OpenRaiser/Intern-Atlas huggingface dataset:
Beyond ISAC: Toward Integrated Heterogeneous Service Provisioning via Elastic Multi-Dimensional Multiple Access
Venue: arXiv cs.IT 2026
Year: 2026

Due to the growing complexity of vertical applications, current integrated sensing and communications (ISAC) in wireless networks remains insufficient for supporting all required beyond communication services. To this end, future networks are evolving toward an integrated heterogeneous service provisioning (IHSP) platform, which seeks to integrate a broad range of heterogeneous services beyond the dual-function scope of ISAC. Nevertheless, this trend intensifies conflicts among concurrent heterogeneous service requirements under constrained resource sharing. In this paper, we overcome this challenge by the joint use of two novel elastic design strategies: compromised service value assessment and flexible multi-dimensional resource multiplexing. Consequently, we propose a value-prioritized elastic multi-dimensional multiple access (MDMA) mechanism for IHSP systems. First, we modify the Value-of-Service (VoS) metric by incorporating elastic parameters to characterize user-specific tolerance and compromise in response to various performance degradations under constrained resources. This VoS metric serves as the foundation for prioritizing services and enabling effective fairness service scheduling among concurrent competing demands. Next, we adapt the MDMA to elastically multiplex services using appropriate multiple access schemes across different resource domains. This protocol leverages user-specific interference tolerances and cancellation capabilities across different domains to reduce resource-demanding conflicts and co-channel interference within the same domain. Then, we maximize the system's VoS by jointly optimizing MDMA and power allocation. Since this problem is non-convex, we develop a monotonic optimization-assisted dynamic programming algorithm for the optimal solution and a VoS-prioritized successive convex approximation algorithm for efficient suboptimal computation.
From Data Behavior to Code Analysis: A Multimodal Study on Security and Privacy Challenges in Blockchain-Based DApp
Venue: arXiv cs.CR 2026
Year: 2026

The recent proliferation of blockchain-based decentralized applications (DApp) has catalyzed transformative advancements in distributed systems, with extensive deployments observed across financial, entertainment, media, and cybersecurity domains. These trustless architectures, characterized by their decentralized nature and elimination of third-party intermediaries, have garnered substantial institutional attention. Consequently, the escalating security challenges confronting DApp demand rigorous scholarly investigation. This study initiates with a systematic analysis of behavioral patterns derived from empirical DApp datasets, establishing foundational insights for subsequent methodological developments. The principal security vulnerabilities in Ethereum-based smart contracts developed via Solidity are then critically examined. Specifically, reentrancy vulnerability attacks are addressed by formally representing contract logic using highly expressive code fragments. This enables precise source code-level detection via bidirectional long short-term memory networks with attention mechanisms (BLSTM-ATT). Regarding privacy preservation challenges, contemporary solutions are evaluated through dual analytical lenses: identity privacy preservation and transaction anonymity enhancement, while proposing future research trajectories in cryptographic obfuscation techniques.
Reinforcement Learning from Human Feedback
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning from human feedback (RLHF) has become an important technical and storytelling tool to deploy the latest machine learning systems. In this book, we hope to give a gentle introduction to the core methods for people with some level of quantitative background. The book starts with the origins of RLHF -- both in recent literature and in a convergence of disparate fields of science in economics, philosophy, and optimal control. We then set the stage with definitions, problem formulation, data collection, and other common math used in the literature. The core of the book details every optimization stage in using RLHF, from starting with instruction tuning to training a reward model and finally all of rejection sampling, reinforcement learning, and direct alignment algorithms. The book concludes with advanced topics -- understudied research questions in synthetic data and evaluation -- and open questions for the field.
Evaluating the Diversity and Quality of LLM Generated Content
Venue: arXiv cs.CL 2026
Year: 2026

Recent work suggests that preference-tuning techniques -- such as Reinforcement Learning from Human Feedback (RLHF) methods like PPO and GRPO, as well as alternatives like DPO -- reduce diversity, creating a dilemma given that these models are widely deployed in applications requiring varied outputs. We argue that diversity without consideration of quality has limited practical value. To address this issue, we introduce a framework for measuring effective semantic diversity -- diversity among outputs that meet quality thresholds -- which better reflects the practical utility of large language models (LLMs). Using open-ended tasks that require no human intervention, we find counterintuitive results: when using diversity metrics that do not explicitly consider quality, preference-tuned models -- particularly those trained via RL -- often produce outputs with lower diversity; however, these same preference-tuned models generate greater effective semantic diversity than supervised fine-tuned (SFT) or base models. Our analysis further shows another trend: while larger models may exhibit greater effective semantic diversity than smaller models, the smaller models are consistently more parameter-efficient at producing unique content within a fixed sampling budget. These findings have practical implications for applications that require diverse yet high-quality outputs, from creative assistance to synthetic data generation.
Provable Secure Steganography Based on Adaptive Dynamic Sampling
Venue: arXiv cs.CR 2026
Year: 2026

