Abstract
As large language models (LLMs) become increasingly capable of generating persuasive content, understanding their effectiveness across different advertising strategies becomes critical. This paper presents a two-part investigation examining LLM-generated advertising through complementary lenses: (1) personality-based and (2) psychological persuasion principles. In our first study (n=400), we tested whether LLMs could generate personalized advertisements tailored to specific personality traits (openness and neuroticism) and how their performance compared to human experts. Results showed that LLM-generated ads achieved statistical parity with human-written ads (51.1% vs. 48.9%, p > 0.05), with no significant performance differences for matched personalities. Building on these insights, our second study (n=800) shifted focus from individual personalization to universal persuasion, testing LLM performance across four foundational psychological principles: authority, consensus, cognition, and scarcity. AI-generated ads significantly outperformed human-created content, achieving a 59.1% preference rate (vs. 40.9%, p < 0.001), with the strongest performance in authority (63.0%) and consensus (62.5%) appeals. Qualitative analysis revealed AI's advantage stems from crafting more sophisticated, aspirational messages and achieving superior visual-narrative coherence. Critically, this quality advantage proved robust: even after applying a 21.2 percentage point detection penalty when participants correctly identified AI-origin, AI ads still outperformed human ads, and 29.4% of participants chose AI content despite knowing its origin. These findings demonstrate LLMs' evolution from parity in personalization to superiority in persuasive storytelling, with significant implications for advertising practice given LLMs' near-zero marginal cost and time requirements compared to human experts.

AI Summary

• AI-generated advertisements achieved a dominant preference rate compared to human-created advertisements. [3]
• The performance of AI-generated content varied depending on the persuasion strategy employed, with strong results in Authority and Consensus conditions. [3]
• Identifying an advertisement as AI-generated influenced user preference, resulting in a bias against known AI content. [3]
• The results suggest that AI-generated content can be a viable alternative to traditional advertising methods, particularly in certain persuasion strategies. [3]
• LLM-generated ads can be competitive with human-written ads in terms of user engagement and purchase intent. [2]

Abstract
AIVisor, an agentic retrieval-augmented LLM for student advising, was used to examine how personalization affects system performance across multiple evaluation dimensions. Using twelve authentic advising questions intentionally designed to stress lexical precision, we compared ten personalized and non-personalized system configurations and analyzed outcomes with a Linear Mixed-Effects Model across lexical (BLEU, ROUGE-L), semantic (METEOR, BERTScore), and grounding (RAGAS) metrics. Results showed a consistent trade-off: personalization reliably improved reasoning quality and grounding, yet introduced a significant negative interaction on semantic similarity, driven not by poorer answers but by the limits of current metrics, which penalize meaningful personalized deviations from generic reference texts. This reveals a structural flaw in prevailing LLM evaluation methods, which are ill-suited for assessing user-specific responses. The fully integrated personalized configuration produced the highest overall gains, suggesting that personalization can enhance system effectiveness when evaluated with appropriate multidimensional metrics. Overall, the study demonstrates that personalization produces metric-dependent shifts rather than uniform improvements and provides a methodological foundation for more transparent and robust personalization in agentic AI.

Abstract
Social science research increasingly demands data-driven insights, yet researchers often face barriers such as lack of technical expertise, inconsistent data formats, and limited access to reliable datasets.Social science research increasingly demands data-driven insights, yet researchers often face barriers such as lack of technical expertise, inconsistent data formats, and limited access to reliable datasets. In this paper, we present a Datalake infrastructure tailored to the needs of interdisciplinary social science research. Our system supports ingestion and integration of diverse data types, automatic provenance and version tracking, role-based access control, and built-in tools for visualization and analysis. We demonstrate the utility of our Datalake using real-world use cases spanning governance, health, and education. A detailed walkthrough of one such use case -- analyzing the relationship between income, education, and infant mortality -- shows how our platform streamlines the research process while maintaining transparency and reproducibility. We argue that such infrastructure can democratize access to advanced data science practices, especially for NGOs, students, and grassroots organizations. The Datalake continues to evolve with plans to support ML pipelines, mobile access, and citizen data feedback mechanisms.

