2 citations · 5 across the 27 of their papers we have counts for
23 papers · 1 filter
ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs
Jackson Hassell, Chen Shen, Estevam Hruschka
Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consistent evidence conditioning, dep…
Who Maintains Agent Skills? A Longitudinal Study of Human-Governed, AI-Assisted Skill Maintenance
Chen Shen, Estevam Hruschka
Lifelong LLM agents increasingly rely on external skill artifacts as one element for preserving and reusing capabilities over time. These skills (usually portable Markdown files su…
Reflective Prompt Tuning through Language Model Function-Calling
Farima Fatahi Bayat, Moin Aminnaseri, Pouya Pezeshkpour +1
Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without par…
Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Naoki Otani, Nikita Bhutani, Hannah Kim +2
Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external data sources. Existing strategi…
AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
Pouya Pezeshkpour, Estevam Hruschka
Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current verifiers face a fundamental…
A Dynamic Self-Evolving Extraction System
Moin Amin-Naseri, Hannah Kim, Estevam Hruschka
The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-qu…