The security of private communication is increasingly at risk due to widespread surveillance. Steganography, a technique for embedding secret messages within innocuous carriers, enables covert communication over monitored channels. Provably Secure Steganography (PSS), which ensures computational indistinguishability between the normal model output and steganography output, is the state-of-the-art in this field. However, current PSS methods often require obtaining the explicit distributions of the model. In this paper, we propose a provably secure steganography scheme that only requires a model API that accepts a seed as input. Our core mechanism involves sampling a candidate set of tokens and constructing a map from possible message bit strings to these tokens. The output token is selected by applying this mapping to the real secret message, which provably preserves the original model's distribution. To ensure correct decoding, we address collision cases, where multiple candidate messages map to the same token, by maintaining and strategically expanding a dynamic collision set within a bounded size range. Extensive evaluations of three real-world datasets and three large language models demonstrate that our sampling-based method is comparable with existing PSS methods in efficiency and capacity.
Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recovery
Venue: arXiv cs.CV 2026
Year: 2026

We propose spatial polarization multiplexing (SPM) for joint sensing of shape and reflectance of a static or dynamic deformable object, which is also invisible to the naked eye. Past structured-light methods are limited to shape acquisition and cannot recover reflectance as they alter scene appearance. Our key idea is to spatially multiplex a polarization pattern to encode the incident ray and also densely sample the reflected light. We derive a quantized polarized light pattern that can be robustly and uniquely decoded from the reflected Angle of Linear Polarization (AoLP) values. It also enables single-shot disentanglement of polarimetric diffuse and specular reflections for accurate BRDF estimation. We achieve this spatial polarization multiplexing (SPM) with a constrained de Bruijn sequence. We validate this novel invisible single-shot shape and reflectance method with real static and dynamic objects. The results demonstrate the effectiveness of SPM for accurate shape and BRDF measurement which opens new avenues of application for 3D sensing thanks to its invisibility and ability to jointly recover the radiometric properties.
Social Human Robot Embodied Conversation (SHREC) Dataset: Benchmarking Foundational Models' Social Reasoning
Venue: arXiv cs.HC 2026
Year: 2026

Our work focuses on the social reasoning capabilities of foundation models for real-world human-robot interactions. We introduce the Social Human Robot Embodied Conversation (SHREC) Dataset, a benchmark of $\sim$400 real-world human-robot interaction videos and over 10K annotations, capturing robot social errors, competencies, underlying rationales, and corrections. Unlike prior datasets focused on human-human interactions, the SHREC Dataset uniquely highlights the social challenges faced by real-world social robots such as emotion understanding, intention tracking, and conversational mechanics. Moreover, current foundation models struggle to recognize these deficits, which manifest as subtle, socially situated failures. To evaluate AI models' capacity for social reasoning, we define eight benchmark tasks targeting critical areas such as (1) detection of social errors and competencies, (2) identification of underlying social attributes, (3) comprehension of interaction flow, and (4) providing rationale and alternative correct actions. Experiments with state-of-the-art foundation models, alongside human evaluations, reveal substantial performance gaps -- underscoring the difficulty and providing directions in developing socially intelligent AI.
On the $p$-adic Skolem Problem
Venue: arXiv cs.LO 2026
Year: 2026

The Skolem Problem asks to determine whether a given linear recurrence sequence (LRS) has a zero term. Showing decidability of this problem is equivalent to giving an effective proof of the Skolem-Mahler-Lech Theorem, which asserts that a non-degenerate LRS has finitely many zeros. The latter result was proven over 90 years ago via an ineffective method showing that such an LRS has only finitely many $p$-adic zeros. In this paper we consider the problem of determining whether a given LRS has a $p$-adic zero, as well as the corresponding function problem of computing exact representations of all $p$-adic zeros. We present algorithms for both problems and report on their implementation. The output of the algorithms is unconditionally correct, and termination is guaranteed subject to the $p$-adic Schanuel Conjecture (a standard number-theoretic hypothesis concerning the $p$-adic exponential function). While these algorithms do not solve the Skolem Problem, they can be exploited to find natural-number and rational zeros under additional hypotheses. To illustrate this, we apply our results to show decidability of the Simultaneous Skolem Problem (determine whether two coprime linear recurrences have a common natural-number zero), again subject to the $p$-adic Schanuel Conjecture.
Towards High-Goodput LLM Serving with Prefill-decode Multiplexing
Venue: arXiv cs.OS 2026
Year: 2026

