Introduces budgeted heteroskedastic multi-judge estimation and proves instance-optimality of an adaptive inverse-variance weighted estimator via matching upper and lower bounds.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6verdicts
UNVERDICTED 6representative citing papers
SASAV introduces the first fully autonomous multi-agent system for scientific data analysis and visualization that operates without external prompting or human-in-the-loop feedback.
NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via RAG-LLM.
Agentic GraphRAG constructs a Neo4j graph via deterministic structured ingestion plus LLM extraction from notices, then deploys modular agents with tool access and reflection to outperform vector-RAG baselines on Swiss commercial gazette data across entity resolution, answer quality, and multi-turn
RPA-Check is a new multi-stage framework using dimension definition, boolean checklist augmentation, semantic filtering, and LLM-as-judge verification to assess role-playing agents, with tests on a legal training game showing smaller instruction-tuned models can be more consistent than larger ones.
RAG-based LLM extraction reaches 89% accuracy on clinical trial protocols versus 62.6% for standalone models and cuts simulated workflow time by 40%.
citing papers explorer
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Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Introduces budgeted heteroskedastic multi-judge estimation and proves instance-optimality of an adaptive inverse-variance weighted estimator via matching upper and lower bounds.
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SASAV: Self-Directed Agent for Scientific Analysis and Visualization
SASAV introduces the first fully autonomous multi-agent system for scientific data analysis and visualization that operates without external prompting or human-in-the-loop feedback.
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NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via RAG-LLM.
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Agentic GraphRAG: Navigating Unstructured Financial Data with Collaborative AI
Agentic GraphRAG constructs a Neo4j graph via deterministic structured ingestion plus LLM extraction from notices, then deploys modular agents with tool access and reflection to outperform vector-RAG baselines on Swiss commercial gazette data across entity resolution, answer quality, and multi-turn
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RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents
RPA-Check is a new multi-stage framework using dimension definition, boolean checklist augmentation, semantic filtering, and LLM-as-judge verification to assess role-playing agents, with tests on a legal training game showing smaller instruction-tuned models can be more consistent than larger ones.
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AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows
RAG-based LLM extraction reaches 89% accuracy on clinical trial protocols versus 62.6% for standalone models and cuts simulated workflow time by 40%.