OpenRaiser/Intern-Atlas huggingface dataset:
Monitoring Monitorability
Venue: arXiv cs.AI 2025
Year: 2025

Observability into the decision making of modern AI systems may be required to safely deploy increasingly capable agents. Monitoring the chain-of-thought (CoT) of today's reasoning models has proven effective for detecting misbehavior. However, this "monitorability" may be fragile under different training procedures, data sources, or even continued system scaling. To measure and track monitorability, we propose three evaluation archetypes (intervention, process, and outcome-property) and a new monitorability metric, and introduce a broad evaluation suite. We demonstrate that these evaluations can catch simple model organisms trained to have obfuscated CoTs, and that CoT monitoring is more effective than action-only monitoring in practical settings. We compare the monitorability of various frontier models and find that most models are fairly, but not perfectly, monitorable. We also evaluate how monitorability scales with inference-time compute, reinforcement learning optimization, and pre-training model size. We find that longer CoTs are generally more monitorable and that RL optimization does not materially decrease monitorability even at the current frontier scale. Notably, we find that for a model at a low reasoning effort, we could instead deploy a smaller model at a higher reasoning effort (thereby matching capabilities) and obtain a higher monitorability, albeit at a higher overall inference compute cost. We further investigate agent-monitor scaling trends and find that scaling a weak monitor's test-time compute when monitoring a strong agent increases monitorability. Giving the weak monitor access to CoT not only improves monitorability, but it steepens the monitor's test-time compute to monitorability scaling trend. Finally, we show we can improve monitorability by asking models follow-up questions and giving their follow-up CoT to the monitor.
MatSpray: Fusing 2D Material World Knowledge on 3D Geometry
Venue: arXiv cs.CV 2025
Year: 2025

Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have enabled accurate approximations of scene geometry and appearance, these methods often fall short in relighting scenarios due to the lack of precise, spatially varying material parameters. At the same time, diffusion models operating on 2D images have shown strong performance in predicting physically based rendering (PBR) properties such as albedo, roughness, and metallicity. However, transferring these 2D material maps onto reconstructed 3D geometry remains a significant challenge. We propose a framework for fusing 2D material data into 3D geometry using a combination of novel learning-based and projection-based approaches. We begin by reconstructing scene geometry via Gaussian Splatting. From the input images, a diffusion model generates 2D maps for albedo, roughness, and metallic parameters. Any existing diffusion model that can convert images or videos to PBR materials can be applied. The predictions are further integrated into the 3D representation either by optimizing an image-based loss or by directly projecting the material parameters onto the Gaussians using Gaussian ray tracing. To enhance fine-scale accuracy and multi-view consistency, we further introduce a light-weight neural refinement step (Neural Merger), which takes ray-traced material features as input and produces detailed adjustments. Our results demonstrate that the proposed methods outperform existing techniques in both quantitative metrics and perceived visual realism. This enables more accurate, relightable, and photorealistic renderings from reconstructed scenes, significantly improving the realism and efficiency of asset creation workflows in content production pipelines.
Implementing Transport Coding in OMNeT++ for Message Delay Reduction
Venue: arXiv cs.IT 2025
Year: 2025

Transport coding reduces message delay in packet-switched networks by introducing controlled redundancy at the transport layer: $k$ original packets are encoded into $n\ge k$ coded packets, and the message is reconstructed after the first $k$ successful deliveries, effectively shifting latency from the maximum packet delay to the $k$-th order statistic. We present a concise, reproducible discrete-event implementation of transport coding in OMNeT++, including a multi-hop Kleinrock-type network, FIFO queues, exponential service and link delays, and explicit receiver-side reconstruction that records message delay and deadline violations. Using paired uncoded ($n{=}k$) and coded ($n{>}k$) configurations at the same message generation rate, we compare delay, reliability, and saturation effects across code rates and input loads. Simulation results show consistent reductions of average delay and late-delivery probability for moderate redundancy, while keeping the saturation throughput close to the uncoded baseline. The proposed model provides a transparent bridge between analytical transport-coding formulas and executable simulation for tuning redundancy in low-latency services.
Faster Vertex Cover Algorithms on GPUs with Component-Aware Parallel Branching
Venue: arXiv cs.DC 2025
Year: 2025

