Inference
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ForeTac: Predictive Contact Guidance for Generative Robot Policies
2026-10-20 10:22:41Zhiyuan Chen, Shuai Chen, Haoran Zhou, Yizhe Zhao, Kaiyuan Yang, Zhixiang Duan, Shaoqing Xu, Ran Cao, Diyun Xiang, Yangwei YouAbstract
In contact-rich manipulation, actions that appear equally plausible from vision can produce different contact outcomes, while reactive control based on current tactile feedback may respond too late to prevent failure. We present ForeTac, a predictive contact-guidance framework organized as a represent-predict-evaluate-guide loop. ForeTac encodes the current tactile state, predicts future tactile latent trajectories conditioned on candidate action sequences, and evaluates each predicted trajectory with a contact-quality objective learned from successful and failed physical interactions. At inference, gradients of this objective are backpropagated through the tactile predictor to guide action generation before execution while keeping all model parameters frozen. We evaluate ForeTac on four real-robot tasks (planar and curved-surface wiping, card swiping, and fragile-object grasping) with diffusion and flow-matching policies. Compared with vision-only Diffusion Policy, ForeTac improves the average task success rate (SR) from 36.3% to 71.3% and the average contact success rate (CSR) from 20.0% to 66.3% across these tasks, and yields gains in both metrics with a flow-matching policy. Component analyses support the learned tactile representation, action-conditioned foresight, and generation-time gradient guidance, while predicted tactile latent trajectories preserve contact-quality ordering. Project page: this https URL.
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URL
https://arxiv.org/abs/2610.18118
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Muse Glimmer 30B: An On-Device Agentic Model
2026-10-20 10:14:52Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed, Juan C. Angeles Acuna, Alexis Audran-Reiss, Asaf Avrahamy, Yoram Bachrach, Can Balioglu, Amar Budhiraja, Onur \c{C}elebi, Gaurav Chaurasia, Joy Chen, Lynn Cherif, Lucia Cipolina-Kun, Gal Cohen, Timoth\'ee Darcet, Beto de Paola, Digant Desai, Sam Devlin, Derek Dunfield, Ivan Evtimov, Maryam Fazel-Zarandi, Rob Fergus, Jean-Christophe Gagnon-Audet, Grant Gardner, Bhavul Gauri, Paris Giampouras, Sujan Kumar Gonugondla, Antoine Grosnit, Young Han, Michael Hassid, Monuril Islam, Masoud Jalili Sabet, Songhao Jia, Narine Kokhlikyan, Svetlana Kurtsman, Sandra Lefdal, Chee Hau Leow, Noam Levi, Mike Lewis, Sanae Lotfi, Xiyang Luo, Despoina Magka, Saeed Mahloujifar, Neal Mangaokar, Ishita Mediratta, Alexander H. Miller, Hela Momand, Saba Nazir, Kelvin Niu, Mattia Opper, Parth Pathak, Alberto Pepe, Danielle Pintz, Mikhail Plekhanov, Sidhartha Reddy Potu, Anton Protopopov, Shengyi Qian, Shashank Rajput, Joseph Rocca, Scott Roy, Luca Sbordone, Amin Shakeri, Tatiana Shavrina, Michael Shvartsman, Ruan Silva, Anya Sims, Eric Michael Smith, Hugo Touvron, Mabel Ubong, Isabel Urrego, Yanzheng Xiang, Zhiyuan Yao, Alex Yarmula, He Ye, Jade Yu, Luke Zettlemoyer, Arman Zharmagambetov, Perpetua Fathima Ahlijah, Tremo Ahmed, Enrique Alfonseca, Pedram Bashiri, Nikolay Bashlykov, Amine Benhalloum, Aaron Berman, Rohith Bhatia, Siddharth Bhattacharya, Prashant Bhende, Chloe Bi, Shuchao Bi, Clement Anthonioz Blanc, Allen Bolourchi, Iva Simon-Bubalo, Pere-Llu\'is Huguet Cabot, Andrea Caciolai, Erica Cai, Xiang Tony Cao, Yue Cao, Andrew Caples, Jinzhao Chang, Lele Chen, Moya Chen, Weijie Chen, Wenhu Chen, Xiaobai Chen, Yinyin Chen, Zhengxing Chen, Wei Cheng, Pavel Chikulaev, Edward Chou, Sy Choudhury, Brian Clerkin, Marta R. Costa-juss\`a, Hanjun Dai, Curtis Davis, Myra Deng, Tiffany Siqi Deng, Dujian Ding, Hantian Ding, Shuangrui Ding, Jesse Dodge, Carl Domond, Shweta Doshi, Shirin Eghtesadi, Faith Eischen, Athyu Eleti, Jordi Armengol-Estape, Alex Fabbri, Han Fang, Paula Farani, Aiman Farooq, Darius Feher, Christoph Feichtenhofer, Amanda Felix, Romain Froger, Carl Chengyan Fu, Sean Gahagan, Yifan Gao, Itai Gat, Jannes Gladrow, Akashdeep Goel, Ahuva Goldstand, Bo He, David Heineman, Dylan Herman, Irene Hjelt, Joonas Hjelt, Alexander Holz, Rui Hou, Annie Hu, Yuan-Ting Hu, Bernie Huang, Jing Huang, Kun Huang, Ashley Hunter, Srinivasan Iyer, Somya Jain, Bei Jia, Michael Jiang, Song Jiang, Wenwen Jiang, Lin Jin, Jacob Kahn, Melanie Kambadur, Yingxin Kang, Justine Kao, Stephane Kasriel, Hossein Kavianihamedani, Madian Khabsa, Ameya Khandekar, Yash Khare, Gefen Kohavi, Kavya Kopparapu, Praveen Krishnan, Venu Kuchibhotla, Ananya Kumar, Leona Lan, Jack Lanchantin, Hunter Lang, Nikolay Pavlovich Laptev, Yuanzhi Li, Yunlu Li, Asad Liaqat, Johnson Lin, Runwei Lin, Zhichao Lin, Shaun Lindsay, Yaron Lipman, Frederick Liu, Mengchen Liu, Qiang Liu, Yinghai Lu, Yujie Lu, Christopher Luc, Faye Ma, Pingchuan Ma, Tengyu Ma, Wufei Ma, Xiaodong Ma, Damir Maksyutov, Josh Malone, Udeep Manchanda, Thomas Mann, Kalyani Marathe, Akhil Mathur, Roberto-Rafael Maura-Rivero, Ruslan Mavlyutov, Thomas Mesnard, Gregoire Mialon, Julian Michael, Sagar Miglani, Todor Mihaylov, Naureen Mitha, Archi Mitra, Leo Mok, Naila Murray, Muthukumaravel Muthuraman, Mergen Nachin, Nese Demir Nalci, Saaketh Narayan, Alyssa Newcomb, Peter Ney, Joycelyn Ng, Devin Norder, Maxwell Nye, Rajvardhan Oak, Cody Ohlsen, Artidoro Pagnoni, Yi Pan, Nilesh Pandey, Guan Pang, Richard Yuanzhe Pang, Mahesh Pasupuleti, Parth Patel, Yash Patel, Vladan Petrovic, Leslie St. Pierre, Norman Ponte, Yundi Qian, Zengyi Qin, Jack Rae, Ramya Raghavendra, Emmanuel Ramirez, Nikhila Ravi, Hongyu Ren, Riandy Riandy, Harrison Rudolph, Kalyan Saladi, Eduardo S\'anchez, Karthik Abinav Sankararaman, Alan Schelten, Thomas Scialom, Aman Shankar, Paresh Shenoy, Alana Shine, Hamid Shojanazeri, Mitesh Kumar Singh, Pawan Sisodiya, Ali Sol, Sten Sootla, DiJia Su, Suraj Subramanian, Brian Sun, Tony Sun, Zhiqing Sun, Natacha Supper, Jason Talpalar, Ellen Xiaoqing Tan, Liang Tan, Bu\u{g}ra Tekin, Jean-Christophe Testud, Parth Thakkar, Joseph Tighe, Ty Toledano, T Tse, Igor Tufanov, Alex Vaughan, Josh Walters, Alex Wang, Bethany Wang, Meng Wang, Rocky Wang, Weiyue Wang, Xiaolan Wang, Xin Wang, Zijian Wang, Bram Wasti, Quentin Wellens, Yeming Wen, Mary Williamson, Yuncheng Wu, Fei Xia, Tian Xie, Anlu Xing, David Xu, Jacob Xu, Puxin Xu, Zheng Xu, Shen Yan, Megan Yang, Yining Yang, Nick Yannopoulos, Kevin Yao, Michihiro Yasunaga, Haoxuan You, Michael You, Dingli Yu, Tianhe Yu, Yue Yu, Summer Yue, Xiang Yue, Shuwei Zeng, Hanwen Zha, Chen Zhang, Fred Zhang, Hongming Zhang, Kaiyu Zhang, Meng Zhang, Mingyang Zhang, Yi Zhang, Shengjia Zhao, Chao Zhou, Claire Zhou, Shuyan Zhou, Yanjun Zhou, Yaxuan Zhou, Jiacheng Zhu, Rosalie Zhu, Yun ZhuAbstract
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer integrates multi-step reasoning, reliable tool use and failure recovery into a single model that runs locally on consumer devices without requiring cloud infrastructure. The model accepts text and image inputs and produces text outputs, was trained on data from over a hundred languages, and is quantized to approximately 4-bit precision for efficient single-GPU inference. Muse Glimmer achieves state-of-the-art performance for its size class on agentic benchmarks, setting a new standard compared to the models of its size: Gemma4-31B and Qwen3.6-27B on tool use, multi-step planning, and agentic task completion. We release the model under the Apache 2.0 license to foster open-source development of capable, safe, local AI agents.
