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新论文 · v1arXiv:2611.16417

VBot Exo2Ego Policy: Lightweight Data Collection and Exocentric-to-Egocentric Video Generation

Tong Rao, Zhihao Zhang, Yuntian Wang, Siyu Zhou, Hailong Qin, Yinan Yu

原文摘要

We present VBot Exo2Ego Policy, a video generation model that turns third-person videos into first-person observations of a selected actor, offering a practical way to expand first-person operation data from abundant real-world footage. To support this goal, we introduce a lightweight data collection pipeline and mobile app that support first-person recording, third-person recording, and synchronized capture of both views. This setup combines accessible single-view collection with paired RGB and camera geometry for learning cross-view correspondence. Building on a pretrained video diffusion transformer, our model incorporates geometric guidance and auxiliary supervision from semantic masks and hand keypoints to facilitate optimization and improve the fidelity of hand-object interactions. Camera geometry is injected stochastically during training and gradually removed, allowing inference without target-camera calibration. To accommodate the diversity of Internet videos, we combine source-video perception and reasoning, optional actor references, and multi-shot adaptation to identify the intended actor and follow an operation across camera cuts. The collection system and generation model provide a foundation for expanding first-person operation data across tasks, scenes, and participants.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16411

Flexible Intelligent Metasurfaces for Enhancing Secure MISO Communications

Zihao Teng, Zihao Zhao, Jiancheng An, Lu Gan, Arumugam Nallanathan, Bo Ai

原文摘要

This letter investigates flexible intelligent metasurface (FIM)-aided secure multiple-input single-output (MISO) communications in the presence of a multi-antenna eavesdropper. By exploiting the morphing capability of the FIM, the transmit array geometry can be dynamically reconfigured to reshape the legitimate and eavesdropping channels. We formulate a secrecy rate maximization (SRM) problem by jointly optimizing the transmit covariance matrix and the FIM surface shape. For a given FIM surface shape, the optimal transmit covariance matrix is shown to be rank one and is obtained in closed form from the dominant generalized eigenvector. For a fixed transmit covariance matrix, a projected gradient ascent (PGA) method is developed to optimize the FIM shape. Based on these two subproblems, a block coordinate ascent (BCA) algorithm is proposed. Moreover, for the special line-of-sight (LoS) scenario, we derive a globally optimal closed-form joint transmit covariance matrix and FIM surface shape design under a sufficient morphing-range condition. Numerical results demonstrate that FIM effectively improves the secrecy rate over a conventional rigid antenna array (RAA). Moreover, FIM achieves a transmit-power saving of about 2.5 dB compared with the RAA benchmark at the same secrecy rate.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16409

Experience-Handoff Reinforcement Learning for Recursive Self-Improvement

Doyoung Kim, Byeongguk Jeon, Kailin Zhang, Sherry Yang

原文摘要

It is common to train Large Language Models (LLMs) for agentic tasks with Reinforcement Learning (RL) from scalar rewards. However, this throws away the fine-grained information about the environment that the agent experienced during the trial (e.g., error logs). To utilize this rich information, recent works wrap the agent in a harness that refines its own inputs through reflection or compaction and hands past experience to future rollouts, but the construction of the handed-off artifacts (e.g., a playbook) is itself not learned. We want the best of both worlds: gradient updates from a scalar reward, while also utilizing environment information to hand off experience. As a concrete testbed, we fix the Agentic Context Engineering (\ace{}) harness and train one model to act, reflect, and curate task-local playbooks. Our method, \acerl{}, evaluates candidate experience-handoff artifacts by their downstream effect on future actor rollouts, and co-optimizes the role-specific behaviors (action generator, insight reflector, and playbook curator) under a fixed scaffold. Across agentic benchmarks, \acerl{} achieves higher held-out performance than baseline methods. The results suggest that agent training should optimize not only the actions in the current rollout, but also the model-written experience that future rollouts inherit.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16408

Gravity and Flight: Learning to Navigate Fine-Grained Remote Sensing Scenes

Yuheng Zong, Minghua Wang, Xin Zhao, Xiaobin Liu, Yongjian Dong, Chanho Eom

原文摘要

Vision-language models are increasingly capable, yet their reasoning in complex remote sensing scenes can remain coarse-grained and insufficiently grounded in task-relevant visual evidence. We propose Gravity and Flight (G&F), a framework for scene specialization with limited supervision that separates visual and reasoning verification while coupling policy updates. It combines Evidence-of-Thought supervised fine-tuning (EoT-SFT) and Relational Transfer Policy Optimization (RTPO). EoT-SFT teaches fine-grained observation and faithful reasoning through description, analysis, and supported conclusions. RTPO independently samples responses to scene-matched image pairs with fine-grained visual differences under a shared task. Its rubric-based verification combines Gravity, which checks visual fidelity and description coverage, and Flight, which takes descriptions as premises to assess evidence use and relations between paired judgments. Their scores and task correctness yield a shared pair-level advantage that couples policy updates from both responses. Using 1,008 demonstrations and 500 training pairs for harbor specialization, G&F achieves a mean score of 69.12 across six evaluation sets, exceeding the backbone and supervised initialization by 9.76 and 4.56 points. Matched controls show gains over single-image and joint-input RL, and removing either verification view reduces performance. Applying G&F to urban specialization also improves on the base model and supervised initialization across three evaluation subsets. Project details and updates are available at https://github.com/Z0ngL1ng/Gravity-and-Flight.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.34302

