Long-horizon memory question answering often requires sparse evidence from heterogeneous histories, including events, object states, visual observations, temporal relations, and causal steps. Existing memory interfaces expand reader context, retrieve semantically related chunks, or expose graph neighborhoods, but they are not explicitly designed to select compact evidence for a fixed reader. We propose Structured Spatiotemporal Scene-Event Memory (S3Mem), a query-time memory interface that writes textual, visual, and agent-use histories into structured scene-event units and routes compact evidence packs to the reader. Its router scores candidate units, query anchors, and anchor-support links, enabling both single-hop selection and short multi-hop evidence chains without reader fine-tuning or test-time training. Across LoCoMo, EMemBench Visual Games, and AMA-Bench, S3Mem provides a strong score-token trade-off, with the clearest gains on localized event, state, temporal, causal, or provenance evidence. On LoCoMo, S3Mem reaches 0.48 F1 and 0.40 BLEU with 1,073 evidence tokens per question, about 15.8 times fewer than the LoCoMo reference. On EMemBench Visual Games, it obtains the best F1 and second-best accuracy with only 189 tokens. On AMA-Bench, it is not the highest-scoring method, but remains competitive while using the fewest reader-visible evidence tokens.

While large language models (LLMs) perform strongly after adaptation within individual scientific domains, their learning dynamics in multi-disciplinary contexts remain poorly understood, despite the promise of improved generalization and broader applicability through cross-domain knowledge synergy. We present a systematic study of multi-discipline LLM fine-tuning across five scientific disciplines and compare the learning dynamics of full fine-tuning, LoRA, LoRA-MoE, and LoRA composition. In our naturally imbalanced setting, we show that multi-disciplinary learning is substantially more variable than single-discipline training and distill four empirical patterns: (1) Balance-then-Diversity: increasing scarce-domain exposure helps, but broad source-pool diversity must be preserved; (2) Merge-then-Align: general instruction data restores instruction-following behavior needed for effective joint adaptation; (3) Optimize-then-Scale: increasing trainable capacity offers limited gains without prior design optimization; and (4) Share-then-Specialize: asymmetric LoRA-MoE improves multi-discipline performance through a shared low-rank projection. Together, these patterns form a practical recipe for principled multi-discipline fine-tuning and provide actionable guidance for developing generalizable scientific LLMs.