Tootfinder

A Survey of Link Prediction in N-ary Knowledge Graphs
Jiyao Wei, Saiping Guan, Da Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
https://arxiv.org/abs/2506.08970

A Survey of Link Prediction in N-ary Knowledge Graphs
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significan…

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
Aaron Goldstein, Ayan Dutta
https://arxiv.org/abs/2507.07251

A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
Traditional recommendation algorithms are not designed to provide personalized recommendations based on user preferences provided through text, e.g., "I enjoy light-hearted comedies with a lot of humor". Large Language Models (LLMs) have emerged as one of the most promising tools for natural language processing in recent years. This research proposes a novel framework that mimics how a close friend would recommend items based on their knowledge of an individual's tastes. We leverage LLMs to enh…

Slowly oscillating periodic solutions in a nonlinear Volterra equation with non-symmetric feedback
Quentin Griette, Franco Herrera
https://arxiv.org/abs/2506.09564

Slowly oscillating periodic solutions in a nonlinear Volterra equation with non-symmetric feedback
In this work we study a nonlinear Volterra equation with non-symmetric feedback that arises as a particular case of the Gurtin-MacCamy model in population dynamics. We are particularly interested in the existence of slowly oscillating periodic solutions when the trivial stationary state is unstable. Here the absence of symmetry of the nonlinearity prevents the use of many traditional strategies to obtain a priori estimates on the solution. Without a precise knowledge of the period of the soluti…

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

This https://arxiv.org/abs/2504.20118 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
Traditional Chinese Medicine (TCM) represents a rich repository of ancient medical knowledge that continues to play an important role in modern healthcare. Due to the complexity and breadth of the TCM literature, the integration of AI technologies is critical for its modernization and broader accessibility. However, this integration poses considerable challenges, including the interpretation of obscure classical Chinese texts and the modeling of intricate semantic relationships among TCM concep…

You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication
Di Wu, Zhuoyin Dai, Yong Zeng
https://arxiv.org/abs/2507.03589

You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication
As one of the key usage scenarios for the sixth generation (6G) wireless networks, integrated sensing and communication (ISAC) provides an efficient framework to achieve simultaneous wireless sensing and communication. However, traditional wireless sensing techniques mainly rely on the line-of-sight (LoS) assumptions, i.e., the sensing targets are directly visible to both the sensing transmitter and receiver. This hinders ISAC systems to be applied in complex environments such as the urban low-…

UNESCO appoints Indigenous co-chairs to protect languages and knowledge amid climate crisis. Valuable traditional ecological knowledge within Indigenous languages is a climate solution. #climatechange #climatesolutions

UNESCO appoints Indigenous co-chairs to protect languages and knowledge amid climate crisis
"A single word like 'X̱maay' contains generations of climate knowledge, laws, and cultural practices."

Human-In-The-Loop Workflow for Neuro- Symbolic Scholarly Knowledge Organization
Lena John, Tim Wittenborg, S\"oren Auer, Oliver Karras
https://arxiv.org/abs/2506.03221

Human-In-The-Loop Workflow for Neuro- Symbolic Scholarly Knowledge Organization
As the volume of scientific literature continues to grow, efficient knowledge organization is an increasingly challenging task. Traditional structuring of scientific content is time-consuming and requires significant domain expertise, increasing the need for tool support. Our goal is to create a Human-in-the-Loop (HITL) workflow that supports researchers in creating and structuring scientific knowledge, leveraging neural models and knowledge graphs, exemplified using the Open Research Knowledge…

Structured Task Solving via Modular Embodied Intelligence: A Case Study on Rubik's Cube
Chongshan Fan, Shenghai Yuan
https://arxiv.org/abs/2507.05607 h…

Structured Task Solving via Modular Embodied Intelligence: A Case Study on Rubik's Cube
This paper presents Auto-RubikAI, a modular autonomous planning framework that integrates a symbolic Knowledge Base (KB), a vision-language model (VLM), and a large language model (LLM) to solve structured manipulation tasks exemplified by Rubik's Cube restoration. Unlike traditional robot systems based on predefined scripts, or modern approaches relying on pretrained networks and large-scale demonstration data, Auto-RubikAI enables interpretable, multi-step task execution with minimal data req…

Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control
Ayoub Farkane, Mohamed Boutayeb, Mustapha Oudani, Mounir Ghogho
https://arxiv.org/abs/2507.06817

Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control
Accurate knowledge of the state variables in a dynamical system is critical for effective control, diagnosis, and supervision, especially when direct measurements of all states are infeasible. This paper presents a novel approach to designing software sensors for nonlinear dynamical systems expressed in their most general form. Unlike traditional model-based observers that rely on explicit transformations or linearization, the proposed framework integrates neural networks with adaptive Sliding …

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Yash Tiwari, Owais Ahmad Lone, Mayukha Pal
https://arxiv.org/abs/2506.00664

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integr…

Predictive posteriors under hidden confounding
Carlos Garc\'ia Meixide, David R\'ios Insua
https://arxiv.org/abs/2507.05170 https://

Predictive posteriors under hidden confounding
Predicting outcomes in external domains is challenging due to hidden confounders that influence both predictors and outcomes, complicating generalization under distribution shifts. Traditional methods often rely on stringent assumptions or overly conservative regularization, compromising estimation and predictive accuracy. Generative Invariance (GI) is a novel framework that facilitates predictions in unseen domains without requiring hyperparameter tuning or knowledge of specific distribution s…

Towards Structure-aware Model for Multi-modal Knowledge Graph Completion
Linyu Li, Zhi Jin, Yichi Zhang, Dongming Jin, Chengfeng Dou, Yuanpeng He, Xuan Zhang, Haiyan Zhao
https://arxiv.org/abs/2505.21973

Towards Structure-aware Model for Multi-modal Knowledge Graph Completion
Knowledge graphs (KGs) play a key role in promoting various multimedia and AI applications. However, with the explosive growth of multi-modal information, traditional knowledge graph completion (KGC) models cannot be directly applied. This has attracted a large number of researchers to study multi-modal knowledge graph completion (MMKGC). Since MMKG extends KG to the visual and textual domains, MMKGC faces two main challenges: (1) how to deal with the fine-grained modality information interacti…

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
Renjie He, Yiqiu Wang, Meixia Tao, Shu Sun
https://arxiv.org/abs/2506.07019

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
This paper investigates the passive detection problem in multi-static integrated sensing and communication (ISAC) systems, where multiple sensing receivers (SRs) jointly detect a target using random unknown communication signals transmitted by a collaborative base station. Unlike traditional active detection, the considered passive detection does not require complete prior knowledge of the transmitted communication signals at each SR. First, we derive a generalized likelihood ratio test detecto…

Transfer Learning for Matrix Completion
Dali Liu, Haolei Weng
https://arxiv.org/abs/2507.02248 https://arxiv.org/pdf/2507.02248

Transfer Learning for Matrix Completion
In this paper, we explore the knowledge transfer under the setting of matrix completion, which aims to enhance the estimation of a low-rank target matrix with auxiliary data available. We propose a transfer learning procedure given prior information on which source datasets are favorable. We study its convergence rates and prove its minimax optimality. Our analysis reveals that with the source matrices close enough to the target matrix, out method outperforms the traditional method using the si…

Going Bayesian on the ages of nearby young stellar systems I. The expansion rate method
J. Olivares, A. Berihuete, H. Bouy
https://arxiv.org/abs/2506.05110

Going Bayesian on the ages of nearby young stellar systems I. The expansion rate method
Context. Determining the ages of young stellar systems is fundamental to test and validate current star-formation theories. Aims. We aim at developing a Bayesian version of the expansion rate method that incorporates the a priori knowledge on the stellar system's age and solves some of the caveats of the traditional frequentist approach. Methods. We upgrade an existing Bayesian hierarchical model with additional parameter hierarchies that include, amongst others, the system's age. For this late…

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Siyuan Li, Ruitong Liu, Yan Wen, Te Sun
https://arxiv.org/abs/2506.23141

