OpenRaiser/Intern-Atlas huggingface dataset:
Aligning Core Aspects: Improving Vulnerability Proof-of-Concepts via Cross-Source Insights
Venue: arXiv cs.CR 2025
Year: 2025

For vulnerabilities, Proof-of-Concept (PoC) plays an irreplaceable role in demonstrating the exploitability. PoC reports may include critical information such as specific usage, test platforms, and more, providing essential insights for researchers. However, in reality, due to various PoC templates across PoC platforms, PoC reports extensively suffer from information deficiency, leading the suboptimal quality and limited usefulness. Fortunately, we found that information deficiency of PoC reports could be mitigated by the completion from multiple sources given the same referred vulnerability. In this paper, we conduct the first study on the deficiency of information in PoC reports across public platforms. We began by collecting 173,170 PoC reports from 4 different platforms and defined 8 key aspects that PoCs should contain. By integrating rule-based matching and a fine-tuned BERT-NER model for extraction of key aspects, we discovered that all PoC reports available on public platforms have at least one missing key aspect. Subsequently, we developed a multi-source information fusion method to complete the missing aspect information in PoC reports by leveraging CVE entries and related PoC reports from different sources. Finally, we successfully completed 69,583 PoC reports (40.18% of all reports).
Compositional Verification in Concurrent Separation Logic with Permissions Regions
Venue: arXiv cs.LO 2025
Year: 2025

Concurrent separation logic with fractional permissions (CSLPerm) provides a promising reasoning system to verify most complex sequential and concurrent fine-grained programs. The logic with strong and weak separating conjunctions offers a solid foundation for producing concise and precise proofs. However, it lacks automation and compositionality support. This paper addresses this limitation by introducing a compositional verification system for concurrent programs that manipulate regions of shared memory. The centre of our system is novel logical principles and an entailment procedure that can infer the residual heaps in the frame rule for a fragment of CSL-Perm with explicit arithmetical constraints for memory heaps' disjointness. This procedure enables the compositional reasoning for concurrent threads and function calls. We have implemented the proposal in a prototype tool called CoSl, tested it with 10 challenging concurrent programs, including those beyond the state-of-the-art, and confirmed the advantage of our approach.
Provable Mixed-Noise Learning with Flow-Matching
Venue: arXiv cs.LG 2025
Year: 2025

We study Bayesian inverse problems with mixed noise, modeled as a combination of additive and multiplicative Gaussian components. While traditional inference methods often assume fixed or known noise characteristics, real-world applications, particularly in physics and chemistry, frequently involve noise with unknown and heterogeneous structure. Motivated by recent advances in flow-based generative modeling, we propose a novel inference framework based on conditional flow matching embedded within an Expectation-Maximization (EM) algorithm to jointly estimate posterior samplers and noise parameters. To enable high-dimensional inference and improve scalability, we use simulation-free ODE-based flow matching as the generative model in the E-step of the EM algorithm. We prove that, under suitable assumptions, the EM updates converge to the true noise parameters in the population limit of infinite observations. Our numerical results illustrate the effectiveness of combining EM inference with flow matching for mixed-noise Bayesian inverse problems.
Views: a hardware-friendly graph database model for storing semantic information
Venue: arXiv cs.DB 2025
Year: 2025

The graph database (GDB) is an increasingly common storage model for data involving relationships between entries. Beyond its widespread usage in database industries, the advantages of GDBs indicate a strong potential in constructing symbolic artificial intelligences (AIs) and retrieval-augmented generation (RAG), where knowledge of data inter-relationships takes a critical role in implementation. However, current GDB models are not optimised for hardware acceleration, leading to bottlenecks in storage capacity and computational efficiency. In this paper, we propose a hardware-friendly GDB model, called Views. We show its data structure and organisation tailored for efficient storage and retrieval of graph data and demonstrate its functional equivalence and storage performance advantage compared to represent traditional graph representations. We further demonstrate its symbolic processing abilities in semantic reasoning and cognitive modelling with practical examples and provide a short perspective on future developments.
SpotEdit: Evaluating Visually-Guided Image Editing Methods
Venue: arXiv cs.CV 2025
Year: 2025

Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models have shown remarkable capabilities, existing evaluations remain simple and insufficiently representative of real-world editing challenges. We present SpotEdit, a comprehensive benchmark designed to systematically assess visually-guided image editing methods across diverse diffusion, autoregressive, and hybrid generative models, uncovering substantial performance disparities. To address a critical yet underexplored challenge, our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task. Our code and benchmark are publicly released at https://github.com/SaraGhazanfari/SpotEdit.
Unveiling the Actual Performance of Neural-based Models for Equation Discovery on Graph Dynamical Systems
Venue: arXiv cs.LG 2025
Year: 2025

