AI Coding Live News

Researchers introduced MemT-1.0 (arXiv:2610.26825), a groundbreaking architecture internalizing memory as an intrinsic component of foundation model computation. Moving beyond exploding KV caches and lossy Vector RAG pipelines, MemT-1.0 employs Gated DeltaNet-2 with stable within-turn token-level updates alongside a novel Mixture-of-Storage mechanism. Trained across a two-stage 34B-token pipeline, the released MemT-1.0-Nano model achieves a 1.86x end-to-end inference speedup and a staggering 66x reduction in persistent storage at 128K tokens relative to full-context KV caching, dramatically elevating procedural task execution in autonomous agents.

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Inception co-founder Stefano Ermon officially unveiled Mercury 3, the company's next-generation diffusion-based large language model. Delivering an astonishing 1,068 tokens per second inference speed on NVIDIA GPUs, Mercury 3 scales to 40x the size of Mercury 1 with a native 1M context window. Benchmarked on IFBench, GPQA Diamond, and SWE-Bench Atlas, it matches frontier closed baselines with standard API pricing of $0.20 / $0.70 per 1M input/output tokens (with 50% promotional pricing of $0.10 / $0.35).

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While looped language models enable depth-adaptive inference by executing fewer layer repetitions on easy tokens and more on complex tokens, variable loop counts break conventional batching engines like vLLM. Researchers from TUM and Imperial College introduced Continuous Depth Batching (CDB). By dynamically reorganizing batches between loop iterations, orchestrating looped KV-caches, and asynchronously predicting token exits, CDB achieves up to 99% of the theoretical maximum inference speedup across Ouro 1.4B and Huginn 3.5B architectures.

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