Major promotional events such as Black Friday and 618 Shopping Day cause drastic, heterogeneous shifts in user and advertiser behavior, posing persistent challenges for conversion rate (CVR) models trained on daily data. Existing methods often lack the flexibility to capture this periodic variability, resulting in poor modeling of diverse behavioral patterns. To address these challenges, we propose TemporalExpertNet(TEN), a cross-temporal transfer learning framework for industrial-scale CVR prediction during promotion cycles. TEN decomposes the model into a stable representation encoder and a promotion-sensitive expert, enabling reusable temporal knowledge transfer. Specifically, we propose BridgeNet to address the mismatch between historical knowledge and current features through temporal representation alignment. We further introduce TemporalExpertGate (TEG) to perform sample-aware expert fusion, enabling fine-grained prediction adjustment and adaptive knowledge reuse across promotion periods. By using a two-stage training strategy, TEN achieves stable alignment and adaptive expert fusion for robust prediction under shifting promotional distributions. TEN was deployed on a large-scale short-video ads platform during the 618 preheating phase, improving conversion rate by 7.52% and platform RPM by 4.27% with only 0.23% model size and 1.8% latency overhead. It was therefore fully launched to all traffic on 618 Shopping Day, bringing substantial commercial gains.

Augmenting large language models (LLMs) with tools significantly enhances their problem-solving potential across multifaceted tasks. However, current tools automatically created by LLMs often serve as a mere summary of specific problems or solutions, which face two main issues: 1) Low reusability: The tools are overly problem-specific and struggle to handle new problems. 2) Limited diversity: The toolsets are too narrow, limiting their application to address a broader range of different problems. In this paper, we propose the Knowledge-grounded Tool Creation with Evolution (KTCE) framework, which aims to craft reusable and comprehensive toolsets for LLMs in a two-stage process. In the first stage (Knowledge-based Tool Creation), we conceptualize tools as a form of executable domain knowledge and propose a problem-knowledge-tool paradigm. Specifically, we leverage LLMs to abstract "knowledge" from "problems" and create a three-layer knowledge tree of topics, concepts, and key points. This hierarchical structure serves as a foundation for inducing atomic "tools" from "knowledge", grounding them in fundamental concepts and enhancing their usability. In the second stage (Tool Evolutionary Search), we evolve the toolsets through several actions including tool selection, mutation, and crossover. This stage mimics the biological evolution process, aiding toolsets in discovering new tools or updating existing ones, thereby increasing the diversity of the toolset. Experiments on challenging mathematical/tabular/scientific reasoning tasks demonstrate that our approach achieves substantial accuracy improvements ranging from 6.23% to 18.49% on average. Moreover, in-depth analyses reveal the superior characteristics of our toolkit, including high reusability, high diversity, and high generalizability on cross-data/LLM performance with low complexity.