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Joined July 12, 2018

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

OVT-MLCS: An Online Visual Tool for MLCS Mining from Long or Big Sequences https://arxiv.org/abs/2604.13037 #cs.DB #cs.AI

arXiv.org

OVT-MLCS: An Online Visual Tool for MLCS Mining from Long or Big Sequences

Mining multiple longest common subsequences (\textit{MLCS}) from a set of sequences of three or more over a finite alphabet $Σ$ (a classical NP-hard problem) is an important task in a wide variety of application fields. Unfortunately, there is still no exact \textit{MLCS} algorithm/tool that can handle long (length $\ge$ 1,000) or big (length $\ge$ 10,000) sequences, which seriously hinders the development and utilization of massive long or big sequences from various application fields today. To

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Independent subcontexts and blocks of concept lattices. Definitions and relationships to decompose fuzzy contexts https://arxiv.org/abs/2604.13039 #cs.DB

arXiv.org

Independent subcontexts and blocks of concept lattices. Definitions and relationships to decompose fuzzy contexts

The decomposition of datasets is a useful mechanism in the processing of large datasets and it is required in many cases. In formal concept analysis (FCA), the dataset is interpreted as a context and the notion of independent context is relevant in the decomposition of a context. In this paper, we have introduced a formal definition of independent context within the multi-adjoint concept lattice framework, which can be translated to other fuzzy approaches. Furthermore, we have analyzed the decom

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Decomposition of contexts into independent subcontexts based on thresholds https://arxiv.org/abs/2604.13040 #cs.DB

arXiv.org

Decomposition of contexts into independent subcontexts based on thresholds

The process of decomposing databases into smaller datasets, with the objective of extrapolating the information obtained in the smaller ones to the original database, represents a relevant and complex challenge in real applications. It is particularly relevant in the context of fuzzy formal concept analysis, where the complexities of knowledge extraction from datasets characterized by incomplete and imperfect data are considerable. This paper will analyze a mechanism and different properties f

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous https://arxiv.org/abs/2604.13041 #cs.DB #cs.AI

arXiv.org

TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous

Table Structure Recognition (TSR) requires the logical reasoning ability of large language models (LLMs) to handle complex table layouts, but current datasets are limited in scale and quality, hindering effective use of this reasoning capacity. We thus present TableNet dataset, a new table structure recognition dataset collected and generated through multiple sources. Central to our approach is the first LLM-powered autonomous table generation and recognition multi-agent system that we developed

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Draft-Refine-Optimize: Self-Evolved Learning for Natural Language to MongoDB Query Generation https://arxiv.org/abs/2604.13045 #cs.DB

arXiv.org

Draft-Refine-Optimize: Self-Evolved Learning for Natural Language to MongoDB Query Generation

Natural Language to MongoDB Query Language (NL2MQL) is essential for democratizing access to modern document-centric databases. Unlike Text-to-SQL, NL2MQL faces unique challenges from MQL's procedural aggregation pipelines, deeply nested schemas, and ambiguous value grounding. Existing approaches use static prompting or one-shot refinement, which inadequately model these complex contexts and fail to systematically leverage execution feedback for persistent improvement. We propose EvoMQL, a self-

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection https://arxiv.org/abs/2604.13046 #cs.DB #cs.CL #cs.IR #cs.LG #cs.PL

arXiv.org

A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection

Data-driven systems depend on task-relevant data, yet data collection pipelines remain passive and indiscriminate. Continuous logging of multimodal sensor streams incurs high storage costs and captures irrelevant data. This paper proposes a declarative framework for intent-driven, on-device data collection that enables selective collection of multimodal sensor data based on high-level user requests. The framework combines natural language interaction with a formally specified domain-specific lan

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

From Natural Language to PromQL: A Catalog-Driven Framework with Dynamic Temporal Resolution for Cloud-Native Observability https://arxiv.org/abs/2604.13048 #cs.DB #cs.AI #cs.SE

arXiv.org

From Natural Language to PromQL: A Catalog-Driven Framework with Dynamic Temporal Resolution for Cloud-Native Observability

