The paper introduces proFILL, a method that transforms the existing rule-based advising chatbot hoBIT into a profile-aware retrieval-augmented generation system. Instead of needing a full student profile at the start, proFILL incrementally gathers only the attributes required for each query, using both the query intent and initially retrieved evidence to guide retrieval from a profile-aware index. Experiments and a human preference study demonstrate that proFILL outperforms various RAG baselines, is favored by users, and remains effective when deployed with open-weight models for cost-efficient on‑premise use.
arXiv:2510. 01800v3 Announce Type: replace Abstract: Academic regulation advising is essential for helping students interpret and comply with institutional policies, yet building effective systems requires domain specific regulatory resources.
The study evaluates six retrieval methods for ranking computer science faculty as potential academic advisors based on graduate applicants’ research interest statements. Using a new dataset of 768 faculty profiles from nine U.S. universities and 162 graded relevance judgments across five queries, the reranked hybrid approach achieved the highest mean NDCG@10 (0.477). Ablation experiments showed that faculty biographies alone outperform the full model, and adding arXiv abstracts actually decreased performance, leading to a late‑fusion design. All code, data, and labels are publicly released.
arXiv:2606. 28360v1 Announce Type: cross Abstract: University students often struggle to navigate complex academic policies, leading to advising bottlenecks and delayed access to critical information.
arXiv:2606. 00809v1 Announce Type: new Abstract: Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person.
arXiv:2607. 00017v1 Announce Type: cross Abstract: Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the right user.
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.
The paper introduces ENOUGH, a method for creating minimal sufficient user profiles for personalized language models. ENOUGH iteratively adds behavioral records or stops, evaluating profile prefixes with a counterfactual search that balances downstream gains, user specificity, and token costs. The resulting profiles are distilled into a lightweight controller that orders records and triggers the generator only once, achieving better effectiveness and efficiency than existing baselines across six tasks.
arXiv:2607. 26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage and semantic steering during retrieval.
arXiv:2603. 27476v2 Announce Type: replace Abstract: AI-powered people search platforms are increasingly used in recruiting, sales prospecting, and professional networking, yet no widely accepted benchmark exists for evaluating their performance.
The study evaluates whether detailed, profession‑specific system prompts improve performance on scientific tasks. Using an open‑source corpus of 503 agent profiles and Gemini 3.8 Flash, the authors compared matched profiles to four control prompts across nine text‑based science benchmarks and a tool‑using bioinformatics benchmark. Results show no consistent accuracy gains; matched profiles actually increased token usage and cost, and in some cases reduced success rates, with only a minor advantage in one benchmark likely due to prompt length rather than domain expertise.
arXiv:2607. 01115v1 Announce Type: cross Abstract: University stakeholders often face difficulties in accessing timely and reliable information, especially in developing countries, where there are very few intelligent support systems.
arXiv:2607. 01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation.