OpenRaiser/Intern-Atlas huggingface dataset:
Developing ChemDFM as a large language foundation model for chemistry
Venue: arXiv cs.CL 2025
Year: 2025

Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require training and tuning for specific tasks. A more generic and efficient solution would be an AI model that could address many tasks and support free-form dialogue in the broad field of chemistry. In its utmost form, such a generalist AI chemist could be referred to as Chemical General Intelligence. Large language models (LLMs) have recently logged tremendous success in the general domain of natural language processing, showing emerging task generalization and free-form dialogue capabilities. However, domain knowledge of chemistry is largely missing when training general-domain LLMs. The lack of such knowledge greatly hinders the performance of generalist LLMs in the field of chemistry. To this end, we develop ChemDFM, a pioneering LLM for chemistry trained on 34B tokens from chemical literature and textbooks, and fine-tuned using 2.7M instructions. As a result, it can understand and reason with chemical knowledge in free-form dialogue. Quantitative evaluations show that ChemDFM significantly surpasses most representative open-source LLMs. It outperforms GPT-4 on a great portion of chemical tasks, despite the substantial size difference. We have open-sourced the inference codes, evaluation datasets, and model weights of ChemDFM on Huggingface (https://huggingface.co/OpenDFM/ChemDFM-v1.0-13B).
Regularized Q-Learning with Linear Function Approximation
Venue: arXiv cs.AI 2025
Year: 2025

Regularized Markov Decision Processes serve as models of sequential decision making under uncertainty wherein the decision maker has limited information processing capacity and/or aversion to model ambiguity. With functional approximation, the convergence properties of learning algorithms for regularized MDPs (e.g. soft Q-learning) are not well understood because the composition of the regularized Bellman operator and a projection onto the span of basis vectors is not a contraction with respect to any norm. In this paper, we consider a bi-level optimization formulation of regularized Q-learning with linear functional approximation. The {\em lower} level optimization problem aims to identify a value function approximation that satisfies Bellman's recursive optimality condition and the {\em upper} level aims to find the projection onto the span of basis vectors. This formulation motivates a single-loop algorithm with finite time convergence guarantees. The algorithm operates on two time-scales: updates to the projection of state-action values are `slow' in that they are implemented with a step size that is smaller than the one used for `faster' updates of approximate solutions to Bellman's recursive optimality equation. We show that, under certain assumptions, the proposed algorithm converges to a stationary point in the presence of Markovian noise. In addition, we provide a performance guarantee for the policies derived from the proposed algorithm.
Improving LLM Reliability with RAG in Religious Question-Answering: MufassirQAS
Venue: arXiv cs.CL 2025
Year: 2025

Religious teachings can sometimes be complex and challenging to grasp, but chatbots can serve as effective assistants in this domain. Large Language Model (LLM) based chatbots, powered by Natural Language Processing (NLP), can connect related topics and provide well-supported responses to intricate questions, making them valuable tools for religious education. However, LLMs are prone to hallucinations as they can generate inaccurate or irrelevant information, and these can include sensitive content that could be offensive, inappropriate, or controversial. Addressing such topics without inadvertently promoting hate speech or disrespecting certain beliefs remains a significant challenge. As a solution to these issues, we introduce MufassirQAS, a system that enhances LLM accuracy and transparency using a vector database-driven Retrieval-Augmented Generation (RAG) approach. We built a dataset comprising fundamental books containing Turkish translations and interpretations of Islamic texts. This database is leveraged to answer religious inquiries while ensuring that responses remain reliable and contextually grounded. Our system also presents the relevant dataset sections alongside the LLM-generated answers, reinforcing transparency. We carefully designed system prompts to prevent harmful, offensive, or disrespectful outputs, ensuring that responses align with ethical and respectful discourse. Moreover, MufassirQAS provides supplementary details, such as source page numbers and referenced articles, to enhance credibility. To evaluate its effectiveness, we tested MufassirQAS against ChatGPT with sensitive questions, and our system demonstrated superior performance in maintaining accuracy and reliability. Future work will focus on improving accuracy and refining prompt engineering techniques to further minimize biases and ensure even more reliable responses.
Social Interpretable Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement Learning (RL) bears the promise of being a game-changer in many applications. However, since most of the literature in the field is currently focused on opaque models, the use of RL in high-stakes scenarios, where interpretability is crucial, is still limited. Recently, some approaches to interpretable RL, e.g., based on Decision Trees, have been proposed, but one of the main limitations of these techniques is their training cost. To overcome this limitation, we propose a new method, called Social Interpretable RL (SIRL), that can substantially reduce the number of episodes needed for training. Our method mimics a social learning process, where each agent in a group learns to solve a given task based both on its own individual experience as well as the experience acquired together with its peers. Our approach is divided into the following two phases. (1) In the collaborative phase, all the agents in the population interact with a shared instance of the environment, where each agent observes the state and independently proposes an action. Then, voting is performed to choose the action that will actually be deployed in the environment. (2) In the individual phase, then, each agent refines its individual performance by interacting with its own instance of the environment. This mechanism makes the agents experience a larger number of episodes with little impact on the computational cost of the process. Our results (on 6 widely-known RL benchmarks) show that SIRL not only reduces the computational cost by a factor varying from a minimum of 43% to a maximum 76%, but it also increases the convergence speed and, often, improves the quality of the solutions.
ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
Venue: arXiv cs.CV 2025
Year: 2025

