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新论文 · v1arXiv:2611.11125

LongCap: Hour-Scale Timestamped Audio Captioning via Chunk-wise Processing

Jinchuan Tian, Sen Chen, Wenbin Huang, Chao Weng, Zhengwei Huang, Jiatong Shi, Yichen Lu, Haoran Wang 等 10 位作者

原文摘要

Describing hours of audio requires accurate event timing and sustained descriptive detail throughout the recording. We present LongCap, a framework for timestamped audio captioning (TAC), with the human-annotated LongCapBench and three diarization-inspired metrics: Timeline Error Rate (TER), Granularity Error Rate (GER), and Caption Similarity (tcpCapSim). LongCap interleaves audio chunks with structured event captions, using relative timestamps and retained context to support length extrapolation within a 1M-token inference context window. Event-level reannotation restores caption detail, while systematic studies examine model scaling, dependency supervision, and self-distillation. LongCap-Diar achieves strong benchmark results and extrapolates to 16 hours of concatenated audio despite training on recordings no longer than 40 minutes. Our TAC models are competitive with Qwen3.5-Omni-Plus on short clips, outperform evaluated baselines in caption similarity and timeline accuracy in segment-level comparisons on LongCapBench-TAC-Long, and caption full recordings approaching two hours. On LongCapBench-TAC, 9B and 35B-A3B offer favorable size-performance tradeoffs, while 122B-A10B substantially improves event-dependency recovery. These findings support long-horizon general audio understanding and provide a foundation for always-on voice agents.

cs.CL · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08577

Meituan-Robotics-0: A Vision-Language-Action Foundation Model for Desktop Manipulation

Meituan Robotics Team, Caicheng Wang, Chen Zhao, Chengpeng Jin, Duo Xu, Fengjiao Chen, Guanglu Wan, Heqing Yang 等 38 位作者

原文摘要

We present Meituan-Robotics-0 (MR0), a vision-language-action foundation model and embodied-agent system for stationary-base desktop manipulation. MR0 combines 147 million vision-language samples with 15,060 hours of curated robot trajectories spanning 12 embodiments. A three-stage training recipe aligns multimodal representations with robot behavior through discrete action-token pre-training, transfers these priors to continuous cross-embodiment control through flow-matching mid-training, and adapts the policy through real-world post-training. We introduce Rollback, which pairs replaced policy segments with human corrections to provide positive and negative supervision for CFG-DAgger without value learning or advantage labeling. A unified end-effector interface supports heterogeneous embodiments, while asynchronous action-chunk merging maintains smooth execution under variable inference latency. An event-driven agent maintains persistent task events and reassesses plans when progress or feasibility changes, enabling cross-task retrieval and conditional task resumption. MR0 achieves 93.6% success on RoboTwin 2.0 and ranks fifth in the RoboDojo VLA track. Under matched HG-DAgger post-training, MR0 achieves higher average success rate and progress score than $π_{0.5}$ across three real-robot tasks. On Ethernet insertion, CFG-DAgger trained with Rollback data reaches 70% success and a progress score of 0.90. Qualitative agent deployments demonstrate cross-task event retrieval and recovery from temporarily unsatisfied task conditions.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06008

Faster Cluster Decomposition with Stronger Bi-Criteria Guarantee

He Sun, Yong Zhang, Yacong Zhou

原文摘要

Partitioning the vertex set of a graph $G=(V,E)$ into clusters is a classical problem in computer science. As vertices within clusters should be well connected and cuts between clusters sparse, Oveis Gharan and Trevisan (SODA 2014, [OGT14]) proposed to measure the partition quality using inner and outer conductance, i.e., the bi-criteria guarantee. Specifically, when partitioning $V$ into vertex-disjoint subsets $P_1,\ldots, P_k$, every subgraph induced by $P_i$ should have high conductance, while the conductance of $P_i$ in $G$ should be low. They studied the existence of such a partition and designed a polynomial-time algorithm achieving this guarantee. For more than a decade, their algorithm remained the only one achieving this guarantee on its output clusters. In this paper, we study faster cluster decomposition algorithms under the same setup as [OGT14] and prove three results: 1. We make their existential result algorithmic: Our algorithms run in nearly-linear time, and achieve the same bi-criteria guarantee as the one in [OGT14] up to constant factors, including the asymptotically tight dependence on $k$. 2. We prove that their existential result holds algorithmically under a much weaker condition on the initial disjoint subsets. To achieve this, we develop a general algorithm framework that boosts the approximation guarantee of a solution: given an $α$-approximate solution to the $k$-way conductance problem, if $ρ_G(k+1)/ρ_G(k)=Ω\left(α/\varepsilon\right)$, then a $(1+\varepsilon)$-approximate solution can be obtained using only $O(k(1+\logα))$ minimum $s$-$t$ cut computations. 3. We present a nearly-linear time algorithm that finds clusters with the bi-criteria guarantee. Our algorithm has the same guarantee as the one in [OGT14], which, for any unweighted graph of $n$ vertices and $m$ edges, runs in $\widetilde{O}(knm^2)$ time.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06954

Certified folding-free deformable image registration: A whole-cell Jacobian certificate and a sequential variational solver

Baihan Zhao, Zicong Zhou, Guojun Liao

原文摘要

Registration maps propagate labels, measure local volume change, and map results back through their inverses; requiring invertibility. Voxelwise checks can miss folds between grid points. We study the canonical continuous extension of a nodal displacement field by trilinear interpolation and give a test that proves its Jacobian determinant positive in every grid cell or rejects the map. The test is rigorous under IEEE 754 binary64 arithmetic (correct rounding, no contraction). With a fixed boundary, certification implies a bi-Lipschitz homeomorphism of the image domain. We introduce SeqVP, built around this certificate. A proximal Jacobian-determinant-curl formulation yields inner problems whose penalties on changes in the map, Jacobian determinant, and curl vanish to first order at the current iterate. The method decreases a fixed outer energy up to a floating-point resolution guard and certifies every committed iterate. After certified initialization, stopping at any time returns a folding-free map. A Poisson-preconditioned gradient method with Jacobian-aware step prediction and affine-scaling damping, initialized by certified handoff from an uncertified binary32 pre-optimizer, handles a 1 mm brain MRI with about 2e7 unknowns in 90 s on one GPU. On 49 held-out OASIS pairs, SeqVP achieves mean Dice 0.821 over 35 structures and 0.889 over 4 tissue classes, with certified zero folding. Gains are 0.022 and 0.025 over ANTs SyN, and 0.022 and 0.023 over uniGradICON with instance optimization at equal time (paired, Holm-adjusted p < 0.001), but maps are rougher. SeqVP is level with ANTs on LPBA40, behind it on IBSR18, and ahead of all label-free baselines on 20 large-deformation pairs. On fields from nine methods, the certificate finds non-positive-Jacobian cells in every ANTs SyN map (median 10, missed by voxelwise checks) and in maps from NiftyReg, ConvexAdam, uniGradICON, and TransMorph.

cs.GR · math.AP

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