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Xinrui Wang

Publications and source records attributed to Xinrui Wang.

At least 19 recordsLinked to original sources
arXiv2026

Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning

Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has begun to use reinforcement learning to make memory control part of the policy, often relying on predefined memory tools within domain-specific training environments of relatively short horizons. This setup ties learned memory behavior to environment-specific interfaces that lie outside the base model's pre-training and must be learned from scratch, so even after post-training, agents struggle to use memory in long-horizon tasks. To address these limitations, we introduce Coding Agent Memory Gym (CAMG), a suite of long-horizon agentic-RL environments spanning Shop, Coding, DeepResearch, and AutoResearch. Alongside each environment's native task interface, CAMG provides executable shell access and an episode-persistent workspace, enabling agents to create, revise, search, and reuse files as memory throughout an episode. We also introduce CAMG-RL, which trains a single policy jointly across all four environments with fully asynchronous PPO, learning this file-based memory behavior directly from downstream task reward, and we train CAMG-RL-4B and CAMG-RL-9B from Qwen3.5 models of matching size. On SWE-bench Verified and MLE-bench Lite, CAMG-RL-4B and CAMG-RL-9B are competitive with Qwen3.5-35B-A3B and Qwen3.5-122B-A10B, respectively.

cs.LG↗
arXiv2026

Chypothermia: Clock Freezing for Static Side-channel Attacks

Static side-channel attacks, which exploit halted-clock conditions to extract sensitive information, pose an increasing threat to chip security. To counter these attacks, various defenses have been proposed that monitor for abnormal clock behavior and trigger the clearing of sensitive data when clock anomalies are detected. In this work, we demonstrate that exposing a chip to cryogenic temperatures interferes with on-chip mixed-signal components, responsible for signal sensing and generation. Based on this observation, we develop Chypothermia, an attack that, without any electrical tampering with the system, disables the target clock sensor, the clock generation circuit, and the voltage sensors, while preserving the secret data. While effective at halting the clock, cooling is a slow process and, on its own, is often insufficient against systems equipped with temperature sensors designed to detect thermal anomalies. To bypass these protections, we combine Chypothermia with Chypnosis (Mitard et al., IEEE SP 2026) and show that, even within a moderately low-temperature operating range, this combination can halt the clock while evading detection. We implement Chypothermia on multiple FPGA/SoC platforms and demonstrate successful disabling of both soft-IP and hard-IP sensor implementations. Finally, we apply Chypothermia to the alert handler of the OpenTitan root of trust, which incorporates a state-of-the-art clock sensor, and show that the attack evades detection and prevents key zeroization. Finally, we introduce and implement an FPGA-compatible self-heating sensor as a countermeasure and demonstrate its robustness against Chypothermia.

cs.CR↗
arXiv2026

EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents

Embodied agents can benefit from skills that guide object search, action execution, and state changes across diverse environments. Since embodied environments vary across layouts, object states, and other execution factors, these skills must self-evolve from trajectories generated during task execution. However, existing skill self-evolution methods are mainly developed in digital environments and often convert trajectories into coarse skill updates. Directly applying this paradigm to embodied settings is problematic, because a failed task execution may reflect not only incorrect skill content, but also an execution lapse in which the agent fails to follow valid guidance. We propose EmbodiSkill, a training-free framework for embodied skill self-evolution through skill-aware reflection and targeted revision. EmbodiSkill interprets each trajectory with respect to the current skill, uses skill-changing evidence to update the skill body, and uses execution-lapse evidence to preserve and emphasize valid guidance. Experiments on ALFWorld and EmbodiedBench show that EmbodiSkill consistently improves embodied task success. On ALFWorld, EmbodiSkill enables a frozen Qwen3.5-27B executor to reach 93.28% task success, outperforming GPT-5.2 used as a direct agent without skills by 31.58%. These results show that skill-aware self-evolution helps embodied agents accumulate reusable procedural knowledge from their own trajectories.

cs.AI↗
arXiv2026

On the trilinear and Ginzburg-Rallis models

Let $k$ be a non-archimedean local field of characteristic zero. We give sufficient conditions under which the Ginzburg-Rallis models of the induced representations of $\mathrm{GL}_6(k)$ from a parabolic subgroup of type $[2^3]$ are isomorphic to the trilinear models of the inducing data. We also give nonvanishing criterion for these trilinear models and Ginzburg-Rallis models.

