Arcee, a developer of open-weight models in the US, has partnered with the Department of Energy to create Genesis-Science-1. This collaboration results in an open model designed specifically for scientific research.
Arcee and Department of Energy partner to build Genesis-Science-1
Arcee AI and DOE announce Genesis-Science-1, a 1T open-weight model for scientific research
The Department of Energy (DOE) and Arcee AI have announced the development of Genesis-Science-1 (GS1), an open-weight language model designed specifically for scientific research. This initiative is part of the Genesis Mission, aiming to bring advanced AI capabilities to a wide range of scientific fields through a joint effort between the government agency and the private company.
Tencent releases HiLS-Attention-7B with chunk-wise sparse attention
Tencent has released the HiLS-Attention-7B checkpoint, a 7-billion parameter model built on an OLMo3-style backbone. The model implements a chunk-wise sparse attention mechanism that learns chunk selection end-to-end under the language-modeling loss.
DiaLLM investigates robustness-generation gap in English dialect adaptation
The DiaLLM study addresses the disconnect between understanding and producing dialectal English by continually pretraining three open-weight language model families on the International Corpus of English. It applies implicit and explicit post-training paradigms combined with three alignment strategies to compare Australian, Indian, and Northern British English.
DiaLLM investigates robustness-generation gap in English dialect adaptation
The DiaLLM study reveals that dialectal robustness and generation are dissociated in large language models, as benchmarks shaped by continual pretraining do not capture how alignment reshapes actual output. The authors continually pretrain three open-weight model families on the International Corpus of English and apply implicit and explicit post-training paradigms combined with three alignment strategies for Australian, Indian, and Northern British English.
Qllm releases O(1) inference model without KV cache
A researcher has released Qllm, a new large language model architecture that achieves O(1) inference time by eliminating the need for a key-value (KV) cache. The 100M parameter model is built on phase associativity and does not rely on transformer or mamba structures.