PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity
Researchers introduce PoTRE, a framework that uses four specialized agents to improve LLM performance on complex, long-horizon reasoning tasks.
- PoTRE uses a multi-agent architecture to solve long-horizon planning issues in LLMs.
- The framework employs four specialized agents to handle different aspects of reasoning.
- Task-Adaptive Aggregation is used to synthesize results from the heterogeneous agents.
- The method aims to reduce brittleness when models encounter novel or abstract constraints.
Current Large Language Models often fail at complex reasoning because they rely on single-stream prompting, which lacks the ability to plan long-term or correct errors iteratively. This limitation becomes particularly evident when models face novel abstractions or strict domain constraints.
To address this, the PoTRE (Poly-Topological Reasoning Ensembles) framework decouples the inference process into four distinct functional agents. These include an Adversarial Refinement Agent for error correction, a Hierarchical Strategic Planning Agent for long-term goals, a Spectrum Search Agent, and a Direct Chain Agent.
By utilizing a Task-Adaptive Aggregation method, the framework allows the model to navigate complex problem spaces more effectively than standard prompting methods. This approach mimics cognitive heterogeneity to ensure more robust and accurate outputs during test-time reasoning.
Provides a new architectural pattern for building complex agentic workflows.
Demonstrates how multi-agent systems can overcome single-stream prompting limitations.
- Test-Time Reasoning
- The process of performing additional computation or iterative steps during the inference phase to improve model accuracy.
- Cognitive Heterogeneity
- The use of diverse, specialized processes or agents to mimic different aspects of human thought patterns.
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