OpenRaiser/Intern-Atlas huggingface dataset:
PADME: Procedure Aware DynaMic Execution
Venue: arXiv cs.AI 2025
Year: 2025

Learning to autonomously execute long-horizon procedures from natural language remains a core challenge for intelligent agents. Free-form instructions such as recipes, scientific protocols, or business workflows encode rich procedural knowledge, but their variability and lack of structure cause agents driven by large language models (LLMs) to drift or fail during execution. We introduce Procedure Aware DynaMic Execution (PADME), an agent framework that produces and exploits a graph-based representation of procedures. Unlike prior work that relies on manual graph construction or unstructured reasoning, PADME autonomously transforms procedural text into executable graphs that capture task dependencies, decision points, and reusable subroutines. Central to PADME is a two-phase methodology; Teach phase, which focuses on systematic structuring, enrichment with executable logic of procedures, followed by Execute phase, which enables dynamic execution in response to real-time inputs and environment feedback. This separation ensures quality assurance and scalability, allowing expert knowledge to be encoded once and reliably reused across varying contexts. The graph representation also provides an inductive bias that reduces error accumulation in long-horizon reasoning, underscoring the importance of structured procedure modeling for reliable agent-driven automation. Empirically, PADME achieves state-of-the-art performance on four diverse benchmarks, including ALFWorld and ScienceWorld. These results demonstrate that agents equipped with graph-based procedure representations offer a powerful intermediate abstraction for robust and generalizable execution.
Gym-TORAX: Open-source software for integrating RL with plasma control simulators
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents Gym-TORAX, a Python package enabling the implementation of Reinforcement Learning (RL) environments for simulating plasma dynamics and control in tokamaks. Users define succinctly a set of control actions and observations, and a control objective from which Gym-TORAX creates a Gymnasium environment that wraps TORAX for simulating the plasma dynamics. The objective is formulated through rewards depending on the simulated state of the plasma and control action to optimize specific characteristics of the plasma, such as performance and stability. The resulting environment instance is then compatible with a wide range of RL algorithms and libraries and will facilitate RL research in plasma control. In its current version, one environment is readily available, based on a ramp-up scenario of the International Thermonuclear Experimental Reactor (ITER).
EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism
Venue: arXiv cs.CV 2025
Year: 2025

Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.
Detection of Performance Changes in MooBench Results Using Nyrkiö on GitHub Actions
Venue: arXiv cs.SE 2025
Year: 2025

In GitHub with its 518 million hosted projects, performance changes within these projects are highly relevant to the project's users. Although performance measurement is supported by GitHub CI/CD, performance change detection is a challenging topic. In this paper, we demonstrate how we incorporated Nyrkiö to MooBench. Prior to this work, Moobench continuously ran on GitHub virtual machines, measuring overhead of tracing agents, but without change detection. By adding the upload of the measurements to the Nyrkiö change detection service, we made it possible to detect performance changes. We identified one major performance regression and examined the performance change in depth. We report that (1) it is reproducible with GitHub actions, and (2) the performance regression is caused by a Linux Kernel version change.
A Denotational Product Construction for Temporal Verification of Effectful Higher-Order Programs
Venue: arXiv cs.LO 2025
Year: 2025

We propose a categorical framework for linear-time temporal verification of effectful higher-order programs, including probabilistic higher-order programs. Our framework provides a generic denotational reduction -- namely, a denotational product construction -- from linear-time safety verification of effectful higher-order programs to computation of weakest pre-conditions of product programs. This reduction enables us to apply existing algorithms for such well-studied computations of weakest pre-conditions, some of which are available as off-the-shelf solvers. We show the correctness of our denotational product construction by proving a preservation theorem under strong monad morphisms and an existence of suitable liftings along a fibration. We instantiate our framework with both probabilistic and angelic nondeterministic higher-order programs, and implement an automated solver for the probabilistic case based on the existing solver developed by Kura and Unno. To the best of our knowledge, this is the first automated verifier for linear-time temporal verification of probabilistic higher-order programs with recursion.
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Venue: arXiv cs.LG 2025
Year: 2025

Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning models generalize in data-scarce domains. While well developed in vision and language, style transfer methods for time series data remain limited. We introduce DiffTSST, a diffusion-based framework that disentangles a time series into content and style representations via convolutional encoders and recombines them through a self-supervised attention-based diffusion process. At inference, encoders extract content and style from two distinct series, enabling conditional generation of novel samples to achieve style transfer. We demonstrate both qualitatively and quantitatively that DiffTSST achieves effective style transfer. We further validate its real-world utility by showing that data augmentation with DiffTSST improves anomaly detection in data-scarce regimes.
A mathematical model for pricing perishable goods for quick-commerce applications
Venue: arXiv cs.CE 2025
Year: 2025

Quick commerce (q-commerce) is one of the fastest growing sectors in India. It provides informal employment to approximately 4,50,000 workers, and it is estimated to become a USD 200 Billion industry by 2026. A significant portion of this industry deals with perishable goods. (e.g. milk, dosa batter etc.) These are food items which are consumed relatively fresh by the consumers and therefore their order volume is high and repetitive even when the average basket size is relatively small. The fundamental challenge for the retailer is that, increasing selling price would hamper sales and would lead to unsold inventory. On the other hand setting a price less, would lead to forgoing of potential revenue. This paper attempts to propose a mathematical model which formalizes this dilemma. The problem statement is not only important for improving the unit economics of the perennially loss making quick commerce firms, but also would lead to a trickle-down effect in improving the conditions of the gig workers as observed in [4]. The sections below describe the mathematical formulation. The results from the simulation would be published in a follow-up study.
A protocol to reduce worst-case latency in deflection-based on-chip networks
Venue: arXiv cs.NI 2025
Year: 2025

We present a novel protocol that reduces worst-case packet latency in deflection-based on-chip interconnect networks. It enforces the deflection of the header of a packet but not its payload, resulting in a reduction in overall network traffic and, more importantly, worst-case packet latency due to decreased pre-injection latency.
VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Venue: arXiv cs.IR 2025
Year: 2025

Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potential in reducing hallucinations and facilitating human verification. Existing citation generation methods primarily rely on either fine-tuning the generator or employing post-processing approaches for citation matching. However, the former approach demands substantial annotated data and computational resources, while the latter often encounters difficulties in managing multiple citations and frequently produces suboptimal results. In this paper, we introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution. Specifically, VeriCite breaks down into a three-stage generation: 1) The initial answer generation first generates a response based on all available contexts and has its claims verified through the NLI model; 2) the supporting evidence selection assesses the utility of each document and extracts useful supporting evidences; 3) the final answer refinement integrates the initial response and collected evidences to produce the final, refined answer.We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.
GenCNER: A Generative Framework for Continual Named Entity Recognition
Venue: arXiv cs.CL 2025
Year: 2025

Traditional named entity recognition (NER) aims to identify text mentions into pre-defined entity types. Continual Named Entity Recognition (CNER) is introduced since entity categories are continuously increasing in various real-world scenarios. However, existing continual learning (CL) methods for NER face challenges of catastrophic forgetting and semantic shift of non-entity type. In this paper, we propose GenCNER, a simple but effective Generative framework for CNER to mitigate the above drawbacks. Specifically, we skillfully convert the CNER task into sustained entity triplet sequence generation problem and utilize a powerful pre-trained seq2seq model to solve it. Additionally, we design a type-specific confidence-based pseudo labeling strategy along with knowledge distillation (KD) to preserve learned knowledge and alleviate the impact of label noise at the triplet level. Experimental results on two benchmark datasets show that our framework outperforms previous state-of-the-art methods in multiple CNER settings, and achieves the smallest gap compared with non-CL results.
Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization
Venue: arXiv cs.CV 2025
Year: 2025

Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities. This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS). This work addresses the limitations of AIS-based monitoring by leveraging non-cooperative satellite imagery and implementing a fusion approach that links visual detections with AIS data to identify dark vessels, validate cooperative traffic, and support advanced MDA. The You Only Look Once (YOLO) v11 object detection model is used to detect and characterize vessels and barges by vessel type, barge cover, operational status, barge count, and direction of travel. An annotated data set of 4,550 instances was developed from $5{,}973~\mathrm{mi}^2$ of Lower Mississippi River imagery. Evaluation on a held-out test set demonstrated vessel classification (tugboat, crane barge, bulk carrier, cargo ship, and hopper barge) with an F1 score of 95.8\%; barge cover (covered or uncovered) detection yielded an F1 score of 91.6\%; operational status (staged or in motion) classification reached an F1 score of 99.4\%. Directionality (upstream, downstream) yielded 93.8\% accuracy. The barge count estimation resulted in a mean absolute error (MAE) of 2.4 barges. Spatial transferability analysis across geographically disjoint river segments showed accuracy was maintained as high as 98\%. These results underscore the viability of integrating non-cooperative satellite sensing with AIS fusion. This approach enables near-real-time fleet inventories, supports anomaly detection, and generates high-quality data for inland waterway surveillance. Future work will expand annotated datasets, incorporate temporal tracking, and explore multi-modal deep learning to further enhance operational scalability.
Investigating Large Language Models' Linguistic Abilities for Text Preprocessing
Venue: arXiv cs.CL 2025
Year: 2025

Text preprocessing is a fundamental component of Natural Language Processing, involving techniques such as stopword removal, stemming, and lemmatization to prepare text as input for further processing and analysis. Despite the context-dependent nature of the above techniques, traditional methods usually ignore contextual information. In this paper, we investigate the idea of using Large Language Models (LLMs) to perform various preprocessing tasks, due to their ability to take context into account without requiring extensive language-specific annotated resources. Through a comprehensive evaluation on web-sourced data, we compare LLM-based preprocessing (specifically stopword removal, lemmatization and stemming) to traditional algorithms across multiple text classification tasks in six European languages. Our analysis indicates that LLMs are capable of replicating traditional stopword removal, lemmatization, and stemming methods with accuracies reaching 97%, 82%, and 74%, respectively. Additionally, we show that ML algorithms trained on texts preprocessed by LLMs achieve an improvement of up to 6% with respect to the $F_1$ measure compared to traditional techniques. Our code, prompts, and results are publicly available at https://github.com/GianCarloMilanese/llm_pipeline_wi-iat.
Rescaling-Aware Training for Efficient Deployment of Deep Learning Models on Full-Integer Hardware
Venue: arXiv cs.LG 2025
Year: 2025

Integer AI inference significantly reduces computational complexity in embedded systems. Quantization-aware training (QAT) helps mitigate accuracy degradation associated with post-training quantization but still overlooks the impact of integer rescaling during inference, which is a hardware costly operation in integer-only AI inference. This work shows that rescaling cost can be dramatically reduced post-training, by applying a stronger quantization to the rescale multiplicands at no model-quality loss. Furthermore, we introduce Rescale-Aware Training, a fine tuning method for ultra-low bit-width rescaling multiplicands. Experiments show that even with 8x reduced rescaler widths, the full accuracy is preserved through minimal incremental retraining. This enables more energy-efficient and cost-efficient AI inference for resource-constrained embedded systems.
How Reinforcement Learning After Next-Token Prediction Facilitates Learning
Venue: arXiv cs.LG 2025
Year: 2025

