When using an LLM agent in a consequential domain, making an informed decision about whether to trust its output or intervene requires calibrated confidence in the agent's success. Confidence estimation for agents is difficult because evidence about success is distributed across heterogeneous, interdependent steps of a...
MAWILE is introduced, a developer-facing workbench for auditing judge sensitivity across four surfaces: the judge prompt, judge rubric, target-system input, and target-system output, and it audits binary, ordinal, and pairwise judges without requiring gold labels.
Jackson Hassell, Farima Fatahi Bayat, Pouya Pezeshkpour et al.· 0 citations
A hybrid LLM-guided Bayesian network structure-learning framework that uses LLMs as bounded semantic guides, and shows that language-derived semantic knowledge can substantially improve scalable probabilistic structure learning when used as bounded guidance within a statistically grounded reasoning pipeline.
Jackson Hassell, Chen Shen, Estevam Hruschka· 0 citations
This work mine the full commit histories of five public AI-skill repositories, a purposive sample of AI-tooling organizations, and a purposive sample of AI-tooling organizations from October 2025 to June 2026, and codes each edit with pre-registered governance, operation, and trigger-evidence codebooks.
HypReflect is introduced, a reliable, scalable framework for continual personalization that infers explicit, uncertainty-aware preference hypotheses from diverse user signals, reflectively refines them as new evidence accumulates, and incorporates the resulting user model through hypotheses-guided self-distillation.
Eunjeong Hwang, Kushan Mitra, Dan Zhang et al.· 1 citation
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