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新论文 · v1arXiv:2611.10263

DAC-MACE: Context-Conditioned Expert Routing for Compact Scene Coordinate Regression in Camera Relocalization

Dingying Fan, Mingkai Liu, Haohua Que, Haojia Gao, Xianliang Huang, Qian Zhang, Xueqian Wang, Fei Qiao

原文摘要

A camera becomes an absolute pose sensor when its images can be relocalized against a prebuilt map of the scene. On mobile robots and augmented-reality devices, that map must be small and quick to build. Scene coordinate regression (SCR) stores the scene in a compact network that predicts a 3D coordinate for every pixel, and mixture-of-experts (MoE) variants extend its capacity to larger outdoor scenes. In existing MoE-based SCR, however, the router assigns each pixel to an expert from the same local descriptor that the experts regress from, so repeated facades and windows at different locations receive similar routing inputs. We present DAC-MACE, which conditions soft expert routing on recalibrated and contextualized features. A residual context adapter (RCA) reweights the channels and locations of frozen encoder features, a global context encoder (GCE) mixes them with multi-scale self-attention, and an image-level pooled descriptor is added to the router input. A dual-threshold RANSAC--Levenberg--Marquardt estimator recovers the pose. On Cambridge Landmarks, DAC-MACE attains a mean per-scene median translation error of 13.0 cm over three seeds, the lowest among the RGB-only SCR methods compared (GLACE 13.6 cm, MACE 14.4 cm), and reduces the error on the largest scene from 24 to 19 cm relative to MACE. In a cumulative study, the MoE head, GCE, RCA, and the dual-threshold estimator lower the mean error from 17.0 cm to 14.4, 14.0, 13.6, and 13.0 cm. On 7-Scenes, adding GCE to a single ACE head raises Recall@(5 cm, 5$^\circ$) from 86.56% to 87.60%.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02629

DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation

Haonan Ping, Jianshu Hu, Yimin Wang, Yi Zhu, Pai Liang, Qiyan Jin, Qirui Bao, Ying Qian 等 15 位作者

原文摘要

Long-horizon dexterous manipulation remains a fundamental challenge for robot learning, requiring the composition of multiple skills, precise bimanual coordination, generalizable visual understanding, and robust skill transitions. Yet existing manipulation benchmarks largely emphasize isolated skills or support only a limited range of end-effectors, making these capabilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 stage-level tasks spanning diverse household and industrial scenarios. Each long-horizon task is composed of explicitly defined stages with physically grounded success conditions, enabling reliable evaluation of both intermediate progress and end-to-end task completion. A unified benchmark interface and verified human-teleoperated demonstrations enable testing different policy learning algorithms, with standardized simulation configurations, scene randomization, trajectory replay, and task-level evaluation. Through extensive baseline experiments, we identify substantial challenges in precise manipulation, generalization to scene variation, and skill composition. DexArena establishes a challenging and reproducible benchmark for developing robot learning methods that move toward generalizable long-horizon dexterous behavior.

cs.RO

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.160ACL Anthology · ACL 2026

Mitigating Context Interference for Reliable and Efficient Search Agents

Boyang Xue, Bin Wu, Shuofei Qiao Zhejiang University, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan University of Edinburgh, University of Edinburgh 等 11 位作者

原文摘要

Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. However, the contexts of multi-turn search agents are lengthy and complex. For example, the retrieved set of documents in each turn would inevitably introduce irrelevant information that distracts LLMs, referring to context interference, potentially hindering the reliability and efficiency of search agents. Therefore, we conduct a systematic study on context interference in multi-turn search agents, focusing on investigating i) which parts of the context of search agents will contribute to the context interference, ii) how to refine the contexts of search agents to mitigate the interference, and iii) can incorporating context refinement into search agent training yield further improvements. We reveal that interference primarily arises from the latest retrieved documents. Based on the explored findings, we then introduce a distill-based context refiner to dynamically mitigate context interference for multi-turn search agents. Finally, we validate that incorporating context refinement into RL training pipelines of search agents can significantly enhance both reliability and efficiency. This study highlights the importance of mitigating context interference of search agents, inspiring a novel paradigm of “refine context and then generate” for AI agents.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1840ACL Anthology · ACL 2026

KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

Baochang Ren, Shuofei Qiao Zhejiang University, Ningyu Zhang Zhejiang University, Da Zheng Ant Group, Huajun Chen Zhejiang University and ZJU-Hangzhou Global Scientific and Technological Innovation Center

原文摘要

Slow-thinking Large Language Models (LLMs) have demonstrated strong reasoning capabilities but often suffer from severe hallucinations due to an inability to recognize their knowledge boundaries. Existing Reinforcement Learning (RL) approaches typically rely on outcome-oriented rewards, which can inadvertently reinforce fabricated reasoning paths when the final answer is correct. To address this, we propose Knowledge-enhanced RL, KnowRL, a framework that integrates factual supervision directly into the reasoning process. By decomposing the chain of thought into atomic facts and verifying them against the corresponding ground-truth knowledge, KnowRL performs fine-grained checks to encourage models to reason faithfully. Crucially, this process-oriented supervision teaches the model to identify its knowledge boundaries, learning to say “I don’t know” instead of fabricating answers when information is missing. Experimental results demonstrate that KnowRL effectively mitigates hallucinations—reducing the Incorrect Rate on SimpleQA by 20.3% for distillation-based slow-thinking models while maintaining strong performance on complex reasoning benchmarks like GPQA and AIME 2025. Furthermore, our method shows robust transferability to out-of-distribution tasks, indicating that the model learns a generalizable verification behavior.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表