Abstract (translated)
URL
https://arxiv.org/abs/2610.24628
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Efficient SAM3 Adaptation for Multi-Class Semantic Segmentation via Dense Competitive Representations
2026-10-20 10:06:19Wenbin Liao, Hao Zhu, Yike Ma, Hao Jiang, Feng DaiAbstract
The Segment Anything Model 3 (SAM3) has achieved significant progress in Promptable Concept Segmentation (PCS) by processing short noun phrases to generate segmentation masks with unique instance identifiers. While SAM3 excels at single-concept segmentation, it falls short in complex, real-world environments where multiple concepts must be segmented simultaneously. In particular, SAM3 typically fails to simultaneously process concepts with semantic overlap, leading to semantic misclassifications and redundant mask predictions. Moreover, as the number of concepts increases, the independent inference paradigm introduces prohibitive inference latency. To address these limitations, we propose the Dense Competitive Representations SAM (DCR-SAM) framework. By decoupling cross-modal interactions, the proposed method alleviates the inference latency. To resolve semantic misclassifications, DCR-SAM dynamically aligns textual concepts with visual representations and incorporates a dense competition head to enforce explicit inter-class competition. Furthermore, a learnable background token is applied to absorb non-target objects dynamically. These mechanisms effectively suppress overlapping predictions and generate high-fidelity masks. Extensive experimental evaluations across multiple benchmarks demonstrate that the proposed framework: (1) achieves a 2.3% $\sim$ 10.7% performance improvement over state-of-the-art methods, (2) delivers a 33$\times$ inference speedup compared to the native SAM3, and (3) exhibits strong robustness across diverse scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27960
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In PRAISE of Attributes: Grounding Retrieval Geometry with Query-Conditioned Subspaces for Person Re-Identification
2026-10-20 09:52:06Rajarshi Bhattacharya, Aryan Shukla, Shakeeb Murtaza, Christian Desrosiers, Jose Dolz, Maguelonne Heritier, Eric GrangerAbstract
Person re-identification (ReID) ranks an image gallery using either an image query or a textual query. In image-to-image (I2I) ReID, viewpoint, illumination, occlusion, and background changes can make distractors appear more similar than the true match. In text-to-image (T2I) ReID, incomplete or ambiguous descriptions may omit key visual evidence. We study this problem for vision-language model (VLM) based ReID, where attributes such as clothing color and carried objects provide useful fine-grained matching cues. However, directly using attribute-prompt similarities as retrieval scores is unreliable because templated prompts can be biased, correlated, and poorly calibrated. We therefore propose \textbf{PR}ompt-guided \textbf{A}ttribute-based \textbf{I}nference-time \textbf{S}imilarity \textbf{E}nhancement (\texttt{PRAISE}), a query-side test-time adaptation framework for VLM-based ReID. Instead of treating prompts as scores, \texttt{PRAISE} uses a transferable attribute-prompt bank to construct an \emph{Attribute-Conditioned Subspace} (ACS): a query-dependent low-rank subspace induced by the most compatible attribute prompts. ACS provides a controlled attribute-conditioned geometry for reshaping query--gallery affinities and guiding lightweight inference-time updates without identity or attribute labels. Experiments across multiple I2I- and T2I-ReID benchmarks show that \texttt{PRAISE} consistently improves VLM retrieval backbones and outperforms both generic and ReID-specific TTA baselines.
