Financial report generation is a complex task that requires gathering and reasoning over multi-source information. Recent advances in Large Language Models have made them a promising solution for automating this process. However, the reasoning paths in traditional Chain-of-Thought paradigms are inherently constrained by predefined, static computational topologies, rendering them ill-equipped to handle the dynamic uncertainties of real-world financial environments. To tackle this challenge, we propose Cogito, a cognitively grounded agentic framework for professional financial report generation. At its core, Cogito is driven by Dynamic Graph of Thoughts, a novel reasoning mechanism that models the agent’s reasoning process as an evolving topology for adaptive exploration.We further introduce a Social Collaboration Mechanism to facilitate coordinated agent interaction. Finally, Cogito is instantiated as a multi-agent system, where four specialized agents collaboratively execute the end-to-end report generation task. Extensive experiments on enterprise- and industry-level financial report generation benchmarks demonstrate the superiority of Cogito in data quality, analytical validity, and presentation quality.

Mixture-of-Experts (MoE) models offer immense capacity via sparsely gated expert subnetworks, yet adapting them to multiple domains without catastrophic forgetting remains an open challenge. Existing approaches either incur prohibitive computation, suffer cross-domain interference, or require separate runs per domain. We propose DES-MoE, a dynamic expert specialization framework for multi-domain adaptation of Mixture-of-Experts models. DES-MoE addresses catastrophic forgetting through three innovations: (1) an adaptive router balancing pre-trained knowledge retention and task-specific updates via distillation, (2) real-time expert-domain correlation mapping to isolate domain-specific gradients, and (3) a three-phase adaptive fine-tuning schedule that progressively freezes non-specialized parameters. Evaluated on six domains (math, code, law, etc.), DES-MoE matches single-domain ESFT performance while training one unified model, reduces forgetting by 89% compared to full fine-tuning as domains scale from 2 to 6, and achieves 68% faster convergence than conventional methods. Our work establishes dynamic expert isolation as a scalable paradigm for multi-task MoE adaptation.

The interpretability of Mixture-of-Experts (MoE) models, especially those with heterogeneous designs, remains underexplored. Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures. To address this issue, we propose a cross-level attribution algorithm to analyze sparse MoE architectures (Qwen 1.5-MoE, OLMoE, Mixtral-8x7B) against dense models (Qwen 1.5-7B, Llama-7B, Mistral-7B). Results show MoE models achieve 31% higher per-layer efficiency via a “mid-activation, late-amplification” pattern: early layers screen experts, while late layers refine knowledge collaboratively. Ablation studies reveal a “basic-refinement” framework—shared experts handle general tasks (entity recognition), while routed experts specialize in domain-specific processing (geographic attributes). Semantic-driven routing is evidenced by strong correlations between attention heads and experts (r=0.68), enabling task-aware coordination. Notably, architectural depth dictates robustness: deep Qwen-MoE mitigates expert failures (e.g., 43% MRR drop in geographic tasks when blocking top-10 experts) through shared expert redundancy, whereas shallow Olmoe suffers severe degradation (76% drop). Task sensitivity further guides design: core-sensitive tasks (geography) require concentrated expertise, while distributed-tolerant tasks (object attributes) leverage broader participation. These insights advance MoE interpretability, offering principles to balance efficiency, specialization, and robustness.