CAT-Free: Calibration-, Annotation-, and Target-Training-Free Multi-View BEV Pedestrian Detection and Tracking

Taigo Sakai, Hiroki Kouno, Naoki Kato, Kazuhiro Hotta

原文摘要

Multi-camera pedestrian detection and tracking often require camera calibration, position annotations, and target-scene training. CAT-Free removes all three requirements and uses only synchronized RGB video. From this video, CAT-Free estimates camera poses and the ground plane. With the estimated poses and ground plane, CAT-Free triangulates detections across cameras and links pedestrian positions into tracks. However, camera errors and mismatched detections produce false positions. Two geometric filters remove these false positions: one rejects unstable estimates, and the other rejects positions off the ground. Both filter thresholds are estimated from the unlabeled video. Without calibration, annotations, or target-scene training, CAT-Free achieves 89.2, 84.1, and 76.1 MODA on WildTrack, MultiviewX, and GMVD. CAT-Free is also less affected by camera errors than methods that assume known camera parameters, and it outperforms a domain adaptation method without any adaptation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16405

AntEmbed: Dual-Mode Multimodal Embedding Model for Efficient and Fine-Grained Retrieval

Kehang Jia, Teng Liu, Juntao Li, Hua Chen, Jinxiong Chang, Xiaobo Liang, Min Zhang

原文摘要

Discriminative embeddings encode original multimodal inputs efficiently but struggle with fine-grained semantics. Generative embeddings use reasoning to capture query intent and relevance evidence, but incur higher computational costs. We propose AntEmbed, a dual-mode multimodal embedding model that supports efficient retrieval and reasoning-enhanced fine-grained matching in a unified embedding space. First, we construct retrieval-oriented reasoning content that captures query intent and confusion-aware semantic evidence. We treat original and reasoning-enhanced candidate inputs as semantically equivalent views, and use multi-positive contrastive learning with a single embedding token to align discriminative and generative embeddings. Second, cross-mode self-distillation transfers fine-grained semantic information from reasoning-enhanced to original representations through candidate similarity-distribution distillation and query representation alignment. This strengthens discriminative embeddings without requiring additional online reasoning. Both retrieval modes share a single candidate index. On MMEB-v2, AntEmbed-8B achieves an overall score of 81.5 with reasoning-enhanced embeddings, outperforming the strongest baseline by 3.1 points.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16404

ReSID: Rethinking Long-Tail Retrieval in Generative Recommendation through Item Reachability

Muyu Zou, Shunye Tang, Yi Xu, Haibo Xing, Yandong Wang, Lingyu Mu, Jinxin Hu

原文摘要

Generative recommendation (GR) represents items with discrete Semantic IDs (SIDs) and casts retrieval as sequence generation. Under skewed interaction distributions and tight serving budgets, GR struggles to retrieve long-tail items. In this work, we are the first to study long-tail reachability across the complete pipeline of SID construction, decoding, and matching. We find that under equal code lengths, parallel SID construction consistently outperforms sequential SIDs on tail items. Moreover, even when full generation fails, decoded sequences carry locally consistent information that retrieves relevant tail items via partial matching. Yet this recovery is bounded by upstream search, failing once beam search prunes all prefixes completable within a fixed mismatch tolerance. Motivated by these findings, we propose ReSID to jointly preserve and recover tail candidates. Built on long parallel SIDs, ReSID pairs popularity-debiased and user-semantic-aware beam search to keep promising paths alive with partial SID matching to recover items from surviving tokens. ReSID achieves state-of-the-art long-tail retrieval while maintaining competitive overall accuracy, with lower latency and higher throughput under narrow beams. ReSID is deployed in our production system, serving main user traffic with substantial business gains. We release our complete code and model checkpoints to support full reproducibility and real-world deployment at https://github.com/Gabriel-reina/ReSID.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16402

Teach with Blinks: Breaking Linguistic Momentum in On-Policy Distillation for Fine-Grained Multimodal Perception

Tangzheng Lian, Hang Zhang, Guanyu Hu, Renjiu Hu, Zhensong Zhang, Jiankang Deng, Jifei Song

原文摘要

On-policy distillation (OPD) has emerged as an effective post-training approach to improving fine-grained perception in multimodal large language models by amortizing costly inference-time zooming operations into a single-pass recipe. However, vanilla OPD is inherently susceptible to linguistic momentum: the teacher can follow the student's erroneous prefixes, thus weakening corrective supervision. To address this, we propose Blink On-Policy Distillation (BlinkOPD), in which the teacher "blinks" at every token: it reads the same prefix once with its eyes open and once with its eyes closed. A first-glance gain further strengthens the correction on rollouts that conflict with the teacher. By extracting more effective supervision from each on-policy trajectory, BlinkOPD achieves state-of-the-art average performance across six fine-grained benchmarks using only 500 training images (12x less data) than existing OPD methods. Beyond fine-grained perception, BlinkOPD also demonstrates strong zero-shot improvements across ten held-out benchmarks spanning general visual perception, visual hallucination, and multimodal reasoning abilities. Code: https://github.com/Supltz/BlinkOPD

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.27025

Improving General Role-Playing Agents via Psychology-Grounded Reasoning and Role-Aware Policy Optimization

Zhenhua Xu, Dongsheng Chen, Jian Li, Yitong Lin, Zhebo Wang, Jiafu Wu, Yizhang Jin, Chengjie Wang 等 10 位作者