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URL
https://arxiv.org/abs/2610.27944
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APRIL: Ambiguity-Aware Expert Routing for Respiratory Sound Domain-Incremental Learning
2026-10-20 09:49:56Xinhong Li, Weiping TuAbstract
Automated respiratory sound classification enables continuous, non-invasive respiratory assessment, yet long-term deployment remains challenging due to domain shifts from evolving recording devices and acquisition periods. When privacy and governance regulations prohibit replaying historical data, sequential learning easily overwrites previously acquired knowledge. Preserving dedicated domain experts prevents parameter interference, but selecting the correct expert at inference is difficult under overlapping acoustic distributions and absent domain labels. To resolve this dilemma, we propose APRIL, an exemplar-free domain-incremental framework for RSC. Built on a shared frozen BEATs backbone, APRIL sequentially inherits and isolates low-rank experts to enable new-domain adaptation without altering earlier models. A multi-level prototype router retrieves the top-two candidate experts and dynamically selects the final model by integrating domain-routing posteriors with classification confidence, adaptively weighted by matching ambiguity. We establish standardized device- and temporal-incremental benchmarks on the ICBHI and SPRSound datasets across forward and reversed domain orders. Evaluated over five random seeds, APRIL consistently achieves the highest final-session average Score among compared continual learning approaches, reaching 64.4% and 65.4% on ICBHI, and 83.1% and 82.2% on SPRSound under forward and reversed sequences, respectively. Crucially, average forgetting remains under 1.0% across all protocols, demonstrating robust domain adaptation and long-term knowledge retention without data replay. Overall, APRIL offers a reliable and privacy-preserving paradigm for deploying continuous respiratory sound classification across real-world healthcare settings.
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URL
https://arxiv.org/abs/2610.27942
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Quantum-JEPA: Quantum Born Priors for Latent-Action JEPA World Models
2026-10-20 09:33:35Zhiyuan Liu, Sicheng Liu, Haiyu WuAbstract
A single observation can admit several plausible futures, making uncertainty an essential part of world modelling. However, quantum generative models remain underexplored for this task. We introduce Quantum-JEPA, a latent-action JEPA framework that learns distributions of future representations using a conditional quantum Born prior. Circuit measurements generate binary latent actions, which a shared predictor maps to alternative future representations. We train the quantum prior through the quality and diversity of these predictions using a sampled energy objective. Our model learns distinct continuations on constructed multimodal tasks and captures latent-action correlations that support coordinated multi-agent futures. On D3IL, a benchmark of diverse human demonstrations, our trained models generate multiple obstacle-crossing routes from a common starting point without observed motion. On the controlled tasks, our classical and quantum prior implementations improve prediction validity and distributional fidelity over the evaluated variational baselines. Our quantum priors combine competitive predictive quality with a parameter-efficient sampling structure: they avoid an exponentially sized categorical output layer while supporting correlated latent actions. We further validate finite-shot parameter-shift training, including training with readout noise. Unlike most prior work, which focuses on simulation, we also verify the feasibility of inference with trained priors on a real QPU. These results establish a trainable quantum component for JEPA future prediction and a basis for exploring quantum-enhanced world models at larger scales.
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URL
https://arxiv.org/abs/2610.27921
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Gaze2HOI: Gaze-Aware Intention Disambiguation for Egocentric Hand-Object Interaction Forecasting
2026-10-20 08:59:54Chuanyang Ma, Chenye Wang, Yixuan Wang, Huiling Zhou, Xingqun Qi, Muyi Sun, Man Zhang, Caifeng Shan, Zhenan SunAbstract
Anticipating how users will interact with their surroundings is essential for proactive systems that provide timely assistance in AR, VR, and embodied applications. Prior work typically forecasts hand-object interactions directly from historical observations, leaving intention and contact geometry implicit. However, similar observation histories can precede semantically or geometrically distinct futures. Inspired by behavioral evidence that gaze can provide early cues for underlying intention, we propose Gaze2HOI, a gaze-aware framework that formulates hand-object interaction forecasting as a hierarchical problem comprising intention inference, contact grounding, and coupled hand-object motion generation. Specifically, Gaze2HOI first weights gaze cues by fixation strength and recency, and uses them to modulate the visual representations of a vision-language model (VLM) through Temporal Gaze Saliency Modulation (TGSM), thereby reducing semantic ambiguity in intention inference. Subsequently, an Anticipatory Contact Grounding (ACG) module maps these structured intentions into explicit contact, support, and placement fields, effectively mitigating geometric ambiguity. Driven by these explicit conditions, a diffusion-based model jointly generates the 6-DoF object trajectories and future articulated motion of both hands. To evaluate Gaze2HOI, we further introduce EgoAIM, a phase-aware benchmark that organizes egocentric hand-object interactions into four phases defined by evolving hand-object relations and provides fine-grained intention annotations. Extensive experiments demonstrate that Gaze2HOI achieves state-of-the-art performance in both intention inference and motion forecasting, even approaching oracle-level end-to-end performance in specific interaction phases. Project page: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27877