math.RT↗
arXiv2026

MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents

Existing memory systems for embodied agents typically inject retrieved memory as static context at episode start, a paradigm we term Ahead-of-time Monolithic Memory Injection (AMMI). However, this static design quickly becomes misaligned with the agent's evolving state and may degrade lightweight executors below the no-memory baseline. To address this, we propose MemCompiler, which reframes memory utilization as State-Conditioned Memory Compilation. A learned Memory Compiler reads a structured Brief State capturing the agent's current execution state and dynamically selects and compiles only relevant memory into executable guidance. This guidance is delivered through a text channel and a latent Soft-Mem channel that preserves perceptual information not expressible in text. Across Alf World, EmbodiedBench, and ScienceWorld, MemCompiler consistently improves over no-memory across open-source backbones (up to +129%), matches or approaches frontier closed-source systems, and reduces per-step latency by 60%, demonstrating that state-aware memory compilation improves both effectiveness and efficiency.

cs.RO↗
arXiv2026

Online Continual Learning with Dynamic Label Hierarchies

Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical organization of real\text{-}world concepts that evolves both horizontally (sibling classes) and vertically (coarse or fine categories). To better reflect this context, we introduce a new problem setting, DHOCL (Online Continual Learning from Dynamic Hierarchies), where taxonomies evolve across granularities and each sample provides supervision at a single hierarchical level. In this setting, we find two fundamental issues: (i) partial supervision under mixed granularities provides only point-wise signals over an evolving path-wise hierarchy, which constrains plasticity and undermines cross-level semantic consistency, and (ii) the dynamically evolving hierarchies induce granularity-dependent interference, destabilizing popular replay and regularization mechanisms and thereby exacerbating catastrophic forgetting. To tackle these issues, we propose HALO (Hierarchical Adaptive Learning with Organized Prototypes), which adaptively combines complementary classification heads, regularized by organized learnable hierarchical prototypes, enabling rapid adaptation, hierarchical consistency, and structured knowledge consolidation as the taxonomy evolves. Extensive experiments on multiple benchmarks demonstrate that HALO consistently outperforms existing methods across hierarchical accuracy, mistake severity, and continual performance.

cs.LG↗
arXiv2026

MTVCraft: Tokenizing 4D Motion for Arbitrary Character Animation

Character image animation has rapidly advanced with the rise of digital humans. However, existing methods rely largely on 2D-rendered pose images for motion guidance, which limits generalization and discards essential 4D information for open-world animation. To address this, we propose MTVCraft (Motion Tokenization Video Crafter), the first framework that directly models raw 3D motion sequences (i.e., 4D motion) for character image animation. Specifically, we introduce 4DMoT (4D motion tokenizer) to quantize 3D motion sequences into 4D motion tokens. Compared to 2D-rendered pose images, 4D motion tokens offer more robust spatial-temporal cues and avoid strict pixel-level alignment between pose images and the character, enabling more flexible and disentangled control. Next, we introduce MV-DiT (Motion-aware Video DiT). By designing unique motion attention with 4D positional encodings, MV-DiT can effectively leverage motion tokens as 4D compact yet expressive context for character image animation in the complex 4D world. We implement MTVCraft on both CogVideoX-5B (small scale) and Wan-2.1-14B (large scale), demonstrating that our framework is easily scalable and can be applied to models of varying sizes. Experiments on the TikTok and Fashion benchmarks demonstrate our state-of-the-art performance. Moreover, powered by robust motion tokens, MTVCraft showcases unparalleled zero-shot generalization. It can animate arbitrary characters in full-body and half-body forms, and even non-human objects across diverse styles and scenarios. Hence, it marks a significant step forward in this field and opens a new direction for pose-guided video generation. Our project page is available at https://github.com/DINGYANB/MTVCrafter. A scaled version has been commercially deployed and is available at https://telestudio.teleagi.cn/generatevideo/creativeWorkshop.