原文摘要

Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. Explicit reasoning can help models deliberate before responding, but generic reasoning does not necessarily preserve character fidelity. We therefore propose Psy-CoT, a psychology-grounded chain-of-thought framework that decomposes pre-response reasoning into three role-specific steps: Interaction Perception, Psychological Empathy, and Logical Construction, so that the model thinks dynamically from the profile rather than merely mimicking surface patterns. While structured reasoning provides a foundation, it alone is insufficient; reinforcement learning is essential to further align the model with character fidelity. However, we observe that under LLM-based reward models, both generic phrases that hack the reward model and genuinely role-specific phrases receive identical gradient signals. This hacking accumulates over training, misleading the model into treating both as equally optimal choices. To address this, we propose Role-Aware Policy Optimization (RAPO), which uses profile-induced entropy differences to weight gradients asymmetrically, amplifying role-specific tokens under positive advantage while attenuating them under negative advantage. Experiments on CoSER, CharacterBench, and CharacterEval demonstrate that Psy-CoT outperforms existing role-playing CoT methods, and RAPO consistently surpasses GRPO across multiple model scales.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.14009

Coding Agent Memory Post-Training II: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via On-Policy Distillation

Lirui Luo, Kelong Mao, Xinwei Yang, Rongqing Li, Luyu Chen, Kieran Wong, Yudong Guo, Xinrui Wang 等 13 位作者

原文摘要

Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. They must therefore decide what to write down and what to read back, and when to do either. Recent work has begun to use on-policy distillation (OPD) to make memory control part of the policy, relying on memory tools and compaction protocols that lie outside either model's pre-training. However, this weakens the distillation on both sides: the teacher cannot give useful guidance on an interface it has never used, and the student samples too few meaningful memory actions for that guidance to take effect. To study this problem, we introduce Coding Agent Memory Gym II (CAMG-II), a suite of long-horizon agentic-RL environments spanning Shop, Coding, AutoResearch, General, and Visual. Alongside each environment's native task interface, CAMG-II provides ordinary Bash access and an episode-persistent workspace, enabling the policy to create, revise, search, and reuse files as memory across context replacements. We also introduce Coding Agent Memory Post-Training II (CAMP-II), which trains a single policy jointly across all five environments with fully asynchronous OPD to learn this file-based memory behavior directly from a frozen teacher with a prior for file operations. On CAMG-II, CAMP-II obtains 2.97 times the gain of the strongest OPD baseline. The memory behavior CAMP-II learns generalizes to two external benchmarks, Terminal-Bench 2.1 and SWE-Bench Pro V2, with improved success rates.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16395

TriGen: A Generative Paradigm for Trigger-Induced Recommendation

Muyu Zou, Haibo Xing, Shunye Tang, Liyan Tie, Lingyu Mu, Yandong Wang, Jinxin Hu

原文摘要

Generative recommendation typically operates under the User-Induced Recommendation (UIR) setting, predicting target items by autoregressively generating semantic IDs as a passive continuation of long-term user history. However, this paradigm overlooks Trigger-Induced Recommendation (TIR), an essential industrial scenario where a user click triggers an immediate follow-up feed. In TIR, recommendations must dynamically align with the instant trigger intent without compromising historical preference continuity. Traditional TIR methods rely on discriminative scoring and cannot guide token-level generation, while existing generative models lack mechanisms to reconcile this structural tension. To address this challenge, we propose TriGen, establishing a tailored generative paradigm for trigger-induced recommendation. TriGen introduces calibrated tail injection to re-encode local history, adaptively arbitrates between persistent tastes and immediate impulses via dynamic gating, and applies position-specific transformations to sustain level-aware trigger guidance across decoding steps. Extensive offline evaluations confirm consistent gains over competitive baselines. Furthermore, online A/B tests across six country markets demonstrate significant revenue and GMV improvements, leading to the full production deployment of TriGen.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16393

TransXplainRRG: A Radiologist-Evaluated LVLM for Chest Radiograph Report Generation

Chayan Mondal, Duc-Son Pham, Tele Tan, Ashu Gupta, Vincent T. Y. Tiong, Shreya Ghosh, Tom Gedeon

原文摘要

Recent advancements in Large Vision-Language Models (LVLM) enhance X-ray report generation, but often neglect clinical context, overlook workflow integration, and lack explainability, which hinders clinical adoption due to interpretability and reliability concerns. This paper addresses the dual challenges of performance and expert explainability in automated X-ray report generation for clinical AI applications. Our proposed approach, TransXplainRRG, utilises an off-the-shelf Swin Transformer model along with a transformer-based text encoder that incorporates patient medical history to generate a radiology report. Further, we explore an expert-guided `inside-out' approach and extract only abnormal findings for radiology report refinement. Thus, this study bridges the gap between high-performance automation and interpretability critical for clinical practice by combining state-of-the-art transformer-based vision encoders, text encoders, and LLMs. TransXplainRRG is trained and tested on the large-scale MIMIC-CXR dataset and further evaluated on the unseen IU X-Ray dataset to demonstrate its generalizability and robustness. We employed the GREEN metric as a complementary measure of clinical consistency, achieving scores of 0.327 on MIMIC-CXR and 0.605 on IU X-Ray, both higher than those achieved by MAIRA-2 under the same evaluation setting. It also performed competitively across standard evaluation metrics, frequently matching or surpassing state-of-the-art models. Additionally, we introduce qualitative evaluation metrics developed from radiologists' viewpoints and conduct a preliminary radiologist evaluation on 100 generated reports to assess their clinical relevance. Quantitative and qualitative analyses further demonstrate the potential of our TransXplainRRG framework for reliable and explainable radiology report generation.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.12109

