OpenRaiser/Intern-Atlas huggingface dataset:
The Comparability of Model Fusion to Measured Data in Confuser Rejection
Venue: arXiv cs.CV 2025
Year: 2025

Data collection has always been a major issue in the modeling and training of large deep learning networks, as no dataset can account for every slight deviation we might see in live usage. Collecting samples can be especially costly for Synthetic Aperture Radar (SAR), limiting the amount of unique targets and operating conditions we are able to observe from. To counter this lack of data, simulators have been developed utilizing the shooting and bouncing ray method to allow for the generation of synthetic SAR data on 3D models. While effective, the synthetically generated data does not perfectly correlate to the measured data leading to issues when training models solely on synthetic data. We aim to use computational power as a substitution for this lack of quality measured data, by ensembling many models trained on synthetic data. Synthetic data is also not complete, as we do not know what targets might be present in a live environment. Therefore we need to have our ensembling techniques account for these unknown targets by applying confuser rejection in which our models will reject unknown targets it is presented with, and only classify those it has been trained on.
TherMod Communication: Low Power or Hot Air?
Venue: arXiv cs.CR 2025
Year: 2025

The Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme leverages statistical physics to enable secure communication with zero average power flow in a wired channel. While the original KLJN scheme requires significant power for operation, a recent wireless modification, TherMod, proposed by Basar claims a "low power" implementation. This paper critically examines this claim. We explain that the additional components inherent in Basar's wireless adaptation substantially increase power consumption, rendering the "low power" assertion inappropriate. Furthermore, we clarify that the security claims of the original KLJN scheme do not directly translate to this wireless adaptation, implying significant security breach. Finally, the scheme looks identical one of the stealth communicators from 2005, which was shown not to be secure.
Beyond the Mirror: Personal Analytics through Visual Juxtaposition with Other People's Data
Venue: arXiv cs.HC 2025
Year: 2025

An individual's data can reveal facets of behavior and identity, but its interpretation is context dependent. We can easily identify various self-tracking applications that help people reflect on their lives. However, self-tracking confined to one person's data source may fall short in terms of objectiveness, and insights coming from various perspectives. To address this, we examine how those interpretations about a person's data can be augmented when the data are juxtaposed with that of others using anonymized online calendar logs from a schedule management app. We develop CALTREND, a visual analytics system that compares an individuals anonymized online schedule logs with using those from other people. Using CALTREND as a probe, we conduct a study with two domain experts, one in information technology and one in Korean herbal medicine. We report our observations on how comparative views help enrich the characterization of an individual based on the experts' comments. We find that juxtaposing personal data with others' can potentially lead to diverse interpretations of one dataset shaped by domain-specific mental models.
Fine-Tuning without Performance Degradation
Venue: arXiv cs.LG 2025
Year: 2025

Fine-tuning policies learned offline remains a major challenge in application domains. Monotonic performance improvement during \emph{fine-tuning} is often challenging, as agents typically experience performance degradation at the early fine-tuning stage. The community has identified multiple difficulties in fine-tuning a learned network online, however, the majority of progress has focused on improving learning efficiency during fine-tuning. In practice, this comes at a serious cost during fine-tuning: initially, agent performance degrades as the agent explores and effectively overrides the policy learned offline. We show across a range of settings, many offline-to-online algorithms exhibit either (1) performance degradation or (2) slow learning (sometimes effectively no improvement) during fine-tuning. We introduce a new fine-tuning algorithm, based on an algorithm called Jump Start, that gradually allows more exploration based on online estimates of performance. Empirically, this approach achieves fast fine-tuning and significantly reduces performance degradations compared with existing algorithms designed to do the same.
Lower Bounds for Non-adaptive Local Computation Algorithms
Venue: arXiv cs.DS 2025
Year: 2025

We study *non-adaptive* Local Computation Algorithms (LCA). A reduction of Parnas and Ron (TCS'07) turns any distributed algorithm into a non-adaptive LCA. Plugging known distributed algorithms, this leads to non-adaptive LCAs for constant approximations of maximum matching (MM) and minimum vertex cover (MVC) with complexity $Δ^{O(\log Δ/ \log \log Δ)}$, where $Δ$ is the maximum degree of the graph. Allowing adaptivity, this bound can be significantly improved to $\text{poly}(Δ)$, but is such a gap necessary or are there better non-adaptive LCAs? Adaptivity as a resource has been studied extensively across various areas. Beyond this, we further motivate the study of non-adaptive LCAs by showing that even a modest improvement over the Parnas-Ron bound for the MVC problem would have major implications in the Massively Parallel Computation (MPC) setting; It would lead to faster truly sublinear space MPC algorithms for approximate MM, a major open problem of the area. Our main result is a lower bound that rules out this avenue for progress. We prove that $Δ^{Ω(\log Δ/ \log \log Δ)}$ queries are needed for any non-adaptive LCA computing a constant approximation of MM or MVC. This is the first separation between non-adaptive and adaptive LCAs, and already matches (up to constants in the exponent) the algorithm obtained by the black-box reduction of Parnas and Ron. Our proof blends techniques from two separate lines of work: sublinear time lower bounds and distributed lower bounds. Particularly, we adopt techniques such as couplings over acyclic subgraphs from the recent sublinear time lower bounds of Behnezhad, Roghani, and Rubinstein (STOC'23, FOCS'23, STOC'24). We apply these techniques to a very different instance, (a modified version of) the construction of Kuhn, Moscibroda and Wattenhoffer (JACM'16) from distributed computing.
SSRLBot: Designing and Developing a Large Language Model-based Agent using Socially Shared Regulated Learning
Venue: arXiv cs.HC 2025
Year: 2025

Large language model (LLM)--based agents have emerged as pivotal tools in assisting human experts across various fields by transforming complex tasks into more efficient workflows and providing actionable stakeholder insights. Despite their potential, the application of LLM-based agents for medical education remains underexplored. The study aims to assist in evaluating the students' process and outcomes on medical case diagnosis and discussion while incorporating the theoretical framework of Socially Shared Regulation of Learning (SSRL) to assess student performance. SSRL emphasizes metacognitive, cognitive, motivational, and emotional interactions, highlighting the collaborative management of learning processes to improve decision-making outcomes. Grounded in SSRL theory, this tool paper introduces SSRLBot, an LLM-based agent designed to enable team members to reflect on their diagnostic performance and the key SSRL skills that foster team success. SSRLBot's core functions include summarizing dialogue content, analyzing participants' SSRL skills, and evaluating students' diagnostic results. Meanwhile, we evaluated SSRLBot through diagnostic conversation data collected from six groups (12 participants, 1926 conversational turns). Results showed that SSRLBot can deliver detailed, theory-aligned evaluations, link specific behaviors to SSRL dimensions, and offer actionable recommendations for improving teamwork. The findings address a critical gap in medical education, advancing the application of LLM agents to enhance team-based decision-making and collaboration in high-stakes environments.
Adaptive Branch-and-Bound Tree Exploration for Neural Network Verification
Venue: arXiv cs.LG 2025
Year: 2025

Formal verification is a rigorous approach that can provably ensure the quality of neural networks, and to date, Branch and Bound (BaB) is the state-of-the-art that performs verification by splitting the problem as needed and applying off-the-shelf verifiers to sub-problems for improved performance. However, existing BaB may not be efficient, due to its naive way of exploring the space of sub-problems that ignores the \emph{importance} of different sub-problems. To bridge this gap, we first introduce a notion of ``importance'' that reflects how likely a counterexample can be found with a sub-problem, and then we devise a novel verification approach, called ABONN, that explores the sub-problem space of BaB adaptively, in a Monte-Carlo tree search (MCTS) style. The exploration is guided by the ``importance'' of different sub-problems, so it favors the sub-problems that are more likely to find counterexamples. As soon as it finds a counterexample, it can immediately terminate; even though it cannot find, after visiting all the sub-problems, it can still manage to verify the problem. We evaluate ABONN with 552 verification problems from commonly-used datasets and neural network models, and compare it with the state-of-the-art verifiers as baseline approaches. Experimental evaluation shows that ABONN demonstrates speedups of up to $15.2\times$ on MNIST and $24.7\times$ on CIFAR-10. We further study the influences of hyperparameters to the performance of ABONN, and the effectiveness of our adaptive tree exploration.
A SCADE Model Verification Method Based on B-Model Transformation
Venue: arXiv cs.SE 2025
Year: 2025

Due to the limitations of SCADE models in expressing and verifying abstract specifications in safety-critical systems, this study proposes a formal verification framework based on the B-Method. By establishing a semantic equivalence transformation mechanism from SCADE models to B models, a hierarchical mapping rule set is constructed, covering type systems, control flow structures, and state machines. This effectively addresses key technical challenges such as loop-equivalent transformation proof for high-order operators and modeling of temporal logic storage structures. The proposed method innovatively leverages the abstraction capabilities of B-Method in set theory and first-order logic, overcoming the constraints of native verification tools of SCADE in complex specification descriptions. It successfully verifies abstract specifications that are difficult to model directly in SCADE. Experimental results show that the transformed B models achieve a higher defect detection rate and improved verification efficiency in the ProB verification environment compared to the native verifier of SCADE, significantly enhancing the formal verification capability of safety-critical systems. This study provides a cross-model verification paradigm for embedded control systems in avionics, rail transportation, and other domains, demonstrating substantial engineering application value.
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Simulation-based testing is crucial for validating autonomous vehicles (AVs), yet existing scenario generation methods either overfit to common driving patterns or operate in an offline, non-interactive manner that fails to expose rare, safety-critical corner cases. In this paper, we introduce an online, retrieval-augmented large language model (LLM) framework for generating safety-critical driving scenarios. Our method first employs an LLM-based behavior analyzer to infer the most dangerous intent of the background vehicle from the observed state, then queries additional LLM agents to synthesize feasible adversarial trajectories. To mitigate catastrophic forgetting and accelerate adaptation, we augment the framework with a dynamic memorization and retrieval bank of intent-planner pairs, automatically expanding its behavioral library when novel intents arise. Evaluations using the Waymo Open Motion Dataset demonstrate that our model reduces the mean minimum time-to-collision from 1.62 to 1.08 s and incurs a 75% collision rate, substantially outperforming baselines.
On the Worst-Case Complexity of Gibbs Decoding for Reed--Muller Codes
Venue: arXiv cs.IT 2025
Year: 2025