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LaLa-World: Scaling Latent World Models with Natural Language Actions
2026-10-20 08:32:14Junha Song, Geonmo Gu, Jeongeun Park, Heejeong Nam, Lucas Maes, Byeongho Heo, Dongyoon Han, Sangdoo YunAbstract
This paper shows that JEPA-style latent world models (LWMs) can learn across diverse domains and data sources by adopting the language-conditioned interface of video generation models. We establish this approach as an effective and efficient basis for planning with imagination and downstream policy fine-tuning. To this end, we introduce LaLa-World, a Language-conditioned Latent World model that learns from simulation and robot trajectories together with broader image and video data. Two designs make this work. First, we describe both numerical actions and visual changes in language, enabling joint training of a single future predictor across heterogeneous datasets. Second, we build the predictor on a language model, allowing it to interpret language conditions while predicting future latent states. The resulting predictor achieves higher average MPC planning performance than existing LWMs trained separately for each domain or task. Adding image and video data further improves average planning performance and robustness to action phrasing. In a matched comparison with an RAEv2-based video generation model baseline, LaLa-World reaches comparable planning performance with $6\times$ less training cost and $13.9\times$ faster planning. Beyond planning, the pretrained LaLa-World serves as an initialization for LaLa-Policy, which jointly predicts actions and future visual representations. LaLa-Policy performs competitively with Cosmos Policy on LIBERO and RoboCasa, with up to $5.5\times$ faster inference.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27851
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GARRET: Generative Feedback for Region Retrieval in Visual Documents
2026-10-20 08:19:14Weijian Jian, Siyang Chen, Yuguang Yang, Yajun Zhang, Chunyu Xie, Dawei Leng, Yuhui YinAbstract
Multimodal retrieval-augmented generation over visually rich documents requires evidence at a finer granularity than entire pages. Existing visual retrievers typically index or return full pages, whereas training region-level retrievers often requires costly query-to-region annotations or noisy model-generated labels. We introduce GARRET, a region-retrieval framework trained from (page, question, answer) triplets through differentiable generative feedback. A shared context-preserving retriever produces page and region multi-vector representations. During training, their retrieval scores define a soft routing distribution over candidate region crops. An auxiliary vision-language model attends to each crop separately, aggregates crop-specific contexts using the routing weights, and predicts the question followed by the answer under a next-token prediction objective. Gradients through the routing distribution assign query-conditioned credit to the indexed region representations, without requiring query-to-region annotations. At inference, the auxiliary model is removed. GARRET also reuses the contextualized region representations, together with page summary tokens, to construct a compact page representation. We introduce ViDoRe-V3-Region, a corpus-wide benchmark containing 200,682 fixed candidate regions across eight domains. GARRET achieves a macro nDCG@10 of 0.4126, outperforming the strongest visual baseline by 2.94 points. On page retrieval, it remains comparable to ColQwen2.5-v0.2 while indexing 47.8% fewer vectors per page.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27829
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Privacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27B
2026-10-20 08:08:46Mahendra Roopa, Srikanth RoopaAbstract
Every prompt sent to a hosted language model is readable by whoever runs it. We study whether the prompt can remain on the client while the host receives transformed vectors that stand in for it. A language model converts text to vectors exactly once, with a public lookup table; we perform that step locally and pass the vectors through a small trained network (55.6M parameters) before they leave the perimeter. The host runs a frozen 27B model (Qwen3.8-27B) on the transformed vectors and never receives text or token ids. We train the transform against the frozen model as a fixed judge, combining a forward-KL utility term with an auxiliary term that pushes each vector away from its original direction. On 1,490 MMLU questions held out from training, the transform costs 1.5 points of accuracy (80.7% vs. 82.3%), breaking 68 of the 1,226 questions the unmodified model answers correctly (5.5%), while 30.1% of content tokens no longer decode to their original token under a nearest-neighbour lookup.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27817
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TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling
2026-10-20 08:02:36Shuaiqi Cheng, Jiahao Zhang, Boyuan Gu, Yuxi Chen, Haiying Xu, Zexin Lu, Huiyu Zhou, Xuming HuAbstract
