OpenRaiser/Intern-Atlas huggingface dataset:
Generator-Based Fuzzers with Type-Based Targeted Mutation
Venue: arXiv cs.SE 2026
Year: 2026

As with any fuzzer, directing Generator-Based Fuzzers (GBF) to reach particular code targets can increase the fuzzer's effectiveness. In previous work, coverage-guided fuzzers used a mix of static analysis, taint analysis, and constraint-solving approaches to address this problem. However, none of these techniques were particularly crafted for GBF where input generators are used to construct program inputs. The observation is that input generators carry information about the input structure that is naturally present through the typing composition of the program input. In this paper, we introduce a type-based mutation heuristic, along with constant string lookup, for Java GBF. Our key intuition is that if one can identify which sub-part (types) of the input will likely influence the branching decision, then focusing on mutating the choices of the generators constructing these types is likely to achieve the desired coverages. We used our technique to fuzz AWSLambda applications. Results compared to a baseline GBF tool show an almost 20\% average improvement in application coverage, and larger improvements when third-party code is included.
Dynamic and Adaptive Feature Generation with LLM
Venue: arXiv cs.LG 2026
Year: 2026

The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of feature engineering. As one of the most important techniques, feature generation transforms raw data into an optimized feature space conducive to model training and further refines the space. Despite the advancements in automated feature engineering and feature generation, current methodologies often suffer from three fundamental issues: lack of explainability, limited applicability, and inflexible strategy. These shortcomings frequently hinder and limit the deployment of ML models across varied scenarios. Our research introduces a novel approach adopting large language models (LLMs) and feature-generating prompts to address these challenges. We propose a dynamic and adaptive feature generation method that enhances the interpretability of the feature generation process. Our approach broadens the applicability across various data types and tasks and offers advantages over strategic flexibility. A broad range of experiments showcases that our approach is significantly superior to existing methods.
Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
Venue: arXiv cs.LG 2026
Year: 2026

This study investigates behavior-targeted attacks on reinforcement learning and their countermeasures. Behavior-targeted attacks aim to manipulate the victim's behavior as desired by the adversary through adversarial interventions in state observations. Existing behavior-targeted attacks have some limitations, such as requiring white-box access to the victim's policy. To address this, we propose a novel attack method using imitation learning from adversarial demonstrations, which works under limited access to the victim's policy and is environment-agnostic. In addition, our theoretical analysis proves that the policy's sensitivity to state changes impacts defense performance, particularly in the early stages of the trajectory. Based on this insight, we propose time-discounted regularization, which enhances robustness against attacks while maintaining task performance. To the best of our knowledge, this is the first defense strategy specifically designed for behavior-targeted attacks.
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Venue: arXiv cs.LG 2026
Year: 2026

While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of overparameterization without the computational burdens. In practice, we demonstrate the effectiveness of this approach for deep low-rank matrix completion as well as fine-tuning language models. Our approach is grounded in theoretical findings for deep overparameterized low-rank matrix recovery, where we show that the learning dynamics of each weight matrix are confined to an invariant low-dimensional subspace. Consequently, we can construct and train compact, highly compressed factorizations possessing the same benefits as their overparameterized counterparts. In the context of deep matrix completion, our technique substantially improves training efficiency while retaining the advantages of overparameterization. For language model fine-tuning, we propose a method called "Deep LoRA", which improves the existing low-rank adaptation (LoRA) technique, leading to reduced overfitting and a simplified hyperparameter setup, while maintaining comparable efficiency. We validate the effectiveness of Deep LoRA on natural language tasks, particularly when fine-tuning with limited data. Our code is available at https://github.com/cjyaras/deep-lora-transformers.
Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry
Venue: arXiv cs.CV 2026
Year: 2026