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowledge graphs, often introduce noise and suffer from information dilution or over-smoothing by indiscriminately aggregating information from all neighboring edges. To address this challenge, we propose a semantic-aware relational message passing. A core innovation of this framework …

SoK: Semantic Privacy in Large Language Models
Baihe Ma, Yanna Jiang, Xu Wang, Guangshen Yu, Qin Wang, Caijun Sun, Chen Li, Xuelei Qi, Ying He, Wei Ni, Ren Ping Liu
https://arxiv.org/abs/2506.23603

SoK: Semantic Privacy in Large Language Models
As Large Language Models (LLMs) are increasingly deployed in sensitive domains, traditional data privacy measures prove inadequate for protecting information that is implicit, contextual, or inferable - what we define as semantic privacy. This Systematization of Knowledge (SoK) introduces a lifecycle-centric framework to analyze how semantic privacy risks emerge across input processing, pretraining, fine-tuning, and alignment stages of LLMs. We categorize key attack vectors and assess how curre…

Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother
Ond\v{r}ej Skalsk\'y, Jakub Dokoupil
https://arxiv.org/abs/2506.14572 https://

Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother
A Bayesian knowledge transfer mechanism that leverages external information to improve the performance of the Kalman fixed-lag interval smoother (FLIS) is proposed. Exact knowledge of the external observation model is assumed to be missing, which hinders the direct application of Bayes' rule in traditional transfer learning approaches. This limitation is overcome by the fully probabilistic design, conditioning the targeted task of state estimation on external information. To mitigate the negati…

Seismic resolution enhancement via deep Learning with Knowledge Distillation and Domain Adaptation
Hanpeng Cai, Haonan Zhang, Liyu Zhang, Suo Cheng
https://arxiv.org/abs/2506.22018

Seismic resolution enhancement via deep Learning with Knowledge Distillation and Domain Adaptation
High-resolution processing of seismic signals is crucial for subsurface geological characterization and thin-layer reservoir identification. Traditional high-resolution algorithms can partially recover high-frequency information but often lack robustness, computational efficiency, and consideration of inter-trace structural relationships. Many deep learning methods use end-to-end architectures that do not incorporate prior knowledge or address data domain disparities, leading to limited general…

This https://arxiv.org/abs/2503.00271 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Why Johnny Signs with Next-Generation Tools: A Usability Case Study of Sigstore
Software signing is the most robust method for ensuring the integrity and authenticity of components in a software supply chain. However, traditional signing tools place key management and signer identification burdens on practitioners, leading to both security vulnerabilities and usability challenges. Next-generation signing tools such as Sigstore have automated some of these concerns, but little is known about their usability and adoption dynamics. This knowledge gap hampers the integration o…

AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models
Zehuan Huang, Haoran Feng, Yangtian Sun, Yuanchen Guo, Yanpei Cao, Lu Sheng
https://arxiv.org/abs/2506.19851

AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models
We present AnimaX, a feed-forward 3D animation framework that bridges the motion priors of video diffusion models with the controllable structure of skeleton-based animation. Traditional motion synthesis methods are either restricted to fixed skeletal topologies or require costly optimization in high-dimensional deformation spaces. In contrast, AnimaX effectively transfers video-based motion knowledge to the 3D domain, supporting diverse articulated meshes with arbitrary skeletons. Our method r…

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
Ryota Okumura, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwara
https://arxiv.org/abs/2506.15468

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowledge to construct shared external representations, a process we interpret as symbol emergence. Unlike traditional AI teaching based on unilateral knowledge transfer, this addresses the challenge of integrating information from inherently different modalities. We empirically test this framework using a human-AI interacti…

Navigating Speech Recording Collections with AI-Generated Illustrations
Sirina H{\aa}land, Trond Karlsen Str{\o}m, Petra Galu\v{s}\v{c}\'akov\'a
https://arxiv.org/abs/2507.04182

Navigating Speech Recording Collections with AI-Generated Illustrations
Although the amount of available spoken content is steadily increasing, extracting information and knowledge from speech recordings remains challenging. Beyond enhancing traditional information retrieval methods such as speech search and keyword spotting, novel approaches for navigating and searching spoken content need to be explored and developed. In this paper, we propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative mode…