SkillDuet: Co-Evolving Models and Skills through Response Feedback

Xingguang Ji, Ziqiang Dong, Yubo Zhang, Xi Leng, Ninghua Yan, Yang Yang, Guanjun Jiang

原文摘要

During reinforcement learning, experience can be both internalized in LLM parameters and retained externally as reusable textual skills. Parameter updates alter the model's behavior and the interaction experience from which new skills are extracted. These skills, in turn, guide the model's subsequent interactions and parameter updates. As the model's capabilities evolve, previously beneficial skills may become redundant or detrimental. We present SkillDuet, a framework that coordinates parameter updates and skill evolution through model response feedback. Skills extracted from on-policy trajectories guide parameter updates through OPD at every step. A response-based score, Strength, ranks candidate skills by the magnitude of the shift they induce in the current model's responses, and the highest-scoring skills are periodically consolidated into a persistent global skill set (Global). The updated model and Global jointly guide subsequent interactions, generating experience for further parameter and skill updates. With Qwen3-4B, SkillDuet achieves success rates of 98.57% on ALFWorld and 92.97% on WebShop, exceeding the matched SEED-API baseline by 2.86 and 5.47 percentage points, respectively. Ablations show that response-based skill selection and Global consolidation improve task performance. Further experiments show that the benefits of retained skills depend on both their content and their compatibility with the model. SkillDuet provides a concrete mechanism for model-skill co-evolution and skill-level recursive self-improvement.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.23670

Automata from Agent Traces: Failure and Next-Step Prediction

Seonglae Cho, Franklin Cardenoso Fernandez, Umar Mohammed, Zekun Wu, Kleyton Da Costa, Ilham Wicaksono, Adriano Koshiyama

原文摘要

LLM-based agents execute multi-step tasks, but their behavioral structure remains opaque: long unstructured traces resist the safety auditing and runtime monitoring that deployment requires. Existing approaches operate per-trace or success-only, so they miss the cross-run topology that links next-step and failure prediction. To recover that shared structure, we collapse an entire trace corpus into a single, compact finite-state machine (FSM) that serves as a structural substrate for the otherwise unpredictable behavior of LLM agents. Across twelve public datasets, the FSMs are compact (7-43 states), replay held-out data at >=0.997 fitness with near-identical topology across splits, and build in milliseconds. This substrate addresses both prediction goals. For next-step prediction, FSM-state context outperforms Agent Workflow Memory on every ground-truth-matched dataset. For failure prediction, per-state behavioral features reach held-out AUROC up to 0.94, and an online monitor ranks failing runs above passing ones from a partial trace, triggering early stopping well before completion. Behavioral topology thus appears shaped more by the deployment harness than by the LLM, providing a model-agnostic structural primitive for safety auditing and runtime monitoring.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16382

RADIANT: Redundancy-Aware Information Allocation for Universal Multi-Modal Fusion

Shenghai Wu, Yue Lin

原文摘要

Universal multi-modal fusion often fails not because modalities are insufficiently aligned, but because the fusion process allocates information poorly: redundant evidence is repeatedly amplified, modality-unique evidence is over-smoothed during alignment, and task-dependent synergistic evidence is insufficiently activated. We present RADIANT (Redundancy-Aware Dynamic Information Allocation Network), a task-general framework that organizes fusion around three complementary roles: suppressing redundant cross-modal overlap, preserving modality-unique evidence, and composing task-conditioned synergy through cross-modal interaction. Specifically, stable matrix-exponential projections are used to reduce interference among shared and modality-unique streams, while task memory with hard positive updates selects which synergistic interactions should be activated for the current fusion regime. Extensive experiments across six heterogeneous fusion tasks show consistent improvements over the compared methods. On FLIR, RADIANT improves downstream detection mAP@0.5 from 91.2% to 96.8%, a gain of 5.6 percentage points (6.1% relative) over GIF. RADIANT also exhibits strong direct cross-dataset transfer, with further gains from inference-time task-memory adaptation while keeping all network weights frozen. Additional ablations and sensitivity studies support the robustness of the allocation mechanism.

cs.CV · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16378

Evidence-Governed Adoption of Self-Generated Updates in Autonomous AI Agents

Subodh Lonkar

原文摘要

Autonomous agents modify prompts, memories, tools, workflows, and policies using interaction histories. Yet a plausible modification is not necessarily suitable for persistent adoption: candidate selection is adaptive, evaluators may be imperfect, and local gains can hide degradation against an earlier baseline. We introduce Evidence-Governed Evolution (EGE), a framework for governing adoption of self-generated agent modifications. EGE separates candidate generation from confirmatory adoption, evaluates frozen candidates on fresh paired audits, requires meaningful improvement on a prespecified primary objective, protects declared capabilities through reference-relative non-inferiority tests, and applies governance constraints before bounded canary deployment. We establish an evaluator-calibrated simultaneous lower-bound construction for a countable sequence of adaptively generated candidates. It combines candidate-wise Hoeffding bounds with independent calibration samples comparing the operational evaluator with a prespecified reference procedure. We also formalise reference replacement through bridge tests, derive a cumulative degradation bound along a versioned reference lineage, and provide an audit-planning condition relating effect-size margin, sample sizes, and acceptance probability. A Monte Carlo stress test illustrates calibration under candidate-dependent evaluator optimism; it is a stylised statistical experiment, not an evaluation of a deployed agent. The guarantees are conditional on bounded paired scores, fresh sampling, a valid reference-evaluation construct, and declared evaluation distributions; they do not establish universal agent safety or robustness to arbitrary distribution shift.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16377

