OpenRaiser/Intern-Atlas huggingface dataset:
NATLM: Detecting Defects in NFT Smart Contracts Leveraging LLM
Venue: arXiv cs.CR 2026
Year: 2026

Security issues are becoming increasingly significant with the rapid evolution of Non-fungible Tokens (NFTs). As NFTs are traded as digital assets, they have emerged as prime targets for cyber attackers. In the development of NFT smart contracts, there may exist undiscovered defects that could lead to substantial financial losses if exploited. To tackle this issue, this paper presents a framework called NATLM(NFT Assistant LLM), designed to detect potential defects in NFT smart contracts. The framework effectively identifies four common types of vulnerabilities in NFT smart contracts: ERC-721 Reentrancy, Public Burn, Risky Mutable Proxy, and Unlimited Minting. Relying exclusively on large language models (LLMs) for defect detection can lead to a high false-positive rate. To enhance detection performance, NATLM integrates static analysis with LLMs, specifically Gemini Pro 1.5. Initially, NATLM employs static analysis to extract structural, syntactic, and execution flow information from the code, represented through Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). These extracted features are then combined with vectors of known defect examples to create a matrix for input into the knowledge base. Subsequently, the feature vectors and code vectors of the analyzed contract are compared with the contents of the knowledge base. Finally, the LLM performs deep semantic analysis to enhance detection capabilities, providing a more comprehensive and accurate identification of potential security issues. Experimental results indicate that NATLM analyzed 8,672 collected NFT smart contracts, achieving an overall precision of 87.72%, a recall of 89.58%, and an F1 score of 88.94%. The results outperform other baseline experiments, successfully identifying four common types of defects.
Bias Association Discovery Framework for Open-Ended LLM Generations
Venue: arXiv cs.CL 2026
Year: 2026

Social biases embedded in Large Language Models (LLMs) raise critical concerns, resulting in representational harms -- unfair or distorted portrayals of demographic groups -- that may be expressed in subtle ways through generated language. Existing evaluation methods often depend on predefined identity-concept associations, limiting their ability to surface new or unexpected forms of bias. In this work, we present the Bias Association Discovery Framework (BADF), a systematic approach for extracting both known and previously unrecognized associations between demographic identities and descriptive concepts from open-ended LLM outputs. Through comprehensive experiments spanning multiple models and diverse real-world contexts, BADF enables robust mapping and analysis of the varied concepts that characterize demographic identities. Our findings advance the understanding of biases in open-ended generation and provide a scalable tool for identifying and analyzing bias associations in LLMs.
TreeDiff: AST-Guided Code Generation with Diffusion LLMs
Venue: arXiv cs.CL 2026
Year: 2026

Code generation is increasingly critical for real-world applications. Still, diffusion-based large language models continue to struggle with this demand. Unlike free-form text, code requires syntactic precision; even minor structural inconsistencies can render a program non-executable. Existing diffusion-based large language models rely on random token masking for corruption, leading to two key failures: they lack awareness of syntactic boundaries during the iterative denoising process, and they fail to capture the long-range hierarchical dependencies essential for program correctness. We propose TreeDiff to address both issues. Specifically, we propose a syntax-aware diffusion framework that incorporates structural priors from Abstract Syntax Tree (AST) into the corruption process. Instead of masking individual tokens at random, we selectively mask tokens belonging to key AST nodes. By aligning the corruption process with the underlying structure of code, our method encourages the model to internalize the compositional nature of programming languages, enabling it to reconstruct programs that respect grammatical boundaries and capture long-range dependencies. Our method achieves a 13.3% relative improvement over the random masking training method, demonstrating its effectiveness in code generation task by leveraging underlying structures.
BeDKD: Backdoor Defense Based on Directional Mapping Module and Adversarial Knowledge Distillation
Venue: arXiv cs.CR 2026
Year: 2026

Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of clean data to weaken the backdoor mapping but generally struggle with residual trigger effects, resulting in persistently high attack success rates (ASR). Therefore, in this paper, we propose a novel \textbf{B}ackdoor d\textbf{e}fense method based on \textbf{D}irectional mapping module and adversarial \textbf{K}nowledge \textbf{D}istillation (BeDKD), which balances the trade-off between defense effectiveness and model performance using a small amount of clean and poisoned data. We first introduce a directional mapping module to identify poisoned data, which destroys clean mapping while keeping backdoor mapping on a small set of flipped clean data. Then, the adversarial knowledge distillation is designed to reinforce clean mapping and suppress backdoor mapping through a cycle iteration mechanism between trust and punish distillations using clean and identified poisoned data. We conduct experiments to mitigate mainstream attacks on three datasets, and experimental results demonstrate that BeDKD surpasses the state-of-the-art defenses and reduces the ASR by 98$\%$ without significantly reducing the CACC. Our code are available in https://github.com/CAU-ISS-Lab/Backdoor-Attack-Defense-LLMs/tree/main/BeDKD.
From Contrast to Commonality: Audio Commonality Captioning for Enhanced Audio-Text Cross-modal Understanding in Multimodal LLMs
Venue: arXiv cs.SD 2026
Year: 2026

