FlavourBench is a new evaluation framework that replaces missing answer keys with dense reward maps generated from a versioned culinary environment. Each task requires selecting a three‑ingredient portfolio from eight options, and all 56 possible portfolios are scored by Epicure before model inference. The authors evaluate 27 endpoints across 534 tasks, perform statistical tests on 351 model contrasts, and conduct a preregistered post‑training study showing that LoRA fine‑tuning on Qwen3‑0.6B improves performance on 84 anchor‑disjoint maps by 13.30 points.
By Josef Chen (Independent Researcher), Erim Hayretci (Imperial College London)
arXiv:2606. 03650v1 Announce Type: cross Abstract: Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks cannot be trusted, their items having likely leaked into pretraining, so scores reflect memorization rather than fitness.
By Alexander Apartsin, Yehudit Aperstein
arXiv:2610.06824v2 Announce Type: replace
Abstract: We introduce TasteVal, a benchmark to evaluate the experimental research taste of frontier models. We define research taste as the ability to pick...
By Oliver Jaffe, Dane Sherburn
arXiv:2608.21382v1 Announce Type: new
Abstract: Multiple-choice benchmarks fix the questions and the correct answers, but not the harness: the order of the options, the wording of the prompt, and whe...
By V. S. Raghu Parupudi
The paper introduces Balance of Benchmarks (BoB), a framework that improves task-conditioned model comparison by weighting benchmark evidence based on semantic density, equating scores across varying difficulty levels, and pooling task-relevant residuals. BoB retains all eligible benchmark data while adjusting its influence, outperforming uniform averaging on the WildScores dataset with higher Spearman correlation, lower MAE, and better shortlist hit rates. The method also reduces ranking instability when benchmarks are repeated or paraphrased, and lowers retrospective regret in model selection.
By Jhen-Ke Lin, Hong-Yun Lin
arXiv:2608.21601v1 Announce Type: new
Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference soluti...
By Aubrey Brueckner, Darshil Patel, Yuhuan He, Timothy Kassis