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
Pankaj Chavan, Tapomoy Guha Sarkar, Anjan A Sen
https://arxiv.org/abs/2506.22064

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
This work serves two-fold purpose. Firstly, we provide an alternative to the traditional method of determining the growth rate of density perturbations $f(z)$. In usual practice, $f(z)$ can not be directly measured from tracer clustering at some redshift without knowledge of the bias. While the bayron acoustic oscillation (BAO) imprint allows the determination of $(D_A(z), H(z))$, redshift space anisotropy (RSD) allows the measurement of a quantity $f_8(z) = f(z) σ_{8,0} D_{+}(z)$. To extract …

Covariant phase space and $L_\infty$ algebras
Vin\'icius Bernardes, Theodore Erler, Atakan Hilmi F{\i}rat
https://arxiv.org/abs/2506.20706 https://

Covariant phase space and $L_\infty$ algebras
We propose a symplectic structure for the phase space of a generic Lagrangian field theory expressed in the framework of $L_\infty$ algebras. The symplectic structure does not require explicit knowledge of the derivative content of the Lagrangian, and therefore is applicable to nonlocal models, such as string field theory, where traditional constructions are difficult to apply. We test our proposal in a number of examples ranging from general relativity to $p$-adic string theory.

PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments
Mengyun Wang, Bo Wang, Yifeng Niu, Chang Wang
https://arxiv.org/abs/2507.00816

PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments
Accurate dynamics modeling is essential for quadrotors to achieve precise trajectory tracking in various applications. Traditional physical knowledge-driven modeling methods face substantial limitations in unknown environments characterized by variable payloads, wind disturbances, and external perturbations. On the other hand, data-driven modeling methods suffer from poor generalization when handling out-of-distribution (OoD) data, restricting their effectiveness in unknown scenarios. To addres…

Quantum Workshop for IT-Professionals
Bettina Just, J\"org Hettel, Gerhard Hellstern
https://arxiv.org/abs/2506.22525 https://ar…

Quantum Workshop for IT-Professionals
Quantum computing is gaining strategic relevance beyond research-driven industries. However, many companies lack the expertise to evaluate its potential for real-world applications. Traditional training formats often focus on physical principles without demonstrating practical relevance, which limits success. This paper presents a user-centered workshop concept tailored to IT professionals without prior quantum knowledge. Using a business game set in a fictitious company, participants explore q…

Distinguishing True Influence from Hyperprolificity with Citation Distance
Lu Li, Yun Wan, Feng Xiao
https://arxiv.org/abs/2506.03527 https://

Distinguishing True Influence from Hyperprolificity with Citation Distance
Accurately evaluating scholarly influence is essential for fair academic assessment, yet traditional bibliometric indicators - dominated by publication and citation counts - often favor hyperprolific authors over those with deeper, long-term impact. We propose the x-index, a novel citation-based metric that conceptualizes citation as a process of knowledge diffusion and incorporates citation distance to reflect the structural reach of scholarly work. By weighting citations according to the coll…

UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge
Yue Zhang, Yankai Chen, Yingli Zhou, Yucan Guo, Xiaolin Han, Chenhao Ma
https://arxiv.org/abs/2506.02784

UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge
In many real-world applications, the evolving relationships between entities can be modeled as temporal graphs, where each edge has a timestamp representing the interaction time. As a fundamental problem in graph analysis, {\it community search (CS)} in temporal graphs has received growing attention but exhibits two major limitations: (1) Traditional methods typically require predefined subgraph structures, which are not always known in advance. (2) Learning-based methods struggle to capture …

Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects
Alexey Burdakov, Max Jaihyun Ahn
https://arxiv.org/abs/2506.02214

Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects
This paper critically evaluates the applicability of the Project Management Body of Knowledge (PMBOK) Guide framework to Artificial Intelligence (AI) software projects, highlighting key limitations and proposing tailored adaptations. Unlike traditional projects, AI initiatives rely heavily on complex data, iterative experimentation, and specialized expertise while navigating significant ethical considerations. Our analysis identifies gaps in the PMBOK Guide, including its limited focus on data …

