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Constructor University

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Selected work

Representative Papers

A Cognitively Motivated Multidimensional Framework for Evaluating Metaphor Explanations

Aug 16, 2026

This study addresses the limitations of holistic scoring in metaphor explanation evaluation, which often overlooks quality structure and human disagreement. We propose a cognition-driven, six-dimensional assessment framework to capture these nuances. Through large-scale annotation and clustering analysis, we reveal the multidimensionality of explanation quality and systematic patterns of disagreement, validating that an automated evaluation pipeline can effectively recover this structure. Our results demonstrate that automatic models can predict key dimensions, with prediction errors significantly correlating with human disagreement. By overcoming the constraints of single-score metrics, this work establishes a fine-grained, diagnostically valuable evaluation paradigm for open-ended generation tasks, offering deeper insights into model performance and human alignment.

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Orbital Detection

Aug 10, 2026

This work addresses the high computational complexity of message-passing detectors in massive MIMO systems by proposing a low-complexity receiver framework based on orbital priors. By relaxing discrete symbol priors into mixed discrete–continuous densities and leveraging an orbital prior decomposition, the symbol distribution is compressed into 3L real-valued scalars, yielding three closed-form denoisers—OBD, OGD, and OPD—that reduce per-iteration complexity to O(1). Combining the Jacobi–Anger expansion, state evolution analysis, and optimal transport theory, the proposed method asymptotically achieves capacity (log₂M), eliminates error floors, attains an MMSE dimension of d = 1/2, and establishes a Wasserstein-distance bound linking constellation ring geometry to achievable rates.

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Recent publications

Latest Papers

A Cognitively Motivated Multidimensional Framework for Evaluating Metaphor Explanations

Aug 16, 2026

This study addresses the limitations of holistic scoring in metaphor explanation evaluation, which often overlooks quality structure and human disagreement. We propose a cognition-driven, six-dimensional assessment framework to capture these nuances. Through large-scale annotation and clustering analysis, we reveal the multidimensionality of explanation quality and systematic patterns of disagreement, validating that an automated evaluation pipeline can effectively recover this structure. Our results demonstrate that automatic models can predict key dimensions, with prediction errors significantly correlating with human disagreement. By overcoming the constraints of single-score metrics, this work establishes a fine-grained, diagnostically valuable evaluation paradigm for open-ended generation tasks, offering deeper insights into model performance and human alignment.

0 citationsRead paper

Orbital Detection

Aug 10, 2026

This work addresses the high computational complexity of message-passing detectors in massive MIMO systems by proposing a low-complexity receiver framework based on orbital priors. By relaxing discrete symbol priors into mixed discrete–continuous densities and leveraging an orbital prior decomposition, the symbol distribution is compressed into 3L real-valued scalars, yielding three closed-form denoisers—OBD, OGD, and OPD—that reduce per-iteration complexity to O(1). Combining the Jacobi–Anger expansion, state evolution analysis, and optimal transport theory, the proposed method asymptotically achieves capacity (log₂M), eliminates error floors, attains an MMSE dimension of d = 1/2, and establishes a Wasserstein-distance bound linking constellation ring geometry to achievable rates.

0 citationsRead paper