Diffusion Transformers (DiTs) deliver high-fidelity image and video generation, but inference requires many sequential model evaluations. Two acceleration strategies are increasingly important: model compression makes each evaluation cheaper, while adaptive inference avoids selected teacher computation through reuse, forecasting, or speculative execution. Their combination, however, is brittle: directly applying the evaluated adaptive methods to a compressed student can degrade quality, an observed failure mode we call the \textbf{Combination Gap}. We link this gap to a mismatch between pointwise distillation and short-horizon execution: a student trained at teacher states need not remain reliable when its own predictions are chained. We propose \textbf{TamedFlow}, which trains a compressed DiT for this deployed role through cross-architecture feature alignment, a near-identity Velocity Alignment Head, and randomized rollout supervision. At inference, a lightweight Learnable Trajectory Estimator routes completed drafts: accepted drafts bypass the teacher, while rejected drafts roll back and receive one teacher rescue step. Distilling DiT-XL/2 into DiT-B/2, TamedFlow reaches FID $2.58$ at $2.25\times$ measured speedup over DDIM-50 on ImageNet $256\times256$. Ablations support trajectory supervision, and routing controls favor LTE over random decisions at nearly matched latency. Evaluations on ImageNet $512\times512$, MS-COCO, and Latte/FaceForensics extend the empirical scope.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26025
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When Does Better Acceptance Pay? A Controlled Study of Speculative Decoding
2026-10-20 07:26:20Yongchao Long, Chunrui Zheng, Yuxuan Zhou, Guangkun Nie, Tian-Chi Ma, Chao Pang, Shuohuan Wang, Yuchen Ding, Xianbin Wen, Dianhai Yu, Junyuan Shang, Yuxi Zhou, Shenda HongAbstract
The throughput gains of speculative decoding depend on the trade-off between acceptance length and execution latency. We ask when additional acceptance is worth the cost of obtaining it. We establish a comparison protocol spanning training, inference, and evaluation, remeasuring released checkpoints and retraining six drafter recipes on shared target-generated data across ten benchmarks and two target models. Alignment changes the picture: gaps that appear large in published comparisons shrink substantially under shared training and inference conditions. This narrowing motivates two deeper questions: how is acceptance improved at different costs, and when do these gains improve throughput? We find that acceptance can be improved through nearly free objective and context changes or more expensive token-to-token conditioning. DCore illustrates the former route as a strong baseline with little additional measured drafting overhead. The payoff is conditional: task uncertainty and serving conditions affect acceptance returns, execution costs, and the preferred drafter and budget. These findings inform future drafter design and deployment choices.
Abstract (translated)
URL
https://arxiv.org/abs/2610.24993
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Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning
2026-10-20 07:26:16Yijia Fan, Ziqi Huang, Zhongang Cai, Yan Li, Zimo Wen, Wanqi Yin, Haiwen Diao, Ziwei LiuAbstract
Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it, observe the result, and diagnose again. Whether a revision helps is known only after it is rendered, so the reflection text and the image generation must be learned jointly, over the whole loop. Supervised fine-tuning (SFT) on reflection trajectories gives a cold start but does not find the high-success repair paths, and naive RL that optimizes only the renderer or only one head leaves most of the gain untapped. We introduce UMM-Reflection, which applies reinforcement learning (RL) to complete reflection trajectories inside one unified model: sibling trajectories share one initial image, so the group-relative advantage compares reflection strategies, and one trajectory-level advantage updates both the reflection tokens and the flow-based revisions, avoiding the combinatorial blow-up of per-round credit assignment. Unlike single-round editing or pipelines with an external critic, credit flows across rounds and to both roles of the same model, and no verifier is needed at inference. On BAGEL, UMM-Reflection improves GenEval by 12.05 points over SFT, and the gains transfer to WISE (+10.97), OneIG-Bench (+3.48), and T2I-CompBench++ (+4.63), none of which is used in training.
Abstract (translated)
URL
https://arxiv.org/abs/2609.35767
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UniAVC: Unified Audio-Video Creation via Progressive Training and Asymmetric Scheduling
2026-10-20 07:19:16Kien T. Pham, Yuhao Liu, Chong Hou Choi, Zhenqi He, Yanghao Wang, Chenyang Lei, Rui Liu, Qifeng Chen, Long ChenAbstract
Unified audio-video creation requires a single model capable of text-conditioned joint synthesis, bidirectional cross-modal generation, and instruction-guided editing across both modalities. Existing methods rely heavily on paired corpora, which are scarce for joint generation and even rarer for editing. Consequently, they are spent learning single-branch generative and editing capabilities that abundant unimodal data could readily teach, rather than on the cross-modal coupling. Furthermore, prevailing systems apply a single shared noise schedule to both modalities, neglecting two critical limitations. First, due to the significant discrepancy in information density, video requires prolonged high noise to organize spatial and motion structure, whereas the sparser audio signal can settle earlier during the synthesis process, the dynamics a uniform schedule might compromise. Second, lockstep denoising leaves asymmetric workflows, such as conditioning on one modality to synthesize or edit another, untrained. We present UniAVC, which addresses both obstacles. It first trains each branch on unimodal dataset to full generative and editing competence, and subsequently dedicates paired data to exploit cross-modal correspondence. Modality-decoupled schedulers meanwhile warp the noise schedule independently from a shared timestep, while subtasks with one clean modality directly supervise asymmetric configurations. These settings respectively enable intent-aware editing, which preserves one modality intact while enlisting it to constrain the other, and post-inference self-refinement, which regenerates only an unsatisfactory branch against an accepted one. UniAVC thus spans multiple creation tasks from a single set of weights without task-specific adapters.