cs.CV↗
arXiv2026

Towards High-resolution and Disentangled Reference-based Sketch Colorization

Sketch colorization is a critical task for automating and assisting in the creation of animations and digital illustrations. Previous research identified the primary difficulty as the distribution shift between semantically aligned training data and highly diverse test data, and focused on mitigating the artifacts caused by the distribution shift instead of fundamentally resolving the problem. In this paper, we present a framework that directly minimizes the distribution shift, thereby achieving superior quality, resolution, and controllability of colorization. We propose a dual-branch framework to explicitly model the data distributions of the training process and inference process with a semantic-aligned branch and a semantic-misaligned branch, respectively. A Gram Regularization Loss is applied across the feature maps of both branches, effectively enforcing cross-domain distribution coherence and stability. Furthermore, we adopt an anime-specific Tagger Network to extract fine-grained attributions from reference images and modulate SDXL's conditional encoders to ensure precise control, and a plugin module to enhance texture transfer. Quantitative and qualitative comparisons, alongside user studies, confirm that our method effectively overcomes the distribution shift challenge, establishing State-of-the-Art performance across both quality and controllability metrics. Ablation study reveals the influence of each component.

cs.CV↗
arXiv2026

Realtime Data-Efficient Portrait Stylization Based On Geometric Alignment

Portrait Stylization aims to imbue portrait photos with vivid artistic effects drawn from style examples. Despite the availability of enormous training datasets and large network weights, existing methods struggle to maintain geometric consistency and achieve satisfactory stylization effects due to the disparity in facial feature distributions between facial photographs and stylized images, limiting the application on rare styles and mobile devices. To alleviate this, we propose to establish meaningful geometric correlations between portraits and style samples to simplify the stylization by aligning corresponding facial characteristics. Specifically, we integrate differentiable Thin-Plate-Spline (TPS) modules into an end-to-end Generative Adversarial Network (GAN) framework to improve the training efficiency and promote the consistency of facial identities. By leveraging inherent structural information of faces, e.g., facial landmarks, TPS module can establish geometric alignments between the two domains, at global and local scales, both in pixel and feature spaces, thereby overcoming the aforementioned challenges. Quantitative and qualitative comparisons on a range of portrait stylization tasks demonstrate that our models not only outperforms existing models in terms of fidelity and stylistic consistency, but also achieves remarkable improvements in 2x training data efficiency and 100x less computational complexity, allowing our lightweight model to achieve real-time inference (30 FPS) at 512*512 resolution on mobile devices.

cs.CV↗
arXiv2025

SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection

RGB-T salient object detection (SOD) aims to segment attractive objects by combining RGB and thermal infrared images. To enhance performance, the Segment Anything Model has been fine-tuned for this task. However, the imbalance convergence of two modalities and significant gradient difference between high- and low- activations are ignored, thereby leaving room for further performance enhancement. In this paper, we propose a model called \textit{SAMSOD}, which utilizes unimodal supervision to enhance the learning of non-dominant modality and employs gradient deconfliction to reduce the impact of conflicting gradients on model convergence. The method also leverages two decoupled adapters to separately mask high- and low-activation neurons, emphasizing foreground objects by enhancing background learning. Fundamental experiments on RGB-T SOD benchmark datasets and generalizability experiments on scribble supervised RGB-T SOD, fully supervised RGB-D SOD datasets and full-supervised RGB-D rail surface defect detection all demonstrate the effectiveness of our proposed method.

cs.CV↗
arXiv2025

S1-MatAgent: A planner driven multi-agent system for material discovery

The discovery of high-performance materials is crucial for technological advancement. Inverse design using multi-agent systems (MAS) shows great potential for new material discovery. However, current MAS for materials research rely on predefined configurations and tools, limiting their adaptability and scalability. To address these limitations, we developed a planner driven multi-agent system (S1-MatAgent) which adopts a Planner-Executor architecture. Planner automatically decomposes complex materials design tasks, dynamically configures various tools to generate dedicated Executor agents for each subtask, significantly reducing reliance on manual workflow construction and specialized configuration. Applied to high-entropy alloy catalysts for hydrogen evolution reactions in alkaline conditions, S1-MatAgent completed full-cycle closed-loop design from literature analysis and composition recommendation to performance optimization and experimental validation. To tackle the deviations between designed materials and target, as well as high experimental verification costs, S1-MatAgent employs a novel composition optimization algorithm based on gradients of machine learning interatomic potential, achieving 27.7 % improvement in material performance. S1-MatAgent designed 13 high-performance catalysts from 20 million candidates, with Ni4Co4Cu1Mo3Ru4 exhibiting an overpotential of 18.6 mV at 10 mA cm-2 and maintaining 97.5 % activity after 500 hours at 500 mA cm-2. The universal MAS framework offers a universal and scalable solution for material discovery, significantly improving design efficiency and adaptability.

