We revisit continual pre-training for large language models and argue that progress now depends less on scaling parameters than on scaling the right structure. We introduce SCALE, a width upscaling architecture that inserts lightweight expansions into linear modules while freezing all pre-trained parameters, preserving residual and attention topologies and increasing capacity without perturbing the base model’s original functionality. SCALE follows two principles: Persistent Preservation, which maintains the base model’s behavior via preservation-oriented initialization and freezing of the pre-trained weights, and Collaborative Adaptation, which trains only selected expansion components to acquire new knowledge with minimal interference. We instantiate these ideas as SCALE-Preserve (preservation-first), SCALE-Adapt (adaptation-first), and SCALE-Route, an optional routing extension that performs token-level routing between preservation and adaptation heads. On a controlled synthetic biography benchmark, SCALE reduces the severe forgetting seen in depth expansion while still learning new knowledge. In continual pre-training on a Korean corpus, SCALE variants forget less on English evaluations and achieve competitive gains on Korean benchmarks, yielding the best overall stability-plasticity trade-off. We further analyze when preservation holds provably and why combining preservation and adaptation stabilizes optimization relative to standard continual learning.

Highlight summarization in baseball requires balancing statistical analysis with narrative coherence. Traditional approaches—such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection—can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold standard but is resource-intensive and not scalable.We introduce DIAMOND, an LLM-driven agent for context-aware baseball highlight summarization that integrates structured sports analytics with natural language reasoning. DIAMOND leverages sabermetric features—Win Expectancy, WPA, and Leverage Index—to quantify play importance, while an LLM module enhances selection based on contextual narrative value. This hybrid approach ensures both quantitative rigor and qualitative richness, surpassing the limitations of purely statistical or vision-based systems.Evaluated on five diverse Korean Baseball Organization League games, DIAMOND improves F1-score from 42.9% (WPA-only) to 84.8%, outperforming both commercial and statistical baselines. Though limited in scale, our results highlight the potential of modular, interpretable agent-based frameworks for event-level summarization in sports and beyond.