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The DeepSeek-V4.1-Flash model, a multimodal Mixture-of-Experts model with 552B backbone parameters and support for contexts of up to one million tokens, is introduced, substantially improving cost efficiency for agentic workloads and pushing the limits of KV cache compression.
Zero-Mem slashes memory operation time costs by over 57% while eliminating unnecessary LLM calls, revolutionizing how LLM agents manage memory.
Dynamically adjusting visual importance at test time can significantly enhance action prediction accuracy in VLA models.
LLMs, like humans, exhibit a "frequency bias," performing better when prompted and fine-tuned with more common textual expressions.