Algorithms for finding minimum or bounded vertex covers in graphs use a branch-and-reduce strategy, which involves exploring a highly imbalanced search tree. Prior GPU solutions assign different thread blocks to different sub-trees, while using a shared worklist to balance the load. However, these prior solutions do not scale to large and complex graphs because their unawareness of when the graph splits into components causes them to solve these components redundantly. Moreover, their high memory footprint limits the number of workers that can execute concurrently. We propose a novel GPU solution for vertex cover problems that detects when a graph splits into components and branches on the components independently. Although the need to aggregate the solutions of different components introduces non-tail-recursive branches which interfere with load balancing, we overcome this challenge by delegating the post-processing to the last descendant of each branch. We also reduce the memory footprint by reducing the graph and inducing a subgraph before exploring the search tree. Our solution substantially outperforms the state-of-the-art GPU solution, finishing in seconds when the state-of-the-art solution exceeds 6 hours. To the best of our knowledge, our work is the first to parallelize non-tail-recursive branching patterns on GPUs in a load balanced manner.
Dynamic Entropy Tuning in Reinforcement Learning Low-Level Quadcopter Control: Stochasticity vs Determinism
Venue: arXiv cs.RO 2025
Year: 2025

This paper explores the impact of dynamic entropy tuning in Reinforcement Learning (RL) algorithms that train a stochastic policy. Its performance is compared against algorithms that train a deterministic one. Stochastic policies optimize a probability distribution over actions to maximize rewards, while deterministic policies select a single deterministic action per state. The effect of training a stochastic policy with both static entropy and dynamic entropy and then executing deterministic actions to control the quadcopter is explored. It is then compared against training a deterministic policy and executing deterministic actions. For the purpose of this research, the Soft Actor-Critic (SAC) algorithm was chosen for the stochastic algorithm while the Twin Delayed Deep Deterministic Policy Gradient (TD3) was chosen for the deterministic algorithm. The training and simulation results show the positive effect the dynamic entropy tuning has on controlling the quadcopter by preventing catastrophic forgetting and improving exploration efficiency.
LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators
Venue: arXiv cs.CL 2025
Year: 2025

We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is "trained" through collaborative interactions among multiple LLM agents rather than traditional backpropagation. The LLM agents produce rule-based Python code for a generator for the given domain, based on RDF triples only, with no in-domain human reference texts. The resulting system is fully interpretable, requires no supervised training data, and generates text nearly instantaneously using only a single CPU. Our experiments on the WebNLG and OpenDialKG data show that outputs produced by our approach reduce hallucination, with only slight fluency penalties compared to finetuned or prompted language models
Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

While imitation learning has shown impressive results in single-task robot manipulation, scaling it to multi-task settings remains a fundamental challenge due to issues such as suboptimal demonstrations, trajectory noise, and behavioral multi-modality. Existing skill-based methods attempt to address this by decomposing actions into reusable abstractions, but they often rely on fixed-length segmentation or environmental priors that limit semantic consistency and cross-task generalization. In this work, we propose AtomSkill, a novel multi-task imitation learning framework that learns and leverages a structured Atomic Skill Space for composable robot manipulation. Our approach is built on two key technical contributions. First, we construct a Semantically Grounded Atomic Skill Library by partitioning demonstrations into variable-length skills using gripper-state keyframe detection and vision-language model annotation. A contrastive learning objective ensures the resulting skill embeddings are both semantically consistent and temporally coherent. Second, we propose an Action Generation module with Keypose Imagination, which jointly predicts a skill's long-horizon terminal keypose and its immediate action sequence. This enables the policy to reason about overarching motion goals and fine-grained control simultaneously, facilitating robust skill chaining. Extensive experiments in simulated and real-world environments show that AtomSkill consistently outperforms state-of-the-art methods across diverse manipulation tasks.
Constant Approximation of Arboricity in Near-Optimal Sublinear Time
Venue: arXiv cs.DS 2025
Year: 2025

We present a randomized algorithm that computes a constant approximation of a graph's arboricity, using $\tilde{O}(n/λ)$ queries to adjacency lists and in the same time bound. Here, $n$ and $λ$ denote the number of nodes and the graph's arboricity, respectively. The $\tilde{O}(n/λ)$ query complexity of our algorithm is nearly optimal. Our constant approximation settles a question of Eden, Mossel, and Ron [SODA'22], who achieved an $O(\log^2 n)$ approximation with the same query and time complexity and asked whether a better approximation can be achieved using near-optimal query complexity. A key technical challenge in the problem is due to recursive algorithms based on probabilistic samplings, each with a non-negligible error probability. In our case, many of the recursions invoked could have bad probabilistic samples and result in high query complexities. The particular difficulty is that those bad recursions are not easy or cheap to detect and discard. Our approach runs multiple recursions in parallel, to attenuate the error probability, using a careful \textit{scheduling mechanism} that manages the speed at which each of them progresses and makes our overall query complexity competitive with the single good recursion. We find this usage of parallelism and scheduling in a sublinear algorithm remarkable, and we are hopeful that similar ideas may find applications in a wider range of sublinear algorithms that rely on probabilistic recursions.
E-RGB-D: Real-Time Event-Based Perception with Structured Light
Venue: arXiv cs.CV 2025
Year: 2025

Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed and efficiency in vision sensing. Despite their high dynamic range, temporal resolution, low power consumption, and computational simplicity, traditional monochrome ECs face limitations in detecting static or slowly moving objects and lack color information essential for certain applications. To address these challenges, we present a novel approach that integrates a Digital Light Processing (DLP) projector, forming Active Structured Light (ASL) for RGB-D sensing. By combining the benefits of ECs and projection-based techniques, our method enables the detection of color and the depth of each pixel separately. Dynamic projection adjustments optimize bandwidth, ensuring selective color data acquisition and yielding colorful point clouds without sacrificing spatial resolution. This integration, facilitated by a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, not only enables frameless RGB-D sensing applications but also achieves remarkable performance milestones. With our approach, we achieved a color detection speed equivalent to 1400 fps and 4 kHz of pixel depth detection, significantly advancing the realm of computer vision across diverse fields from robotics to 3D reconstruction methods. Our code is publicly available: https://github.com/MISTLab/event_based_rgbd_ros
Snowveil: A Framework for Decentralised Preference Discovery
Venue: arXiv cs.GT 2025
Year: 2025

Aggregating subjective preferences of a large group is a fundamental challenge in computational social choice, traditionally reliant on central authorities. To address the limitations of this model, this paper introduces Decentralised Preference Discovery (DPD), the problem of determining the collective will of an electorate under constraints of censorship resistance, partial information, and asynchronous communication. We propose Snowveil, a novel framework for this task. Snowveil uses an iterative, gossip-based protocol where voters repeatedly sample the preferences of a small, random subset of the electorate to progressively converge on a collective outcome. We demonstrate the framework's modularity by designing the Constrained Hybrid Borda (CHB), a novel aggregation rule engineered to balance broad consensus with strong plurality support, and provide a rigorous axiomatic analysis of its properties. By applying a potential function and submartingale theory, we develop a multi-level analytical method to show that the system almost surely converges to a stable, single-winner in finite time, a process that can then be iterated to construct a set of winning candidates for multi-winner scenarios. This technique is largely agnostic to the specific aggregation rule, requiring only that it satisfies core social choice axioms like Positive Responsiveness, thus offering a formal toolkit for a wider class of DPD protocols. Furthermore, we present a comprehensive empirical analysis through extensive simulation, validating Snowveil's $O(n)$ scalability. Overall, this work advances the understanding of how a stable consensus can emerge from subjective, complex, and diverse preferences in decentralised systems for large electorates.
Agent-Based Output Drift Detection for Breast Cancer Response Prediction in a Multisite Clinical Decision Support System
Venue: arXiv cs.AI 2025
Year: 2025

Modern clinical decision support systems can concurrently serve multiple, independent medical imaging institutions, but their predictive performance may degrade across sites due to variations in patient populations, imaging hardware, and acquisition protocols. Continuous surveillance of predictive model outputs offers a safe and reliable approach for identifying such distributional shifts without ground truth labels. However, most existing methods rely on centralized monitoring of aggregated predictions, overlooking site-specific drift dynamics. We propose an agent-based framework for detecting drift and assessing its severity in multisite clinical AI systems. To evaluate its effectiveness, we simulate a multi-center environment for output-based drift detection, assigning each site a drift monitoring agent that performs batch-wise comparisons of model outputs against a reference distribution. We analyse several multi-center monitoring schemes, that differ in how the reference is obtained (site-specific, global, production-only and adaptive), alongside a centralized baseline. Results on real-world breast cancer imaging data using a pathological complete response prediction model shows that all multi-center schemes outperform centralized monitoring, with F1-score improvements up to 10.3% in drift detection. In the absence of site-specific references, the adaptive scheme performs best, with F1-scores of 74.3% for drift detection and 83.7% for drift severity classification. These findings suggest that adaptive, site-aware agent-based drift monitoring can enhance reliability of multisite clinical decision support systems.
SoK: Understanding (New) Security Issues Across AI4Code Use Cases
Venue: arXiv cs.CR 2025
Year: 2025

AI-for-Code (AI4Code) systems are reshaping software engineering, with tools like GitHub Copilot accelerating code generation, translation, and vulnerability detection. Alongside these advances, however, security risks remain pervasive: insecure outputs, biased benchmarks, and susceptibility to adversarial manipulation undermine their reliability. This SoK surveys the landscape of AI4Code security across three core applications, identifying recurring gaps: benchmark dominance by Python and toy problems, lack of standardized security datasets, data leakage in evaluation, and fragile adversarial robustness. A comparative study of six state-of-the-art models illustrates these challenges: insecure patterns persist in code generation, vulnerability detection is brittle to semantic-preserving attacks, fine-tuning often misaligns security objectives, and code translation yields uneven security benefits. From this analysis, we distill three forward paths: embedding secure-by-default practices in code generation, building robust and comprehensive detection benchmarks, and leveraging translation as a route to security-enhanced languages. We call for a shift toward security-first AI4Code, where vulnerability mitigation and robustness are embedded throughout the development life cycle.
Enhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation
Venue: arXiv cs.CR 2025
Year: 2025