This https://arxiv.org/abs/2504.02712 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

TeleMoM: Consensus-Driven Telecom Intelligence via Mixture of Models
Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Telecom) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coor…

A Greedy Version of the Frame Algorithm
Brody Dylan Johnson
https://arxiv.org/abs/2506.18865 https://arxiv.org/pdf/2506.18865

A Greedy Version of the Frame Algorithm
The frame algorithm uses a simple recursive formula to approximate an unknown vector from its frame coefficients. This note introduces an adaptive version of the frame algorithm that maximizes the error reduction between steps in terms of an equivalent norm. This greedy version of the frame algorithm is proven to achieve the same guaranteed convergence rate as the traditional frame algorithm, yet, unlike its classical counterpart, does not require knowledge of the frame bounds. The robustness o…

Procedural Knowledge Libraries: Towards Executable (Research) Memory
Hamidah Oderinwale
https://arxiv.org/abs/2506.14715 https://arxi…

Procedural Knowledge Libraries: Towards Executable (Research) Memory
Procedural Knowledge Libraries (PKLs) are frameworks for capturing the full arc of scientific inquiry, not just its outcomes. Whereas traditional libraries store static end products, PKLs preserve the process that leads to those results, including hypotheses, failures, decisions, and iterations. By addressing the loss of tacit knowledge -- typically buried in notebooks, emails, or memory -- PKLs lay a foundation for reproducible, collaborative, and adaptive research. PKLs provide executable, ve…

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Giacomo Meanti, Thomas Ryckeboer, Michael Arbel, Julien Mairal
https://arxiv.org/abs/2506.14605

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowledge of the forward model or access to paired degraded and ground-truth images -- the proposed method operates under minimal assumptions and relies only on small, unpaired datasets. This makes it particularly well-suited for real-world scenarios, where the forward model is often unknown or misspecified, and collecting…

Learning Before Filtering: Real-Time Hardware Learning at the Detector Level
Bo\v{s}tjan Ma\v{c}ek
https://arxiv.org/abs/2506.11981 https://

Learning Before Filtering: Real-Time Hardware Learning at the Detector Level
Advances in sensor technology and automation have ushered in an era of data abundance, where the ability to identify and extract relevant information in real time has become increasingly critical. Traditional filtering approaches, which depend on a priori knowledge, often struggle to adapt to dynamic or unanticipated data features. Machine learning offers a compelling alternative-particularly when training can occur directly at or near the detector. This paper presents a digital hardware archit…

Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
Sajratul Y. Rubaiat, Hasan M. Jamil
https://arxiv.org/abs/2506.17580

Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
The exponential growth of scientific literature challenges researchers extracting and synthesizing knowledge. Traditional search engines return many sources without direct, detailed answers, while general-purpose LLMs may offer concise responses that lack depth or omit current information. LLMs with search capabilities are also limited by context window, yielding short, incomplete answers. This paper introduces WISE (Workflow for Intelligent Scientific Knowledge Extraction), a system addressing…

AgenticControl: An Automated Control Design Framework Using Large Language Models
Mohammad Narimani, Seyyed Ali Emami
https://arxiv.org/abs/2506.19160 http…

AgenticControl: An Automated Control Design Framework Using Large Language Models
Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or uncertain dynamics. This paper introduces AgenticControl, a novel multi-agent framework that automates controller design using coordinated Large Language Model (LLM) agents. Through structured JSON communication, these agents handle tasks including controller selection, scenario design, parameter optimization, performance evaluation, and decision-making. Through an actor-crit…

Self-Balancing of Cell Populations via Martingale Turnover with Amplification
Tomoyuki Yamaguchi
https://arxiv.org/abs/2506.16770 https://

Self-Balancing of Cell Populations via Martingale Turnover with Amplification
The mechanisms underlying adaptive control in biological systems, such as intestinal immune regulation, remain poorly understood, despite detailed knowledge of their regulatory networks. Here, we propose an alternative to traditional deterministic regulation. We theoretically demonstrate that stochastic martingale turnover, in which cells proliferate through mutual competition and decay without component-specific control, naturally converges to an appropriate population balance characterized by…

