OSC : Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration (2025)

Abstract: This paper introduces OSC (Orchestrating Cognitive Synergy), a knowledge-aware adaptive collaboration framework designed to enhance cognitive synergy in multi-agent systems with large language models. While prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck. OSC addresses this gap as a pivotal intermediate layer between selection and aggregation, introducing Collaborator Knowledge Models (CKM) to enable each agent to dynamically perceive its collaborators’ cognitive states. Through real-time cognitive gap analysis, agents adaptively adjust communication behaviors, including content focus, detail level, and expression style, using learned strategies. Experiments on complex reasoning and problem-solving benchmarks demonstrate that OSC significantly improves task performance and communication efficiency, transforming “parallel-working individuals” into a “deeply collaborative cognitive team”.

## introduction recently, large language models (llms) @xcite @xcite have shown exceptional capabilities in tackling complex tasks, greatly advancing artificial intelligence. however, scaling a single llm often leads to high computational costs and performance bottlenecks. multi-agent systems (mas) @xcite @xcite ) offer a scalable alternative by leveraging diverse agents' expertise to solve problems beyond the reach of individual models, improving cost-efficiency and unlocking llms' full potential. recent research @xcite @xcite has focused on efficient mas collaboration, with "dynamic expert selection" and knowledge-aware routing frameworks effectively matching tasks to expert subsets, boosting adaptability and resource efficiency. moreover, "aggregation strategies" aim to combine multi-agent outputs into high-quality final solutions. a critical challenge still remains for enabling experts (even with an optimal expert combination) to dynamically adapt their linguistic interactions, i.e., fostering shared understanding, resolving discrepancies, and producing coherent, high-quality outputs, remains a key bottleneck in mas-llm research. attempting to address this issue, we propose orchestrating cognitive synergy (osc), an endto-end, knowledge-aware adaptive collaboration framework. osc serves as an intermediate layer, enhancing linguistic interactions among selected experts without replacing expert selection or aggregation. in its "inter-expert collaborative communication" phase, each agent e i uses a dynamically learned collaborator knowledge model (ckm) to track collaborators' cognitive states (knowledge, reasoning, task understanding). the parameters of ckm, initially pre-trained, are fine-tuned end-to-end within osc's rl loop, tailoring them for effective collaboration. a learnable cognitive gap analysis module informs a policy π comm , which dynamically shapes communication behavior m @xmath0, style, objectives; φ (t) ## experiments the highest lc win rate (81.4%) among osc setups, outperforming kabb (full) (77.9%) at a slightly higher cost ( 0.97vs.0.91). compared to proprietary models like gpt-4o and claude-3.7, osc (@xmath1. even @xmath2. osc's expert routing and adaptive communication enable precise control over the price-performance curve, making it a versatile, cost-effective solution for top results across budgets. ## appendix a: osc framework implementation details this appendix elaborates on the specific implementation choices and learning paradigms for the core components of the osc (orchestrating cognitive synergy) framework, as deployed in the experiments reported in this paper. these details directly support the methodology described in section 3, focusing on the end-to-end learning of the adaptive communication strategy, the dynamic operationalization of the collaborator knowledge model (ckm), and the learned mechanisms for cognitive gap analysis and adaptive communication objective determination. ## ckm initialization and end-to-end the parameters θ ckm of f ckm and θ update of the gru-based update function f update (d @xmath3, diverse corpus of multi-turn dialogues (e.g., >1m turns from educational forums, collaborative problem-solving datasets). pre-training objectives include: this pre-training provides a robust initialization. subsequently, during the main rl training of π comm , both θ ckm and θ update are actively fine-tuned. gradients from the overall ppo objective (equation 8 ) are propagated back to these parameters. additionally, auxiliary prediction tasks can be introduced during fine-tuning, such as predicting specific elements of a collaborator's next utterance if it can be reliably estimated, or a self-supervisory signal that rewards ckm states that accurately predict successful intermediate steps in the collaboration. this ensures the ckm representations are not only descriptive but also maximally useful for the policy π comm . ## osc computational resource efficiency results we adopt the alpacaeval 2.0 dataset (160 development examples, 160 validation examples), six agents (e.g., llama-3-13b-instruct and other compressed models) with a qwen2-13b aggregator in the osc system, running on a single nvidia a100 gpu. training uses mixed precision for 1 × 10 6 steps, freezing the ckm and f gap modules and training only π comm . during inference, we