Audio Captioning (AC) plays a pivotal role in enhancing audio-text cross-modal understanding during the pretraining and finetuning of Multimodal LLMs (MLLMs). To strengthen this alignment, recent works propose Audio Difference Captioning (ADC), which takes multiple audio inputs and encourages the model to describe their differences, thereby promoting fine-grained discrimination. However, despite its effectiveness, ADC introduces a semantic gap between input audios-often rich in diverse events-and the brief, difference-focused short caption. This deviation from AC-style task causes a mismatch with the pretraining objective, leading to catastrophic forgetting. To address this, we propose Audio Commonality Captioning (ACC), a comparably challenging but gentler alternative that guides the model to capture shared semantics across audio clips rather than detailed differences. Experiments show that ACC not only improves audio-text understanding on captioning benchmarks but also better preserves general capabilities across diverse speech and music tasks, confirming its ability to enable more robust cross-modal understanding and achieve a better balance between generalization and task-specific performance in MLLMs.
The Bidirectional Process Reward Model
Venue: arXiv cs.CL 2026
Year: 2026

Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promising approach to enhance the reasoning quality of Large Language Models (LLMs). However, most existing PRMs rely on a unidirectional left-to-right (L2R) evaluation scheme, which restricts their utilization of global context. In light of this challenge, we propose a novel bidirectional evaluation paradigm, named Bidirectional Process Reward Model (BiPRM). BiPRM incorporates a parallel right-to-left (R2L) evaluation stream, implemented via prompt reversal, alongside the conventional L2R flow. Then a gating mechanism is introduced to adaptively fuse the reward scores from both streams to yield a holistic quality assessment. Remarkably, compared to the original PRM, BiPRM introduces only a 0.3% parameter increase for the gating module, and the parallel execution of two streams incurs merely 5% inference time latency. Our extensive empirical evaluations spanning diverse benchmarks, LLM backbones, PRM objectives and sampling policies demonstrate that BiPRM consistently surpasses unidirectional baselines, achieving an average relative gain of 10.6% over 54 solution-level configurations and 37.7% in 12 step-level error detection scenarios. Generally, our results highlight the effectiveness, robustness and general applicability of BiPRM, offering a promising new direction for process-based reward modeling.
Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinity and Auxiliary Regularization
Venue: arXiv cs.LG 2026
Year: 2026

Recent advances in conditional generative modeling have introduced Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM) for estimating high-dimensional data distributions conditioned on scalar, continuous regression labels (e.g., angles, ages, or temperatures). However, these approaches face fundamental limitations: CcGAN suffers from data imbalance due to fixed-size vicinity constraints, while CCDM requires computationally expensive iterative sampling. To address these issues, we propose CcGAN-AVAR, an enhanced CcGAN framework featuring (1) two novel components for handling data imbalance - an adaptive vicinity mechanism that dynamically adjusts vicinity size and a multi-task discriminator that enhances generator training through auxiliary regression and density ratio estimation - and (2) the GAN framework's native one-step generator, enable 30x-2000x faster inference than CCDM. Extensive experiments on four benchmark datasets (64x64 to 256x256 resolution) across eleven challenging settings demonstrate that CcGAN-AVAR achieves state-of-the-art generation quality while maintaining sampling efficiency.
Toward Efficient Spiking Transformers: Synapse Pruning Meets Synergistic Learning-Based Compensation
Venue: arXiv cs.LG 2026
Year: 2026

As a foundational architecture of artificial intelligence models, Transformer has been recently adapted to spiking neural networks with promising performance across various tasks. However, existing spiking Transformer(ST)-based models require a substantial number of parameters and incur high computational costs, thus limiting their deployment in resource-constrained environments. To address these challenges, we propose combining synapse pruning with a synergistic learning-based compensation strategy to derive lightweight ST-based models. Specifically, two types of tailored pruning strategies are introduced to reduce redundancy in the weight matrices of ST blocks: an unstructured $\mathrm{L_{1}P}$ method to induce sparse representations, and a structured DSP method to induce low-rank representations. In addition, we propose an enhanced spiking neuron model, termed the synergistic leaky integrate-and-fire (sLIF) neuron, to effectively compensate for model pruning through synergistic learning between synaptic and intrinsic plasticity mechanisms. Extensive experiments on benchmark datasets demonstrate that the proposed methods significantly reduce model size and computational overhead while maintaining competitive performance. These results validate the effectiveness of the proposed pruning and compensation strategies in constructing efficient and high-performing ST-based models.
CellForge: Agentic Design of Virtual Cell Models
Venue: arXiv cs.LG 2026
Year: 2026

Virtual cell modeling aims to predict cellular responses to diverse perturbations but faces challenges from biological complexity, multimodal data heterogeneity, and the need for interdisciplinary expertise. We introduce CellForge, a multi-agent framework that autonomously designs and synthesizes neural network architectures tailored to specific single-cell datasets and perturbation tasks. Given raw multi-omics data and task descriptions, CellForge discovers candidate architectures through collaborative reasoning among specialized agents, then generates executable implementations. Our core contribution is the framework itself: showing that multi-agent collaboration mechanisms - rather than manual human design or single-LLM prompting - can autonomously produce executable, high-quality computational methods. This approach goes beyond conventional hyperparameter tuning by enabling entirely new architectural components such as trajectory-aware encoders and perturbation diffusion modules to emerge from agentic deliberation. We evaluate CellForge on six datasets spanning gene knockouts, drug treatments, and cytokine stimulations across multiple modalities (scRNA-seq, scATAC-seq, CITE-seq). The results demonstrate that the models generated by CellForge are highly competitive with established baselines, while revealing systematic patterns of architectural innovation. CellForge highlights the scientific value of multi-agent frameworks: collaboration among specialized agents enables genuine methodological innovation and executable solutions that single agents or human experts cannot achieve. This represents a paradigm shift toward autonomous scientific method development in computational biology. Code is available at https://github.com/gersteinlab/CellForge.
From Commits to Confidence: Towards Stability-Informed Risk Assessment in Open Source Software
Venue: arXiv cs.SE 2026
Year: 2026

Open source software (OSS) generates trillions of dollars in economic value and has become essential to the technical infrastructures that power organizations worldwide. As these systems increasingly depend on OSS, understanding the evolution of these projects is critical. While existing metrics provide insights into project health, one dimension remains understudied: project resilience, or the ability to return to normal operations after disturbances such as contributor departures,security vulnerabilities and bug report spikes. We hypothesize that stable commit patterns may serve as an indicator of underlying project characteristics such as mature governance, sustained contributors, and robust development processes, factors that existing research associates with resilience. Our findings reveal that only 2% of repositories exhibit daily stability, 29% achieve weekly stability, and 50\% demonstrate monthly stability, while the remaining half are unstable across all levels of granularity. Analysis of the 50 unstable repositories indicate that 86% of activity is concentrated among a few maintainers, with the top 3 contributors accounting for over 50% of commits in the past 5 years. In contrast, the 50 stable repositories distribute work more evenly, with the top 3 contributors representing less than 50% of commits. Our insights thus far indicate the fragile and multi-dimensional nature of OSS project stability, suggesting a need to go beyond commits to understand how our understanding of stability can be enriched with other considerations such as community engagement metrics and issue or pull request churn. Though our efforts only identified two repositories that achieved stability at all three temporal commit granularities, further investigation into their processes and policies can provide insights and foundations for stability-informed risk assessment in practice.
Highlight & Summarize: RAG without the jailbreaks
Venue: arXiv cs.CL 2026
Year: 2026

Preventing jailbreaking and model hijacking of Large Language Models (LLMs) is an important yet challenging task. When interacting with a chatbot, malicious users can input specially crafted prompts that cause the LLM to generate undesirable content or perform a different task from its intended purpose. Existing systems attempt to mitigate this by hardening the LLM's system prompt or using additional classifiers to detect undesirable content or off-topic conversations. However, these probabilistic approaches are relatively easy to bypass due to the very large space of possible inputs and undesirable outputs. We present and evaluate Highlight & Summarize (H&S), a new design pattern for retrieval-augmented generation (RAG) systems that prevents these attacks by design. The core idea is to perform the same task as a standard RAG pipeline (i.e., to provide natural language answers to questions, based on relevant sources) without ever revealing the user's question to the generative LLM. This is achieved by splitting the pipeline into two components: a highlighter, which takes the user's question and extracts ("highlights") relevant passages from the retrieved documents, and a summarizer, which takes the highlighted passages and summarizes them into a cohesive answer. We describe and implement several possible instantiations of H&S and evaluate their responses in terms of correctness, relevance, and quality. For certain question-answering (QA) tasks, the responses produced by H&S are judged to be as good, if not better, than those of a standard RAG pipeline.
Reliable Non-Leveled Homomorphic Encryption for Web Services
Venue: arXiv cs.CR 2026
Year: 2026

