The paper introduces ACORN, a method that blends machine‑learning predictions with occupancy models to guide ecologists in selecting which samples to review. By strategically choosing the most informative labels, ACORN achieves ecological conclusions nearly identical to fully human‑labeled data while dramatically reducing the number of expert reviews needed. The approach is evaluated on camera‑trap and bioacoustic datasets, demonstrating its effectiveness across real‑world biodiversity surveys.
By Timm Haucke, Lauren Harrell, Justin Kay, Mary Clapp, Sara Beery
The paper investigates how AI systems that perform best‑of‑n search require different validation strategies as the search width changes. It shows that auditing only small search widths leaves a gap in reliability estimates for larger widths, and proposes retaining candidate ranks and truth labels to estimate reliability across all widths up to N. The authors derive theoretical bounds on the minimax mean‑squared error, design procedures that achieve these bounds, and demonstrate that a shared audit can significantly reduce maximum error across many widths in practical CodeRM pools.
By Ricardo Fitas
The paper introduces a risk‑controlled framework for using large language models (LLMs) as judges in tasks without reference answers. By calibrating uncertainty thresholds on a held‑out set, the method ensures that the false discovery rate of accepted verdicts stays below a user‑specified level α with high probability, using finite‑sample Clopper–Pearson intervals. When the parametric judge lacks confidence, the instance is routed to a retrieval‑augmented mode with a second calibrated threshold, preserving the error guarantee while achieving higher coverage than single‑mode baselines.
The paper discusses how active learning (AL) can alleviate the expert annotation bottleneck in biodiversity monitoring by selecting the most informative samples under a fixed budget. It highlights that while AL reduces labeling effort, its non-random sample selection complicates model validation, calibration, and ecological inference, issues often overlooked in current studies. The authors review existing AL research across acoustic and image data, identify gaps such as limited species coverage and lack of real-world deployments, and propose a tutorial framework and roadmap for developing AL methods that support efficient training, reliable validation, and trustworthy ecological conclusions.
By Ben McEwen, Shiqi Zhang, Dan Stowell
arXiv:2610.35698v1 Announce Type: new
Abstract: For large language models (LLMs) and multimodal large language models (MLLMs), model-reported self-confidence is often miscalibrated, yet it may still...
By Harine Choi (Texas A&M Transportation Institute, Texas A&M University System), Chenchen Kuai (Zachry Department of Civil and Environmental Engineering, Texas A&M University), Zihao Li (Department of Civil, Construction and Environmental Engineering, Marquette University), Jiwan Jiang (Zachry Department of Civil and Environmental Engineering, Texas A&M University), Yunlong Zhang (Zachry Department of Civil and Environmental Engineering, Texas A&M University), Zhiwen Fan (Department of Electrical and Computer Engineering, Texas A&M University), Zhengzhong Tu (Department of Computer Science and Engineering, Texas A&M University), Yang Zhou (Zachry Department of Civil and Environmental Engineering, Texas A&M University)
arXiv:2609.33676v2 Announce Type: replace
Abstract: LLM agents increasingly take consequential actions through interactions with users, policies, and external tools. Auditing these agents requires au...
By Yifan Liu, Praveen Venkateswaran, Abdulhamid Adebayo, Dong Wang
The paper introduces a framework for online monitoring of AI systems that produce decisions with associated confidence scores. It formulates the problem as an online constrained optimization task, aiming to minimize expert auditing while ensuring that the proportion of unaudited AI errors remains below a user‑defined threshold. The authors present a family of algorithms with provable error‑control guarantees, including an optimally efficient method that achieves the best possible auditing rate for both adversarial and i.i.d. data streams, and demonstrate its effectiveness on tasks such as screening harmful requests, text annotation, and data labeling.
By Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani, Tijana Zrnic
Active learning is now standard practice in labeling ecological data, enabling ecologists to quickly process large volumes of field data to understand and monitor natural environments. Current practices evaluate active learning inductively, estimating predictive performance on a held-out test set.
The paper introduces ARGUS, a language‑model pipeline that audits evidence for identification assumptions in difference‑in‑differences studies of climate policy. ARGUS evaluates reported evidence against an eleven‑dimension rubric, abstaining when evidence cannot be retrieved. In tests, ARGUS detects 73% of injected flaws versus 18% for a keyword approach, abstains on about 40% of assessments in 26 economics papers, and often assigns higher risk than human labels in a five‑paper pilot.
By Yonghong Zhang, Yong Xie, Isabel M. Parra, Ricardo Correia
The paper introduces a new consistency criterion for auditing decision systems that combines ensemble margin with local prediction variability to address predictive multiplicity, or the Rashomon effect. It shows that finite ensembles converge to the expected model’s consistency score as ensemble size and sample count grow, and demonstrates that ensembling models from the Rashomon set reduces unchecked incorrect predictions while keeping diversions moderate. Experiments on transformer and fine‑tuned language models for NLP and tabular classification confirm the method’s effectiveness and stronger alignment with existing multiplicity metrics.
By Sinjini Banerjee, Tim Marrinan, Anand D. Sarwate
arXiv:2606. 03821v1 Announce Type: new Abstract: Active learning is now standard practice in labeling ecological data, enabling ecologists to quickly process large volumes of field data to understand and monitor natural environments.
By Rupa Kurinchi-Vendhan, Sara Beery
arXiv:2606. 13436v1 Announce Type: new Abstract: Evaluation in machine learning is typically treated as a neutral measurement process.
By Raymond Vasquez