arXiv:2607. 23696v1 Announce Type: new Abstract: Ad creative optimization is increasingly constrained by evaluation rather than generation.
arXiv:2608. 07243v1 Announce Type: new Abstract: Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges through iterative generation, appraisal, and refinement.
arXiv:2603. 11863v2 Announce Type: replace Abstract: The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve.
The paper introduces CreaEval, an automated creativity evaluator designed for complex multi-step tasks (CGPST). It separates the evaluation process into two phases: a memory‑augmented analysis that transforms responses into structured evidence, and an evidence‑based judging step that scores without seeing raw outputs. Experiments show CreaEval outperforms existing baselines by an average of 22.74% across CGPST and two simpler creativity tasks.
arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.
arXiv:2510. 20091v3 Announce Type: replace-cross Abstract: Creativity is often seen as a hallmark of human intelligence.
arXiv:2608.30047v1 Announce Type: new Abstract: Recent AI systems promise autonomous scientific discovery, claiming to discover algorithms and produce research papers, yet understanding whether they...
arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.
PinDCO is a scalable dynamic creative optimization system designed for Pinterest’s billion‑scale visual discovery platform. It uses a Creative Component Fusion Network to score ad creatives by modeling individual components (image, title, layout) with dedicated towers and fusing their representations, while a Pixel‑aware Adjustment Module tailors scores to creative size for better whole‑page outcomes. The system incorporates a lightweight pre‑selection model, caching, and dynamic batching to handle large candidate volumes, achieving a 3.09% lift in ad click‑through rate in online experiments.
arXiv:2605. 28556v2 Announce Type: replace Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated.
arXiv:2606. 02863v1 Announce Type: new Abstract: AI-Driven Research Systems (ADRS) -- systems coupling LLMs with automated evaluation to discover algorithms, proofs, and designs -- are being optimized and adopted across domains, but the tools to analyze them have not kept pace.
arXiv:2607. 29241v1 Announce Type: cross Abstract: Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes.