AI Research Analysis
MARINE: Theoretical Optimization and Design for Multi-Agent Recursive IN-context Enhancement
This paper introduces MARINE, a theoretically grounded framework that redefines test-time reasoning as iterative refinement of a persistent reference trajectory. It systematically converts a base model's pass@N capabilities into near-optimal pass@1 performance, offering significant advancements for LLM-based agents in complex reasoning tasks. Notably, it enables parameter-efficient reasoning, matching the performance of much larger models with significantly fewer parameters.
Quantifiable Impact for Your Enterprise
MARINE's innovative approach translates into tangible benefits, offering a new paradigm for efficient and reliable AI deployment.
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Pass@1 Accuracy (685B LLM)
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Parameter Reduction
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Equivalent Performance (80B Model)
Deep Analysis & Enterprise Applications
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Multi-Agent Systems: Collaborative AI for Enhanced Reasoning
Multi-agent systems leverage multiple AI instances that collaborate or compete to solve complex problems. This approach mitigates the limitations of single-agent reflection and allows for more robust, structured problem-solving. MARINE advances this field by organizing agents around a shared reference trajectory, ensuring local improvements integrate into a globally coherent reasoning path, unlike free-form dialogue systems.
Iterative Trajectory Refinement Process
MARINE redefines test-time reasoning as an iterative optimization process, refining a persistent reference trajectory through multi-agent collaboration rather than one-shot decoding. This approach ensures monotonic improvement by systematically aggregating candidate trajectories.
Initial Exploration (M1 agents)
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Reference Trajectory Selection
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Recursive Enhancement (Mk agents)
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Trajectory Refinement Operator R
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Final Answer Generation
Parameter Efficiency Breakthrough
10x+ Parameter Reduction vs. Standalone 1000B AgentsAn 80B-parameter model augmented with MARINE matches the performance of standalone 1000B-parameter agents, reducing parameter requirements by over an order of magnitude. This establishes a new paradigm for parameter-efficient reasoning.
| Constraint | Optimal Batch Size (Mk) | Outcome |
|---|---|---|
| Fixed Invocation Budget | Minimal Feasible (Mk=2) |
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| Unlimited Invocation Budget | Logarithmically Growing (O(log k)) |
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State-of-the-Art Performance
Challenge: Achieving high pass@1 accuracy for complex reasoning tasks with LLM agents under practical constraints.
Solution: MARINE's multi-agent iterative refinement process, strategically allocating computational budget to transform pass@N capabilities into reliable pass@1 performance.
Outcome: 685B LLM with MARINE achieves 46.0% pass@1 accuracy, outperforming leading baselines and matching 1000B-parameter models with an 80B agent.
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Your Roadmap to Multi-Agent AI Integration
A structured approach ensures seamless adoption and maximized value from MARINE's advanced reasoning capabilities.
Phase 1: Initial Exploration & Reference Selection
M1 agents generate initial diverse trajectories, exploring a wide range of problem-solving paths. A high-quality reference trajectory is then selected from this initial set to serve as the baseline for refinement.
Phase 2: Recursive Enhancement Loop
For a specified number of rounds (K), multiple Mk agents generate new candidate trajectories, conditioned on the current reference. A sophisticated refinement operator aggregates these candidates, detects conflicts (factual and logical), resolves them, and updates the reference trajectory for continuous improvement.
Phase 3: Final Answer Generation
After the recursive enhancement rounds are complete, a single agent leverages the highly refined reference trajectory to generate the ultimate, robust, and accurate response to the original query.
Phase 4: Continuous Monitoring & Optimization
Deployment of MARINE in real-world scenarios, leveraging high-quality samples for post-training alignment. This includes continuous monitoring for failure modes, performance validation, and strategic optimization for ongoing efficiency gains.
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