Modern cloud-native platforms expose thousands of time series metrics through systems like Prometheus, yet formulating correct queries in domain-specific languages such as PromQL remains a significant barrier for platform engineers and site reliability teams. We present a catalog-driven framework that translates natural language questions into executable PromQL queries, bridging the gap between human intent and observability data. Our approach introduces three contributions: (1) a hybrid metrics

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization https://arxiv.org/abs/2604.11810 #cs.DB #cs.AI

arXiv.org

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their immense number of parameters and complex transformer-based architectures result in significant resource demands and computational complexity during training, making it challenging to optimize them efficiently on large datasets. To reduce training costs while preserving performance, researchers have investigated coreset selection techniques, which aim to identify

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

A Pythonic Functional Approach for Semantic Data Harmonisation in the ILIAD Project https://arxiv.org/abs/2604.13042 #cs.DB #cs.AI #cs.SE

arXiv.org

A Pythonic Functional Approach for Semantic Data Harmonisation in the ILIAD Project

Semantic data harmonisation is a central requirement in the ILIAD project, where heterogeneous environmental data must be harmonised according to the Ocean Information Model (OIM), a modular family of ontologies for enabling the implementation of interoperable Digital Twins of the Ocean. Existing approaches to Semantic Data Harmonisation, such as RML and OTTR, offer valuable abstractions but require extensive knowledge of the technical intricacies of the OIM and the Semantic Web standards, inclu

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Green by Design? Investigating the Energy and Carbon Footprint of Chia Network https://arxiv.org/abs/2604.13044 #cs.CY

arXiv.org

Green by Design? Investigating the Energy and Carbon Footprint of Chia Network

This paper presents a detailed analysis of the environmental impact of Chia Network (Chia for short), a green-claimed blockchain, which uses a Proof of Space and Time (PoST) consensus mechanism. While Chia claims to be a sustainable alternative to Proof-of-Work-based blockchains, our results show that its resource-intensive initialization phase and ongoing operations lead to carbon emissions 18x higher than claimed (0.88 MtCO2/year), exceeding mainstream "green" blockchains by orders of magnitud

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Integration of Deep Reinforcement Learning and Agent-based Simulation to Explore Strategies Counteracting Information Disorder https://arxiv.org/abs/2604.13047 #cs.SI #cs.AI #cs.CY

arXiv.org

Integration of Deep Reinforcement Learning and Agent-based Simulation to Explore Strategies Counteracting Information Disorder

In recent years, the spread of fake news has triggered a growing interest in Information Disorders (ID) on social media, a phenomenon that has become a focal point of research across fields ranging from complexity theory and computer science to cognitive sciences. Overall, such a body of research can be traced back to two main approaches. On the one hand, there are works focused on exploiting data mining to analyze the content of news and related metadata data-driven approach; on the other hand,

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

M$^\star$: Every Task Deserves Its Own Memory Harness https://arxiv.org/abs/2604.11811 #cs.PL #cs.AI #cs.CL #cs.LG

$M$^\star$: Every Task Deserves Its Own Memory Harness$

arXiv.org

M$^\star$: Every Task Deserves Its Own Memory Harness

Large language model agents rely on specialized memory systems to accumulate and reuse knowledge during extended interactions. Recent architectures typically adopt a fixed memory design tailored to specific domains, such as semantic retrieval for conversations or skills reused for coding. However, a memory system optimized for one purpose frequently fails to transfer to others. To address this limitation, we introduce M$^\star$, a method that automatically discovers task-optimized memory harness

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Refined Differentially Private Linear Regression via Extension of a Free Lunch Result https://arxiv.org/abs/2604.11820 #math.IT #cs.IT #cs.LG

arXiv.org

Refined Differentially Private Linear Regression via Extension of a Free Lunch Result

As data-privacy regulations tighten and statistical models are increasingly deployed on sensitive human-sourced data, privacy-preserving linear regression has become a critical necessity. For the add-remove DP model, Kulesza et al. (2024) and Fitzsimons et al. (2024) have independently shown that the size of the dataset -- an important statistic for linear regression -- can be privately estimated for "free", via a simplex transformation of bounded variables and private sum queries on the transfo

