Related papers: Using Process Mining to Improve Digital Service Delivery

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Harnessing LLM Agents with Skill Programs [58.356514745548026]
We introduce HASP, a new framework that upgrades skills into executable Program Functions (PFs)<n>PFs act as executable guardrails that activate on failure-prone states and modify the next action or inject corrective context.<n> HASP drives substantial gains compared to both training-free and training-based methods on web-search, math reasoning, and coding tasks.
arXiv Detail & Related papers (2026-05-18T01:35:11Z)
SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data [49.370849653460716]
We propose SOPE, an algorithm that uses an actor-aligned Off-Policy Policy Evaluation signal as an automated early-stopping mechanism.<n> evaluated on 25 continuous control tasks from the Minari benchmark suite.<n>SOPE improves baseline performance by up to 45.6% while reducing the required TFLOPs by up to 22x.
arXiv Detail & Related papers (2026-05-07T08:32:09Z)
Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation [121.31598641762251]
Long-tail distribution of training demonstrations hinders policy learning.<n>Approaching-Phase Augmentation (APA) transfers knowledge from data-rich head tasks to data-scarce tail tasks without requiring demonstrations.
arXiv Detail & Related papers (2026-02-06T09:10:02Z)
Verified Critical Step Optimization for LLM Agents [67.05296684575445]
Critical Step Optimization focuses preference learning on verified critical steps.<n>Method starts from failed policy trajectories rather than expert demonstrations.<n>Experiments on GAIA-Text-103 and XBench-DeepSearch show that CSO achieves 37% and 26% relative improvement over the SFT baseline.
arXiv Detail & Related papers (2026-02-03T11:41:02Z)
Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application [7.018547803286914]
This work evaluates the behavioural and retention effects of agentic personalisation on a financial service application's customer communication system.<n>We compare an agentic messaging approach against a business-as-usual (BAU) rule-based campaign system.
arXiv Detail & Related papers (2025-12-19T11:25:18Z)
DrP: Meta's Efficient Investigations Platform at Scale [0.644402572758926]
DrP is an end-to-end framework and system to automate investigations.<n>It consists of an expressive and flexible SDK to author investigation playbooks in code.<n>DrP has been running in production for the past 5 years and executes 50K automated analyses per day.
arXiv Detail & Related papers (2025-12-03T20:34:51Z)
Measuring Agents in Production [133.77818981073457]
We present the first large-scale systematic study of AI agents in production.<n>We find that production agents are typically built using simple, controllable approaches.<n> Reliability remains the top development challenge, driven by difficulties in ensuring and evaluating agent correctness.
arXiv Detail & Related papers (2025-12-02T16:45:10Z)
What is the Return on Investment of Digital Engineering for Complex Systems Development? Findings from a Mixed-Methods Study on the Post-production Design Change Process of Navy Assets [0.0]
Complex engineered systems routinely face schedule and cost overruns, along with poor post-deployment performance.<n>Championed by both INCOSE and the U.S. Department of Defense (DoD), the systems engineering (SE) community has increasingly looked to Digital Engineering (DE) as a potential remedy.<n>This research presents findings from a case study on a Navy SE team responsible for the preliminary design phase of post-production design change projects for Navy assets.
arXiv Detail & Related papers (2025-10-29T15:37:44Z)
RRO: LLM Agent Optimization Through Rising Reward Trajectories [52.579992804584464]
Large language models (LLMs) have exhibited extraordinary performance in a variety of tasks.<n>In practice, agents sensitive to the outcome of certain key steps which makes them likely to fail the task.<n>We propose Reward Rising Optimization (RRO) to mitigate this issue.
arXiv Detail & Related papers (2025-05-27T05:27:54Z)
Learning Long-Context Diffusion Policies via Past-Token Prediction [48.86967836229684]
We propose an alternative approach that explicitly regularizes the retention of past information.<n>We introduce Past-Token Prediction, an auxiliary task in which the policy learns to predict past action tokens alongside future ones.<n> Experiments across four real-world and six simulated tasks demonstrate that our proposed method improves the performance of long-context diffusion policies by 3x and accelerates policy training by more than 10x.
