CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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CoreThink Claims Big LLM Reasoning Gains, But Benchmarking Looks Suspect

This paper introduces CoreThink, a "symbolic reasoning layer" that supposedly boosts LLMs' reasoning abilities by 30-60% across various tasks. However, there are concerns about potential overfitting to benchmarks and a lack of clear comparisons to equally-sized models without the layer, making the true impact unclear.

Possible Conflicts of Interest

The paper acknowledges support from CoreThink AI, suggesting a potential conflict of interest, especially given the lack of external validation.

Identified Weaknesses

Overfitting/Contamination Concerns

The paper briefly mentions that current benchmarks suffer from potential overfitting and contamination, which makes it difficult to ascertain whether improved performance is due to actual reasoning gains or simply memorizing patterns. The evaluations of CoreThink don't sufficiently address this, raising doubts about the generalizability of the improvements.

Unclear Baseline Comparisons

The paper does not provide a rigorous apples-to-apples comparison. It's difficult to tell how much of the improvement comes from CoreThink specifically, versus the larger models used in some evaluations, or other architectural differences.

Limited Transparency of General Symbolics

While "General Symbolics" is presented as a novel symbolic reasoning method, the details provided are high-level and lack clarity. This makes it difficult to understand how the method actually works and evaluate its novelty.

Lack of External Validation

All evaluations are performed by the authors, with no independent third-party verification of the results. This raises concerns about potential biases in the evaluation process.

Rating Explanation

While the paper presents an interesting approach to LLM reasoning, the methodological weaknesses and potential conflicts of interest raise significant concerns about the validity and generalizability of the reported performance gains. A more rigorous and transparent evaluation is needed to substantiate the claims.

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