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NVIDIA Rewrites KV and Beam Search for Its Generative Recommendation Stack, Making 5K-Context Inference 2.27× Faster

NVIDIA has assembled HSTU, dynamic embeddings, and Semantic ID-based generative recommendation into an executable PyTorch reference stack. It rewrites the caching and decoding paths for recommendation workloads characterized by long histories, generation of only a few tokens, and extremely large beam widths, though the results are currently limited to NVIDIA hardware and synthetic configurations.

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RuleMaze Breaks Visual Planning Into Tool Calls, Raising a 3B Model’s Success Rate on Unseen Rules From 56.3% to 88.0%

RuleMaze uses executable verifiers to test whether multimodal models can understand mazes, follow natural-language rules, and complete path planning. By separating perception, movement, and rule verification, DMP substantially outperforms standard fine-tuning, though testing remains limited to synthetic grids and the verifiers underwent manual correction.

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TMI Reconstructs Task Trees from Interleaved Computer Activity, Raising Step-Description Accuracy from 30.3% to 74.9%

Task Model Induction does not compress an entire screen and input log into a single workflow. Instead, it first disentangles noncontiguous tasks, then reconstructs goal hierarchies and loop-based control flow. The resulting agent skills improved held-out accuracy from the best baseline’s 14.29% to 18.57%, but the full pipeline depends on frontier models, while raw interaction traces introduce significant privacy risks.