Recent advances in reasoning domains with neural networks have primarily been enabled by a training recipe that optimizes Large Language Models, previously trained to predict the next-token in a sequence, with reinforcement learning algorithms. We introduce a framework to study the success of this paradigm, and we theoretically expose the optimization mechanisms by which reinforcement learning improves over next-token prediction in this setting. We study learning from mixture distributions of short and long ``chain-of-thought'' sequences encoding a single task. In particular, when the task consists of predicting the parity of $d$ bits and long sequences are rare, we show how reinforcement learning after next-token prediction enables autoregressive transformers to generalize, whereas mere next-token prediction requires extreme statistical or computational resources to do so. We further explain how reinforcement learning leverages increased test-time computation, manifested in longer responses, to facilitate this learning process. In a simplified setting, we theoretically prove that autoregressive linear models following this training recipe can efficiently learn to predict the parity of $d$ bits as long as the proportion of long demonstrations in the data mix is not exponentially small in the input dimension $d$. Finally, we demonstrate these same phenomena in other settings, including the post-training of Llama-series models on mixture variations of common mathematical reasoning benchmarks.
ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding
Venue: arXiv cs.LG 2025
Year: 2025

While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate--diagnose--refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent uses the MLLM-in-the-loop both as a visual critic--scoring code with screenshots--and as a source of actionable, vision-grounded feedback; a strict zero-reward rule for invalid renders anchors renderability and prevents reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training-inference decoupling.
Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
Venue: arXiv cs.LG 2025
Year: 2025

Autonomous vehicles have shown promising potential to be a groundbreaking technology for improving the safety of road users. For these vehicles, as well as many other safety-critical robotic technologies, to be deployed in real-world applications, we require algorithms that can generalize well to unseen scenarios and data. Model-based reinforcement learning algorithms (MBRL) have demonstrated state-of-the-art performance and data efficiency across a diverse set of domains. However, these algorithms have also shown susceptibility to changes in the environment and its transition dynamics. In this work, we explore the performance and generalization capabilities of MBRL algorithms for autonomous driving, specifically in the simulated autonomous racing environment, Roboracer (formerly F1Tenth). We frame the head-to-head racing task as a learning problem using contextual Markov decision processes and parameterize the driving behavior of the adversaries using the context of the episode, thereby also parameterizing the transition and reward dynamics. We benchmark the behavior of MBRL algorithms in this environment and propose a novel context-aware extension of the existing literature, cMask. We demonstrate that context-aware MBRL algorithms generalize better to out-of-distribution adversary behaviors relative to context-free approaches. We also demonstrate that cMask displays strong generalization capabilities, as well as further performance improvement relative to other context-aware MBRL approaches when racing against adversaries with in-distribution behaviors.
Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errors
Venue: arXiv cs.LG 2025
Year: 2025

Research on reasoning in language models (LMs) predominantly focuses on improving the correctness of their outputs. But some important applications require modeling reasoning patterns that are incorrect. For example, automated systems that can reason about and simulate student errors are useful for providing real-time feedback in the classroom or offline practice for educators-in-training. This paper presents a new method, MISTAKE, that (1) constructs high-quality synthetic examples of reasoning errors by leveraging cycle consistency between incorrect answers and latent misconceptions; and (2) uses the generated data to learn models for student simulation, misconception classification, and answer generation. We evaluate MISTAKE on three educational tasks and find that it results in (1) higher accuracy when simulating incorrect student answers based on specific misconceptions, (2) increased performance inferring latent misconceptions from observed incorrect answers, and (3) higher alignment with expert-written distractor answers when generating incorrect answers (e.g., for multiple-choice tests).
Towards Fast and Scalable Normal Integration using Continuous Components
Venue: arXiv cs.CV 2025
Year: 2025

Surface normal integration is a fundamental problem in computer vision, dealing with the objective of reconstructing a surface from its corresponding normal map. Existing approaches require an iterative global optimization to jointly estimate the depth of each pixel, which scales poorly to larger normal maps. In this paper, we address this problem by recasting normal integration as the estimation of relative scales of continuous components. By constraining pixels belonging to the same component to jointly vary their scale, we drastically reduce the number of optimization variables. Our framework includes a heuristic to accurately estimate continuous components from the start, a strategy to rebalance optimization terms, and a technique to iteratively merge components to further reduce the size of the problem. Our method achieves state-of-the-art results on the standard normal integration benchmark in as little as a few seconds and achieves one-order-of-magnitude speedup over pixel-level approaches on large-resolution normal maps.