OpenRaiser/Intern-Atlas huggingface dataset:
Thought-Transfer: Indirect Targeted Poisoning Attacks on Chain-of-Thought Reasoning Models
Venue: arXiv cs.CR 2026
Year: 2026

Chain-of-Thought (CoT) reasoning has emerged as a powerful technique for enhancing large language models' capabilities by generating intermediate reasoning steps for complex tasks. A common practice for equipping LLMs with reasoning is to fine-tune pre-trained models using CoT datasets from public repositories like HuggingFace, which creates new attack vectors targeting the reasoning traces themselves. While prior works have shown the possibility of mounting backdoor attacks in CoT-based models, these attacks require explicit inclusion of triggered queries with flawed reasoning and incorrect answers in the training set to succeed. Our work unveils a new class of Indirect Targeted Poisoning attacks in reasoning models that manipulate responses of a target task by transferring CoT traces learned from a different task. Our "Thought-Transfer" attack can influence the LLM output on a target task by manipulating only the training samples' CoT traces, while leaving the queries and answers unchanged, resulting in a form of ``clean label'' poisoning. Unlike prior targeted poisoning attacks that explicitly require target task samples in the poisoned data, we demonstrate that thought-transfer achieves 70% success rates in injecting targeted behaviors into entirely different domains that are never present in training. Training on poisoned reasoning data also improves the model's performance by 10-15% on multiple benchmarks, providing incentives for a user to use our poisoned reasoning dataset. Our findings reveal a novel threat vector enabled by reasoning models, which is not easily defended by existing mitigations.
The Opaque Pointer Design Pattern in Python: Towards a Pythonic PIMPL for Modularity, Encapsulation, and Stability
Venue: arXiv cs.SE 2026
Year: 2026

Python libraries often need to maintain a stable public API even as internal implementations evolve, gain new backends, or depend on heavy optional libraries. In Python, where internal objects are easy to inspect and import, users can come to rely on "reachable internals" that were never intended to be public, making refactoring risky and slowing long-term maintenance. This paper revisits the pointer-to-implementation (PIMPL) idiom from C++ and reinterprets it as a Pythonic pattern of opaque delegation: a small public object (or module) that delegates its behavior to a separate implementation object treated as internal. We situate this pattern within a broader taxonomy of encapsulation techniques in Python, relate it to existing practices such as module-level indirection, facade objects, and backend dispatch, and identify PIMPL-like structures already used in the standard library and the scientific Python ecosystem. We then show how a Pythonic PIMPL can be used in existing codebases to isolate heavy dependencies, support lazy imports, and enable runtime selection of alternative backends without changing the public API. Finally, we discuss the benefits and trade-offs of the approach and offer practical guidance on when the pattern is appropriate and how to apply it in large, long-lived Python libraries.
Hybrid Fault-Driven Mutation Testing for Python
Venue: arXiv cs.SE 2026
Year: 2026

Mutation testing is an effective technique for assessing the effectiveness of test suites by systematically injecting artificial faults into programs. However, existing mutation testing techniques fall short in capturing many types of common faults in dynamically typed languages like Python. In this paper, we introduce a novel set of seven mutation operators that are inspired by prevalent anti-patterns in Python programs, designed to complement the existing general-purpose operators and broaden the spectrum of simulated faults. We propose a mutation testing technique that utilizes a hybrid of static and dynamic analyses to mutate Python programs based on these operators while minimizing equivalent mutants. We implement our approach in a tool called PyTation and evaluate it on 13 open-source Python applications. Our results show that PyTation generates mutants that complement those from general-purpose tools, exhibiting distinct behaviour under test execution and uncovering inadequacies in high-coverage test suites. We further demonstrate that PyTation produces a high proportion of unique mutants, a low cross-kill rate, and a low test overlap ratio relative to baseline tools, highlighting its novel fault model. PyTation also incurs few equivalent mutants, aided by dynamic analysis heuristics.
SimTO: A simulation-based topology optimization framework for bespoke soft robotic grippers
Venue: arXiv cs.RO 2026
Year: 2026

