arXiv:2607. 26545v1 Announce Type: cross Abstract: We propose an index for predicting the U.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces PersonaLink, a training‑free method that distills a user’s interaction history into a bounded three‑field persona and iteratively refines it by self‑evaluating a frozen 7B language model on held‑out labeled data. Each refinement rewrites the persona only if it does not regress on that slice, ensuring the persona remains bounded and query‑independent. On a 200‑user news categorization task (LaMP‑2), PersonaLink achieves 0.745–0.755 accuracy, statistically indistinguishable from BM25 retrieval’s 0.760–0.765 accuracy, demonstrating that distilled personas can match retrieval for classification but not for regression tasks.
arXiv:2607. 18310v1 Announce Type: cross Abstract: Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent.
The paper introduces A-B-D, a method that infers personality traits of large language model agents from their behavioral data, combining functional actions and linguistic outputs across 345,667 real-world trajectories of 80 models. After filtering for stability and discriminability, 79 features are reduced to six reliable factors—two functional and four linguistic—revealing differences such as Claude‑Sonnet‑4.6’s planfulness versus GPT‑5.5’s low energy. The study also demonstrates a weak correlation between these inferred traits and agents’ self-reported Big Five scores, highlighting a knowledge‑action gap.
The paper investigates how well model providers can detect distributed model‑extraction attacks by monitoring only the queries sent to their services. Using a dataset of one million real chat turns and a frozen evaluation period, the authors show that detection depends more on the distinctiveness of a campaign than on specific extraction techniques. They demonstrate that a calibrated monitor can catch most naive distributed extraction attempts, while more sophisticated campaigns that mimic ordinary user behavior evade all query‑only detectors.
The study evaluates whether large language models (LLMs) used as synthetic personas can predict real audience responses to marketing copy. Using thousands of headline A/B tests from the Upworthy Research Archive, the authors compare a ten-persona panel grounded in real audience demographics to a no-persona zero‑shot baseline that asks the model for a typical reader’s click likelihood. Results show that the no‑persona baseline outperforms the persona‑based approach, with higher predictive validity and top‑1 accuracy, indicating that forcing the model to role‑play specific personas introduces bias and noise.
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying...