NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
arXiv preprint arXiv:2505.11581 , year=
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Vision models converge on universal object dimensions that are semantically interpretable and align more closely with biological vision than model-specific ones.
Compositionality emerges in neural networks only in a narrow depth-connectivity regime, with gradient descent converging to fractured solutions outside it.
Cross-modal representational alignment between text, image, audio, and video models degrades substantially when evaluated at million-sample scale and under realistic many-to-many pairing assumptions.
CreativityNeuro applies contrastive weight steering to LLMs, yielding up to 14 percentile gains on the Divergent Association Task and improved originality in human-rated tests while reducing mode collapse.
citing papers explorer
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Gradient-Based Program Synthesis with Neurally Interpreted Languages
NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
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Characterizing Universal Object Representations Across Vision Models
Vision models converge on universal object dimensions that are semantically interpretable and align more closely with biological vision than model-specific ones.
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Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds
Compositionality emerges in neural networks only in a narrow depth-connectivity regime, with gradient descent converging to fractured solutions outside it.
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Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
Cross-modal representational alignment between text, image, audio, and video models degrades substantially when evaluated at million-sample scale and under realistic many-to-many pairing assumptions.
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CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
CreativityNeuro applies contrastive weight steering to LLMs, yielding up to 14 percentile gains on the Divergent Association Task and improved originality in human-rated tests while reducing mode collapse.