Universal optimality on finite Lee tori

Tao Zhang

原文摘要

We study subsets of finite Lee tori that minimize pair energy among sets of the same cardinality for every discretely completely monotone potential. At half occupancy on a product of even cycles, the two checkerboards minimize both total pair energy and maximum individual energy. They are the only minimizers for every nonconstant classical completely monotone potential on $(0,\infty)$, proving the checkerboard conjecture of Bouman, Draisma, and van Leeuwaarden for this class. At the independence number of the distance one graph on $\Z_q^n$, universally optimal sets exist exactly when $q$ is even, $q=3$, $n=1$, or $(q,n)=(5,2)$. In these cases they are precisely the maximum independent sets and form a single Lee isometry class. At density $1/5$ in $\Z_5^n$, $n\ge2$, universal optimality is characterized by a weighted count of pairs at distances one and two. We prove these results using integer box occupancy bounds that strengthen Fourier linear programming and, in the quinary case, Fourier certificates with a common zero set.

math.CO · cs.IT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.14856

RAIN: Region-Aware Inversion Network for Semantic Watermark Extraction

Zilai Li

原文摘要

Semantic watermarks for diffusion models embed ownership information into the generative process while preserving perceptual quality, but Gaussian-Shading extraction conventionally requires multi-step diffusion inversion to recover the initial noise. Recent one-step methods show that this cost can be reduced substantially. We study this problem through extended flow matching and conditional regression. The key observation is that, near the high-SNR image endpoint, recovering a useful noise statistic given by the first-step output of the extended flow matching in the high-SNR regime is much simpler than reconstructing the full inverse trajectory, and Gaussian Shading only requires the recovered latent to remain in the correct watermark decision region. Based on this observation, we propose a lightweight, prompt-free extractor that decomposes endpoint recovery into an image-like anchor and a noise-oriented residual, which increases the capability of the model to utilize GPU parallel computation. The resulting method avoids iterative inversion and repeated evaluation of a diffusion-scale U-Net, providing an efficient one-step extraction pipeline with a concise theoretical interpretation. The computational cost of extracting noise is lower than that of both OSI and FARI. The github repo is there: https://github.com/TheLovesOfLadyPurple/RAIN-lightweight-NN-for-one-step-semantic-watermark-extraction

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.30768

CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy

Zekang Yang, Jiamin Li, Zhenghua Li, Jiaqi Fan, Zengcai Guo, Xiaolin Hu

原文摘要

Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved encouraging results on local image blocks, it remains unclear whether such progress translates to reconstruction that is both structurally accurate and scalable to the whole-brain scale. We present CORAL, the first benchmark for structure-aware evaluation of automatic neuron reconstruction from light microscopy images at both local and whole-brain scales. Built on a high-quality whole-brain fMOST dataset with carefully curated annotations, CORAL establishes two progressive tasks: block-level reconstruction, which evaluates reconstruction methods under limited spatial context, and brain-wide reconstruction, which assesses complete neuron reconstruction at the whole-brain scale. To account for topological correctness beyond geometric distance similarity, we introduce a structure-aware metric based on fiber prediction. To further achieve complete neuron reconstruction across the entire brain, we develop a brain-wide neuron tracing framework that extends arbitrary local reconstruction methods to the whole-brain scale through an iterative local-to-global process. Using this benchmark, we provide the first structure-aware comparison of mainstream methods for local neuron reconstruction and further evaluate their performance in brain-wide reconstruction. Our results underscore the importance of structure-aware evaluation and the need for more robust methods for complete neuron reconstruction.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16376

SPEC CPU2026 Under the Microscope: Programs, Phases, and Samples

Peijun Li, Pei-Hung Lin, Tianming Cui, Caiwen Ding, Yang Katie Zhao, Maya Gokhale

原文摘要

SPEC CPU2026, the latest generation of the SPEC CPU benchmarks, is set to become the standard benchmark suite in computer architecture research, where new designs are often evaluated through sampled simulation with tools such as SimPoint. Existing characterizations of the suite focus on whole-run averages, which reveal neither how behavior changes within a run nor how well sampling captures it. We characterize CPU2026 on real hardware at three levels: programs, phases, and samples. At the program level, we report whole-run metrics and top-down bottleneck classes for all four suites and examine how they change with workload size, compiler version, and platform, across machines with three different instruction set architectures: x86, Arm, and RISC-V. Notably, changing only the compiler version can halve or nearly double the IPC of individual benchmarks. At the phase level, we track hardware counters every 10 ms over all 90 SPECrate inputs, identify recurring patterns of time-varying behavior, and divide the inputs into three groups: steady, multi-phase, and bursty. More than half of the inputs show time-varying behavior, and IPC within a single input can vary by more than an order of magnitude. At the sample level, we validate SimPoint against hardware measurements of every interval it can select. SimPoint is accurate on steady inputs, but its error reaches 43% on the others, and changing only the random seed spreads the error of one input over a range of 58 percentage points. Keeping SimPoint's clusters but choosing each representative by measured CPI brings every input within 2.6%, so the error comes from the choice of representatives, not from the clustering. We conclude with practical recommendations on sampling strategy for CPU2026, which give researchers a deeper understanding of the suite and show where additional simulation effort is needed to keep sampled results accurate.