Abstract (translated)
URL
https://arxiv.org/abs/2610.24791
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Stable Signer V3: 4D Deformable Primitive Splatting Is All You Need for Sign Language Production
2026-10-20 07:14:53Sen Fang, Liwei Che, Dimitris N. MetaxasAbstract
We present Stable Signer V3, an interactive high-resolution sign language avatar system built on 4D Deformable Primitive Splatting. It addresses three limitations of existing avatars: restricted facial expression, inaccurate identity-specific body deformation, and loss of fine appearance details. Shared Face Primitive Splatting adds transferable superquadric primitives for richer expression control; Test-Time Body Primitive Splatting learns an identity-specific virtual muscle layer for more faithful motion; and Test-Time Tunneling Super-Resolution uses TunnelingNet to recover identity-specific high-resolution details. Both test-time modules are optimized once per signer identity; inference then runs entirely from the stored identity parameters. Together, they reduce facial geometry error by 71.4%, body-boundary error by 35.1% across ten identities, and high-resolution RGB reconstruction error by 24.2% on held-out evaluations, while retaining interactive streaming.
Abstract (translated)
URL
https://arxiv.org/abs/2610.24926
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AWLS: Advantage-Weighted Latent Steering for One-Step Generative Robotic Manipulation Policies
2026-10-20 06:58:12Jiahong Tang, Jiaju He, Zhiyuan Yang, Ruyang Han, Haiyue ZhuAbstract
Human-collected real-world robotic manipulation demonstrations often contain mixed-quality trajectories, including hesitant, indirect, or inefficient action segments. While diffusion- and flow-based policies can model such multimodal data, standard behavior cloning (BC) assigns equal likelihood to all demonstrated actions and may therefore reproduce suboptimal modes. Offline RL value estimates offer a natural signal for biasing policies toward higher-value behavioral modes, but directly fine-tuning the generative policy or using critic gradients for policy steering can amplify critic errors and induce prior drift. We propose Advantage-Weighted Latent Steering (AWLS), an offline post-training method for improving frozen one-step generative manipulation policies without updating their decoders. AWLS learns a residual noise shaper, an observation-conditioned residual correction to the policy's latent noise, and trains it with an advantage-weighted action reconstruction objective. The advantages are detached, scale-normalized, and used only as supervised weights on demonstrated actions, avoiding critic-gradient policy updates while preserving fast single-step inference. We further introduce a rollout-free decoder-sensitivity criterion for selecting pretrained checkpoints that remain responsive to latent-noise perturbations. Experiments on simulated and real-world robotic manipulation tasks with clean and mixed-quality demonstrations show that AWLS improves over the frozen base policy while limiting drift from the pretrained behavior prior. Project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2610.27736
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VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse
2026-10-20 06:54:08Sicheng He, Yi Pan, Qiuting Yu, Jiaming Huang, Qi Lu, Shuyan Wu, Hongyan Feng, Xuanyu Liu, Jun Zhang, Jie Zhang, Wenqi Zhang, Xuhong Zhang, Jianwei YinAbstract
Flow-matching vision-language-action (VLA) models generate an action chunk by iteratively denoising Gaussian noise, but typically execute only a short prefix before replanning with updated observations. Under this receding-horizon paradigm, the unexecuted remainder---the action tail---is discarded, and the next chunk is regenerated from fresh noise. We show that this discarded tail retains transferable denoising progress: action-tail initialization can match or outperform a native 10-step Gaussian baseline using fewer denoising steps. However, the optimal denoising budget varies substantially across samples, indicating that tail reuse should be adapted to the updated environment. Based on this observation, we introduce VLA-Rolling, a lightweight inference framework that reuses action tails through three stages: action-tail inheritance, per-action confidence estimation, and confidence-guided refinement through renoising and denoising budget allocation, while the underlying VLA remains frozen. Across $\pi_{0.5}$, SmolVLA, X-VLA, and MolmoAct2 on LIBERO, MetaWorld, LIBERO-Plus, and RoboTwin 2.0, VLA-Rolling reduces action-chunk inference cost in all 16 model--benchmark pairs, achieving up to a $1.48\times$ speedup, while improving success rate in 14 pairs by up to $15.08$ percentage points. These results show that action tails are not merely discarded predictions, but reusable intermediate states that enable more efficient and accurate VLA inference. These results demonstrate that the action tail can support the generation of better actions with fewer denoising steps. Our project is available at this https URL.