Large Language Model (LLM) serving must meet stringent Service Level Objectives (SLOs) for both the prefill and decode phases. Some existing solutions disaggregate the two phases, causing potential resource idleness or compute redundancy. Others split the prefill phase into chunks and fuse it with decode iteration, creating a dilemma between SLO compliance and high utilization. To address these issues, an efficient serving system should dynamically adapt compute allocation, decouple compute from memory management, and execute prefill and decode independently. We present MuxWise, an LLM serving framework that adopts a new paradigm, intra-GPU prefill-decode multiplexing, to meet these requirements. To fully exploit the paradigm, MuxWise integrates a bubble-less multiplex engine, a contention-tolerant estimator, and an SLO-aware dispatcher. Evaluation shows that MuxWise improves peak throughput under SLO guarantees by an average of 2.20x (up to 3.06x) over state-of-the-art baselines.
Synthesising Asynchronous Automata from Fair Specifications
Venue: arXiv cs.FL 2026
Year: 2026

Asynchronous automata are a model of distributed finite state processes synchronising on shared actions. A celebrated result by Zielonka shows how a deterministic asynchronous automaton (AA) can be synthesised, starting from two inputs: a global specification given as a deterministic finite-state automaton (DFA) and a distribution of the alphabet into local alphabets for each process. The DFA to AA translation is particularly complex and has been revisited several times, with no complete prototype tool provided for the full construction. In this work, we revisit this construction on a restricted class of "fair" specifications: a DFA describes a fair specification if in every loop, all processes participate in at least one action, so no process is starved. For fair specifications, we present a new construction to synthesise an AA. Our construction results in an AA where every process has a number of local states that is linear in the number of states of the DFA, and where the only exponential explosion is related to a fairness parameter: the length of the longest word that can be read in the DFA in which not every process participates. We have implemented a prototype tool showing how it can be applied to some examples, in particular, a concrete one: the dining philosophers problem. Finally, we show how this construction can be combined with an existing construction for hierarchical process architectures, in order to relax the fairness assumption. We have implemented a prototype tool showing how it can be applied to some examples, in particular, a concrete one: the dining philosophers problem. Finally, we show how this construction can be combined with an existing construction for hierarchical process architectures, in order to relax the fairness assumption.
Emergence and Evolution of Interpretable Concepts in Diffusion Models
Venue: arXiv cs.CV 2026
Year: 2026

Diffusion models have become the go-to method for text-to-image generation, producing high-quality images from pure noise. However, the inner workings of diffusion models is still largely a mystery due to their black-box nature and complex, multi-step generation process. Mechanistic interpretability techniques, such as Sparse Autoencoders (SAEs), have been successful in understanding and steering the behavior of large language models at scale. However, the great potential of SAEs has not yet been applied toward gaining insight into the intricate generative process of diffusion models. In this work, we leverage the SAE framework to probe the inner workings of a popular text-to-image diffusion model, and uncover a variety of human-interpretable concepts in its activations. Interestingly, we find that even before the first reverse diffusion step is completed, the final composition of the scene can be predicted surprisingly well by looking at the spatial distribution of activated concepts. Moreover, going beyond correlational analysis, we design intervention techniques aimed at manipulating image composition and style, and demonstrate that (1) in early stages of diffusion image composition can be effectively controlled, (2) in the middle stages image composition is finalized, however stylistic interventions are effective, and (3) in the final stages only minor textural details are subject to change.
Sound and Complete Invariant-Based Heap Encodings (Technical Report)
Venue: arXiv cs.LO 2026
Year: 2026

Verification of programs operating on heap-allocated data structures, for instance lists or trees, poses significant challenges due to the potentially unbounded size of such data structures. We present time-indexed heap invariants, a novel invariant-based heap encoding leveraging uninterpreted predicates and prophecy variables to reduce verification of heap-manipulating programs to verification of programs over integers only. Our encoding of heap is general and agnostic to specific data structures. To the best of our knowledge, our approach is the first heap invariant-based method that achieves both soundness and completeness. We provide formal proofs establishing the correctness of our encodings. Through an experimental evaluation, we demonstrate that time-indexed heap invariants significantly extend the capability of existing verification tools, allowing automatic verification of programs with heap that were previously out of reach for state-of-the-art tools.
Boosting Generative Image Modeling via Joint Image-Feature Synthesis
Venue: arXiv cs.CV 2026
Year: 2026