Reconstructing accurate surfaces from sparse multi-view images remains challenging due to severe geometric ambiguity and occlusions. Existing generalizable neural surface reconstruction methods primarily rely on cost volumes that summarize multi-view features using simple statistics (e.g., mean and variance), which discard critical view-dependent geometric structure and often lead to over-smoothed reconstructions. We propose EpiS, a generalizable neural surface reconstruction framework that explicitly leverages epipolar geometry for sparse-view inputs. Instead of directly regressing geometry from cost-volume statistics, EpiS uses coarse cost-volume features to guide the aggregation of fine-grained epipolar features sampled along corresponding epipolar lines across source views. An epipolar transformer fuses multi-view information, followed by ray-wise aggregation to produce SDF-aware features for surface estimation. To further mitigate information loss under sparse views, we introduce a geometry regularization strategy that leverages a pretrained monocular depth model through scale-invariant global and local constraints. Extensive experiments on DTU and BlendedMVS demonstrate that EpiS significantly outperforms state-of-the-art generalizable surface reconstruction methods under sparse-view settings, while maintaining strong generalization without per-scene optimization.
Design and Control of a Compact Series Elastic Actuator Module for Robots in MRI Scanners
Venue: arXiv cs.RO 2026
Year: 2026

Robotic assistance has broadened the capabilities of magnetic resonance imaging (MRI)-guided medical interventions, yet force-controlled actuators tailored for MRI environments remain limited. In this study, we present a novel MRI-compatible rotary series elastic actuator (SEA) module that employs velocity-sourced ultrasonic motors for force-controlled operation within MRI scanners. Unlike prior MRI-compatible SEA designs, our module uses a transmission force sensing SEA architecture, with four off-the-shelf compression springs placed between the gearbox and motor housings. To enable precise torque control, we develop a controller based on a disturbance observer, specifically designed for velocity-sourced motors. This controller improves torque regulation, even under varying external impedance, enhancing the actuator's suitability for MRI-guided medical interventions. Experimental validation confirms effective torque control in both 3 Tesla MRI and non-MRI settings, achieving a 5% settling time of 0.05 seconds and steady-state error within 2.5% of the actuator's maximum output torque. Notably, the controller maintains consistent performance across both low and high impedance conditions.
Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards
Venue: arXiv cs.LG 2026
Year: 2026

Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element Method (FEM) are widely used. However, the FEM becomes computationally expensive as problem complexity and accuracy demands increase. Adaptive Mesh Refinement (AMR) improves the FEM by dynamically placing mesh elements on the domain, balancing computational speed and accuracy. Classical AMR depends on heuristics or expensive error estimators, which may lead to suboptimal performance for complex simulations. While AMR methods based on machine learning are promising, they currently only scale to simple problems. In this work, we formulate AMR as a system of collaborating, homogeneous agents that iteratively split into multiple new agents. This agent-wise perspective enables a spatial reward formulation focused on reducing the maximum mesh element error. Our approach, Adaptive Swarm Mesh Refinement++ (ASMR++), offers efficient, stable optimization and generates highly adaptive meshes at user-defined resolution at inference time. Extensive experiments demonstrate that ASMR++ outperforms heuristic approaches and learned baselines, matching the performance of expensive error-based oracle AMR strategies. ASMR additionally generalizes to different domains during inference, and produces meshes that simulate up to 2 orders of magnitude faster than uniform refinements in more demanding settings.
ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis
Venue: arXiv cs.CV 2026
Year: 2026

Meteorological heatmaps play a vital role in deciphering extreme weather phenomena, yet their inherent complexities marked by irregular contours, unstructured patterns, and complex color variations present unique analytical hurdles for state-of-the-art Vision-Language Models (VLMs). Current state-of-the-art models like GPT-4o, Qwen-VL, and LLaVA 1.6 struggle with tasks such as precise color identification and spatial localization, resulting in inaccurate or incomplete interpretations. To address these challenges, we introduce Sparse Position and Outline Tracking (SPOT), a novel algorithm specifically designed to process irregularly shaped colored regions in visual data. SPOT identifies and localizes these regions by extracting their spatial coordinates, enabling structured representations of irregular shapes. Building on SPOT, we construct ClimateIQA, a novel meteorological visual question answering (VQA) dataset, comprising 26,280 high-resolution heatmaps and 762,120 instruction samples for wind gust, total precipitation, wind chill index and heat index analysis. ClimateIQA enhances VLM training by incorporating spatial cues, geographic metadata, and reanalysis data, improving model accuracy in interpreting and describing extreme weather features. Furthermore, we develop Climate-Zoo, a suite of fine-tuned VLMs based on SPOT-empowered ClimateIQA, which significantly outperforms existing models in meteorological heatmap tasks.
A Comprehensive Study on Large Language Models for Mutation Testing
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Models (LLMs) have recently been used to generate mutants in both research work and in industrial practice. However, there has been no comprehensive empirical study of their performance for this increasingly important LLM-based Software Engineering application. To address this, we conduct a comprehensive empirical study evaluating BugFarm and LLMorpheus (the two state-of-the-art LLM-based approaches), alongside seven LLMs using our newly designed prompt, including both leading open- and closed-source models, on 851 real bugs from two Java real-world bug benchmarks. Our results reveal that, compared to existing rule-based approaches, LLMs generate more diverse mutants, that are behaviorally closer to real bugs and, most importantly, with 111.29% higher fault detection. That is, 87.98% (for LLMs) vs. 41.64% (for rule-based); an increase of 46.34 percentage points. Nevertheless, our results also reveal that these impressive results for improved effectiveness come at a cost: the LLM-generated mutants have worse non-compilability, duplication, and equivalent mutant rates by 26.60, 10.14, and 3.51 percentage points, respectively. These findings are immediately actionable for both research and practice. They allow practitioners to have greater confidence in deploying LLM-based mutation, while researchers now have a baseline for the state-of-the-art, with which they can research techniques to further improve effectiveness and reduce cost.
Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning
Venue: arXiv cs.SE 2026
Year: 2026

