly greater stylistic accommodation, semantic alignment, and register formalization than matched controls. These effects vary markedly by feed identity -- Blacksky produces the deepest psycholinguistic restructuring, with significant shifts in cognitive processing, affective expression, and pronoun use, while News and Science effects are largely confined to register and topical focus. Regression models reveal that reposting is the most consistent predictor of linguistic convergence across all feeds, whereas posting and bookmarking show feed-dependent effects, with effects differing more than fourfold across feeds. Our work extends Cultivation Theory beyond belief formation to linguistic behavior, demonstrating that feeds function as persistent linguistic environments that gradually shape what and how users write online. Our work has implications for studying algorithmic influence, online identity formation, and the design and governance of feed-based platforms that mediate online interactions.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

State Contamination in Memory-Augmented LLM Agents

LLM agents increasingly rely on persistent state, including transcripts, summaries, retrieved context, and memory buffers, to support long-horizon interaction. This makes safety depend not only on individual model outputs, but also on what an agent stores and later reuses. We study a failure mode we call memory laundering: toxic or adversarial context can be compressed into memory summaries that no longer appear toxic under standard detectors, while still preserving hostile framing or conflict structure that influences future generations. Using paired counterfactual multi-agent rollouts, we show that toxic-origin memory summaries can remain below common toxicity thresholds while nevertheless increasing downstream toxicity relative to matched neutral baselines. To measure this hidden influence, we introduce the sub-threshold propagation gap (SPG), which quantifies downstream behavioral differences conditioned on memory states that a deployed monitor would classify as safe. Our experiments show that toxicity propagates through distinct state channels: raw transcript reuse drives overt downstream toxicity, while compressed memory carries hidden sub-threshold influence. We further find that mitigation depends critically on intervention placement. Sanitizing toxic state before summarization substantially reduces the hidden propagation gap, whereas cleaning only the completed summary can leave laundered influence intact. These results suggest that safety in memory-augmented agents should be treated as a state-control problem over evolving context, with sanitization applied before unsafe information is compressed into persistent memory.

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preprint2026arXiv

VASTU: Value-Aligned Social Toolkit for Online Content Curation

Detecting what content communities value is a foundational challenge for social computing systems -- from feed curation and content ranking to moderation tools and personalized recommendation systems. Yet existing approaches remain fragmented across methodological paradigms, and it remains unclear which methods best capture community-specific notions of value. We introduce VASTU (Value-Aligned Social Toolkit for Online Content Curation), a benchmark and evaluation framework for systematically comparing approaches to detecting community-valued content. VASTU includes a dataset of 75,000 comments from 15 diverse Reddit communities, annotated with community approval labels and rich linguistic features. Using VASTU, we evaluate feature-based models, transformers, prompted and fine-tuned language models under global versus community-specific training regimes. We find that community-specific models consistently outperform global approaches, with fine-tuned transformers achieving the strongest performance (0.72 AUROC). Notably, fine-tuned SLMs (0.65 AUROC) substantially outperform prompted LLMs (0.60 AUROC) despite being 100 times smaller. Counterintuitively, chain-of-thought prompting provides no benefit, and reasoning models perform the worst (0.53 AUROC), suggesting this task requires learning community norms rather than test-time reasoning. By releasing VASTU, we provide a standardized benchmark to advance research on value-aligned sociotechnical systems.

0 citations0 reviewsNo code linkedOpen access