Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Manish Bhatt
TL;DR
The paper proposes a hierarchical benchmarking framework for AI reasoning that emphasizes genuine cognitive abilities over pattern matching, supported by an implementation showing current models' limitations in complex reasoning tasks.
Contribution
It introduces the Bhatt Conjectures framework for rigorous AI reasoning benchmarks and demonstrates its practical application through the agentreasoning-sdk.
Findings
Current AI models struggle with complex reasoning tasks
The framework emphasizes representation invariance and robustness
Implementation reveals gaps in AI reasoning capabilities
Abstract
The Bhatt Conjectures framework introduces rigorous, hierarchical benchmarks for evaluating AI reasoning and understanding, moving beyond pattern matching to assess representation invariance, robustness, and metacognitive self-awareness. The agentreasoning-sdk demonstrates practical implementation, revealing that current AI models struggle with complex reasoning tasks and highlighting the need for advanced evaluation protocols to distinguish genuine cognitive abilities from statistical inference. https://github.com/mbhatt1/agentreasoning-sdk
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference · Computability, Logic, AI Algorithms