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University of Arkansas

Academic institutionnorthamerica · us
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Research library163linked papers
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Selected work

Representative Papers

Resonant Sparse Geometry Networks

Jan 26, 2026

This work proposes a brain-inspired neural architecture to address the high computational complexity, large parameter count, and lack of biological plausibility in Transformers when handling long-range dependencies and hierarchical classification tasks. The approach uniquely integrates hyperbolic space embeddings, input-dependent dynamic sparse connectivity, and Hebbian structural learning, modulating connection strengths via geodesic distance decay and incorporating a dual-timescale learning mechanism with fast and slow components. The resulting model reduces computational complexity to O(n·k), achieving 96.5% accuracy on long-range dependency tasks with only 1/15 the parameters of a standard Transformer. On a 20-class hierarchical classification benchmark, it attains 23.8% accuracy using just 41,672 parameters—approximately five times the random baseline—demonstrating substantial gains in both efficiency and biological plausibility.

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CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models

May 21, 2025arXiv.org

This work addresses the critical gap in causal reasoning capabilities—specifically causal structure identification, intervention prediction, and counterfactual prediction—of Large Vision-Language Models (LVLMs). We introduce the first multimodal causal reasoning benchmark tailored for LVLMs and establish the first standardized evaluation protocol. Methodologically, we propose a unified three-tier assessment framework covering structural, interventional, and counterfactual reasoning; design a context-learning protocol grounded in causal representation learning datasets; conduct zero-shot and few-shot cross-task evaluations on leading open-source LVLMs; and integrate causal graph modeling with vision-language alignment analysis. Experimental results reveal severe limitations: current LVLMs achieve <35% average accuracy on counterfactual reasoning and rely excessively on superficial statistical correlations rather than mechanistic causal modeling for structure identification. Our findings provide essential empirical evidence and concrete directions for developing causally enhanced LVLM architectures.

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