Reed--Muller (RM) codes are known to achieve capacity on binary symmetric channels (BSC) under the Maximum a Posteriori (MAP) decoder. However, it remains an open problem to design a capacity achieving polynomial-time RM decoder. Due to a lemma by Liu, Cuff, and Verdú, it can be shown that decoding by sampling from the posterior distribution is also capacity-achieving for RM codes over BSC. The Gibbs decoder is one such Markov Chain Monte Carlo (MCMC) based method, which samples from the posterior distribution by flipping message bits according to the posterior, and can be modified to give other MCMC decoding methods. In this paper, we analyze the mixing time of the Gibbs decoder for RM codes. Our analysis reveals that the Gibbs decoder can exhibit slow mixing for certain carefully constructed sequences. This slow mixing implies that, in the worst-case scenario, the decoder requires super-polynomial time to converge to the desired posterior distribution.
On-demand Test-time Adaptation for Edge Devices
Venue: arXiv cs.LG 2025
Year: 2025

Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data. While achieving optimal accuracy, existing CTTA approaches present poor real-world applicability on resource-constrained edge devices, due to the substantial memory overhead and energy consumption. In this work, we first introduce a novel paradigm -- on-demand TTA -- which triggers adaptation only when a significant domain shift is detected. Then, we present OD-TTA, an on-demand TTA framework for accurate and efficient adaptation on edge devices. OD-TTA comprises three innovative techniques: 1) a lightweight domain shift detection mechanism to activate TTA only when it is needed, drastically reducing the overall computation overhead, 2) a source domain selection module that chooses an appropriate source model for adaptation, ensuring high and robust accuracy, 3) a decoupled Batch Normalization (BN) update scheme to enable memory-efficient adaptation with small batch sizes. Extensive experiments show that OD-TTA achieves comparable and even better performance while reducing the energy and computation overhead remarkably, making TTA a practical reality.
Detecting the Root Cause Code Lines in Bug-Fixing Commits by Heterogeneous Graph Learning
Venue: arXiv cs.SE 2025
Year: 2025

With the continuous growth in the scale and complexity of software systems, defect remediation has become increasingly difficult and costly. Automated defect prediction tools can proactively identify software changes prone to defects within software projects, thereby enhancing software development efficiency. However, existing work in heterogeneous and complex software projects continues to face challenges, such as struggling with heterogeneous commit structures and ignoring cross-line dependencies in code changes, which ultimately reduce the accuracy of defect identification. To address these challenges, we propose an approach called RC_Detector. RC_Detector comprises three main components: the bug-fixing graph construction component, the code semantic aggregation component, and the cross-line semantic retention component. The bug-fixing graph construction component identifies the code syntax structures and program dependencies within bug-fixing commits and transforms them into heterogeneous graph formats by converting the source code into vector representations. The code semantic aggregation component adapts to heterogeneous data by using heterogeneous attention to learn the hidden semantic representation of target code lines. The cross-line semantic retention component regulates propagated semantic information by using attenuation and reinforcement gates derived from old and new code semantic representations, effectively preserving cross-line semantic relationships. Extensive experiments were conducted to evaluate the performance of our model by collecting data from 87 open-source projects, including 675 bug-fixing commits. The experimental results demonstrate that our model outperforms state-of-the-art approaches, achieving significant improvements of 83.15%,96.83%,78.71%,74.15%,54.14%,91.66%,91.66%, and 34.82% in MFR, respectively, compared with the state-of-the-art approaches.
Regular expressions over countable words
Venue: arXiv cs.LO 2025
Year: 2025

We investigate the expressive power of regular expressions for languages of countable words and establish their expressive equivalence with logical and algebraic characterizations. Our goal is to extend the classical theory of regular languages - defined over finite words and characterized by automata, monadic second-order logic, and regular expressions - to the setting of countable words. In this paper, we introduce and study five classes of expressions: marked star-free expressions, marked expressions, power-free expressions, scatter-free expressions, and scatter expressions. We show that these expression classes characterize natural fragments of logic over countable words and possess decidable algebraic characterizations. As part of our algebraic analysis, we provide a precise description of the relevant classes in terms of their J-class structure. These results complete a triad of equivalences - between logic, algebra, and expressions - in this richer setting, thereby generalizing foundational results from classical formal language theory.
Good News for Script Kiddies? Evaluating Large Language Models for Automated Exploit Generation
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in code-related tasks, raising concerns about their potential for automated exploit generation (AEG). This paper presents the first systematic study on LLMs' effectiveness in AEG, evaluating both their cooperativeness and technical proficiency. To mitigate dataset bias, we introduce a benchmark with refactored versions of five software security labs. Additionally, we design an LLM-based attacker to systematically prompt LLMs for exploit generation. Our experiments reveal that GPT-4 and GPT-4o exhibit high cooperativeness, comparable to uncensored models, while Llama3 is the most resistant. However, no model successfully generates exploits for refactored labs, though GPT-4o's minimal errors highlight the potential for LLM-driven AEG advancements.
Retrieval Augmented Learning: A Retrial-based Large Language Model Self-Supervised Learning and Autonomous Knowledge Generation
Venue: arXiv cs.AI 2025
Year: 2025

The lack of domain-specific data in the pre-training of Large Language Models (LLMs) severely limits LLM-based decision systems in specialized applications, while post-training a model in the scenarios requires significant computational resources. In this paper, we present Retrial-Augmented Learning (RAL), a reward-free self-supervised learning framework for LLMs that operates without model training. By developing Retrieval-Augmented Generation (RAG) into a module for organizing intermediate data, we realized a three-stage autonomous knowledge generation of proposing a hypothesis, validating the hypothesis, and generating the knowledge. The method is evaluated in the LLM-PySC2 environment, a representative decision-making platform that combines sufficient complexity with domain-specific knowledge requirements. Experiments demonstrate that the proposed method effectively reduces hallucination by generating and utilizing validated knowledge, and increases decision-making performance at an extremely low cost. Meanwhile, the approach exhibits potential in out-of-distribution(OOD) tasks, robustness, and transferability, making it a cost-friendly but effective solution for decision-making problems and autonomous knowledge generation.
Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study
Venue: arXiv cs.CV 2025
Year: 2025

Multiple Instance Learning (MIL) has emerged as the best solution for Whole Slide Image (WSI) classification. It consists of dividing each slide into patches, which are treated as a bag of instances labeled with a global label. MIL includes two main approaches: instance-based and embedding-based. In the former, each patch is classified independently, and then the patch scores are aggregated to predict the bag label. In the latter, bag classification is performed after aggregating patch embeddings. Even if instance-based methods are naturally more interpretable, embedding-based MILs have usually been preferred in the past due to their robustness to poor feature extractors. However, recently, the quality of feature embeddings has drastically increased using self-supervised learning (SSL). Nevertheless, many authors continue to endorse the superiority of embedding-based MIL. To investigate this further, we conduct 710 experiments across 4 datasets, comparing 10 MIL strategies, 6 self-supervised methods with 4 backbones, 4 foundation models, and various pathology-adapted techniques. Furthermore, we introduce 4 instance-based MIL methods never used before in the pathology domain. Through these extensive experiments, we show that with a good SSL feature extractor, simple instance-based MILs, with very few parameters, obtain similar or better performance than complex, state-of-the-art (SOTA) embedding-based MIL methods, setting new SOTA results on the BRACS and Camelyon16 datasets. Since simple instance-based MIL methods are naturally more interpretable and explainable to clinicians, our results suggest that more effort should be put into well-adapted SSL methods for WSI rather than into complex embedding-based MIL methods.
MateICL: Mitigating Attention Dispersion in Large-Scale In-Context Learning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in In-Context Learning (ICL). However, the fixed position length constraints in pre-trained models limit the number of demonstration examples. Recent efforts to extend context suffer from attention dispersion as the number of demonstrations increases. In this paper, we introduce Mitigating Attention Dispersion in large-scale ICL (MateICL) that enables LLMs to maintain effective self-attention as the context size grows. We first split the context into multiple windows, each filled to the model's context capacity, which are processed separately. Then, we introduce an additional layer to recalibrate the attention weights, prioritizing the query tokens as the number of demonstrations increases. Our empirical results show that MateICL can effectively leverage larger contexts to improve ICL performance. Compared to retrieval-based baselines, MateICL consistently achieves better performance without requiring an externally trained retrieval model. Despite recent advances in inference strategies (e.g., 32k token contexts), our results demonstrate that MateICL remains beneficial in computationally resource-constrained settings. The code is publicly available at https://github.com/amurtadha/MateICL.