The ``black-box'' nature of deep learning models presents a significant barrier to their adoption for scientific discovery, where interpretability is paramount. This challenge is especially pronounced in discovering the governing equations of dynamical processes on networks or graphs, since even their topological structure further affects the processes' behavior. This paper provides a rigorous, comparative assessment of state-of-the-art symbolic regression techniques for this task. We evaluate established methods, including sparse regression and MLP-based architectures, and introduce a novel adaptation of Kolmogorov-Arnold Networks (KANs) for graphs, designed to exploit their inherent interpretability. Across a suite of synthetic and real-world dynamical systems, our results demonstrate that both MLP and KAN-based architectures can successfully identify the underlying symbolic equations, significantly surpassing existing baselines. Critically, we show that KANs achieve this performance with greater parsimony and transparency, as their learnable activation functions provide a clearer mapping to the true physical dynamics. This study offers a practical guide for researchers, clarifying the trade-offs between model expressivity and interpretability, and establishes the viability of neural-based architectures for robust scientific discovery on complex systems.
AdLoCo: adaptive batching significantly improves communications efficiency and convergence for Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Scaling distributed training of Large Language Models (LLMs) requires not only algorithmic advances but also efficient utilization of heterogeneous hardware resources. While existing methods such as DiLoCo have demonstrated promising results, they often fail to fully exploit computational clusters under dynamic workloads. To address this limitation, we propose a three-stage method that combines Multi-Instance Training (MIT), Adaptive Batched DiLoCo, and switch mode mechanism. MIT allows individual nodes to run multiple lightweight training streams with different model instances in parallel and merge them to combine knowledge, increasing throughput and reducing idle time. Adaptive Batched DiLoCo dynamically adjusts local batch sizes to balance computation and communication, substantially lowering synchronization delays. Switch mode further stabilizes training by seamlessly introducing gradient accumulation once adaptive batch sizes grow beyond hardware-friendly limits. Together, these innovations improve both convergence speed and system efficiency. We also provide a theoretical estimate of the number of communications required for the full convergence of a model trained using our method.
Unraveling the cognitive patterns of Large Language Models through module communities
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and medical diagnostics to Chatbots. Despite their ubiquity and utility, the underlying mechanisms of LLM remain concealed within billions of parameters and complex structures, making their inner architecture and cognitive processes challenging to comprehend. We address this gap by adopting approaches to understanding emerging cognition in biology and developing a network-based framework that links cognitive skills, LLM architectures, and datasets, ushering in a paradigm shift in foundation model analysis. The skill distribution in the module communities demonstrates that while LLMs do not strictly parallel the focalized specialization observed in specific biological systems, they exhibit unique communities of modules whose emergent skill patterns partially mirror the distributed yet interconnected cognitive organization seen in avian and small mammalian brains. Our numerical results highlight a key divergence from biological systems to LLMs, where skill acquisition benefits substantially from dynamic, cross-regional interactions and neural plasticity. By integrating cognitive science principles with machine learning, our framework provides new insights into LLM interpretability and suggests that effective fine-tuning strategies should leverage distributed learning dynamics rather than rigid modular interventions.
HypER: Hyperbolic Echo State Networks for Capturing Stretch-and-Fold Dynamics in Chaotic Flows
Venue: arXiv cs.LG 2025
Year: 2025

Forecasting chaotic dynamics beyond a few Lyapunov times is difficult because infinitesimal errors grow exponentially. Existing Echo State Networks (ESNs) mitigate this growth but employ reservoirs whose Euclidean geometry is mismatched to the stretch-and-fold structure of chaos. We introduce the Hyperbolic Embedding Reservoir (HypER), an ESN whose neurons are sampled in the Poincare ball and whose connections decay exponentially with hyperbolic distance. This negative-curvature construction embeds an exponential metric directly into the latent space, aligning the reservoir's local expansion-contraction spectrum with the system's Lyapunov directions while preserving standard ESN features such as sparsity, leaky integration, and spectral-radius control. Training is limited to a Tikhonov-regularized readout. On the chaotic Lorenz-63 and Roessler systems, and the hyperchaotic Chen-Ueta attractor, HypER consistently lengthens the mean valid-prediction horizon beyond Euclidean and graph-structured ESN baselines, with statistically significant gains confirmed over 30 independent runs; parallel results on real-world benchmarks, including heart-rate variability from the Santa Fe and MIT-BIH datasets and international sunspot numbers, corroborate its advantage. We further establish a lower bound on the rate of state divergence for HypER, mirroring Lyapunov growth.
Deep Learning and Matrix Completion-aided IoT Network Localization in the Outlier Scenarios
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we propose a deep learning and matrix completion aided approach for recovering an outlier contaminated Euclidean distance matrix D in IoT network localization. Unlike conventional localization techniques that search the solution over a whole set of matrices, the proposed technique restricts the search to the set of Euclidean distance matrices. Specifically, we express D as a function of the sensor coordinate matrix X that inherently satisfies the unique properties of D, and then jointly recover D and X using a deep neural network. To handle outliers effectively, we model them as a sparse matrix L and add a regularization term of L into the optimization problem. We then solve the problem by alternately updating X, D, and L. Numerical experiments demonstrate that the proposed technique can recover the location information of sensors accurately even in the presence of outliers.
Aligning the Evaluation of Probabilistic Predictions with Downstream Value
Venue: arXiv cs.LG 2025
Year: 2025

