A driven Duffing ring on a cycle graph produces shape-dependent harmonics through cubic mode mixing and dissipation-broken time-reversal symmetry, captured by observable φ₀ that stays informative down to 0 dB SNR on synthetic signals.
Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , url =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
unclear 1representative citing papers
Vision-language models use semantic signals more than syntactic ones to bind words like 'image' to actual visual inputs, with implications for robustness in multimodal systems.
NSFL adapts t-norms and t-conorms to embedding spaces with NS-Delta and SQO to enable logical operations, reporting up to 81% mAP gains in retrieval tasks.
Gated DeltaNet integrates gating and delta rules into linear transformers, outperforming Mamba2 and DeltaNet on language modeling, reasoning, retrieval, and long-context tasks.
citing papers explorer
-
Broken-symmetry shape discrimination on a driven Duffing ring
A driven Duffing ring on a cycle graph produces shape-dependent harmonics through cubic mode mixing and dissipation-broken time-reversal symmetry, captured by observable φ₀ that stays informative down to 0 dB SNR on synthetic signals.
-
Source-Modality Monitoring in Vision-Language Models
Vision-language models use semantic signals more than syntactic ones to bind words like 'image' to actual visual inputs, with implications for robustness in multimodal systems.
-
NSFL: A Post-Training Neuro-Symbolic Fuzzy Logic Framework for Boolean Operators in Neural Embeddings
NSFL adapts t-norms and t-conorms to embedding spaces with NS-Delta and SQO to enable logical operations, reporting up to 81% mAP gains in retrieval tasks.
-
Gated Delta Networks: Improving Mamba2 with Delta Rule
Gated DeltaNet integrates gating and delta rules into linear transformers, outperforming Mamba2 and DeltaNet on language modeling, reasoning, retrieval, and long-context tasks.