In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps and insufficient characterization of the global column features. To tackle this problem, we propose a novel infrared image destriping method, called Asymmetric Sampling Correction Network (ASCNet), that can effectively capture global column relationships and embed them into a U-shaped framework, providing comprehensive discriminative representation and seamless semantic connectivity. Our ASCNet consists of three core elements: Residual Haar Discrete Wavelet Transform (RHDWT), Pixel Shuffle (PS), and Column Non-uniformity Correction Module (CNCM). Specifically, RHDWT is a novel downsampler that employs double-branch modeling to effectively integrate stripe-directional prior knowledge and data-driven semantic interaction to enrich the feature representation. Observing the semantic patterns crosstalk of stripe noise, PS is introduced as an upsampler to prevent excessive apriori decoding and performing semantic-bias-free image reconstruction. After each sampling, CNCM captures the column relationships in long-range dependencies. By incorporating column, spatial, and self-dependence information, CNCM well establishes a global context to distinguish stripes from the scene's vertical structures. Extensive experiments on synthetic data, real data, and infrared small target detection tasks demonstrate that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively. Our code will be made publicly available at https://github.com/xdFai/ASCNet.
Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel
Venue: arXiv cs.CL 2025
Year: 2025

Existing linguistic steganography schemes often overlook the conditional probability (CP) of tokens in the candidate pool, allocating the one coding to all tokens, which results in identical selection likelihoods. This approach leads to the selection of low-CP tokens, degrading the quality of stegos and making them more detectable. This paper proposes a scheme based on the interval allocated, called DAIRstega. DAIRstega first uses a portion of the read secret to build the roulette area. Then, this scheme uses the idea of the roulette wheel and takes the CPs of tokens as the main basis for allocating the roulette area (i.e., the interval length). Thus, tokens with larger CPs are allocated more area. The secret will have an increased likelihood of selecting a token with a higher CP. During allocation, we designed some allocation functions and three constraints to optimize the process. Additionally, DAIRstega supports prompt-based controllable generation of stegos. Rich experiments show that the proposed embedding way and DAIRstega perform better than the existing ways and baselines, which shows strong perceptual, statistical, and semantic concealment, as well as anti-steganalysis ability. It can also generate high-quality longer stegos, addressing the deficiencies in this task. DAIRstega is confirmed to have potential as a secure watermarking, offering insights for its development.
HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation
Venue: arXiv cs.HC 2025
Year: 2025

Routes represent an integral part of triggering emotions in drivers. Navigation systems allow users to choose a navigation strategy, such as the fastest or shortest route. However, they do not consider the driver's emotional well-being. We present HappyRouting, a novel navigation-based empathic car interface guiding drivers through real-world traffic while evoking positive emotions. We propose design considerations, derive a technical architecture, and implement a routing optimization framework. Our contribution is a machine learning-based generated emotion map layer, predicting emotions along routes based on static and dynamic contextual data. We evaluated HappyRouting in a real-world driving study (N=13), finding that happy routes increase subjectively perceived valence by 11% (p=.007). Although happy routes take 1.25 times longer on average, participants perceived the happy route as shorter, presenting an emotion-enhanced alternative to today's fastest routing mechanisms. We discuss how emotion-based routing can be integrated into navigation apps, promoting emotional well-being for mobility use.
Quantum Circuit Reconstruction from Power Side-Channel Attacks on Quantum Computer Controllers
Venue: arXiv cs.CR 2025
Year: 2025

