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#multi-llm

Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection

arXiv cs.CL ↗ · 2026-10-01 Cached

The paper proposes a heterogeneity-driven framework for selecting complementary LLM teams by profiling error decorrelation and predictive divergence, then greedily optimizing a quality–complementarity objective, showing gains over quality-only top-k baselines across multiple benchmarks.

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#multi-llm

COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference

arXiv cs.CL ↗ · 2026-09-24 Cached

CoMed introduces a post-anchor controller for selective cross-model collaboration in multi-LLM systems, improving accuracy on benchmarks like MedQA and HLE while reducing computational costs compared to dense collaboration.

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#multi-llm

@bkdgiffug: Want a real AI that can chat with you and play games? This AIRI is pretty interesting. It's not just a simple chatbot, …

X AI KOLs Timeline ↗ · 2026-09-18 Cached

AIRI is an AI companion that features real-time voice chat, gaze-tracking avatars, memory retention, and gameplay support for Minecraft and Factorio, with compatibility for over 30 LLMs and local inference via WebGPU.

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MIDAS: Multi-LLM Iterative Data-Adaptive Summarization

arXiv cs.CL ↗ · 2026-08-06 Cached

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.

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Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning

arXiv cs.AI ↗ · 2026-07-28 Cached

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.

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Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement

arXiv cs.LG ↗ · 2026-07-24 Cached

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.

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SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

arXiv cs.CL ↗ · 2026-07-09 Cached

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.

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@degenrsc: https://x.com/degenrsc/status/2064714047241736302

X AI KOLs Timeline ↗ · 2026-06-10 Cached

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.

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