cs papers | Gist.Science

Turn Complexity of Context-free Languages, Pushdown Automata and One-Counter Automata

This paper investigates the computational complexity of context-free, pushdown, and one-counter automata based on the number of "turns" (switches between increasing and decreasing stack height) in accepting computations, proving that determining whether this number is bounded is undecidable, establishing non-recursive trade-offs between automata types, and demonstrating an infinite hierarchy of complexity classes defined by sublinear turn bounds.

Giovanni Pighizzini2026-03-10💻 cs

Detecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method

The paper proposes DS-DGA-GCN, an adaptive graph learning model that integrates diversity- and similarity-aware dynamic graph attention with a Network Feature Scoring system to effectively detect organized fake reviewer groups in dynamic networks, achieving state-of-the-art performance on real-world datasets.

Jing Zhang, Ke Huang, Yao Zhang, Bin Guo, