decoding

DecodeShare proposes a method to identify a low-dimensional subspace consistently shared across tasks in LLM decode-time hidden states and shows that disturbing this subspace degrades decision performance more than random or prefill-derived subspaces, with implications for activation steering.

Introduces Deco-G, a decoding framework that separates format adherence from problem-solving in LLMs, using a Format Estimation Module to ensure compliance without degrading reasoning. Achieves improved accuracy on mathematical reasoning, event extraction, and LLM-as-a-judge tasks.

Discusses methods for generating structured outputs from large language models using constrained sampling techniques.

A blog post details the discovery and decoding of an obfuscated bash script printed on a Uniqlo t-shirt as part of Akamai's Peace for All campaign, which reveals a hidden Easter egg message when executed.

TACG is a training-free decoder for diffusion language models that uses trajectory-aware signals to decide when to commit tokens, improving accuracy and efficiency on code and math benchmarks.

An analysis comparing prefill and decoding phases in LLM inference, questioning whether prefill is underappreciated in terms of ROI for local LLM deployments.

Fable 5 demonstrates impressive ability to decode shorthand where users type the first letter of each word, accurately reconstructing the intended message.

Set Diffusion introduces a new class of language models that interpolates between autoregressive and diffusion models by factorizing token generation over flexible-position, flexible-length token sets. This enables faster decoding and flexible token ordering, achieving better speed-quality tradeoffs on reasoning, summarization, and unconditional generation tasks.

Proposes Dual-Confidence Contrastive Decoding (DCCD), a training-free method for retrieval-augmented generation that handles intra-context conflicts in multi-document settings by combining document-level and token-level confidence signals, and introduces the DRQA benchmark for factual-conflict QA.

Dr. Julie Elie of UC Berkeley won the 2026 Coller-Dolittle Prize for decoding the core vocabulary of zebra finches, identifying 11 distinct calls by combining observation, machine learning, and behavioral experiments. Her work marks a significant step in interspecies communication.

This paper proposes FADE, a training-free method that mitigates hallucinations in Large Vision-Language Models by attenuating FFN outputs at critical layers to reduce language-prior dominance, demonstrating effectiveness across multiple benchmarks.

This paper reinterprets masked diffusion language model decoding as continuous clean-state prediction, introducing a flow-based framework where tokens are updated continuously and asynchronously based on confidence, achieving 97% of LLaDA's performance with 25% of the decoding budget.

This paper proposes Multi-Block Diffusion Language Models (MBD-LMs), extending single-block diffusion to concurrent multi-block decoding with improved training strategies like Multi-block Teacher Forcing and an optimized Block Buffer decoding algorithm. Experiments show increased tokens per forward pass and improved accuracy on benchmarks.

A tweet listing 16 inference optimization techniques for achieving sub-second LLM responses, including KV-caching, speculative decoding, FlashAttention, and various parallelism methods.

Introduces Speculative Refinement (SpecRef), a training-free hybrid decoding strategy that warm-starts a masked diffusion language model from an autoregressive draft using entropy-guided selective masking. Evaluated across six benchmarks, it reveals that code benchmarks conflate structural discovery with logical correctness, identifies a refinement tension phenomenon, and shows that evaluation protocols can produce different model rankings.

huff12 is a 12-stream Huffman decoder optimized for Apple Silicon processors, aiming to improve decoding performance through parallel stream processing.

A tweet highlights the value of understanding decoding and sampler mechanisms in LLMs for gaining an edge.

This paper proposes TIE, a knowledge fusion framework for masked diffusion language models that tracks confidence dynamics to identify reliable decoding trajectories and iteratively transfers partially denoised sequences between models, improving generation quality on reasoning tasks.

Announced release of Luce KVFlash, a tool to run models inside Lucebox at 256k context without KVCache OOM, achieving up to 2.9x faster decoding at long context using speculative prefill and dynamic offloading.

LatticeBridge proposes a twisted sequential Monte Carlo decoder for structured sequence generation that improves constraint satisfaction by treating the problem as rare-event inference, outperforming greedy and beam baselines on CommonGen, E2E NLG, and WikiBio.