LLaDA 1.5
PulseAugur coverage of LLaDA 1.5 — every cluster mentioning LLaDA 1.5 across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Step-dLLM boosts diffusion LLM inference speed with adaptive sparse attention
Researchers have developed Step-dLLM, a novel method to improve the efficiency of diffusion large language models (dLLMs) during inference. Unlike autoregressive models, dLLMs cannot use KV caching and recompute attenti…
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Diffusion Language Models Show Sampling-Dependent Bias
A new research paper explores the fairness of Diffusion Language Models (DLMs), which are emerging as an alternative to autoregressive language models. The study, conducted across LLaDA, LLaDA-1.5, and Dream models usin…
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HaloBlock improves dLLM decoding speed and accuracy
Researchers have introduced HaloBlock, a novel mechanism designed to improve the efficiency and quality of decoding in diffusion large language models (dLLMs). This method addresses limitations such as decoding difficul…
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New framework explains LLM jailbreaks via energy landscape analysis
Researchers have developed a new framework to understand why jailbreaks are successful in diffusion-based large language models (dLLMs). They propose that safety alignment can be viewed as shaping the energy landscape o…
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Diffusion Language Models Advance with New Efficiency and Safety Techniques · 10 sources tracked
Recent research explores advancements in diffusion language models (DLMs), focusing on improving their efficiency, safety, and capabilities. Papers introduce methods like Q-Skew for privacy risk assessment and PII extra…
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New methods accelerate Diffusion LLMs, addressing speed-quality trade-offs · 3 sources tracked
Researchers are developing new methods to accelerate Diffusion Large Language Models (dLLMs), which are computationally intensive due to their sequence length scaling. Two new frameworks, Dynamic-dLLM and Streaming-dLLM…