Showing papers from Xiaomi Corporation Show all papers
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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RotVLA introduces continuous rotational latent actions on SO(n) for vision-language-action pretraining, using a flow-matching head guided by latent planning to achieve state-of-the-art robot control.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Memory Grafting uses frozen hidden states from a grafting model as conditional n-gram memory for language models, improving average benchmarks to 53.86 versus 52.43 for vanilla Engram at 2.8B scale with minimal overhead.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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PointForward reconstructs driving scenes via world-space 3D queries and scene graphs, achieving state-of-the-art feedforward results with explicit cross-view and instance consistency.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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LaST-VLA replaces explicit chain-of-thought reasoning with a physically grounded latent spatio-temporal framework, achieving record NAVSIM scores and improved reasoning.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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CoDMD adds a copula-aware relational regularizer to distribution matching distillation that improves few-step video generation, achieving 84.46/84.87 VBench scores at 4 steps with ~25× speedup.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Embodied chain-of-thought improves generalization when grounded in action guidance, and ERVLA uses reasoning-dropout supervision to avoid unstable autoregressive reasoning at inference, achieving state-of-the-art robot manipulation results.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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CausalMix frames data mixture optimization as causal inference to dynamically estimate optimal mixtures via conditional average treatment effects, improving LLM performance without retraining proxy models.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 19 on Hugging Face
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