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https://arxiv.org/abs/2610.25043
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RLSplat: Resource-Linear Budget Control for Feed-forward 3D Gaussian Splatting
2026-10-20 06:30:48Injae Kim, Minseok Joo, Jihwan Park, Sehyung Kim, Minseong Bae, Hyunwoo J. KimAbstract
Feed-forward 3D Gaussian Splatting (FF3DGS) reconstructs 3D scenes from images in a single forward pass, making it well-suited to applications that must operate within limited resource budgets. To further improve its practicality, recent budget-controllable FF3DGS methods allow the number of Gaussians to be adjusted at inference, so that the output meets a given storage limit or rendering-latency constraint. However, these methods overlook the cost of inference itself: to produce a representation with the target number of Gaussians, they first predict a dense set of Gaussians and then prune or select from it, requiring nearly constant latency and peak memory regardless of the budget. We therefore propose RLSplat, a resource-linear budget-controllable FF3DGS framework whose representation size, inference latency, and peak memory all decrease linearly as the Gaussian budget is reduced. Instead of pruning or selection, RLSplat learns a Gaussian allocation policy that distributes the budget across local scene regions, so that exactly the requested number of Gaussians is generated. These Gaussians are then refined through resource-linear Gaussian decoding, in which each Gaussian attends only to nearby Gaussians found via an efficient hierarchical neighbor search, so that the decoding cost decreases linearly with fewer Gaussians. In zero-shot evaluation, RLSplat outperforms prior budget-controllable methods at every budget, surpassing the strongest baseline even with 4$\times$ fewer Gaussians, while running up to 4.1$\times$ faster with 9.6$\times$ less peak memory at low budgets.
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https://arxiv.org/abs/2610.27712
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Beyond Calibration: Temporal LLM Confidence Assessment Across Languages and Local Settings
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Large language models are expected to estimate their own uncertainty, with confidence signals used to guide retrieval, adaptive compute allocation, routing, and selective abstention. We investigate whether confidence signals measured at different temporal stages of problem solving encode distinct forms of epistemic information and remain reliable beyond English-centric settings. Inspired by classical meta-memory theory, we distinguish anticipatory pre-solve confidence (Feeling-of-Knowing, FOK) from retrospective post-solve confidence (Judgement-of-Learning, JOL). We evaluate these signals across two frontier LLMs, multiple reasoning domains, translated Marathi benchmarks, and a newly constructed benchmark of genuinely local Marathi factual questions. Across settings, the cheaper pre-solve signal remains weakly calibrated, while post-solve confidence generally provides stronger epistemic alignment despite its additional inference cost. Confidence behaviour transfers surprisingly well under direct language translation, with calibration trends largely preserved between English and Marathi, but degrades substantially on local Marathi knowledge, suggesting that linguistic transfer alone is insufficient for region-specific information needs. Finally, hallucinated responses maintain persistently elevated confidence after answer generation, highlighting important limitations of current LLM self-assessment mechanisms.
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URL
https://arxiv.org/abs/2610.27702
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CovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation
2026-10-20 05:47:26Pengying Wu, Zhejia Wen, Kang Ding, Kangjie Zhou, Chunxuan Jiao, Wei Pan, Xiangli Nie, Chang LiuAbstract
In object goal navigation (ObjectNav), an embodied agent must find a target object (e.g., "find a bed") in a previously unseen environment, relying on spatial commonsense such as object co-occurrence. Large Language Models (LLMs) encode this commonsense, but deploying them on robots exposes a trade-off between reasoning quality and efficiency: cloud-based models incur latency and privacy risks, whereas small on-device LLMs are fast but prone to hallucinated spatial judgments. Hybrid reasoning LLMs offer both a fast non-thinking mode and a slow thinking mode, which raises a key question for embodied agents: when is slow thinking worth its cost? We propose CovNav, a training-free framework that answers this question with Conformal Prediction (CP), turning dual-process (System 1 / System 2) inference into an uncertainty-aware decision. Much like recent decision models such as Jev, CovNav casts each spatial query as a structured binary decision rather than free-form generation: a local LLM in fast mode judges whether the target is likely near an observed object, CP converts its token probabilities into a prediction set, and only ambiguous queries are escalated to slow chain-of-thought reasoning. The object-pair formulation keeps calibration tractable and supports open-set objects through off-the-shelf detectors. Experiments on Gibson and HM3D show that CovNav surpasses the strongest zero-shot baseline by 0.8 and 1.7 percentage points in success rate on HM3D v1 and v2, while invoking slow reasoning for only 12.4% of queries and retaining most of the gain of always-on thinking at a fraction of its latency. Real-robot experiments on a TurtleBot 4 further validate the approach. CovNav shows how calibrated uncertainty can decide when an embodied agent should think slowly, offering a principled form of adaptive test-time reasoning for LLM-based robots.
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https://arxiv.org/abs/2610.27677
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