FLUID is a continuous-time transformer using Liquid Attention Networks to model attention as stable ODE solutions that interpolate between discrete SDPA and CT-RNNs, with an explicit sink gate and liquid hyper-connections for better information flow.
hub
Gomez, Lukasz Kaiser, and Illia Polosukhin
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
hub tools
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Averaging and temporally interpolating text latents in VLAs enables 83% success on novel task combinations in the libero-ood benchmark where SOTA models achieve under 15%.
ChronoVAE-HOPE proposes a VAE foundation model for time series classification that replaces attention with a HOPE Block dual-memory system and uses disentangled trend-seasonal latent representations, pre-trained on Monash and evaluated on UCR datasets.
Adam-SHANG is a convergent Adam variant for stochastic smooth convex optimization that uses a stable lagged-preconditioner update and a computable trace-ratio stepsize rule.
S-FLM is a hyperspherical latent flow language model that learns velocity fields on the unit sphere to generate token sequences via deterministic ODE integration without materializing one-hot vectors.
WhisperRT converts Whisper to a causal streaming ASR model via encoder causality, decoder synchronization on partial states, and fine-tuning, achieving better performance than non-fine-tuned streaming methods on sub-300ms chunks with lower complexity.
LLMs form functional subspaces in activation space where in-context learning tasks are solved by vector algebra operations such as addition and subtraction.
An adapted scaling law predicts GPU energy consumption for diffusion model inference with R² > 0.9 within architectures and strong cross-architecture generalization.
JAM aligns frozen vision and language models via joint autoencoders and multimodal Spread Loss, reliably inducing cross-modal alignment across layer depths, objectives, and model scales.
KairosHope proposes a HOPE block with dual-memory (Titans + CMS) and hybrid statistical-deep decision head for TSFM classification, pre-trained via MTSM and InfoNCE on Monash then adapted via LP-FT to UCR, claiming superior results on causal domains.
Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.
citing papers explorer
-
FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning
FLUID is a continuous-time transformer using Liquid Attention Networks to model attention as stable ODE solutions that interpolate between discrete SDPA and CT-RNNs, with an explicit sink gate and liquid hyper-connections for better information flow.
-
VLAs are Confined yet Capable of Generalizing to Novel Instructions
Averaging and temporally interpolating text latents in VLAs enables 83% success on novel task combinations in the libero-ood benchmark where SOTA models achieve under 15%.
-
ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification
ChronoVAE-HOPE proposes a VAE foundation model for time series classification that replaces attention with a HOPE Block dual-memory system and uses disentangled trend-seasonal latent representations, pre-trained on Monash and evaluated on UCR datasets.
-
Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
Adam-SHANG is a convergent Adam variant for stochastic smooth convex optimization that uses a stable lagged-preconditioner update and a computable trace-ratio stepsize rule.
-
Language Modeling with Hyperspherical Flows
S-FLM is a hyperspherical latent flow language model that learns velocity fields on the unit sphere to generate token sequences via deterministic ODE integration without materializing one-hot vectors.
-
Functional Subspace, where language models can use vector algebra to solve problems
LLMs form functional subspaces in activation space where in-context learning tasks are solved by vector algebra operations such as addition and subtraction.
-
Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
An adapted scaling law predicts GPU energy consumption for diffusion model inference with R² > 0.9 within architectures and strong cross-architecture generalization.
-
Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models
JAM aligns frozen vision and language models via joint autoencoders and multimodal Spread Loss, reliably inducing cross-modal alignment across layer depths, objectives, and model scales.
-
KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture
KairosHope proposes a HOPE block with dual-memory (Titans + CMS) and hybrid statistical-deep decision head for TSFM classification, pre-trained via MTSM and InfoNCE on Monash then adapted via LP-FT to UCR, claiming superior results on causal domains.
-
Improving Spatio-Temporal Residual Error Propagation by Mitigating Over-Squashing
Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.