Merge2Docs is a multilevel graph fusion framework that leverages the computational power of Fixed-Parameter Tractable (FPT) algorithms to enable the synergistic combination of ideas from disparate documents - a capability traditionally limited by the NP-hard nature of optimal document merging problems. The underlying rationale for this approach is that FPT algorithms can solve many NP-hard problems efficiently when parameterized appropriately. In particular, we utilize novel parameters from the Machine Learning (ML) domain, like Vapnik-Chervonenkis (VC) dimension, Kolmogorov complexity, and treewidth. We prove theoretically and demonstrate experimentally that by setting the threshold to ensure the discrete graphs produced have bounded treewidth, significant improvements in model accuracy and results are obtained. Where traditional approaches either concatenate documents or rely purely on statistical methods, Merge2Docs employs biologically inspired algorithms to identify optimal merge points, enabling our novel SAT solver to generate genuinely new insights by uncovering beneficial logical relationships. The framework implements a toolkit of semi-exact parameterized heuristics, including: Cluster Editing with Vertex-Splitting (CE-VS), Bipartite Cluster Editing with Vertex-Splitting (BCE-VS), Vertex Cover (VC), Red-Black Domination (RB-domination), and treewidth-bounded graph analysis to transform document merging from an intractable combinatorial explosion into a parameterized problem solvable in practice. These algorithms were combined with category-theoretic knowledge representation, multigranularity semantic graphs, monadic second-order logic (MSOL) queries, and agentic LLM orchestration. Category-Theoretic Foundation: Documents are modeled using both syntactic-semantic networks with connecting edges that work like functors and show how, utilizing a genetic read-repair mechanism very similar to BCE-CS, we can preserve well-quasi-ordering (w.q.o) properties. The significance of the identified w.q.o is that they enable principled reasoning about document relationships through Monadic Second-Order Logic (MSOL) queries. We then utilize Courcelle’s Theorem to extract insights from bounded-treewidth knowledge graphs efficiently. Using a multi-granularity semantic architecture, the system integrates word-level precision through Word2Vec/GloVe. This provides category-like relationships for the semantic embeddings of words that form a word-based bridge between the syntactic and semantic graphs. And embeddings that allow for semantic matching of the topic. This structure provides a basis for discussions on the fundamental limitations and advantages of our approach and guidance on how to build a GNN to model other lncRNA-inspired relationships. An LLM-based conceptual understanding via Pydantic-AI orchestrated knowledge graphs, Agentic processing pipelines with type-safe graph operations, and sophisticated error handling. Rather than concatenating documents, the system identifies structural patterns in which ideas from different sources can be mathematically combined to generate novel hypotheses. The FPT parameterization makes previously intractable optimal solutions computationally feasible, enabling genuine intellectual synthesis.