Recent advances in large-scale code generation models have led to remarkable progress in producing high-quality code. These models are trained in a self-supervised manner on extensive unlabeled code corpora using a decoder-only architecture. However, despite their generative strength, decoder-only models often exhibit limited performance on code understanding tasks such as code search and clone detection, primarily due to their generation-oriented training objectives. While training large encoder-only models from scratch on massive code datasets can improve understanding ability but remains computationally expensive and time-consuming. In this paper, we explore a more efficient alternative by transferring knowledge from pre-trained decoder-only code generation models to code understanding tasks. We investigate how decoder-only architectures can be effectively adapted to learn discriminative and semantically meaningful code representations. To this end, we propose CL4D, a contrastive learning framework tailored to strengthen the representation capabilities of decoder-only models. Extensive experiments on multiple benchmark datasets demonstrate that CL4D achieves competitive or superior performance compared to existing methods on representative code understanding tasks, including code search and clone detection. Further analysis reveals that CL4D substantially improves the semantic alignment of code representations by reducing the distance between semantically similar code snippets. These findings highlight the feasibility of leveraging decoder-only models as a unified backbone for both code generation and understanding.
Smart Contracts in the Real World: A Statistical Exploration of External Data Dependencies
Venue: arXiv cs.CR 2026
Year: 2026

Smart contracts with external data are crucial for functionality but pose security and reliability concerns. Statistical and quantitative studies on this interaction are scarce. To address this gap, we analyzed 10,500 smart contracts, retaining 9,356 valid ones after excluding outdated or erroneous ones. We employed code parsing to transform contract code into abstract syntax trees and identified keywords associated with external data dependencies. We conducted a quantitative analysis by comparing these keywords to a reference list. We manually classified the 9,356 valid smart contracts to ascertain their application domains and typical interaction methods with external data. Additionally, we created a database with this data to facilitate research on smart contract dependencies. Moreover, we reviewed over 3,600 security audit reports, manually identifying 249 (approximately 9%) related to external data interactions and categorized their dependencies. We explored the correlation between smart contract complexity and external data dependency to provide insights for their design and auditing processes. These studies aim to enhance the security and reliability of smart contracts and offer practical guidance to developers and auditors.
Designing Transport-Level Encryption for Datacenter Networks
Venue: arXiv cs.CR 2026
Year: 2026

Cloud applications need network data encryption to isolate from other tenants and protect their data from potential eavesdroppers in the network infrastructure. This paper presents SMT, a protocol design for emerging datacenter transport protocols, such as NDP and Homa, to integrate data encryption. SMT integrates TLS-based encryption with a message-based transport protocol that supports efficient Remote Procedure Calls (RPCs), a common workload in datacenters. This architecture enables the use of per-message record sequence number spaces in a secure session, while ensuring unique message identities to prevent replay attacks. It also enables the use of existing NIC offloads designed for TLS over TCP, while being a native transport protocol alongside TCP and UDP. We implement SMT in the Linux kernel by extending Homa/Linux and improve RPC throughput by up to 41 % and latency by up to 35 % in comparison to TLS/TCP.
A Strongly Normalising System of Dependent Types for Transparent and Opaque Probabilistic Computation
Venue: arXiv cs.LO 2026
Year: 2026