cs.AR · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16375

Recursive State-Augmented Language Models: Persistent Latent-State Adaptation of Pretrained Language Models

Omer Haq

原文摘要

Language models rely on finite token contexts, limiting access to earlier interactions. We introduce Recursive State-Augmented Language Models (Recursive SALM), a framework in which a persistent latent state evolves with each observation and generates adaptations of a pretrained language model. A modular read-transition-write architecture separates observation encoding, latent dynamics, and predictor adaptation. We develop deterministic and stochastic formulations. The experiments evaluate only deterministic SALM with frozen GPT-2 small and exclude earlier evidence from its decoder context. On a four-turn hypothesis-inference task, latent-gated LoRA reaches 89.1% accuracy, comparable to 88.0% for a trained full-context LoRA baseline, with earlier evidence available solely through a 64-dimensional state. Zeroing or shuffling the state substantially reduces accuracy. In a two-turn task with codewords unseen in training, several interfaces improve candidate ranking over chance, but rarely generate the exact codeword under the evaluated training schedule. These experiments show that persistent latent-conditioned adaptation can support sequential inference when earlier evidence is absent from the decoder context.

cs.LG · cs.AI · cs.CL · stat.ML

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新论文 · v1arXiv:2611.16374

ManiLadder: Benchmarking Robot Manipulation Through a Categorized and Multi-Difficulty Level Task Ladder

Chongkai Gao, Jieao Shi, Zhaoyu Mu, Yudi Lin, Xuanwei Liu, Wei Ken Ng, Baiye Cheng, Anqi Chen 等 17 位作者

原文摘要

Recent advances in robotic manipulation have enabled increasingly complex and dexterous behaviors, yet existing benchmarks still focus on simple pick-and-place tasks and lack a systematic framework for measuring progress in robot learning along the axis of task difficulty. We present ManiLadder, a large-scale and organized simulation benchmark for robotic manipulation. ManiLadder contains 112 carefully designed tasks stratified into 4 difficulty levels, with 8 categories in each level, spanning rigid-body and deformable-object manipulation, single-arm and dual-arm settings, and gripper and dexterous-hand end-effectors. To calibrate difficulty, we fix the data amount the same for every task and use a unified flow- matching imitation learning policy under the same training and evaluation protocols, then use its evaluation success rate as an operational empirical difficulty signal. All tasks are iteratively refined until they fall into target difficulty ranges, which are then verified across different random seeds, policies, and agentic learning systems. We further analyze the task properties, compare different policy performances and training settings, analyze task transfer and data scaling behaviors, and conduct real-world experiments showing that the designed tasks preserve their difficulty ordering beyond simulation. ManiLad- der provides a calibrated ladder for measuring, comparing, and advancing robotic manipulation capabilities. More results are available at https://maniladder-benchmark.github.io/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.03327

TIDE: Token-Level Credit Assignment via Entropy-Gated Bounded Deviation

Qiaoman Zhang, Shunhua Min, Xiang He, Zhenghan Zhang, Yuting Zhang, Guangxian Li, Yunzhu Kuang, Rui Jiang 等 11 位作者

原文摘要

Reinforcement learning (RL) is central for aligning large language models (LLMs) with complex reasoning, yet the leading critic-free algorithm, Group Relative Policy Optimization (GRPO), broadcasts a single sequence-level advantage uniformly across long Chain-of-Thought (CoT) generations and constrains exploration with an unbounded Kullback-Leibler (KL) penalty, which dilutes token-level supervision and induces gradient instability and mode-seeking conservatism. We propose TIDE (Token-level Importance via Deviation and Entropy), a critic-free RL framework that reinterprets distribution deviation as a guiding signal rather than a rigid penalty. TIDE replaces the volatile reverse KL divergence with the bounded Hellinger distance, safely quantifying token-level exploration without risk of gradient explosion. To separate genuine reasoning breakthroughs from confident hallucinations, an entropy gating mechanism scales this deviation by the policy's predictive uncertainty, and the gated scores redistribute the sequence-level advantage across tokens. The resulting updates amplify exploratory steps while suppressing low-entropy deviations, without a value network and with negligible overhead. Across in-domain, held-out, and out-of-domain benchmarks, TIDE outperforms strong critic-free baselines: on the Qwen2.5-32B backbone it reaches 60.0% and 46.0% Avg@32 accuracy on AIME 2024 and AIME 2025, respectively, above competitive baselines such as DAPO. Token-level attribution, causal perturbation, and exploration analyses further support the interpretation that the entropy-gated deviation identifies pivotal reasoning steps rather than merely perturbing optimization dynamics.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.08439

Label-Wording Augmentation in a Typed-Decision Encoder: Gains on Unfamiliar Words, Not on Abbreviations or Codes

