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PoTRE: Test-Time Reasoning inspired by Cognitive He... | AI Research

Key Takeaways

Large Language Models (LLMs) are powerful, but they often struggle with complex tasks that require long-term planning or the ability to correct their own mis...
While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction.
Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constraints.
A final Task-Adaptive Aggregation Layer dynamically reconciles these perspectives -- via final candidate selection, semantic synthesis, or neuro-symbolic verification -- to produce a robust global solution.
We evaluate PoTRE on three frontier benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance.

Paper AbstractExpand

While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction. Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constraints. We introduce PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents: (1) Adversarial Refinement Agent, (2) Hierarchical strategic Planning Agent, (3) Spectrum Search Agent, and (4) Direct Chain Agent. A final Task-Adaptive Aggregation Layer dynamically reconciles these perspectives -- via final candidate selection, semantic synthesis, or neuro-symbolic verification -- to produce a robust global solution. We evaluate PoTRE on three frontier benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance. PoTRE achieves state-of-the-art accuracy of 49.92% on HLE, surpassing the previous best official score. We demonstrate that this architectural heterogeneity achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines.

Large Language Models (LLMs) are powerful, but they often struggle with complex tasks that require long-term planning or the ability to correct their own mistakes. When faced with new, abstract problems or strict rules, standard prompting methods often fail. The paper "PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity" introduces a new framework called PoTRE (Poly-Topological Reasoning Ensembles) designed to overcome these limitations by using a team of specialized agents to handle reasoning tasks more effectively.

A Multi-Agent Approach

Instead of relying on a single stream of thought, PoTRE breaks the inference process into four distinct agents, each with a specific role:

Adversarial Refinement Agent: Focuses on iterative error correction.
Hierarchical Strategic Planning Agent: Manages long-horizon planning.
Spectrum Search Agent: Explores different reasoning paths.
Direct Chain Agent: Provides a straightforward reasoning path.
By decoupling these functions, the model can approach a problem from multiple angles simultaneously rather than relying on a single, potentially brittle, line of reasoning.

Reconciling Perspectives

Once the four agents have generated their insights, the framework uses a "Task-Adaptive Aggregation Layer" to combine them. This layer acts as a final judge, reconciling the different perspectives through methods like candidate selection, semantic synthesis, or neuro-symbolic verification. This ensures that the final output is a robust, well-vetted solution rather than just the result of a single attempt.

Performance and Efficiency

The researchers tested PoTRE on three challenging benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance. The framework achieved a state-of-the-art accuracy of 49.92% on HLE, setting a new record. Notably, the authors found that this heterogeneous approach achieves better reasoning performance while using the same amount of—or even fewer—inference tokens compared to larger, homogeneous models. This suggests that architectural diversity is a highly efficient way to improve LLM reasoning capabilities.

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