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新论文 · v1arXiv:2611.15771

SAME: Semantic Watermark Embedding for Open-Source Large Pretrained Models

Xinyao Song, Jialiang Shen, Hui Kang, Huajie Chen, Ruiqi Liu, Jiyang Zheng, Huaxi Huang, Zhenliang Lu 等 10 位作者

原文摘要

Open-source large pretrained models (LPMs) have become increasingly valuable assets for model development and downstream applications, but their accessibility also facilitates unauthorised copying and modification. To protect their intellectual property (IP), existing approaches typically embed tailored trigger-response patterns as watermarks for ownership verification. However, such trigger designs need to be tailored to task-specific data that constrain their transferability across downstream tasks, and are not durable under subsequent benign fine-tuning. To address these limitations, we propose SemAntic waterMark Embedding (SAME), which introduces a semantically meaningful private vocabulary that enables flexible composition of adaptive and persistent watermarks. Specifically, SAME introduces a fixed private vocabulary that assigns each native token a private embedding counterpart, while aligning the semantic expressiveness of the two vocabularies within the model's representation space. This alignment allows private tokens to be flexibly composed into semantic triggers that elicit meaningful responses, enabling adaptive watermarks beyond predefined trigger-response pairs. By grounding the private vocabulary in the model's native semantics, SAME couples the watermark with its evolving semantic capabilities, allowing this connection to be preserved throughout downstream fine-tuning. Extensive experiments demonstrate that SAME provides reliable and robust watermarks across LPMs of varying scales and architectures, enabling effective ownership verification for IP protection.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11798

PAIR: Proprioception-Aligned Image-Ray Representations for Viewpoint-Robust Manipulation

Sung-Wook Lee, Xuhui Kang, Dylan Losey, Yen-Ling Kuo

原文摘要

While imitation learning has made substantial progress toward generalizable robot manipulation through scaling, viewpoint robustness remains a major bottleneck. Visuomotor policies rely heavily on visual inputs, yet images provide an inherently ambiguous signal under viewpoint change: similar scenes may appear markedly different across viewpoints, while distinct states may share certain visual overlaps. A policy must therefore infer not only the task-relevant state, but also how it is visually projected under a particular camera configuration. This entanglement of viewpoint-dependent appearance and task-relevant state can make learning inefficient and limit generalization to unseen viewpoints. Recent work has begun to address this challenge through camera-ray conditioning and representation learning using geometric cues to induce invariance. However, providing camera information as additional context does not by itself ground visual representations in how the task scene is visually projected, while geometric alignment without camera context can introduce spurious correspondences by ignoring viewpoint-dependent projection. We propose PAIR, a contrastive learning framework that aligns joint image-ray representations using robot proprioception. PAIR encourages observations with similar robot states to remain close in representation space across viewpoints, providing a structured grounding signal for learning viewpoint-robust features. PAIR is a highly adaptable framework: it can be used to train Diffusion Policy (DP) or post-train vision-language-action (VLA) models. In simulated and real-world manipulation benchmarks with camera variation, PAIR consistently outperforms the behavior cloning baseline without ray conditioning by 12.7% and 26.7% points, respectively, demonstrating stronger generalization to unseen viewpoints. Project website: PAIRrobot.github.io.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38204

Two Birds with One Stone: Skill-Augmented Generation via Unified Retrieval and Utilization

Zihan Luo, Jiahui Kang, Wei Zhou, Qilin Fan, Mingyan Li, Chao Chen

原文摘要

Skills, as reusable external knowledge, have demonstrated substantial potential to enhance the ability of Large Language Models (LLMs) to solve complex tasks. However, existing skill-augmented generation methods typically rely on separate models for skill retrieval and skill utilization, resulting in disconnected optimization of these two processes. To address this gap, we propose Skill-Uni, a unified reinforcement learning framework that jointly optimizes skill retrieval and utilization. Specifically, we adopt a generative skill retrieval paradigm that enables a single model to both learn how to retrieve relevant skills and leverage them for task solving. To effectively train this unified framework, we introduce a two-stage training strategy consisting of warm-up instruction tuning and GRPO, allowing the model to progressively improve its skill retrieval and utilization capabilities. Extensive experiments on five skill-related benchmarks demonstrate the effectiveness of Skill-Uni, yielding average performance gains of 66.59% and 34.82% over the base Qwen3-4B and LLaMA3.1-8B models, respectively. Besides, Skill-Uni is compatible with various skill retrieval engines, demonstrating its flexibility and generalizability. Additional in-depth analyses also provide insights into the effectiveness of Skill-Uni.

cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.932ACL Anthology · ACL 2026

Beyond Ranking: Fine-Grained Diagnostics and Self-Improvement for MLLMs

Mingze Xu, Zijing Zhao, Qiming Peng, Houwen Peng, Han Hu, Zhanhui Kang, Yuxing Han Tsinghua University, Tsinghua University

原文摘要

While Multimodal Large Language Models (MLLMs) are advancing rapidly, accurately evaluating their capabilities remains challenging. Current paradigms primarily rely on holistic scoring and static leaderboards, which fail to disentangle fine-grained competencies. Specifically, they suffer from “Outcome Bias” by validating only final answers and ignoring intermediate reasoning. To address these limitations, we introduce ATOM (AnaTomy Of MLLM), a novel MLLM-as-a-judge framework designed to shift the focus from ranking to fine-grained diagnosis. ATOM decomposes complex reasoning into atomic criteria anchored in visual elements, enforcing verification against explicit visual facts. Validated on a newly constructed benchmark with rigorous human rankings, ATOM achieves state-of-the-art accuracy, surpassing the strongest baseline by up to 7.92%. Moving beyond ranking, ATOM bridges the gap between assessment and alignment: by pinpointing atomic-level failures, it establishes a closed-loop mechanism for targeted self-correction. This approach enables models to identify and rectify errors autonomously, successfully resolving up to 39.95% of previously failed queries without human intervention.

cs.CL · cs.CV

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