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable (PE) malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM (LGBM) malware detector by integrating Neural Networks (NN), PriorNet, and Neural Network Ensembles, evaluated across three benchmark datasets: EMBER, BODMAS, and UCSB. The UCSB dataset, composed mainly of packed malware, introduces a substantial distributional shift relative to EMBER and BODMAS, making it a challenging testbed for robustness. We study uncertainty-aware decision strategies, including probability thresholding, PriorNet, ensemble-derived estimates, and Inductive Conformal Evaluation (ICE). Our main contribution is the use of ensemble-based uncertainty estimates as Non-Conformity Measures within ICE, combined with a novel threshold optimisation method. On the UCSB dataset, where the shift is most severe, the state-of-the-art probability-based ICE (SOTA) yields an incorrect acceptance rate (IA%) of 22.8%. In contrast, our method reduces this to 16% a relative reduction of about 30% while maintaining competitive correct acceptance rates (CA%). These results demonstrate that integrating ensemble-based uncertainty with conformal prediction provides a more reliable safeguard against misclassifications under extreme dataset shifts, particularly in the presence of packed malware, thereby offering practical benefits for real-world security operations.
Adaptive-VoCo: Complexity-Aware Visual Token Compression for Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, large-scale vision-language models (VLMs) have demonstrated remarkable performance on multimodal understanding and reasoning tasks. However, handling high-dimensional visual features often incurs substantial computational and memory costs. VoCo-LLaMA alleviates this issue by compressing visual patch tokens into a few VoCo tokens, reducing computational overhead while preserving strong cross-modal alignment. Nevertheless, such approaches typically adopt a fixed compression rate, limiting their ability to adapt to varying levels of visual complexity. To address this limitation, we propose Adaptive-VoCo, a framework that augments VoCo-LLaMA with a lightweight predictor for adaptive compression. This predictor dynamically selects an optimal compression rate by quantifying an image's visual complexity using statistical cues from the vision encoder, such as patch token entropy and attention map variance. Furthermore, we introduce a joint loss function that integrates rate regularization with complexity alignment. This enables the model to balance inference efficiency with representational capacity, particularly in challenging scenarios. Experimental results show that our method consistently outperforms fixed-rate baselines across multiple multimodal tasks, highlighting the potential of adaptive visual compression for creating more efficient and robust VLMs.
PlantDiseaseNet-RT50: A Fine-tuned ResNet50 Architecture for High-Accuracy Plant Disease Detection Beyond Standard CNNs
Venue: arXiv cs.CV 2025
Year: 2025

Plant diseases pose a significant threat to agricultural productivity and global food security, accounting for 70-80% of crop losses worldwide. Traditional detection methods rely heavily on expert visual inspection, which is time-consuming, labour-intensive, and often impractical for large-scale farming operations. In this paper, we present PlantDiseaseNet-RT50, a novel fine-tuned deep learning architecture based on ResNet50 for automated plant disease detection. Our model features strategically unfrozen layers, a custom classification head with regularization mechanisms, and dynamic learning rate scheduling through cosine decay. Using a comprehensive dataset of distinct plant disease categories across multiple crop species, PlantDiseaseNet-RT50 achieves exceptional performance with approximately 98% accuracy, precision, and recall. Our architectural modifications and optimization protocol demonstrate how targeted fine-tuning can transform a standard pretrained model into a specialized agricultural diagnostic tool. We provide a detailed account of our methodology, including the systematic unfreezing of terminal layers, implementation of batch normalization and dropout regularization and application of advanced training techniques. PlantDiseaseNet-RT50 represents a significant advancement in AI-driven agricultural tools, offering a computationally efficient solution for rapid and accurate plant disease diagnosis that can be readily implemented in practical farming contexts to support timely interventions and reduce crop losses.
Teaching and Critiquing Conceptualization and Operationalization in NLP
Venue: arXiv cs.CL 2025
Year: 2025

NLP researchers regularly invoke abstract concepts like "interpretability," "bias," "reasoning," and "stereotypes," without defining them. Each subfield has a shared understanding or conceptualization of what these terms mean and how we should treat them, and this shared understanding is the basis on which operational decisions are made: Datasets are built to evaluate these concepts, metrics are proposed to quantify them, and claims are made about systems. But what do they mean, what should they mean, and how should we measure them? I outline a seminar I created for students to explore these questions of conceptualization and operationalization, with an interdisciplinary reading list and an emphasis on discussion and critique.