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap https://arxiv.org/abs/2604.11828 #math.OC #cs.AI #cs.CY

arXiv.org

The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap

Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet the \emph{trajectory} of scientific discovery is rarely examined as an optimization problem in its own right. This paper argues that the body of scientific knowledge, at any given historical moment, represents a \emph{local optimum} rather than a global one--that the frameworks, formalisms, and paradigms through which we understand nature are substantially shaped by historical conting

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Learning on the Temporal Tangent Bundle for Physics-Informed Neural Networks https://arxiv.org/abs/2604.11829 #math.NA #cs.NA

arXiv.org

Learning on the Temporal Tangent Bundle for Physics-Informed Neural Networks

This paper addresses the limitations of Physics-Informed Neural Networks for time-dependent problems by introducing a tangent bundle learning framework. Instead of directly approximating the solution, we parameterize its temporal derivative and reconstruct the state through a Volterra integral operator that enforces initial conditions exactly. This approach eliminates competing soft constraints and naturally amplifies high-frequency errors through differentiation, countering spectral bias. We pr

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks https://arxiv.org/abs/2604.11833 #cs.LG

arXiv.org

Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks

Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severely limits the application of CNN in certain areas, such as medicine, where prediction uncertainty is critically important. Among the few existing UQ approaches that have been proposed for deep learning, none of them has theoretical consistency that can guarantee the uncertainty quality. To address this issue, we propose

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning https://arxiv.org/abs/2604.11835 #cs.LG #cs.AI

arXiv.org

Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning

Machine learning for tabular data remains constrained by poor schema generalization, a challenge rooted in the lack of semantic understanding of structured variables. This challenge is particularly acute in domains like clinical medicine, where electronic health record (EHR) schemas vary significantly. To solve this problem, we propose Schema-Adaptive Tabular Representation Learning, a novel method that leverages large language models (LLMs) to create transferable tabular embeddings. By transfor

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Design and Deployment of a Course-Aware AI Tutor in an Introductory Programming Course https://arxiv.org/abs/2604.11836 #cs.CY #cs.SE

arXiv.org

Design and Deployment of a Course-Aware AI Tutor in an Introductory Programming Course

Large Language Models (LLMs) have become part of how students solve programming tasks, offering immediate explanations and even full solutions. Previous work has highlighted that novice programmers often heavily rely on LLMs, thereby neglecting their own problem-solving skills. To address this challenge, we designed a course-specific online Python tutor that provides retrieval-augmented, course-aligned guidance without generating complete solutions. The tutor integrates a web-based programming e

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

A Layer-wise Analysis of Supervised Fine-Tuning https://arxiv.org/abs/2604.11838 #cs.LG #cs.AI

arXiv.org

A Layer-wise Analysis of Supervised Fine-Tuning

While critical for alignment, Supervised Fine-Tuning (SFT) incurs the risk of catastrophic forgetting, yet the layer-wise emergence of instruction-following capabilities remains elusive. We investigate this mechanism via a comprehensive analysis utilizing information-theoretic, geometric, and optimization metrics across model scales (1B-32B). Our experiments reveal a distinct depth-dependent pattern: middle layers (20\%-80\%) are stable, whereas final layers exhibit high sensitivity. Leveraging

arXiv Computer Science @arxiv_cs@qoto.org

· 5mo ago

Beyond Static Sandboxing: Learned Capability Governance for Autonomous AI Agents https://arxiv.org/abs/2604.11839 #cs.CR #cs.AI

arXiv.org

Beyond Static Sandboxing: Learned Capability Governance for Autonomous AI Agents

Autonomous AI agents built on open-source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A summarization task receives the same shell execution, subagent spawning, and credential access capabilities as a code deployment task, a 15x overprovision ratio that we call the capability overprovisioning problem. Existing defenses, including the NemoClaw container sandbox and the Cisco DefenseClaw skill scanner, address containment and threat de