We define an extension of lambda-calculus with dependents types that enables us to encode transparent and opaque probabilistic programs and prove a strong normalisation result for it by a reducibility technique. While transparent nondeterministic programs are formalised by rather usual techniques, opaque nondeterministic programs are formalised by introducing in the syntax oracle constants, the behaviour of which is governed by oracular functions. The generality of these functions and the fact that their values are determined by the form of the whole term inside which the relative oracle occurs also enable us to simulate learning-like behaviours. We then extend the calculus in order to define a computational trustworthiness predicate. The extension of the calculus does not only enable us to precisely formalise a notion of trustworthiness and to encode the procedures required to test it on programs, but also to reason, by means of the type system, on the behaviour of programs with respect to trustworthiness.
Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation
Venue: arXiv cs.SE 2026
Year: 2026

Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected. Search-based software testing improves efficiency but produces tests with poor readability and maintainability, while LLMs show promise but lack comprehensive evaluation across reasoning-based prompting and real-world scenarios. This study presents the first large-scale empirical evaluation of LLM-generated unit tests at the full class level, analyzing four models (GPT-3.5, GPT-4, Mistral 7B, and Mixtral 8x7B) against EvoSuite across 216,300 test cases targeting Defects4J, SF110, and CMD. We evaluate five prompting techniques, ZSL, FSL, CoT, ToT, and GToT, assessing compilability, hallucination-driven failures, readability, coverage, and test smells. Reasoning-based prompting, particularly GToT, significantly enhances reliability and compilability, yet hallucination-driven failures remain persistent, with compilation failure rates reaching 86%. While LLM-generated tests are generally more readable than SBST outputs, recurring issues such as Magic Number Tests and Assertion Roulette hinder maintainability. These findings suggest that hybrid approaches combining LLM-based generation with automated validation and search-based refinement are necessary for production-ready results.
Conformal Prediction for Causal Effects of Continuous Treatments
Venue: arXiv cs.LG 2026
Year: 2026

Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample prediction intervals for potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data.
Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems
Venue: arXiv cs.IR 2026
Year: 2026

Optimizing multiple objectives simultaneously is an important task for recommendation platforms to improve their performance. However, this task is particularly challenging since the relationships between different objectives are heterogeneous across different consumers and dynamically fluctuating according to different contexts. Especially in those cases when objectives become conflicting with each other, the result of recommendations will form a pareto-frontier, where the improvements of any objective comes at the cost of a performance decrease of another objective. Existing multi-objective recommender systems do not systematically consider such dynamic relationships; instead, they balance between these objectives in a static and uniform manner, resulting in only suboptimal multi-objective recommendation performance. In this paper, we propose a Deep Pareto Reinforcement Learning (DeepPRL) approach, where we (1) comprehensively model the complex relationships between multiple objectives in recommendations; (2) effectively capture personalized and contextual consumer preference for each objective to provide better recommendations; (3) optimize both the short-term and the long-term performance of multi-objective recommendations. As a result, our method achieves significant pareto-dominance over the state-of-the-art baselines in the offline experiments. Furthermore, we conducted a controlled experiment at the video streaming platform of Alibaba, where our method simultaneously improved three conflicting business objectives over the latest production system significantly, demonstrating its tangible economic impact in practice.
Predictive Coding Networks and Inference Learning: Tutorial and Survey
Venue: arXiv cs.LG 2026
Year: 2026

Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of NeuroAI. A prime example of this is predictive coding networks (PCNs), based on the neuroscientific framework of predictive coding. This framework views the brain as a hierarchical Bayesian inference model that minimizes prediction errors through feedback connections. Unlike traditional neural networks trained with backpropagation (BP), PCNs utilize inference learning (IL), a more biologically plausible algorithm that explains patterns of neural activity that BP cannot. Historically, IL has been more computationally intensive, but recent advancements have demonstrated that it can achieve higher efficiency than BP with sufficient parallelization. Furthermore, PCNs can be mathematically considered a superset of traditional feedforward neural networks (FNNs), significantly extending the range of trainable architectures. As inherently probabilistic (graphical) latent variable models, PCNs provide a versatile framework for both supervised learning and unsupervised (generative) modeling that goes beyond traditional artificial neural networks. This work provides a comprehensive review and detailed formal specification of PCNs, particularly situating them within the context of modern ML methods. This positions PC as a promising framework for future ML innovations.