# LivingArena: Do LLMs Know What Other LLMs Don't? Peer-Probing as Scalable Evaluation

arXiv: https://arxiv.org/abs/2607.24780 · HTML: https://mlsanity.com/paper/2607.24780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 26 of 26)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Humans or LLMs as the Judge? A Study on Judgement Biases](https://mlsanity.com/paper/2402.10669/) (2024)
- [Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2406.07791/) (2024)
- [LiveBench: A Challenging, Contamination-Limited LLM Benchmark](https://mlsanity.com/paper/2406.19314/) (2024)
- [Benchmarking Foundation Models with Language-Model-as-an-Examiner](https://mlsanity.com/paper/2306.04181/) (2023)
- [ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities](https://mlsanity.com/paper/2409.19839/) (2024)
- [SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks](https://mlsanity.com/paper/2506.11791/) (2025)
- [Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions](https://mlsanity.com/paper/2405.20267/) (2024)
- [Debate, Deliberate, Decide (D3): A Cost-Aware Adversarial Framework for Reliable and Interpretable LLM Evaluation](https://mlsanity.com/paper/2410.04663/) (2024)
- [CodeArena: A Collective Evaluation Platform for LLM Code Generation](https://mlsanity.com/paper/2503.01295/) (2025)
- [Judging the Judges: A Systematic Evaluation of Bias Mitigation Strategies in LLM-as-a-Judge Pipelines](https://mlsanity.com/paper/2604.23178/) (2026)
- [How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework](https://mlsanity.com/paper/2507.19219/) (2025)
- [PeerRank: Autonomous LLM Evaluation Through Web-Grounded, Bias-Controlled Peer Review](https://mlsanity.com/paper/2602.02589/) (2026)
- [PeerPrism: Peer Evaluation Expertise vs Review-writing AI](https://mlsanity.com/paper/2604.14513/) (2026)
- [SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges](https://mlsanity.com/paper/2508.06111/) (2025)
- [AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment](https://mlsanity.com/paper/2510.22593/) (2025)
- [QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks](https://mlsanity.com/paper/2604.17842/) (2026)

## Built on by (most cited first, 1 of 1)

- [Measuring Intelligence Beyond Human Scale](https://mlsanity.com/paper/2607.07040/) (2026)

## Abstract (arXiv)

Fixed benchmarks are costly to renew and cannot adapt their questions to model-specific failures. We ask whether LLMs can instead discover one another's weaknesses and turn those observations into an evaluation process. To study this question, we introduce LivingArena, an automated peer-probing framework in which models take turns testing one another. Using the interaction history, each questioner identifies potential weaknesses of its opponent and constructs targeted, verifiable questions to probe them. A 3,600-round tournament of ten models reveals a clear role asymmetry: strong answerers are not always reliable questioners, because they may generate internally inconsistent tests or fail to verify their own reference answers. After a questioner exposes an answerer's failure, it is more likely to pursue the same capability domain, while the answerer's weakness recurs on independently generated questions, including questions written by different models. These findings show that peer probing can reveal persistent model-specific weaknesses while separately evaluating answering and reliable test construction. By automating this process and allowing test difficulty to evolve with model capabilities, LivingArena provides a "living" benchmark for model development, red-teaming, and capability-aware multi-agent coordination. We publicly release our code: https://github.com/galaxyChen/LivingArena