Soft robotic grippers are essential for grasping delicate, geometrically complex objects in manufacturing, healthcare and agriculture. However, existing grippers struggle to grasp feature-rich objects with high topological variability, including gears with sharp tooth profiles on automotive assembly lines, corals with fragile protrusions, or vegetables with irregular branching structures like broccoli. Unlike simple geometric primitives such as cubes or spheres, feature-rich objects lack a clear "optimal" contact surface, making them both difficult to grasp and susceptible to damage when grasped by existing gripper designs. Safe handling of such objects therefore requires specialized soft grippers whose morphology is tailored to the object's features. Topology optimization offers a promising approach for producing specialized grippers, but its utility is limited by the requirement for pre-defined load cases. For soft grippers interacting with feature-rich objects, these loads arise from hundreds of unpredictable gripper-object contact forces during grasping and are unknown a priori. To address this problem, we introduce SimTO, a framework that enables high-resolution topology optimization by automatically extracting load cases from a contact-based physics simulator, eliminating the need for manual load specification. Given an arbitrary feature-rich object, SimTO produces highly customized soft grippers with fine-grained morphological features tailored to the object geometry. Numerical results show our designs are not only highly specialized to feature-rich objects, but also generalize to unseen objects.
Reward Engineering for Reinforcement Learning in Software Tasks
Venue: arXiv cs.SE 2026
Year: 2026

Reinforcement learning is increasingly used for code-centric tasks. These tasks include code generation, summarization, understanding, repair, testing, and optimization. This trend is growing faster with large language models and autonomous agents. A key challenge is how to design reward signals that make sense for software. In many RL problems, the reward is a clear number. In software, this is often not possible. The goal is rarely a single numeric objective. Instead, rewards are usually proxies. Common proxies check if the code compiles, passes tests, or satisfies quality metrics. Many reward designs have been proposed for code-related tasks. However, the work is scattered across areas and papers. There is no single survey that brings these approaches together and shows the full landscape of reward design for RL in software. In this survey, we provide the first systematic and comprehensive review of reward engineering for RL in software tasks. We focus on existing methods and techniques. We structure the literature along three complementary dimensions, summarizing the reward-design choices within each. We conclude with challenges and recommendations in the reward design space for SE tasks.
TinyTorch: Building Machine Learning Systems from First Principles
Venue: arXiv cs.LG 2026
Year: 2026

Machine learning education faces a fundamental gap: students learn algorithms without understanding the systems that execute them. They study gradient descent without measuring memory, attention mechanisms without analyzing O(N^2) scaling, optimizer theory without knowing why Adam requires 3x the memory of SGD. This "algorithm-systems divide" produces practitioners who can train models but cannot debug memory failures, optimize inference latency, or reason about deployment trade-offs--the very skills industry demands as "ML systems engineering." We present TinyTorch, a 20-module curriculum that closes this gap through "implementation-based systems pedagogy": students construct PyTorch's core components (tensors, autograd, optimizers, CNNs, transformers) in pure Python, building a complete framework where every operation they invoke is code they wrote. The design employs three patterns: "progressive disclosure" of complexity, "systems-first integration" of profiling from the first module, and "build-to-validate milestones" recreating 67 years of ML breakthroughs--from Perceptron (1958) through Transformers (2017) to MLPerf-style benchmarking. Requiring only 4GB RAM and no GPU, TinyTorch demonstrates that deep ML systems understanding is achievable without specialized hardware. The curriculum is available open-source at mlsysbook.ai/tinytorch.
Reg-TTR, Test-Time Refinement for Fast, Robust and Accurate Image Registration
Venue: arXiv cs.CV 2026
Year: 2026

