n-gram
PulseAugur coverage of n-gram — every cluster mentioning n-gram across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Code completer's ghost text feature fails eval due to self-prediction gap
A developer has created a code completion tool called pycomplete that utilizes a transformer model blended with n-gram models and a cache. While the tool's next-token prediction accuracy is 54.6%, its performance on gen…
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N-gram models better predict reading time than transformers, study finds
A new paper proposes that traditional n-gram language models may be better predictors of naturalistic reading time than complex transformer models. The research suggests that while transformers excel at next-word predic…
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DeepSeek's Engram Module Made Tokenizer-Agnostic
Researchers have developed a tokenizer-agnostic engram module for large language models, building upon DeepSeek's original design. The new approach replaces XOR-based hashing with polynomial hashing, creating a joint em…
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Qwen3.6-27B benchmark reveals DFlash leads speculative decoding speedups
A recent benchmark compared speculative decoding methods across vLLM and SGLang frameworks using the Qwen3.6-27B model on a single RTX PRO 6000 Max-Q GPU. The DFlash method emerged as the most effective, offering speedu…
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Speculative decoding research boosts LLM inference speed on consumer hardware
Researchers are exploring speculative decoding techniques to accelerate large language model (LLM) inference. Two papers, one from arXiv and another from dev.to, detail methods for improving efficiency on consumer hardw…
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New speculative decoding methods boost LLM inference speed and efficiency · 6 sources tracked
Researchers have introduced DominoTree, a novel method for speculative decoding that significantly accelerates LLM inference by using a conditional tree-structured approach. This method achieves up to 6.6x speedup on Qw…
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New Hamm-Grams Algorithm Enhances Malware Detection with Robust Features
Researchers have developed a new algorithm called Hamm-Grams, designed to improve malware detection and classification by creating more robust features than traditional n-grams. These hamm-grams are a type of regular ex…