T^2MLR: Transformer with Temporal Middle-Layer Recurrence
T²MLR fuses cached middle-layer representations across decoding steps to enable persistent reasoning in transformers with minimal inference overhead.
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T²MLR fuses cached middle-layer representations across decoding steps to enable persistent reasoning in transformers with minimal inference overhead.
FlashDecoder: Transformer-based streaming video decoder with fixed temporal window enabling constant-latency real-time generation at high resolutions.
Analysis of Transformer feedforward architecture: skip connections and normalization as rank-preservation mechanisms across depth at initialization.
Decoder-only Transformer (VAIOM) for continuous financial time-series prediction with categorical output distribution.
Kaleido: algorithm-hardware co-design for video diffusion transformers exploiting latent space correlations to accelerate compute-bound self-attention.
MxGPS addresses topology overfitting in GNNs for power grid modeling via multiplex graph transformers, improving generalization across unseen grid structures.
ROBIN method localizes and repairs bias in transformer attention heads via inference-time intervention without retraining.
Adaptive Vector-Quantized Attention allocates codebook capacity proportionally to attention importance, reducing transformer complexity from O(N²) to O(MN) with precision targeting.
Encoder-decoder transformer optimizes quantum circuits for fault-tolerant computing by minimizing T gates while maintaining functional equivalence.
Theoretical framework proves Transformers develop inductive reasoning via low-dimensional invariant manifolds, unifying in-context learning and multi-hop reasoning.
Hybrid NAS framework combining Transformer RL controller with Artificial Bee Colony for efficient architecture search on consumer hardware.
Theory paper on Transformer sample complexity via C-RASP, analyzing learnability of attention-based solutions beyond expressivity.
NITROGEN: imputation-free transformer for Alzheimer's disease prediction from heterogeneous clinical data with uncertainty quantification.
PAC-ACT reinforcement-learning post-training for Action Chunking Transformers improves industrial robot contact manipulation policy.
CoCoT-EEG: contrastive self-supervised foundation model with multiscale convolution for EEG signal decoding.
Foveated Dynamic Transformer (FDT) applies human visual system principles for robust, efficient vision transformers with inherent noise/adversarial resilience.
BioModule: lightweight temporal transformer predicting biomechanical attributes from 3D pose estimates for rehab and sports science.
Systematic evaluation of 25 learning-rate scheduler configurations across 30 architectures (CNNs, transformers) via automated source-code injection.
Analysis of transformer linearization via state update design; introduces sink tokens to improve long-context inference while preserving model quality.
Analysis of RoPE frequency selection in transformers showing data-driven matching between positional frequencies and training data structure.
FourierQK applies FFT-based spectral preprocessing to query-key projections in transformer attention, achieving 79% perplexity reduction on character-level language modeling.
SAMPA: Whisper-based speech segmentation model for prosodic boundary detection in Brazilian Portuguese.
Domain-adapted Sentence Transformers automate cloud security compliance mapping using 13k synthetic training pairs from five European standards.
X-FEMR: token-level explainability framework for Electronic Health Records foundation models using Transformer surrogates to address bias and clinical interpretability.
Nemotron-Labs Audex-30B: unified audio-text MoE LLM enabling seamless multimodal generation via single Transformer decoder with shared embedding space.
PLGSA-Transformer uses periocular landmarks and adaptive thresholding for masked face recognition across modalities.
IVD-SSM state-space model for SemEval-2026 narrative similarity task isolating causal patterns without Transformer quadratic cost.
SemEval-2026 task entry using transformer ensembles and LLM annotations for dimensional aspect-based sentiment regression.
OrbitQuant data-agnostic quantization for diffusion transformers; uses randomized permuted block-Hadamard rotation for stable timestep-invariant inference.