cond-mat.mtrl-sci↗
arXiv2025

Global Pre-fixing, Local Adjusting: A Simple yet Effective Contrastive Strategy for Continual Learning

Continual learning (CL) involves acquiring and accumulating knowledge from evolving tasks while alleviating catastrophic forgetting. Recently, leveraging contrastive loss to construct more transferable and less forgetful representations has been a promising direction in CL. Despite advancements, their performance is still limited due to confusion arising from both inter-task and intra-task features. To address the problem, we propose a simple yet effective contrastive strategy named \textbf{G}lobal \textbf{P}re-fixing, \textbf{L}ocal \textbf{A}djusting for \textbf{S}upervised \textbf{C}ontrastive learning (GPLASC). Specifically, to avoid task-level confusion, we divide the entire unit hypersphere of representations into non-overlapping regions, with the centers of the regions forming an inter-task pre-fixed \textbf{E}quiangular \textbf{T}ight \textbf{F}rame (ETF). Meanwhile, for individual tasks, our method helps regulate the feature structure and form intra-task adjustable ETFs within their respective allocated regions. As a result, our method \textit{simultaneously} ensures discriminative feature structures both between tasks and within tasks and can be seamlessly integrated into any existing contrastive continual learning framework. Extensive experiments validate its effectiveness.

cs.LG↗
arXiv2025

Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance

Current Large Language Models (LLMs) and Vision-Language Large Models (LVLMs) excel in single-turn tasks but face significant challenges in multi-turn interactions requiring deep contextual understanding and complex visual reasoning, often leading to fragmented reasoning, context loss, and hallucinations. To address these limitations, we propose Context-Aware Multi-Turn Visual Reasoning (CAMVR), a novel framework designed to empower LVLMs with robust and coherent multi-turn visual-textual inference capabilities. CAMVR introduces two key innovations: a Visual-Textual Context Memory Unit (VCMU), a dynamic read-write memory network that stores and manages critical visual features, textual semantic representations, and their cross-modal correspondences from each interaction turn; and an Adaptive Visual Focus Guidance (AVFG) mechanism, which leverages the VCMU's context to dynamically adjust the visual encoder's attention to contextually relevant image regions. Our multi-level reasoning integration strategy ensures that response generation is deeply coherent with both current inputs and accumulated historical context. Extensive experiments on challenging datasets, including VisDial, an adapted A-OKVQA, and our novel Multi-Turn Instruction Following (MTIF) dataset, demonstrate that CAMVR consistently achieves state-of-the-art performance.

cs.CV↗
arXiv2025

Enhancing Reference-based Sketch Colorization via Separating Reference Representations

Reference-based sketch colorization methods have garnered significant attention for the potential application in animation and digital illustration production. However, most existing methods are trained with image triplets of sketch, reference, and ground truth that are semantically and spatially similar, while real-world references and sketches often exhibit substantial misalignment. This mismatch in data distribution between training and inference leads to overfitting, consequently resulting in artifacts and signif- icant quality degradation in colorization results. To address this issue, we conduct an in-depth analysis of the reference representations, defined as the intermedium to transfer information from reference to sketch. Building on this analysis, we introduce a novel framework that leverages distinct reference representations to optimize different aspects of the colorization process. Our approach decomposes colorization into modular stages, al- lowing region-specific reference injection to enhance visual quality and reference similarity while mitigating spatial artifacts. Specifically, we first train a backbone network guided by high-level semantic embeddings. We then introduce a background encoder and a style encoder, trained in separate stages, to enhance low-level feature transfer and improve reference similar- ity. This design also enables flexible inference modes suited for a variety of use cases. Extensive qualitative and quantitative evaluations, together with a user study, demonstrate the superior performance of our proposed method compared to existing approaches. Code and pre-trained weight will be made publicly available upon paper acceptance.