The interest in quantum computing has grown rapidly in recent years, and with it grows the importance of securing quantum circuits. A novel type of threat to quantum circuits that dedicated attackers could launch are power trace attacks. To address this threat, this paper presents first formalization and demonstration of using power traces to unlock and steal quantum circuit secrets. With access to power traces, attackers can recover information about the control pulses sent to quantum computers. From the control pulses, the gate level description of the circuits, and eventually the secret algorithms can be reverse engineered. This work demonstrates how and what information could be recovered. This work uses algebraic reconstruction from power traces to realize two new types of single trace attacks: per-channel and total power attacks. The former attack relies on per-channel measurements to perform a brute-force attack to reconstruct the quantum circuits. The latter attack performs a single-trace attack using Mixed-Integer Linear Programming optimization. Through the use of algebraic reconstruction, this work demonstrates that quantum circuit secrets can be stolen with high accuracy. Evaluation on 32 real benchmark quantum circuits shows that our technique is highly effective at reconstructing quantum circuits. The findings not only show the veracity of the potential attacks, but also the need to develop new means to protect quantum circuits from power trace attacks. Throughout this work real control pulse information from real quantum computers is used to demonstrate potential attacks based on simulation of collection of power traces.
On PIR and SPIR Over Gaussian MAC
Venue: arXiv cs.IT 2025
Year: 2025

This paper revisits the problems of Private Information Retrieval (PIR) and Symmetric PIR (SPIR). In PIR, a user retrieves a desired message from $N$ replicated, non-communicating databases, each storing the same $M$ messages, while preserving the privacy of the requested message index. SPIR extends this notion further by additionally protecting the privacy of the databases, ensuring that the user learns no information beyond the requested message. In this paper, we assume a block-fading Additive White Gaussian Noise Multiple Access Channel (AWGN MAC) linking the user and the databases. Previous work by Shmuel et al. presented a joint channel-PIR scheme utilizing the Compute and Forward (C\&F) protocol, demonstrating the potential of a joint PIR-channel coding scheme over a separated one, yet still lagging behind the channel capacity and requiring significant computational complexity. We propose an improved scheme that offers reduced computational complexity while improving the achievable rate for finite parameters, as well as its scaling laws. Specifically, the achievable rate outperforms the C\&F-based approach and scales with the number of databases $N$ and the power $P$ similarly to the channel capacity \textit{without the privacy constraint}. Furthermore, the analysis demonstrates that the improved rate exhibits only a finite gap from this unconstrained channel capacity -- $1$ $bit/sec/Hz$ as $N$ increases. Finally, we provide two SPIR schemes. The first is a modification for our PIR scheme to attain SPIR with no rate loss, which is accomplished by introducing shared common randomness among databases. The second is a novel joint channel-SPIR scheme that utilizes the channel and lattice codes characteristics to nontrivially achieve SPIR without requiring common randomness, at the price of a loss in the achievable rate.
Pilotfish: Distributed Execution for Scalable Blockchains
Venue: arXiv cs.DC 2025
Year: 2025

Scalability is a crucial requirement for modern large-scale systems, enabling elasticity and ensuring responsiveness under varying load. While cloud systems have achieved scalable architectures, blockchain systems remain constrained by the need to over-provision validator machines to handle peak load. This leads to resource inefficiency, poor cost scaling, and limits on performance. To address these challenges, we introduce Pilotfish, the first scale-out transaction execution engine for blockchains. Pilotfish enables validators to scale horizontally by distributing transaction execution across multiple worker machines, allowing elasticity without compromising consistency or determinism. It integrates seamlessly with the lazy blockchain architecture, completing the missing piece of execution elasticity. To achieve this, Pilotfish tackles several key challenges: ensuring scalable and strongly consistent distributed transactions, handling partial crash recovery with lightweight replication, and maintaining concurrency with a novel versioned-queue scheduling algorithm. Our evaluation shows that Pilotfish scales linearly up to at least eight workers per validator for compute-bound workloads, while maintaining low latency. By solving scalable execution, Pilotfish brings blockchains closer to achieving end-to-end elasticity, unlocking new possibilities for efficient and adaptable blockchain systems.
Detecting LLM-assisted writing in scientific communication: Are we there yet?
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs), exemplified by ChatGPT, have significantly reshaped text generation, particularly in the realm of writing assistance. While ethical considerations underscore the importance of transparently acknowledging LLM use, especially in scientific communication, genuine acknowledgment remains infrequent. A potential avenue to encourage accurate acknowledging of LLM-assisted writing involves employing automated detectors. Our evaluation of four cutting-edge LLM-generated text detectors reveals their suboptimal performance compared to a simple ad-hoc detector designed to identify abrupt writing style changes around the time of LLM proliferation. We contend that the development of specialized detectors exclusively dedicated to LLM-assisted writing detection is necessary. Such detectors could play a crucial role in fostering more authentic recognition of LLM involvement in scientific communication, addressing the current challenges in acknowledgment practices.
Higher-Dimensional Timed Automata for Real-Time Concurrency
Venue: arXiv cs.FL 2025
Year: 2025

