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Received 10 September 2025; revised 3 January 2026 and 5 March 2026; accepted 7 April 2026
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Achieving 89.9% accuracy in loop closures, this framework revolutionizes UGV exploration in challenging environments by significantly cutting down travel distance and time.
MLLMs exhibit alarming Stochastic Collapse, failing to maintain randomness even under explicit random instructions, which could undermine their utility in diverse applications.
State-of-the-art shot boundary detection gets a major upgrade with a Transformer-based approach that not only improves accuracy but also offers more interpretable boundaries, thanks to a novel relational prediction framework and synthetic training data.
Achieve 2.6x faster autoregressive world model inference without retraining by caching and selectively reusing block-level residuals across generation chunks.