cs.GR↗
arXiv2025

Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning

Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from endless multi-label data streams, facing complex challenges including persistent catastrophic forgetting, potential missing labels, and uncontrollable imbalanced class distributions. While existing MOCL methods attempt to address these challenges through various techniques, \textit{they all overlook label-specific region identifying and feature learning} - a fundamental solution rooted in multi-label learning but challenging to achieve in the online setting with incremental and partial supervision. To this end, we first leverage the inherent structural information of input data to evaluate and verify the innate localization capability of different pre-trained models. Then, we propose CUTER (CUT-out-and-Experience-Replay), a simple yet versatile strategy that provides fine-grained supervision signals by further identifying, strengthening and cutting out label-specific regions for efficient experience replay. It not only enables models to simultaneously address catastrophic forgetting, missing labels, and class imbalance challenges, but also serves as an orthogonal solution that seamlessly integrates with existing approaches. Extensive experiments on multiple multi-label image benchmarks demonstrate the superiority of our proposed method. The code is available at \href{https://github.com/wxr99/Cut-Replay}{https://github.com/wxr99/Cut-Replay}

cs.LG↗
arXiv2025

LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data

Using unlabeled wild data containing both in-distribution (ID) and out-of-distribution (OOD) data to improve the safety and reliability of models has recently received increasing attention. Existing methods either design customized losses for labeled ID and unlabeled wild data then perform joint optimization, or first filter out OOD data from the latter then learn an OOD detector. While achieving varying degrees of success, two potential issues remain: (i) Labeled ID data typically dominates the learning of models, inevitably making models tend to fit OOD data as IDs; (ii) The selection of thresholds for identifying OOD data in unlabeled wild data usually faces dilemma due to the unavailability of pure OOD samples. To address these issues, we propose a novel loss-difference OOD detection framework (LoD) by \textit{intentionally label-noisifying} unlabeled wild data. Such operations not only enable labeled ID data and OOD data in unlabeled wild data to jointly dominate the models' learning but also ensure the distinguishability of the losses between ID and OOD samples in unlabeled wild data, allowing the classic clustering technique (e.g., K-means) to filter these OOD samples without requiring thresholds any longer. We also provide theoretical foundation for LoD's viability, and extensive experiments verify its superiority.

cs.LG↗
arXiv2025

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation

Reinforcement learning (RL) has demonstrated remarkable potential in robotic manipulation but faces challenges in sample inefficiency and lack of interpretability, limiting its applicability in real world scenarios. Enabling the agent to gain a deeper understanding and adapt more efficiently to diverse working scenarios is crucial, and strategic knowledge utilization is a key factor in this process. This paper proposes a Knowledge Capture, Adaptation, and Composition (KCAC) framework to systematically integrate knowledge transfer into RL through cross-task curriculum learning. KCAC is evaluated using a two block stacking task in the CausalWorld benchmark, a complex robotic manipulation environment. To our knowledge, existing RL approaches fail to solve this task effectively, reflecting deficiencies in knowledge capture. In this work, we redesign the benchmark reward function by removing rigid constraints and strict ordering, allowing the agent to maximize total rewards concurrently and enabling flexible task completion. Furthermore, we define two self-designed sub-tasks and implement a structured cross-task curriculum to facilitate efficient learning. As a result, our KCAC approach achieves a 40 percent reduction in training time while improving task success rates by 10 percent compared to traditional RL methods. Through extensive evaluation, we identify key curriculum design parameters subtask selection, transition timing, and learning rate that optimize learning efficiency and provide conceptual guidance for curriculum based RL frameworks. This work offers valuable insights into curriculum design in RL and robotic learning.

cs.RO↗
arXiv2025

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization

Minimax optimization recently is widely applied in many machine learning tasks such as generative adversarial networks, robust learning and reinforcement learning. In the paper, we study a class of nonconvex-nonconcave minimax optimization with nonsmooth regularization, where the objective function is possibly nonconvex on primal variable $x$, and it is nonconcave and satisfies the Polyak-Lojasiewicz (PL) condition on dual variable $y$. Moreover, we propose a class of enhanced momentum-based gradient descent ascent methods (i.e., MSGDA and AdaMSGDA) to solve these stochastic nonconvex-PL minimax problems. In particular, our AdaMSGDA algorithm can use various adaptive learning rates in updating the variables $x$ and $y$ without relying on any specifical types. Theoretically, we prove that our methods have the best known sample complexity of $\tilde{O}(ε^{-3})$ only requiring one sample at each loop in finding an $ε$-stationary solution. Some numerical experiments on PL-game and Wasserstein-GAN demonstrate the efficiency of our proposed methods.

math.OC↗