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Most methods that optimize LLM prompts and agent workflows assume that task-specific output schemas, extraction instructions, and evaluation criteria are predefined. For scientific extraction agents, however, a short task goal may not fully determine these components, while specifying them manually is costly. We study...
The results show that simple parameter averaging, when paired with lightweight dimensional adaptation and carefully controlled ratios, is a surprisingly strong baseline for heterogeneous LLM merging, suggesting that the limits of direct weighted fusion may also bound what more complex heterogeneous merging methods can...
Cross-scale heterogeneous MLLM fusion is recast as selective language-side reasoning transfer within a narrow, low-interference regime, rather than broad capability inheritance, to recast cross-scale capability transfer within a narrow, low-interference regime.
Evidence is provided that cross-scale heterogeneous fusion can succeed without explicit semantic alignment when the donor contribution is sufficiently concentrated and carefully selected, and that activation-guided extraction improves the quality of the transferable donor slice while preserving the small-ratio fusion r...
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