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This paper proposes MIDAS, a multi-LLM framework for data-adaptive summarization that automates prompt optimization for domain-specific enterprise use cases, achieving strong improvements over prior methods on customer ticket summarization benchmarks.
Opti-Q is a database-inspired optimizer for multi-LLM question answering that plans execution DAGs to optimize answer quality under cost, latency, and energy constraints, achieving significant improvements on benchmarks.
This paper introduces an uncertainty-aware trust estimation method for aggregating predictions from multiple LLMs, adapting structured expert judgment with Cooke-style log weighting to penalize overconfident incorrect predictions. Evaluations on MMLU and MMLU-Pro show that this approach achieves superior accuracy-reliability balance under heterogeneous and contaminated expert panels.
This paper presents SynthAVE, a large-scale human-validated benchmark for attribute value extraction in e-commerce, using a multi-LLM arena framework with 21 judge configurations to validate synthetic labels efficiently and cost-effectively while maintaining quality parity with human review.
A detailed guide on building an agentic research framework using a multi-LLM system with persistent memory, allowing researchers to avoid re-explaining context across sessions by leveraging file-based identity, project docs, and memory indices.