Vladimir Beskorovainyi

原文摘要

Typed-decision models score options that the caller supplies at request time, so their accuracy depends on label wording that the model's developer does not control. We compare two releases of a 307M-parameter multilingual cross-encoder, Ru-Jev, trained on thirteen Russian-language tasks; the later release adds an augmentation that presents labels in several surface forms. Holding questions fixed and varying only how eighteen routing labels are worded, we evaluate ten label vocabularies at five and eighteen candidates, checked for overlap with the training data. On held-out vocabularies made of ordinary or borrowed words (jargon, office slang, department names), the augmented release has higher point accuracy in all fourteen cells, by up to 0.24. Several of these gains rest partly on one label that the earlier release almost never chose; fitting one bias per option removes part of them, while the jargon gain largely survives. On abbreviations and opaque codes the augmented release gives no usable gain: its loss on 17 abbreviations unrelated to training (-0.060) is one label and disappears after the same correction. The comparison is between two trained releases, not a controlled ablation, and the earlier recipe varies across seeds by up to 0.098 on the strictest slice. On two datasets neither release saw, accuracy moves by -8.4 and +5.6 points, narrowed to -3.4 and +1.4 by the per-option biases; on a third, scientific rubrics, the augmented release is ahead of both runs of the earlier recipe after the correction. Rankings of ten training checkpoints disagree across the three unseen datasets, so none of them should choose the checkpoint. We also report four evaluation hazards from comparing the two releases.

cs.CL · cs.AI · cs.LG

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新论文 · v1arXiv:2611.16373

Description-Guided CLIP Adaptation with Local Evidence Calibration for Few-Shot Plant Disease Recognition

Zhenkun Ye, Qian Zhou, Hua Zou

原文摘要

Accurate plant disease recognition is essential for intelligent agriculture, but few-shot settings provide limited labeled samples that may not capture diagnostic symptoms and intra-class variations. Pretrained vision-language models (VLMs), such as CLIP, offer transferable visual-semantic knowledge. However, existing CLIP-based methods often rely on coarse class semantics and global visual representations, overlooking disease-specific diagnostic information and fine-grained local cues. Excessive few-shot adaptation may also cause over-specialization and weaken the generalization ability of large-scale pre-training. We propose a description-guided, two-stage CLIP adaptation method to address these issues. Symptom and comparative descriptions enrich class semantics with typical visual symptoms and discriminative cues between similar diseases. The enhanced semantics guide the aggregation of class-relevant patch evidence to calibrate global image-text matching with fine-grained cues. For adaptation, we first update the vision-language encoders with lightweight parameters, then freeze them and train a linear classifier. At inference, the classifier is used for in-domain prediction, while class prototypes from the adapted text encoder support cross-domain prediction and retain vision-language transferability. Experiments with two backbones show that our method achieves the highest mean accuracy in most few-shot settings on PlantVillage, Cassava, and Pear, reaching 93.16%, 79.30%, and 96.56%, respectively, with ViT-L/14 under 16-shot. On class-aligned cross-domain evaluation across 21 shared PlantDoc classes, it achieves 56.96% accuracy, outperforming all compared methods. These results demonstrate its effectiveness in fine-grained few-shot plant disease recognition and cross-domain generalization.

cs.CV

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版本更新 · v3arXiv:2610.05688

Square-Root Regret for Adversarial Multiplayer Bandits without Collision Information or Shared Randomness

Chenyu Gan

原文摘要

We study cooperative multiplayer bandits with adversarial rewards and no collision information. In each of $T$ rounds, each of $m$ labeled players picks one of $K>m$ arms. A player alone on its arm receives that arm's reward in $[0,1]$; players who share an arm receive $0$. Each player observes only its own reward, so it cannot distinguish a collision from an arm with zero reward, and there is neither shared randomness nor a communication channel. Bubeck, Li, Peres and Sellke (2020) obtained regret $\widetilde O(KT^{3/4})$ for two players and $\widetilde O(mK^{3/2}T^{1-1/(2m)})$ for $m$ players, and conjectured that regret $Ω(T^{2/3})$ is unavoidable already for two players. We refute this conjecture. We give a strategy whose expected regret against every oblivious adversary is $O(m^{3/2}\sqrt{KT}\log^2(KmT))$, which is optimal in $K$ and $T$ up to logarithmic factors. Three ideas make this possible. (i) Bits are sent through deliberate collisions as random parity checks, and the regret spent while a message is delayed is charged to the number of arms with positive reward rather than to elapsed time: an adversary that jams the channel with zero rewards forfeits the regret it could inflict. (ii) An exponential-weights learner over sets of arms combines a frequently resampled coordinator arm with a follower assignment that changes only $O(\sqrt{mT/K})$ times in expectation; delayed updates are reconstructed exactly. (iii) We teach the followers a privately chosen row index in a fixed public table using reward observations alone, allowing them to coordinate without shared randomness. The bound holds simultaneously for all reward matrices, even ones chosen with knowledge of the table, and a random table works with probability $1-O(1/(KmT))$.

cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.21666

A Contradiction Judge Counted Reported Teammate Errors as Hallucinations: An Assertion-Aware Re-Score of One Multi-Agent LLM Harness

