arXiv Machine Learning
Certified Adaptive List Decoding for Error-Correcting Codes
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6d ago
Certified Adaptive List Decoding for Error-Correcting Codes
Maximum-likelihood (ML) decoding of linear block codes is NP-hard, so neural decoders are evaluated by how closely they approach it. We observe that proving a candidate optimal can be far easier than...
arXiv Machine Learning
Jun 24
ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs
arXiv:2510. 04767v2 Announce Type: replace Abstract: While most autoregressive LLMs are constrained to one-by-one decoding, diffusion LLMs (dLLMs) have attracted growing interest for their potential to dramatically accelerate inference through parallel decoding.
arXiv AI
Jul 3
Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability
arXiv:2607. 01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcomplete dictionary $\mathbf{W}\in\mathbb{R}^{m\times n}$ with $m<n$, and inferring a sparse code $\mathbf{x}\in\mathbb{R}^n$ from $\mathbf{h}\approx\mathbf{W}\mathbf{x}$.
arXiv Machine Learning
Jun 3
Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
arXiv:2606. 02628v1 Announce Type: new Abstract: We investigate whether open-source LLMs encode a linearly separable truthfulness signal in their hidden states, and at which network depth this signal is strongest.
arXiv Machine Learning
Sep 1
Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting
arXiv:2608.28910v1 Announce Type: new Abstract: Coded computing has emerged as a powerful paradigm for mitigating the impact of straggling workers in distributed computing systems. However, existing...
arXiv AI
Sep 30
Reliable Parallel Decoding in Masked Diffusion Language Models
The paper introduces Reliable Parallel Decoding (RPD) for masked diffusion language models, addressing the unreliability of committing multiple predictions from a single forward pass. Diagnostics reveal that confidence alone is insufficient, as confident end‑sequence predictions can preempt necessary upstream computations, and downstream predictions degrade with upstream uncertainty. RPD selects candidates based on layer‑wise stability and final confidence, committing them under an entropy budget while deferring uncertain predictions, achieving superior throughput and competitive accuracy on LLaDA and Dream benchmarks.