Every prediction is ultimately used in a downstream task. Consequently, evaluating prediction quality is more meaningful when considered in the context of its downstream use. Metrics based solely on predictive performance often diverge from measures of real-world downstream impact. Existing approaches incorporate the downstream view by relying on multiple task-specific metrics, which can be burdensome to analyze, or by formulating cost-sensitive evaluations that require an explicit cost structure, typically assumed to be known a priori. We frame this mismatch as an evaluation alignment problem and propose a data-driven method to learn a proxy evaluation function aligned with the downstream evaluation. Building on the theory of proper scoring rules, we explore transformations of scoring rules that ensure the preservation of propriety. Our approach leverages weighted scoring rules parametrized by a neural network, where weighting is learned to align with the performance in the downstream task. This enables fast and scalable evaluation cycles across tasks where the weighting is complex or unknown a priori. We showcase our framework through synthetic and real-data experiments for regression tasks, demonstrating its potential to bridge the gap between predictive evaluation and downstream utility in modular prediction systems.
Technology-assisted Personalized Yoga for Better Health -- Challenges and Outlook
Venue: arXiv cs.HC 2025
Year: 2025

Yoga is a discipline of physical postures, breathing techniques, and meditative practices rooted in ancient Indian traditions, now embraced worldwide for promoting overall well-being and inner balance. The practices are a large set of items, our term for executable actions like physical poses or breath exercises, to offer for a person's well-being. However, to get benefits of Yoga tailored to a person's unique needs, a person needs to (a) discover their subset from the large and seemingly complex set with inter-dependencies, (b) continue to follow them with interest adjusted to their changing abilities and near-term objectives, and (c) as appropriate, adapt to alternative items based on changing environment and the person's health conditions. In this vision paper, we describe the challenges for the Yoga personalization problem. Next, we sketch a preliminary approach and use the experience to provide an outlook on solving the challenging problem using existing and novel techniques from a multidisciplinary computing perspective. To the best of our knowledge, this is the first paper that comprehensively examines decision support issues around Yoga personalization, from pose sensing to recommendation of corrections for a complete regimen, and illustrates with a case study of Surya Namaskar -- a set of 12 choreographed poses.
Consensus Is All You Need: Gossip-Based Reasoning Among Large Language Models
Venue: arXiv cs.MA 2025
Year: 2025

Large language models have advanced rapidly, but no single model excels in every area -- each has its strengths and weaknesses. Instead of relying on one model alone, we take inspiration from gossip protocols in distributed systems, where information is exchanged with peers until they all come to an agreement. In this setup, models exchange answers and gradually work toward a shared solution. Each LLM acts as a node in a peer-to-peer network, sharing responses and thought processes to reach a collective decision. Our results show that this "gossip-based consensus" leads to robust, resilient, and accurate multi-agent AI reasoning. It helps overcome the weaknesses of individual models and brings out their collective strengths. This approach is similar to how humans build consensus, making AI seem more collaborative and trustworthy instead of just a black-box program.
Can VLMs Recall Factual Associations From Visual References?
Venue: arXiv cs.CV 2025
Year: 2025

Through a controlled study, we identify a systematic deficiency in the multimodal grounding of Vision Language Models (VLMs). While VLMs can recall factual associations when provided a textual reference to an entity; their ability to do so is significantly diminished when the reference is visual instead. Forcing VLMs to rely on image representations of an entity halves their ability to recall factual knowledge, suggesting that VLMs struggle to link their internal knowledge of an entity with its image representation. We show that such linking failures are correlated with the expression of distinct patterns in model internal states, and that probes on these internal states achieve over 92% accuracy at flagging cases where the VLM response is unreliable. These probes can be applied, without retraining, to identify when a VLM will fail to correctly answer a question that requires an understanding of multimodal input. When used to facilitate selective prediction on a visual question answering task, the probes increase coverage by 7.87% (absolute) while also reducing the risk of error by 0.9% (absolute). Addressing the systematic, detectable deficiency is an important avenue in language grounding, and we provide informed recommendations for future directions.
SALMAN: Stability Analysis of Language Models Through the Maps Between Graph-based Manifolds
Venue: arXiv cs.LG 2025
Year: 2025