Carson Rodrigues

原文摘要

LLM judges that score hallucination flag any statement that contradicts a reference fact. Agents in multi-agent LLM systems quote and dispute each other's claims, so such a judge can count a correct report of a teammate's error as a hallucination. An earlier version of this study, a preprint (arXiv:2606.21666v1), reported that broadcasting raised a hallucination rate from 0.492 to 0.658 (+34%) and called it contamination; this version withdraws that claim, and the preprint's runs cannot be re-scored. An unpublished revision of that harness reported 0.289 to 0.554 (+0.265, 95% CI [0.225, 0.305]), and its saved outputs are re-scored here. Under an analysis plan fixed before any judge call, three judges from three vendors labeled each of six facts in 1,832 outputs as asserted wrong, attributed to another source, asserted correct or absent. On values that outputs assert as their own, the broadcast effect was -0.0208 (95% CI [-0.0458, +0.0041]) on 10 Full-Broadcast trials: not distinguishable from zero, and equivalent to zero only within a post hoc margin of +/-0.0663 and under the plan's prompt. On these LLM-judge labels, reports of a teammate's wrong value accounted for nearly all of the Haiku travel effect. Scored for mentions, the judges reproduced the effect (+0.1996). With Llama agents, 2 of 40 teammate outputs asserted the wrong destination. One of twelve Claude judge-prompt combinations gave an asserted increase (Sonnet 4.6, one paraphrase: +0.0246, 95% CI [+0.0055, +0.0436]). In 40 new trials per arm, judged by Claude models like the agents, the asserted difference was -0.0505 when both judges agreed and +0.0161 (95% CI [+0.0024, +0.0299]) when either did. Main limit: the labels have no human validation; human labels for 120 outputs are pending. The outputs come from one harness and 13 hand-written scenarios, so the shares do not transfer.

cs.AI · cs.CL · cs.MA

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新论文 · v1arXiv:2611.16369

Bait: Mitigating Harmful Agentic Reinforcement Learning via Probing Reward Function

Tiansheng Huang, Ananth Balashankar, Virat Shejwalkar, Santosh Pande, Ling Liu

原文摘要

We expose a safety vulnerability on nascent agentic reinforcement-learning-as-a-service. Specifically, with a cost as low as \$20, we show that safety alignment of a large language model (e.g., Inkling) can be completely subverted through rounds of harmful agentic reinforcement learning (agentic RL) with a toxic environment from its users. We also demonstrate that upon a successful harmful agentic RL attack via the service API, the attackers can potentially use the safety-degraded model to assist with real-world harmful activities, e.g., exploit software vulnerabilities and launch code re-use attacks. To mitigate harmful agentic RL, we propose Bait, a defense that mitigates the attack via proactively probing the reward function. Experimental results show that such a simple defense can sufficiently mitigate learning from a harmful reward function and can still learn from a benign reward function that promotes benign agentic tasks. Further analysis on both empirical and theoretical aspects shows that an adaptive attacker who assumes the knowledge of Bait defense cannot perfectly subvert it due to a systematic challenge. Source code is available at https://github.com/git-disl/Bait.

cs.LG · cs.CR

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新论文 · v1arXiv:2611.16368

ReliaGuard: Reconstruction-Aware Watermarking for Source Attribution and Blind Edit Localization

Hailong Wang, Wuzheng Tan, Zixin Zhang, Xujun Fang, Fengjuan Gong

原文摘要

Generative image editing facilitates content creation but complicates source attribution and edit verification. Existing watermarking methods combine copyright protection with tamper localization, yet background reconstruction can weaken spatial evidence even when source identifiers remain recoverable. We propose ReliaGuard, a reconstruction-aware watermarking framework for source attribution and blind edit localization. The writer optimizes keyed feature responses in both the protected image and its variational autoencoder reconstruction under image-distortion and source-preservation constraints. Using only the received image and a secret key, the reader combines watermark responses with local image context through residual evidence refinement and predicts an edit mask through native-resolution-guided multiscale reading. It is trained on complete editing outputs, locally spliced counterparts, and processed versions of both. On an independent test set of 300 source images and 900 editing cases from three image domains, our method achieves edit-region intersection-over-union scores of 85.61% for complete outputs and 94.90% for locally spliced outputs at a threshold calibrated to a nominal 1% background false-positive rate on complete outputs. The complete-output result exceeds the strongest baseline by 45.02 percentage points, while decoded identifier-bit accuracy reaches 100% in both conditions. Across seven families of image processing, the corresponding localization scores are 81.01% and 86.49%, with identifier-bit accuracies of 92.76% and 92.79%, respectively. These results support source recovery and blind edit localization under background reconstruction and subsequent image processing.

cs.CV · cs.AI · cs.CR

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新论文 · v1arXiv:2611.16363

Vector-Space Projection and Unified Complex-Valued Acceleration for Scaling Matrix-Based Brain-Inspired Representations

William Youngwoo Chung, Hyunwoo Oh, Calvin Yeung, Hansen Jin Lillemark, Hamza Errahmouni Barkam, Mohsen Imani

原文摘要

Symbolic reasoning at scale requires representations that are both expressive and hardware-efficient. Hyperdimensional Computing (HDC) provides a promising brain-inspired framework, encoding concepts as high-dimensional vectors composed through efficient algebraic operations. However, the widely used Fourier Holographic Reduced Representation (FHRR) relies on commutative binding, which cannot encode order or directionality. Prior FPGA/ASIC accelerators recover this through costly permutation logic, incurring irregular memory access and high control overhead. Generalized Holographic Reduced Representations (GHRR) instead redefine binding as non-commutative matrix multiplication, naturally encoding sequences, graphs, and hierarchies without permutations-but at the cost of turning every element into a matrix, introducing compute and storage overhead that limits scalability. In this work, we propose a flattening methodology that preserves GHRR's matrix-based encoding while executing training and inference directly in vector space, functionally equivalent to FHRR inference yet free of permutation logic. We further design a custom 28 nm ASIC that fuses binding and similarity into a unified complex-valued datapath, with dual-DMA streaming and runtime normalization for accurate inference. Against a PyTorch baseline on an NVIDIA RTX 4090 GPU, the prototype delivers 1.36x-1.56x higher throughput and 16.2x-18.6x better energy efficiency.

cs.AR

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