With the ubiquitous deployment of web services, ensuring data confidentiality has become a challenging imperative. Fully Homomorphic Encryption (FHE) presents a powerful solution for processing encrypted data; however, its widespread adoption is severely constrained by two fundamental bottlenecks: substantial computational overhead and the absence of a built-in automatic error correction mechanism. These limitations render the deployment of FHE in real-world, complex network environments impractical. To address this dual challenge, this work puts forward a new FHE framework that enhances computational efficiency and integrates an automatic error correction capability through new encoding techniques and an algebraic reliability layer. Our system was validated across several web workloads, including encrypted inference on MNIST and CIFAR-10, federated aggregation with non-IID data, and streaming analytics on household power consumption data. Experimental results demonstrate significant performance improvements, particularly with large polynomial modulus degrees such as $N=8192$, while maintaining task accuracy within $0.5\%$ of the plaintext baseline. Furthermore, our error correction mechanism reduces the service failure rate to below $0.5\%$ even under harsh, bursty network fault conditions.
Revisiting Deep Information Propagation: Fractal Frontier and Finite-size Effects
Venue: arXiv cs.LG 2026
Year: 2026

Information propagation characterizes how input correlations evolve across layers in deep neural networks. This framework has been well studied using mean-field theory, which assumes infinitely wide networks. However, these assumptions break down for practical, finite-size networks. In this work, we study information propagation in randomly initialized neural networks with finite width and reveal that the boundary between ordered and chaotic regimes exhibits a fractal structure. This shows the fundamental complexity of neural network dynamics, in a setting that is independent of input data and optimization. To extend this analysis beyond multilayer perceptrons, we leverage recently introduced Fourier-based structured transforms, and show that information propagation in convolutional neural networks also follow the same behavior. In practice, our investigation highlights the importance of finite network depth with respect to the tradeoff between separation and robustness.
When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs
Venue: arXiv cs.SD 2026
Year: 2026

As large language models (LLMs) become increasingly integrated into daily life, audio has emerged as a key interface for human-AI interaction. However, this convenience also introduces new vulnerabilities, making audio a potential attack surface for adversaries. Our research introduces WhisperInject, a two-stage adversarial audio attack framework that manipulates state-of-the-art audio language models to generate harmful content. Our method embeds harmful payloads as subtle perturbations into audio inputs that remain intelligible to human listeners. The first stage uses a novel reward-based white-box optimization method, Reinforcement Learning with Projected Gradient Descent (RL-PGD), to jailbreak the target model and elicit harmful native responses. This native harmful response then serves as the target for Stage 2, Payload Injection, where we use gradient-based optimization to embed subtle perturbations into benign audio carriers, such as weather queries or greeting messages. Our method achieves average attack success rates of 60-78% across two benchmarks and five multimodal LLMs, validated by multiple evaluation frameworks. Our work demonstrates a new class of practical, audio-native threats, moving beyond theoretical exploits to reveal a feasible and covert method for manipulating multimodal AI systems.
Dual Domain Expurgated Error Exponents for Source Coding with Side Information
Venue: arXiv cs.IT 2026
Year: 2026

We introduce an expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents. Dual-domain methods yield optimization problems over few parameters, with any sub-optimal choice resulting in an achievable exponent, as opposed to primal-domain optimization over distributions. In addition, dual-domain methods naturally allow for general alphabets and/or memory. We derive two such expurgated error exponents for different random-coding ensembles in the case where the decoder is possibly mismatched with respect to the source and side information joint distribution. We show the better of the exponents coincides with the Csiszár-Körner exponent obtained via a graph decomposition lemma. We show some numerical examples that illustrate the differences between the two exponents and show that in the case of source coding without side information, the expurgated exponent coincides with the error exponent of the source optimal code.
DSKC: Domain Style Modeling with Adaptive Knowledge Consolidation for Exemplar-free Lifelong Person Re-Identification
Venue: arXiv cs.CV 2026
Year: 2026

Lifelong Person Re-identification (LReID) aims to continuously match individuals across camera views from sequential data streams. Existing LReID methods often ignore domain-specific style awareness and unified knowledge consolidation, which are crucial for mitigating forgetting when adapting to new information. We propose DSKC, a novel rehearsal-free and distillation-free framework for LReID. DSKC designs a domain-style encoder (DSE) to dynamically model domain-specific styles, and a unified knowledge consolidation (UKC) mechanism to adaptively integrate instance-level representations with domain-specific style into a cross-domain unified representation. By leveraging unified representation as a bridge, DSKC explicitly models inter-domain associations at both instance and domain levels to enhance anti-forgetting and generalization. Experimental results demonstrate that our DSKC outperforms state-of-the-art methods in two training orders and enhances the model's strong performance. Our code is available at https://github.com/LiuShiBen/DKUA.