arXiv AI
Aug 10
TransSLR: A Lightweight Transformer for Sign Language Recognition
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
ODE-Based Transformer Decoders for Iterative Sign Language Translation
arXiv:2608. 11352v1 Announce Type: cross Abstract: Sign language translation has achieved strong results with Transformer architectures, yet recent improvements largely rely on scaling model capacity at the cost of increased computation.
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arXiv AI
5d ago
SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning
arXiv:2608. 15360v1 Announce Type: cross Abstract: While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT through parameter sharing.
Hugging Face Trending Papers
Jul 9
Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models
Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining. We study whether an existing backbone can keep improving by allocating more computation to each token while leaving the Transformer backbone fixed.
arXiv AI
6d ago
CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing
arXiv:2608. 13925v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) accelerate language generation by predicting multiple masks in a single forward pass.
arXiv Machine Learning
Jul 9
FPTQuant: Function-Preserving Transforms for LLM Quantization
arXiv:2506. 04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time.
arXiv AI
Aug 11
Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production
arXiv:2608. 09045v1 Announce Type: cross Abstract: Recent advances in sign language (SL) research have shown a trend toward unifying multiple sign language understanding (SLU) subtasks, such as isolated sign language recognition (ISLR), continuous sign language recognition (CSLR), and sign language translation (SLT), within a single framework, leading to substantial progress.
arXiv Machine Learning
Jul 9
SNLP: Layer-Parallel Inference via Structured Newton Corrections
arXiv:2605. 17842v3 Announce Type: replace Abstract: Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism.
arXiv Machine Learning
Aug 4
Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Models
arXiv:2511. 21759v2 Announce Type: replace-cross Abstract: Diffusion-based large language models (dLLMs) have recently gained significant attention for their exceptional performance and inherent potential for parallel decoding.
arXiv Machine Learning
Jun 3
KVarN: Variance-Normalized KV-Cache Quantization Mitigates Error Accumulation in Reasoning Tasks
arXiv:2606. 03458v1 Announce Type: new Abstract: Test-time scaling is a powerful approach to obtain better reasoning in large language models, but it becomes memory-bottlenecked during long-horizon decoding, as the KV-cache grows.
arXiv Machine Learning
Jul 13
HALO: Hybrid Adaptive Latent Reasoning for Language Models
arXiv:2607. 08775v1 Announce Type: cross Abstract: We study how to improve a frozen pretrained language model with a small amount of adaptive extra computation.
arXiv Machine Learning
Jun 26
Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM
arXiv:2606. 26120v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms.
arXiv AI
Jun 15
Residual Context Diffusion Language Models
arXiv:2601. 22954v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel.