OpenRaiser/Intern-Atlas huggingface dataset:
Symmaries: Automatic Inference of Formal Security Summaries for Java Programs
Venue: arXiv cs.CR 2025
Year: 2025

We introduce a scalable, modular, and sound approach for automatically constructing formal security specifications for Java bytecode programs in the form of method summaries. A summary provides an abstract representation of a method's security behavior, consisting of the conditions under which the method can be securely invoked, together with specifications of information flows and aliasing updates. Such summaries can be consumed by static code analysis tools and also help developers understand the behavior of code segments, such as libraries, in order to evaluate their security implications when reused in applications. Our approach is implemented in a tool called Symmaries, which automates the generation of security summaries. We applied Symmaries to Java API libraries to extract their security specifications and to large real-world applications to evaluate its scalability. Our results show that the tool successfully scales to analyze applications with hundreds of thousands of lines of code, and that Symmaries achieves a promising precision depending on the heap model used. We prove the soundness of our approach in terms of guaranteeing termination-insensitive non-interference.
Sentiment-Aware Extractive and Abstractive Summarization for Unstructured Text Mining
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid growth of unstructured data from social media, reviews, and forums, text mining has become essential in Information Systems (IS) for extracting actionable insights. Summarization can condense fragmented, emotion-rich posts, but existing methods-optimized for structured news-struggle with noisy, informal content. Emotional cues are critical for IS tasks such as brand monitoring and market analysis, yet few studies integrate sentiment modeling into summarization of short user-generated texts. We propose a sentiment-aware framework extending extractive (TextRank) and abstractive (UniLM) approaches by embedding sentiment signals into ranking and generation processes. This dual design improves the capture of emotional nuances and thematic relevance, producing concise, sentiment-enriched summaries that enhance timely interventions and strategic decision-making in dynamic online environments.
Simplifying Multi-Task Architectures Through Task-Specific Normalization
Venue: arXiv cs.LG 2025
Year: 2025

Multi-task learning (MTL) aims to leverage shared knowledge across tasks to improve generalization and parameter efficiency, yet balancing resources and mitigating interference remain open challenges. Architectural solutions often introduce elaborate task-specific modules or routing schemes, increasing complexity and overhead. In this work, we show that normalization layers alone are sufficient to address many of these challenges. Simply replacing shared normalization with task-specific variants already yields competitive performance, questioning the need for complex designs. Building on this insight, we propose Task-Specific Sigmoid Batch Normalization (TS$σ$BN), a lightweight mechanism that enables tasks to softly allocate network capacity while fully sharing feature extractors. TS$σ$BN improves stability across CNNs and Transformers, matching or exceeding performance on NYUv2, Cityscapes, CelebA, and PascalContext, while remaining highly parameter-efficient. Moreover, its learned gates provide a natural framework for analyzing MTL dynamics, offering interpretable insights into capacity allocation, filter specialization, and task relationships. Our findings suggest that complex MTL architectures may be unnecessary and that task-specific normalization offers a simple, interpretable, and efficient alternative.
Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systems
Venue: arXiv cs.CV 2025
Year: 2025

Landmines remain a persistent humanitarian threat, with 110 million actively deployed mines across 60 countries, claiming 26,000 casualties annually. This research evaluates adaptive Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) fusion for Unmanned Aerial Systems (UAS)-based detection of surface-laid landmines, leveraging the thermal contrast between the ordnance and the surrounding soil to enhance feature extraction. Using You Only Look Once (YOLO) architectures (v8, v10, v11) across 114 test images, generating 35,640 model-condition evaluations, YOLOv11 achieved optimal performance (86.8% mAP), with 10 to 30% thermal fusion at 5 to 10m altitude identified as the optimal detection parameters. A complementary architectural comparison revealed that while RF-DETR achieved the highest accuracy (69.2% mAP), followed by Faster R-CNN (67.6%), YOLOv11 (64.2%), and RetinaNet (50.2%), YOLOv11 trained 17.7 times faster than the transformer-based RF-DETR (41 minutes versus 12 hours), presenting a critical accuracy-efficiency tradeoff for operational deployment. Aggregated multi-temporal training datasets outperformed season-specific approaches by 1.8 to 9.6%, suggesting that models benefit from exposure to diverse thermal conditions. Anti-Tank (AT) mines achieved 61.9% detection accuracy, compared with 19.2% for Anti-Personnel (AP) mines, reflecting both the size differential and thermal-mass differences between these ordnance classes. As this research examined surface-laid mines where thermal contrast is maximized, future research should quantify thermal contrast effects for mines buried at varying depths across heterogeneous soil types.
Bridging Modalities and Transferring Knowledge: Enhanced Multimodal Understanding and Recognition
Venue: arXiv cs.CV 2025
Year: 2025

This manuscript explores multimodal alignment, translation, fusion, and transference to enhance machine understanding of complex inputs. We organize the work into five chapters, each addressing unique challenges in multimodal machine learning. Chapter 3 introduces Spatial-Reasoning Bert for translating text-based spatial relations into 2D arrangements between clip-arts. This enables effective decoding of spatial language into visual representations, paving the way for automated scene generation aligned with human spatial understanding. Chapter 4 presents a method for translating medical texts into specific 3D locations within an anatomical atlas. We introduce a loss function leveraging spatial co-occurrences of medical terms to create interpretable mappings, significantly enhancing medical text navigability. Chapter 5 tackles translating structured text into canonical facts within knowledge graphs. We develop a benchmark for linking natural language to entities and predicates, addressing ambiguities in text extraction to provide clearer, actionable insights. Chapter 6 explores multimodal fusion methods for compositional action recognition. We propose a method fusing video frames and object detection representations, improving recognition robustness and accuracy. Chapter 7 investigates multimodal knowledge transference for egocentric action recognition. We demonstrate how multimodal knowledge distillation enables RGB-only models to mimic multimodal fusion-based capabilities, reducing computational requirements while maintaining performance. These contributions advance methodologies for spatial language understanding, medical text interpretation, knowledge graph enrichment, and action recognition, enhancing computational systems' ability to process complex, multimodal inputs across diverse applications.
LLM-Based Authoring of Agent-Based Narratives through Scene Descriptions
Venue: arXiv cs.GR 2025
Year: 2025

This paper presents a system for procedurally generating agent-based narratives using large language models (LLMs). Users could drag and drop multiple agents and objects into a scene, with each entity automatically assigned semantic metadata describing its identity, role, and potential interactions. The scene structure is then serialized into a natural language prompt and sent to an LLM, which returns a structured string describing a sequence of actions and interactions among agents and objects. The returned string encodes who performed which actions, when, and how. A custom parser interprets this string and triggers coordinated agent behaviors, animations, and interaction modules. The system supports agent-based scenes, dynamic object manipulation, and diverse interaction types. Designed for ease of use and rapid iteration, the system enables the generation of virtual agent activity suitable for prototyping agent narratives. The performance of the developed system was evaluated using four popular lightweight LLMs. Each model's process and response time were measured under multiple complexity scenarios. The collected data were analyzed to compare consistency across the examined scenarios and to highlight the relative efficiency and suitability of each model for procedural agent-based narratives generation. The results demonstrate that LLMs can reliably translate high-level scene descriptions into executable agent-based behaviors.
Composing Mini Oscilloscope on Embedded Systems
Venue: arXiv cs.AR 2025
Year: 2025

In this paper, our goal is to reproduce the basic functionalities of a regular oscilloscope, using the Nuvoton NUC-140 embedded systems development platform as the front-end and display method. A custom-built daughter board connects the NUC-140 to a variety of peripherals, including two BNC scope-probe connections, an external nine-button keypad, and a calibration signal. The LCD of the NUC-140 development board serves as the waveform display. From the experimental results, it is demonstrated that our proposed system became a very competent debugging tool. It implements 90% of the features we typically use on original oscilloscopes, including: automatic, edge-triggered, and single modes; waveform visualization using vertical and horizontal scaling; probe calibration.
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
Venue: arXiv cs.LG 2025
Year: 2025

Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. During RL, these models explore by generating new outputs, one token at a time. However, sampling actions token-by-token can result in highly inefficient learning, particularly when rewards are sparse. Here, we show that it is possible to overcome this problem by acting and exploring within the internal representations of an autoregressive model. Specifically, to discover temporally-abstract actions, we introduce a higher-order, non-causal sequence model whose outputs control the residual stream activations of a base autoregressive model. On grid world and MuJoCo-based tasks with hierarchical structure, we find that the higher-order model learns to compress long activation sequence chunks onto internal controllers. Critically, each controller executes a sequence of behaviorally meaningful actions that unfold over long timescales and are accompanied with a learned termination condition, such that composing multiple controllers over time leads to efficient exploration on novel tasks. We show that direct internal controller reinforcement, a process we term "internal RL", enables learning from sparse rewards in cases where standard RL finetuning fails. Our results demonstrate the benefits of latent action generation and reinforcement in autoregressive models, suggesting internal RL as a promising avenue for realizing hierarchical RL within foundation models.
LongVideoAgent: Multi-Agent Reasoning with Long Videos
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in multimodal LLMs and systems that use tools for long-video QA point to the promise of reasoning over hour-long episodes. However, many methods still compress content into lossy summaries or rely on limited toolsets, weakening temporal grounding and missing fine-grained cues. We propose a multi-agent framework in which a master LLM coordinates a grounding agent to localize question-relevant segments and a vision agent to extract targeted textual observations. The master agent plans with a step limit, and is trained with reinforcement learning to encourage concise, correct, and efficient multi-agent cooperation. This design helps the master agent focus on relevant clips via grounding, complements subtitles with visual detail, and yields interpretable trajectories. On our proposed LongTVQA and LongTVQA+ which are episode-level datasets aggregated from TVQA/TVQA+, our multi-agent system significantly outperforms strong non-agent baselines. Experiments also show reinforcement learning further strengthens reasoning and planning for the trained agent. Code and data will be shared at https://longvideoagent.github.io/.
How Feasible are Passive Network Attacks on 5G Networks and Beyond? A Survey
Venue: arXiv cs.NI 2025
Year: 2025

Privacy concerns around 5G, the latest generation of mobile networks, are growing, with fears that its deployment may increase exposure to privacy risks. This perception is largely driven by the use of denser deployments of small antenna systems, which enable highly accurate data collection at higher speeds and closer proximity to mobile users. At the same time, 5G's unique radio communication features can make the reproduction of known network attacks more challenging. In particular, passive network attacks, which do not involve direct interaction with the target network and are therefore nearly impossible to detect, remain a pressing concern. Such attacks can reveal sensitive information about users, their devices, and active applications, which may then be exploited through known vulnerabilities or spear-phishing schemes. This survey examines the feasibility of passive network attacks in 5G and beyond (B5G/6G) networks, with emphasis on two major categories: information extraction (system identification, website and application fingerprinting) and geolocation (user identification and position tracking). These attacks are well documented and reproducible in existing wireless and mobile systems, including short-range networks (IEEE 802.11) and, to a lesser extent, LTE. Current evidence suggests that while such attacks remain theoretically possible in 5G, their practical execution is significantly constrained by directional beamforming, high-frequency propagation characteristics, and encryption mechanisms. For B5G and early 6G networks, the lack of public tools and high hardware cost currently renders these attacks infeasible in practice, which highlights a critical gap in our understanding of future network threat models.
BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization
Venue: arXiv cs.AI 2025
Year: 2025

Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural language commands via Google Home/IFTTT integration and learns from implicit feedback through manual overrides. Our comparative analysis shows 1-bit models achieve 5.07 times speedup over 2-bit alternatives on ARM processors while maintaining 92% task accuracy. This work establishes a practical framework for deploying adaptive AI on resource-constrained IoT devices, enabling intelligent home automation without cloud dependencies.
Cross-Domain Elephant Flow Detection: A Unified Machine Learning Approach with Application-Aware and Security Features
Venue: arXiv cs.NI 2025
Year: 2025