We present a new language semantics for real-time concurrency. Its operational models are higher-dimensional timed automata (HDTAs), a generalization of both higher-dimensional automata and timed automata. In real-time concurrent systems, both concurrency of events and timing and duration of events are of interest. Thus, HDTAs combine the non-interleaving concurrency model of higher-dimensional automata with the real-time modeling, using clocks, of timed automata. We define languages of HDTAs as sets of interval-timed pomsets with interfaces. We show that language inclusion of HDTAs is undecidable. On the other hand, using a region construction we can show that untimings of HDTA languages have enough regularity so that untimed language inclusion is decidable. On a more practical note, we give new insights on when practical applications, like checking reachability, might benefit from using HDTAs instead of classical timed automata.
On The Theory of Semantic Information and Communication for Logical Inference
Venue: arXiv cs.IT 2025
Year: 2025

First-Order Logic (FOL), also called first-order predicate calculus, is a formal language that provides a framework to comprehensively represent a world and its present state, including all of its entities, attributes, and complex interrelations, irrespective of their physical modality (e.g., text, image, or sensor data). Grounded in this universal representation, this paper develops a mathematical theory for semantic information and communication tailored to tasks involving logical reasoning and inference. For semantic communication, our framework distinguishes between two fundamental components: the physical cost of transmitting symbols of the FOL language and the logical content those symbols represent. A calibrated measure for semantic content is proposed, which allows for the consistent comparison of information value across different logical systems. This measure quantifies the degree to which a message reduces uncertainty about the true state of the world. Building on this measure, semantic entropy, conditional and mutual information metrics are defined. These metrics are then used to formulate optimizable objectives for semantic communication, designed to preserve the information most relevant for logical reasoning task at the receiver while adhering to a transmission budget. The framework's operational value is demonstrated through experiments in semantic compression, where the proposed objectives are used to manage the trade-off between transmission cost and the preservation of logical content; and deductive inference, where increasing world-state awareness improves deduction performance.
Robustness in Wireless Distributed Learning: An Information-Theoretic Analysis
Venue: arXiv cs.IT 2025
Year: 2025

In recent years, the application of artificial intelligence (AI) in wireless communications has demonstrated inherent robustness against wireless channel distortions. Most existing works empirically leverage this robustness to yield considerable performance gains through AI architectural designs. However, there is a lack of direct theoretical analysis of this robustness and its potential to enhance communication efficiency, which restricts the full exploitation of these advantages. In this paper, we adopt an information-theoretic approach to evaluate the robustness in wireless distributed learning by deriving an upper bound on the task performance loss due to imperfect wireless channels. Utilizing this insight, we define task outage probability and characterize the maximum transmission rate under task accuracy guarantees, referred to as the task-aware $ε$-capacity resulting from the robustness. To achieve the utility of the theoretical results in practical settings, we present an efficient algorithm for the approximation of the upper bound. Subsequently, we devise a robust training framework that optimizes the trade-off between robustness and task accuracy, enhancing the robustness against channel distortions. Extensive experiments validate the effectiveness of the proposed upper bound and task-aware $ε$-capacity and demonstrate that the proposed robust training framework achieves high robustness, thus ensuring a high transmission rate while maintaining inference performance.
On the Challenges of Fuzzing Techniques via Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

In the modern era where software plays a pivotal role, software security and vulnerability analysis are essential for secure software development. Fuzzing test, as an efficient and traditional software testing method, has been widely adopted across various domains. Meanwhile, the rapid development in Large Language Models (LLMs) has facilitated their application in the field of software testing, demonstrating remarkable performance. As existing fuzzing test techniques are not fully automated and software vulnerabilities continue to evolve, there is a growing interest in leveraging large language models to generate fuzzing test. In this paper, we present a systematic overview of the developments that utilize large language models for the fuzzing test. To our best knowledge, this is the first work that covers the intersection of three areas, including LLMs, fuzzing test, and fuzzing test generated based on LLMs. A statistical analysis and discussion of the literature are conducted by summarizing the state-of-the-art methods up to date of the submission. Our work also investigates the potential for widespread deployment and application of fuzzing test techniques generated by LLMs in the future, highlighting their promise for advancing automated software testing practices.