AI Summary

• The Datalake is a cloud-based platform that enables users to perform simple and complex analytical tasks on multiple datasets. [3]
• It provides an end-to-end solution for data management, including provenance and version tracking. [3]
• The ease of use of the Datalake is key in democratizing access to data and good data science practices. [3]
• Datalake: A cloud-based platform that enables users to perform simple and complex analytical tasks on multiple datasets. [3]
• Provenance: The origin or history of a dataset or its components. [3]
• Version tracking: The process of keeping track of changes made to a dataset over time. [3]
• Funding support by Mphasis AI Lab at Ashoka University. [3]
• Access to data and analysis tools are the most important factors in lowering barriers for NGOs, grassroots organizations, and students who may not be well-versed in using computer science tools for data processing. [2]
• Limited user engagement with social scientists. [1]

Abstract
This paper reflects on the literature that rejects the use of Large Language Models (LLMs) in qualitative data analysis. It illustrates through empirical evidence as well as critical reflections why the current critical debate is focusing on the wrong problems. The paper proposes that the focus of researching the use of the LLMs for qualitative analysis is not the method per se, but rather the empirical investigation of an artificial system performing an analysis. The paper builds on the seminal work of Alan Turing and reads the current debate using key ideas from Turing "Computing Machinery and Intelligence". This paper therefore reframes the debate on qualitative analysis with LLMs and states that rather than asking whether machines can perform qualitative analysis in principle, we should ask whether with LLMs we can produce analyses that are sufficiently comparable to human analysts. In the final part the contrary views to performing qualitative analysis with LLMs are analysed using the same writing and rhetorical style that Turing used in his seminal work, to discuss the contrary views to the main question.

Abstract
This paper develops a new perspective on parameter-efficient fine-tuning for LLMs, inspired by the classical theory of subspace minimization. We introduce a unifying framework, Parameter-Efficient Subspace Optimization (PESO), which not only recovers many existing methods such as LoRA but also bridges them with the principled algorithmic and theoretical foundations of subspace optimization. This connection highlights a natural ``exploration--exploitation'' view of subspace methods, guiding the design of new algorithms that achieve strong convergence performance while still preserving memory efficiency. Importantly, our framework establishes the convergence in the full-parameter space, resolving a critical gap of LoRA variants where low-rank updates lack such guarantees. We further instantiate the framework into a practical algorithm named {PESO-LoRA}, based on LoRA-type parameterization. Our algorithm achieves notable improvements over existing methods on standard benchmarks.

AI Summary

• One critical limitation in the literature is the absence of convergence guarantees toward valid optimality conditions of (1). [2]
• Most existing works establish convergence only with respect to the low-dimensional parameters—such as the factors A and B in LoRA—but do not address convergence with respect to the full-parameter matrix W. [1]

Abstract
For a remote estimation system, we study age of incorrect information (AoII), which is a recently proposed semantic-aware freshness metric. In particular, we assume an information source observing a discrete-time finite-state Markov chain (DTMC) and employing push-based transmissions of status update packets towards the monitor which is tasked with remote estimation of the source. The source-to-monitor channel delay is assumed to have a general discrete-time phase-type (DPH) distribution, whereas the zero-delay reverse channel ensures that the source has perfect information on AoII and the remote estimate. A multi-threshold transmission policy is employed where packet transmissions are initiated when the AoII process exceeds a threshold which may be different for each estimation value. In this general setting, our goal is to minimize the weighted sum of time average of an arbitrary function of AoII and estimation, and transmission costs, by suitable choice of the thresholds. We formulate the problem as a semi-Markov decision process (SMDP) with the same state-space as the original DTMC to obtain the optimum multi-threshold policy whereas the parameters of the SMDP are obtained by using a novel stochastic tool called dual-regime absorbing Markov chain (DR-AMC), and its corresponding absorption time distribution named as dual-regime DPH (DR-DPH).