EntropyCache: Decoded Token Entropy Guided KV Caching for Diffusion Language Models

Diffusion-based large language models (dLLMs) rely on bidirectional attention, which prevents lossless KV caching and requires a full forward pass at every denoising step. Existing approximate KV caching methods reduce this cost by selectively updating cached states, but their decision overhead scales with context length or model depth. We propose EntropyCache, a training-free KV caching method that uses the maximum entropy of newly decoded token distributions as a constant-cost signal for deciding when to recompute. Our design is grounded in two empirical observations: (1) decoded token entropy correlates with KV cache drift, providing a cheap proxy for cache staleness, and (2) feature volatility of decoded tokens persists for multiple steps after unmasking, motivating recomputation of the $k$ most recently decoded tokens. The skip-or-recompute decision requires only $O(V)$ computation per step, independent of context length and model scale. Experiments on LLaDA-8B-Instruct and Dream-7B-Instruct show that EntropyCache achieves $15.2\times$-$26.4\times$ speedup on standard benchmarks and $22.4\times$-$24.1\times$ on chain-of-thought benchmarks, with competitive accuracy and decision overhead accounting for only $0.5\%$ of inference time. Code is available at https://github.com/mscheong01/EntropyCache.

InFerActive: Towards Scalable Human Evaluation of Large Language Models through Interactive Inference

Human evaluation remains the gold standard for evaluating outputs of Large Language Models (LLMs). The current evaluation paradigm reviews numerous individual responses, leading to significant scalability challenges. LLM outputs can be more efficiently represented as a tree structure, reflecting their autoregressive generation process and stochastic token selection. However, conventional tree visualization cannot scale to the exponentially large trees generated by modern sampling methods of LLMs. To address this problem, we present InFerActive, an interactive inference system for scalable human evaluation. InFerActive enables on-demand exploration through probability-based filtering and evaluation features, while bridging the semantic gap between computational tokens and human-readable text through adaptive visualization techniques. Through a technical evaluation and user study (N=12), we demonstrate that InFerActive significantly improves evaluation efficiency and enables more comprehensive assessment of model behavior. We further conduct expert case studies that demonstrate InFerActive's practical applicability and potential for transforming LLM evaluation workflows.