Read a study called "The Leaderboard Illusion" which credibly alleged that Meta Google OpenAI and Amazon got unfair treatment from LM Arena that distorted the benchmarks
They gave them special access to privately test and let them benchmark over and over without showing the failed tests
Meta got to privately test Llama 4 27 times to optimize it for high benchmark scores and then was allowed to report the only the highest cherry picked benchmark
Which makes sense because in real world applications Llama is recognized to be markedly inferior to models that scored lower
Which is one study that touches exactly one benchmark - and "credibly alleged" is being way too generous to it. The only case that was anywhere close to being proven LMArena fraud is Meta and Llama 4. Which is a nonentity now - nowhere near SOTA on anything, LMArena included.
Not that it makes LMArena a perfect benchmark. By now, everyone who wanted to push LMArena ratings at any cost knows what the human evaluators there are weak to, and what should they aim for.
But your claim of "we know that ChatGPT, Google, Grok and Claude have explicitly gamed <benchmarks> to inflate their capabilities" still has no leg to stand on.
There are a lot of other cases that extend well beyond LMArena where it was shown certain benchmark performance increases by the major US labs were only attributable to being over-optimized for the specific benchmarks. Some in ways that are not explainable by the benchmark tests merely contaminating the corpus.
There are cases where merely rewording the questions or assigning different letters to the answer dropped models like Llama 30% in the evaluations while others were unchanged
Open-LLM-Leaderboard had to rate limit because a "handful of labs" were doing so many evals in a single day that it hogged the entire eval cluster
“Coding Benchmarks Are Already Contaminated” (Ortiz et al., 2025)
“GSM-PLUS: A Re-translation Reveals Data Contamination” (Shi et al., ACL 2024).
“Prompt-Tuning Can Add 30 Points to TruthfulQA” (Perez et al., 2023).
“HellaSwag Can Be Gamed by a Linear Probe” (Rajpurohit & Berg-Kirkpatrick, EMNLP 2024).
“Label Bias Explains MMLU Jumps” (Hassan et al., arXiv 2025)
“HumanEval-Revival: A Re-typed Test for LLM Coding Ability” (Yang & Liu, ICML 2024 workshop).
“Data Contamination or Over-fitting? Detecting MMLU Memorisation in Open LLMs” (IBM, 2024)
And yes I relied on LLM to summarize these instead of reading the full papers