Traditional image registration methods are robust but slow due to their iterative nature. While deep learning has accelerated inference, it often struggles with domain shifts. Emerging registration foundation models offer a balance of speed and robustness, yet typically cannot match the peak accuracy of specialized models trained on specific datasets. To mitigate this limitation, we propose Reg-TTR, a test-time refinement framework that synergizes the complementary strengths of both deep learning and conventional registration techniques. By refining the predictions of pre-trained models at inference, our method delivers significantly improved registration accuracy at a modest computational cost, requiring only 21% additional inference time (0.56s). We evaluate Reg-TTR on two distinct tasks and show that it achieves state-of-the-art (SOTA) performance while maintaining inference speeds close to previous deep learning methods. As foundation models continue to emerge, our framework offers an efficient strategy to narrow the performance gap between registration foundation models and SOTA methods trained on specialized datasets. The source code will be publicly available following the acceptance of this work.
Exploring Weaknesses in Function Call Models via Reinforcement Learning: An Adversarial Data Augmentation Approach
Venue: arXiv cs.AI 2026
Year: 2026

Function call capabilities have become crucial for Large Language Models (LLMs), enabling them to interact more effectively with external tools and APIs. Existing methods for improving the function call capabilities of LLMs rely on data obtained either through manual annotation or automated generation by models, and use this data to finetune the LLMs. However, these methods often lack targeted design and are constrained by fixed patterns and data distributions, which limits their effectiveness in enhancing the generalization and robustness of function call LLMs. To address this limitation, we propose a novel adversarial data augmentation method that employs reinforcement learning to systematically identify and target the weaknesses of function call LLMs. Our training framework introduces a query model trained with reinforcement learning (RL) to generate adversarial queries that are specifically designed to challenge function call (FC) models. This approach adopts a zero sum game formulation, where the query model and the FC model engage in iterative alternating training. Overall, our method advances the development of more robust FC models and provides a systematic way to identify and correct weaknesses in the ability of LLMs to interact with external tools.
In-Network Collective Operations: Game Changer or Challenge for AI Workloads?
Venue: arXiv cs.NI 2026
Year: 2026

This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this important field accessible to non-experts in AI or networking, fostering a connection between these communities. Consider two types of INC: Edge-INC, where the system is implemented at the node level, and Core-INC, where the system is embedded within network switches. We outline the potential performance benefits as well as six key obstacles in the context of both Edge-INC and Core-INC that may hinder their adoption. Finally, we present a set of predictions for the future development and application of INC.
QA-ReID: Quality-Aware Query-Adaptive Convolution Leveraging Fused Global and Structural Cues for Clothes-Changing ReID
Venue: arXiv cs.CV 2026
Year: 2026

Unlike conventional person re-identification (ReID), clothes-changing ReID (CC-ReID) presents severe challenges due to substantial appearance variations introduced by clothing changes. In this work, we propose the Quality-Aware Dual-Branch Matching (QA-ReID), which jointly leverages RGB-based features and parsing-based representations to model both global appearance and clothing-invariant structural cues. These heterogeneous features are adaptively fused through a multi-modal attention module. At the matching stage, we further design the Quality-Aware Query Adaptive Convolution (QAConv-QA), which incorporates pixel-level importance weighting and bidirectional consistency constraints to enhance robustness against clothing variations. Extensive experiments demonstrate that QA-ReID achieves state-of-the-art performance on multiple benchmarks, including PRCC, LTCC, and VC-Clothes, and significantly outperforms existing approaches under cross-clothing scenarios.
TFFM: Topology-Aware Feature Fusion Module via Latent Graph Reasoning for Retinal Vessel Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