arXiv Detail & Related papers (2025-05-14T17:00:47Z)
Towards an Interpretable Analysis for Estimating the Resolution Time of Software Issues [1.4039240369201997]
We build an issue monitoring system that extracts the actual effort required to fix issues on a per-project basis.<n>Our approach employs topic modeling to capture issue semantics and leverages metadata for interpretable resolution time analysis.
arXiv Detail & Related papers (2025-05-02T08:38:59Z)
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning [90.23629291067763]
A promising approach for improving reasoning in large language models is to use process reward models (PRMs) PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs) To improve a base policy by running search against a PRM or using it as dense rewards for reinforcement learning (RL), we ask: "How should we design process rewards?" We theoretically characterize the set of good provers and our results show that optimizing process rewards from such provers improves exploration during test-time search and online RL.
arXiv Detail & Related papers (2024-10-10T17:31:23Z)
Multimodal video analysis for crowd anomaly detection using open access tourism cameras [76.93566452564627]
We propose the detection of crowd anomalies through the extraction of information in the form of time series from video format using a multimodal approach. The application of this methodology on the webcam of Turisme Comunitat Valenciana in the town of Morella has provided excellent results.
arXiv Detail & Related papers (2024-05-21T11:56:01Z)
Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents [0.8158530638728501]
We train a Markov decision process using event data from a care process. The goal was to find optimal policies for staff members when clients are displaying any type of aggressive behavior. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used.
arXiv Detail & Related papers (2023-10-02T08:43:29Z)
Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs [65.9077733300329]
Large Language Models (LLMs) have the potential to greatly enhance the analysis of public affairs documents. LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.
arXiv Detail & Related papers (2023-06-05T13:35:01Z)
Web-based Management Information System of Cases Filed with the National Labor Relations Commission [0.0]
This study was developed to describe the daily operations and encountered problems of the National Labor Relations Commission Regional Arbitration Branch No. IV (NLRC RAB IV) through conducted observations and interviews. The research methodology utilized in this project was the descriptive developmental approach. The system covered managing filed complaints, Single-Entry Approach (SEnA), labor cases, and report generation.
arXiv Detail & Related papers (2022-11-23T06:30:01Z)
Robotic Process Automation Using Process Mining $\unicode{x2013}$ A Systematic Literature Review [0.7252027234425332]
This paper aims to assess the applicability of process mining in accelerating and improving the implementation of RPA. A systematic literature review was conducted to examine the approaches where PM techniques were used to understand the as-is processes that can be automated with software robots. There is a steady increase in the number of publications in this domain, especially during the year 2022, which suggests a raising interest in the combined use of PM with RPA.
arXiv Detail & Related papers (2022-04-02T03:13:17Z)
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z)
Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
The paper studies existing methods in this field via a Systematic Literature Review ( SLR) The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
arXiv Detail & Related papers (2021-12-03T08:06:24Z)
Estimating Effectiveness of Identifying Human Trafficking via Data Envelopment Analysis [0.0]
Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border. We developed a system that uses data envelopment analysis (DEA) to help LJI decision-makers evaluate the performance of these stations.
arXiv Detail & Related papers (2020-12-09T17:06:50Z)
On the Composition of the Long Tail of Business Processes: Implications from a Process Mining Study [0.0]
Digital transformation forces companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide the means to structure and tackle this change. Most approaches to BPM face restrictions on the number of processes they can optimize at a time due to complexity and resource restrictions. Investigating this shortcoming, the concept of the long tail of business processes suggests a hybrid approach that entails managing important processes centrally, while incrementally improving the majority of processes at their place of execution.
arXiv Detail & Related papers (2020-11-26T09:04:15Z)
Provably Good Batch Reinforcement Learning Without Great Exploration [51.51462608429621]
Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Recent algorithms have shown promise but can still be overly optimistic in their expected outcomes. We show that a small modification to Bellman optimality and evaluation back-up to take a more conservative update can have much stronger guarantees.
arXiv Detail & Related papers (2020-07-16T09:25:54Z)

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