This https://arxiv.org/abs/2411.06256 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Annotative Indexing
This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support retrieval augmented generation, knowledge graphs, entity retrieval, semi-structured data, and ranked retrieval. While we primarily focus on human language data in the form of text, annotative indexing is sufficiently gener…

Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning
Anvi Alex Eponon, Moein Shahiki-Tash, Ildar Batyrshin, Christian E. Maldonado-Sifuentes, Grigori Sidorov, Alexander Gelbukh
https://arxiv.org/abs/2506.13778

Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning
This study presents a question-based knowledge encoding approach that improves retrieval-augmented generation (RAG) systems without requiring fine-tuning or traditional chunking. We encode textual content using generated questions that span the lexical and semantic space, creating targeted retrieval cues combined with a custom syntactic reranking method. In single-hop retrieval over 109 scientific papers, our approach achieves a Recall@3 of 0.84, outperforming traditional chunking methods by …

Tracing Errors, Constructing Fixes: Repository-Level Memory Error Repair via Typestate-Guided Context Retrieval
Xiao Cheng, Zhihao Guo, Huan Huo, Yulei Sui
https://arxiv.org/abs/2506.18394

Tracing Errors, Constructing Fixes: Repository-Level Memory Error Repair via Typestate-Guided Context Retrieval
Memory-related errors in C programming continue to pose significant challenges in software development, primarily due to the complexities of manual memory management inherent in the language. These errors frequently serve as vectors for severe vulnerabilities, while their repair requires extensive knowledge of program logic and C's memory model. Automated Program Repair (APR) has emerged as a critical research area to address these challenges. Traditional APR approaches rely on expert-designed …

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
https://

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, wh…

This https://arxiv.org/abs/2505.20368 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents
Retrieval-augmented generation (RAG) based large language models (LLMs) are widely used in finance for their excellent performance on knowledge-intensive tasks. However, standardized documents (e.g., SEC filing) share similar formats such as repetitive boilerplate texts, and similar table structures. This similarity forces traditional RAG methods to misidentify near-duplicate text, leading to duplicate retrieval that undermines accuracy and completeness. To address these issues, we propose the …

Mapping the Evolution of Research Contributions using KnoVo
Sajratul Y. Rubaiat, Syed N. Sakib, Hasan M. Jamil
https://arxiv.org/abs/2506.17508 https://

Mapping the Evolution of Research Contributions using KnoVo
This paper presents KnoVo (Knowledge Evolution), an intelligent framework designed for quantifying and analyzing the evolution of research novelty in the scientific literature. Moving beyond traditional citation analysis, which primarily measures impact, KnoVo determines a paper's novelty relative to both prior and subsequent work within its multilayered citation network. Given a target paper's abstract, KnoVo utilizes Large Language Models (LLMs) to dynamically extract dimensions of comparison…

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat-Seng Chua
https://arxiv.org/abs/2506.21579

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have mo…

XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
Ke Wang, Bo Pan, Yingchaojie Feng, Yuwei Wu, Jieyi Chen, Minfeng Zhu, Wei Chen
https://arxiv.org/abs/2506.13782

XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an intermediate representation to capture better structured relational knowledge in the corpus, elevating the precision and comprehensiveness of generation results. However, developers usually face challenges in analyzing the effectiveness of GraphRAG on their dataset due to GraphRAG's …

From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Yankai Chen, Chunkit Chan, Peilin Zhou, Xinyang Zhang, Chenwei Zhang, Jingbo Shang, Ming Zhang, Yangqiu Song, Irwin King, Philip S. Yu

From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engines are increasingly inadequate for handling complex, multi-step information needs. Our position is that Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research. These systems transcend conventional information search techniques by tigh…

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Chia-Heng Yu, Yen-Lung Tsai
https://arxiv.org/abs/2506.13607

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant c…

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
Abdul Haque, Umm e Hani, Ahmad Din, Muhammad Babar, Ali Abbas, Insaf Ullah
https://arxiv.org/abs/2506.11600

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based s…