Latent diffusion models (LDMs) dominate high-quality image generation, yet integrating representation learning with generative modeling remains a challenge. We introduce a novel generative image modeling framework that seamlessly bridges this gap by leveraging a diffusion model to jointly model low-level image latents (from a variational autoencoder) and high-level semantic features (from a pretrained self-supervised encoder like DINO). Our latent-semantic diffusion approach learns to generate coherent image-feature pairs from pure noise, significantly enhancing both generative quality and training efficiency, all while requiring only minimal modifications to standard Diffusion Transformer architectures. By eliminating the need for complex distillation objectives, our unified design simplifies training and unlocks a powerful new inference strategy: Representation Guidance, which leverages learned semantics to steer and refine image generation. Evaluated in both conditional and unconditional settings, our method delivers substantial improvements in image quality and training convergence speed, establishing a new direction for representation-aware generative modeling. Project page and code: https://representationdiffusion.github.io
Leveraging Noisy Manual Labels as Useful Information: An Information Fusion Approach for Enhanced Variable Selection in Penalized Logistic Regression
Venue: arXiv cs.LG 2026
Year: 2026

In large-scale supervised learning, penalized logistic regression (PLR) effectively mitigates overfitting through regularization, yet its performance critically depends on robust variable selection. This paper demonstrates that label noise introduced during manual annotation, often dismissed as a mere artifact, can serve as a valuable source of information to enhance variable selection in PLR. We theoretically show that such noise, intrinsically linked to classification difficulty, helps refine the estimation of non-zero coefficients compared to using only ground truth labels, effectively turning a common imperfection into a useful information resource. To efficiently leverage this form of information fusion in large-scale settings where data cannot be stored on a single machine, we propose a novel partition insensitive parallel algorithm based on the alternating direction method of multipliers (ADMM). Our method ensures that the solution remains invariant to how data is distributed across workers, a key property for reproducible and stable distributed learning, while guaranteeing global convergence at a sublinear rate. Extensive experiments on multiple large-scale datasets show that the proposed approach consistently outperforms conventional variable selection techniques in both estimation accuracy and classification performance, affirming the value of intentionally fusing noisy manual labels into the learning process.
MEC Task Offloading in AIoT: A User-Centric DRL Model Splitting Inference Scheme
Venue: arXiv cs.NI 2026
Year: 2026

With the rapid development of the Artificial Intelligence of Things (AIoT), mobile edge computing (MEC) becomes an essential technology underpinning AIoT applications. However, multi-angle resource constraints, multi-user task competition, and the complexity of task offloading decisions in dynamic MEC environments present new technical challenges. Therefore, a user-centric deep reinforcement learning (DRL) model splitting inference scheme is proposed to address the problem. This scheme combines model splitting inference technology and designs a UCMS_MADDPG-based offloading algorithm to realize efficient model splitting inference responses in the dynamic MEC environment with multi-angle resource constraints. Specifically, we formulate a joint optimization problem that integrates resource allocation, server selection, and task offloading, aiming to minimize the weighted sum of task execution delay and energy consumption. We also introduce a user-server co-selection algorithm to address the selection issue between users and servers. Furthermore, we design an algorithm centered on user pre-decision to coordinate the outputs of continuous and discrete hybrid decisions, and introduce a priority sampling mechanism based on reward-error trade-off to optimize the experience replay mechanism of the network. Simulation results show that the proposed UCMS_MADDPG-based offloading algorithm demonstrates superior overall performance compared with other benchmark algorithms in dynamic environments.
Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity
Venue: arXiv cs.CL 2026
Year: 2026

Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but its complexity, which is marked by high dimensionality, sparsity, and batch effects, which poses major computational challenges. Transformer-based models have made significant advances in this domain but are often limited by their quadratic complexity and suboptimal handling of long-range dependencies. In this work, we introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling. Leveraging the Bi-Mamba architecture, GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines. The model is pretrained on nearly 30 million cells and incorporates biologically informed objectives, including pathway-aware contrastive loss and rank-based gene encoding. We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness. These results position GeneMamba as a practical and powerful alternative to transformer-based methods, advancing the development of biologically grounded, scalable tools for large-scale single-cell data analysis.