Network traffic classification, particularly elephant flow detection, faces significant challenges when deployed across heterogeneous network environments. While existing approaches demonstrate high accuracy within single domains, they suffer from poor generalization due to domain shift phenomena. This paper presents a unified machine learning framework for cross domain elephant flow detection that incorporates application aware and security features to enhance robustness across diverse network environments. Our approach addresses the critical gap in existing literature by evaluating model performance across three distinct domains: Campus networks, UNSW-NB15, and CIC-IDS2018 datasets. This paper proposes a unified pipeline that employs adaptive thresholding, comprehensive feature engineering, and cross-domain evaluation to quantify and mitigate domain shift effects. Experimental results demonstrate significant performance variations across domains (F1-scores ranging from 0.37 to 0.97), highlighting the importance of cross-domain validation. The unified model achieves an overall cross-validation F1 score of 0.99 while maintaining interpretability through feature importance analysis. Our findings reveal that while size based features dominate elephant flow detection (33.80% importance for total bytes), application-aware and security features contribute to improved classification accuracy and provide valuable insights for network management and security applications.
Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of large language models (LLMs). While prior work focuses on improving model performance through internal reasoning strategies, little is known about the interchangeability of reasoning across different models. In this work, we explore whether a partially completed reasoning chain from one model can be reliably continued by another model, either within the same model family or across families. We achieve this by assessing the sufficiency of intermediate reasoning traces as transferable scaffolds for logical coherence and final answer accuracy. We interpret this interchangeability as a means of examining inference-time trustworthiness, probing whether reasoning remains both coherent and reliable under model substitution. Using token-level log-probability thresholds to truncate reasoning at early, mid, and late stages from our baseline models, Gemma-3-4B-IT and LLaMA-3.1-70B-Instruct, we conduct continuation experiments with Gemma-3-1B-IT and LLaMA-3.1-8B-Instruct to test intra-family and cross-family behaviors. Our evaluation pipeline leverages truncation thresholds with a Process Reward Model (PRM), providing a reproducible framework for assessing reasoning stability via model interchange. Evaluations with a PRM reveal that hybrid reasoning chains often preserve, and in some cases even improve, final accuracy and logical structure. Our findings point towards interchangeability as an emerging behavioral property of reasoning models, offering insights into new paradigms for reliable modular reasoning in collaborative AI systems.
Eidoku: A Neuro-Symbolic Verification Gate for LLM Reasoning via Structural Constraint Satisfaction
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) frequently produce hallucinated statements that are assigned high likelihood by the model itself, exposing a fundamental limitation of probability-based verification. This suggests that hallucination is often not a low-confidence phenomenon, but a failure of structural consistency. In this work, we reformulate the verification of LLM reasoning as a Constraint Satisfaction Problem (CSP) operating independently of the generation likelihood. Rather than optimizing for statistical plausibility, we model verification as a feasibility check based on structural violation cost -- the computational cost required to embed a candidate reasoning step into the contextual graph structure. We define a total cost function composed of three proxies: (i) graph connectivity (structural), (ii) feature space consistency (geometric), and (iii) logical entailment (symbolic). Crucially, verification is performed via a lightweight System-2 gate, Eidoku, which rejects candidates exceeding a context-calibrated cost threshold. The threshold is not learned but is derived from the intrinsic statistics of the context, avoiding ad hoc heuristics. We demonstrate that this approach successfully rejects ``smooth falsehoods'' -- statements that are highly probable yet structurally disconnected -- that probability-based verifiers are principally incapable of detecting. Our experiments on a controlled diagnostic dataset show that explicitly enforcing structural constraints allows for the deterministic rejection of this specific class of hallucinations, serving as a neuro-symbolic sanity check for generative reasoning.
Forward Only Learning for Orthogonal Neural Networks of any Depth
Venue: arXiv cs.LG 2025
Year: 2025

Backpropagation is still the de facto algorithm used today to train neural networks. With the exponential growth of recent architectures, the computational cost of this algorithm also becomes a burden. The recent PEPITA and forward-only frameworks have proposed promising alternatives, but they failed to scale up to a handful of hidden layers, yet limiting their use. In this paper, we first analyze theoretically the main limitations of these approaches. It allows us the design of a forward-only algorithm, which is equivalent to backpropagation under the linear and orthogonal assumptions. By relaxing the linear assumption, we then introduce FOTON (Forward-Only Training of Orthogonal Networks) that bridges the gap with the backpropagation algorithm. Experimental results show that it outperforms PEPITA, enabling us to train neural networks of any depth, without the need for a backward pass. Moreover its performance on convolutional networks clearly opens up avenues for its application to more advanced architectures. The code is open-sourced at https://github.com/p0lcAi/FOTON .
Signal, Noise, and Burnout: A Human-Information Interaction Analysis of Voter Verification in a High-Volatility Environment
Venue: arXiv cs.SI 2025
Year: 2025

The 2024 U.S. Presidential Election unfolded within an information environment of unprecedented volatility, challenging citizens to navigate a torrent of rapidly evolving, often contradictory information while determining what to believe. This study investigates the cognitive mechanisms underlying epistemic self-efficacy - the perceived ability to distinguish accurate news from misinformation - across different information channels during this high-stakes election cycle. Drawing on data from the Pew Research Center's American Trends Panel (Wave 155, September 2024, N = 9,360), we test three hypotheses: (H1) whether reliance on social media predicts lower epistemic self-efficacy compared to mainstream news sources; (H2) whether perceived exposure to inaccurate information mediates this relationship; and (H3) whether information fatigue moderates the cognitive burden of verification across platforms. Contrary to expectations rooted in algorithmic filtering theory, we find no significant differences in reported difficulty determining truth between social media and mainstream news users. Instead, epistemic burden is driven by demographics (age, education) and universal information fatigue, suggesting a "leveling" of the information landscape during periods of extreme volatility. This finding challenges platform-deterministic theories and suggests that interventions to support informed citizenship must address cognitive resilience and attention management rather than platform choice alone.