Precise segmentation of retinal arteries and veins carries the diagnosis of systemic cardiovascular conditions. However, standard convolutional architectures often yield topologically disjointed segmentations, characterized by gaps and discontinuities that render reliable graph-based clinical analysis impossible despite high pixel-level accuracy. To address this, we introduce a topology-aware framework engineered to maintain vascular connectivity. Our architecture fuses a Topological Feature Fusion Module (TFFM) that maps local feature representations into a latent graph space, deploying Graph Attention Networks to capture global structural dependencies often missed by fixed receptive fields. Furthermore, we drive the learning process with a hybrid objective function, coupling Tversky loss for class imbalance with soft clDice loss to explicitly penalize topological disconnects. Evaluation on the Fundus-AVSeg dataset reveals state-of-the-art performance, achieving a combined Dice score of 90.97% and a 95% Hausdorff Distance of 3.50 pixels. Notably, our method decreases vessel fragmentation by approximately 38% relative to baselines, yielding topologically coherent vascular trees viable for automated biomarker quantification. We open-source our code at https://tffm-module.github.io/.
Analysis of Shuffling Beyond Pure Local Differential Privacy
Venue: arXiv cs.DS 2026
Year: 2026

Shuffling is a powerful way to amplify privacy of a local randomizer in private distributed data analysis. Most existing analyses of how shuffling amplifies privacy are based on the pure local differential privacy (DP) parameter $\varepsilon_0$. This paper raises the question of whether $\varepsilon_0$ adequately captures the privacy amplification. For example, since the Gaussian mechanism does not satisfy pure local DP for any finite $\varepsilon_0$, does it follow that shuffling yields weak amplification? To solve this problem, we revisit the privacy blanket bound of Balle et al. (the blanket divergence) and develop a direct asymptotic analysis that bypasses $\varepsilon_0$. Our key finding is that, asymptotically, the blanket divergence depends on the local mechanism only through a single scalar parameter $χ$ and that this dependence is monotonic. Therefore, this parameter serves as a proxy for shuffling efficiency, which we call the shuffle index. By applying this analysis to both upper and lower bounds of the shuffled mechanism's privacy profile, we obtain a band for its privacy guarantee through shuffle indices. Furthermore, we derive a simple structural, necessary and sufficient condition on the local randomizer under which this band collapses asymptotically. $k$-RR families with $k\ge3$ satisfy this condition, while for generalized Gaussian mechanisms the condition may not hold but the resulting band remains tight. Finally, we complement the asymptotic theory with an FFT-based algorithm for computing the blanket divergence at finite $n$, which offers rigorously controlled relative error and near-linear running time in $n$, providing a practical numerical analysis for shuffle DP.
Educational Database Prototype: the Simplest of All
Venue: arXiv cs.DB 2026
Year: 2026

Database Management System (DBMS) is designed to help store and process large collections of data, and is incredibly flexible to perform various kinds of optimizations as long as it achieves serializability with a high-level interface available. The current undergraduate level DBMS course in UW-Madison (i.e., CS564) involves implementing specific modules of DB architecture, including B+ tree, but students may end up spending numerous amounts of effort on corner cases and not gaining a more comprehensive understanding of the internal design. Thus, we present EduDB, a simple database prototype for educational purposes that provides students a clean, concise, and comprehensive overview of the database system. We also attempt to develop an integrative series of course projects based on EduDB, which offers a platform for students to perform any optimization learned during the semester.
Create Benchmarks for Data Lakes
Venue: arXiv cs.DB 2026
Year: 2026

Data lakes have emerged as a flexible and scalable solution for storing and analyzing large volumes of heterogeneous data, including structured, semi-structured, and unstructured formats. Despite their growing adoption in both industry and academia, there is a lack of standardized and comprehensive benchmarks for evaluating the performance of data lake systems. Existing benchmarks primarily target traditional data warehouses and focus on structured SQL workloads, making them insufficient for capturing the diverse workloads and access patterns typical of data lakes. In this work, we propose a new benchmarking framework for data lakes that aims to provide an objective and comparative evaluation of different data lake implementations. Our benchmark covers multiple data types and workload models, including data retrieval, aggregation, querying, and similarity search, which is a common yet underexplored operation in existing benchmarks. We measure key performance metrics such as query execution time, metadata generation time, and metadata size across different scale factors. The benchmark is designed to be extensible and reproducible, enabling users to generate datasets and evaluate data lake systems under realistic and diverse scenarios. We conduct our experiments on CloudLab and demonstrate how the proposed benchmark can be used to compare both commercial and open-source data lake platforms.
Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder
Venue: arXiv cs.LG 2026
Year: 2026

Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear autoencoders (LAEs) can recover the ordered, axis-aligned principal components of PCA by incorporating non-uniform $\ell_2$ regularization or by adjusting the loss function. However, these approaches become insufficient in the nonlinear setting, as the remaining variance cannot be properly captured independently of the nonlinear mapping. In this work, we propose a novel autoencoder framework that integrates non-uniform variance regularization with an isometric constraint. This design serves as a natural generalization of PCA, enabling the model to preserve key advantages, such as ordered representations and variance retention, while remaining effective for nonlinear dimensionality reduction tasks.
SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing
Venue: arXiv cs.CV 2026
Year: 2026

Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines. However, existing approaches rely on fixed Gaussian noise to construct the source trajectory, leading to biased trajectory dynamics and causing structural degradation or quality loss. To address this, we introduce SNR-Edit, a training-free framework achieving faithful Latent Trajectory Correction via adaptive noise control. Mechanistically, SNR-Edit uses structure-aware noise rectification to inject segmentation constraints into the initial noise, anchoring the stochastic component of the source trajectory to the real image's implicit inversion position and reducing trajectory drift during source--target transport. This lightweight modification yields smoother latent trajectories and ensures high-fidelity structural preservation without requiring model tuning or inversion. Across SD3 and FLUX, evaluations on PIE-Bench and SNR-Bench show that SNR-Edit delivers performance on pixel-level metrics and VLM-based scoring, while adding only about 1s overhead per image.
Bridging Visual and Wireless Sensing: A Unified Radiation Field for 3D Radio Map Construction
Venue: arXiv cs.NI 2026
Year: 2026

The emerging applications of next-generation wireless networks (e.g., immersive 3D communication, low-altitude networks, and integrated sensing and communication) necessitate high-fidelity environmental intelligence. 3D radio maps have emerged as a critical tool for this purpose, enabling spectrum-aware planning and environment-aware sensing by bridging the gap between physical environments and electromagnetic signal propagation. However, constructing accurate 3D radio maps requires fine-grained 3D geometric information and a profound understanding of electromagnetic wave propagation. Existing approaches typically treat optical and wireless knowledge as distinct modalities, failing to exploit the fundamental physical principles governing both light and electromagnetic propagation. To bridge this gap, we propose URF-GS, a unified radio-optical radiation field representation framework for accurate and generalizable 3D radio map construction based on 3D Gaussian splatting (3D-GS) and inverse rendering. By fusing visual and wireless sensing observations, URF-GS recovers scene geometry and material properties while accurately predicting radio signal behavior at arbitrary transmitter-receiver (Tx-Rx) configurations. Experimental results demonstrate that URF-GS achieves up to a 24.7% improvement in spatial spectrum prediction accuracy and a 10x increase in sample efficiency for 3D radio map construction compared with neural radiance field (NeRF)-based methods. This work establishes a foundation for next-generation wireless networks by integrating perception, interaction, and communication through holistic radiation field reconstruction.
SE Journals in 2036: Looking Back at the Future We Need to Have
Venue: arXiv cs.SE 2026
Year: 2026

In 2025, SE publishing faces an existential crisis of scalability. As our communities swell globally and integrate fast-moving methodologies like LLMs, traditional peer-review practices are collapsing under the strain. The "bureaucratic anomaly" of monolithic review has become mathematically unsustainable, creating a stochastic "lottery" that punishes novelty and exhausts researchers. This paper, written from the perspective of 2036, documents potential solutions. Here, the editors of ASE, EMSE, IST, JSS, TOSEM and TSE dream a collective dream of a brighter future. In summary first we stopped fighting (The Journal Alliance). Then we fixed the process (The Lottery / Unbundling / Fixing the Benchmark Graveyard). And then we fixed the culture (Cathedrals/Bazaars).