Recent strides in pretrained transformer-based language models have propelled state-of-the-art performance in numerous NLP tasks. Yet, as these models grow in size and deployment, their robustness under input perturbations becomes an increasingly urgent question. Existing robustness methods often diverge between small-parameter and large-scale models (LLMs), and they typically rely on labor-intensive, sample-specific adversarial designs. In this paper, we propose a unified, local (sample-level) robustness framework (SALMAN) that evaluates model stability without modifying internal parameters or resorting to complex perturbation heuristics. Central to our approach is a novel Distance Mapping Distortion (DMD) measure, which ranks each sample's susceptibility by comparing input-to-output distance mappings in a near-linear complexity manner. By demonstrating significant gains in attack efficiency and robust training, we position our framework as a practical, model-agnostic tool for advancing the reliability of transformer-based NLP systems.
REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs) have shown strong capabilities in document re-ranking, a key component in modern Information Retrieval (IR) systems. However, existing LLM-based approaches face notable limitations, including ranking uncertainty, unstable top-k recovery, and high token cost due to token-intensive prompting. To effectively address these limitations, we propose REALM, an uncertainty-aware re-ranking framework that models LLM-derived relevance as Gaussian distributions and refines them through recursive Bayesian updates. By explicitly capturing uncertainty and minimizing redundant queries, REALM achieves better rankings more efficiently. Experimental results demonstrate that our REALM surpasses state-of-the-art re-rankers while significantly reducing token usage and latency, improving NDCG@10 by 0.7-11.9 and simultaneously reducing the number of LLM inferences by 23.4-84.4%, promoting it as the next-generation re-ranker for modern IR systems.
Engineering a Digital Twin for the Monitoring and Control of Beer Fermentation Sampling
Venue: arXiv cs.SE 2025
Year: 2025

Successfully engineering interactive industrial DTs is a complex task, especially when implementing services beyond passive monitoring. We present here an experience report on engineering a safety-critical digital twin (DT) for beer fermentation monitoring, which provides continual sampling and reduces manual sampling time by 91%. We document our systematic methodology and practical solutions for implementing bidirectional DTs in industrial environments. This includes our three-phase engineering approach that transforms a passive monitoring system into an interactive Type 2 DT with real-time control capabilities for pressurized systems operating at seven bar. We contribute details of multi-layered safety protocols, hardware-software integration strategies across Arduino controllers and Unity visualization, and real-time synchronization solutions. We document specific engineering challenges and solutions spanning interdisciplinary integration, demonstrating how our use of the constellation reporting framework facilitates cross-domain collaboration. Key findings include the critical importance of safety-first design, simulation-driven development, and progressive implementation strategies. Our work thus provides actionable guidance for practitioners developing DTs requiring bidirectional control in safety-critical applications.
Skeptik: A Hybrid Framework for Combating Potential Misinformation in Journalism
Venue: arXiv cs.HC 2025
Year: 2025

The proliferation of misinformation in journalism, often stemming from flawed reasoning and logical fallacies, poses significant challenges to public understanding and trust in news media. Traditional fact-checking methods, while valuable, are insufficient for detecting the subtle logical inconsistencies that can mislead readers within seemingly factual content. To address this gap, we introduce Skeptik, a hybrid framework that integrates Large Language Models (LLMs) with heuristic approaches to analyze and annotate potential logical fallacies and reasoning errors in online news articles. Operating as a web browser extension, Skeptik automatically highlights sentences that may contain logical fallacies, provides detailed explanations, and offers multi-layered interventions to help readers critically assess the information presented. The system is designed to be extensible, accommodating a wide range of fallacy types and adapting to evolving misinformation tactics. Through comprehensive case studies, quantitative analyses, usability experiments, and expert evaluations, we demonstrate the effectiveness of Skeptik in enhancing readers' critical examination of news content and promoting media literacy. Our contributions include the development of an expandable classification system for logical fallacies, the innovative integration of LLMs for real-time analysis and annotation, and the creation of an interactive user interface that fosters user engagement and close reading. By emphasizing the logical integrity of textual content rather than relying solely on factual accuracy, Skeptik offers a comprehensive solution to combat potential misinformation in journalism. Ultimately, our framework aims to improve critical reading and protect the public from deceptive information online and enhance the overall credibility of news media.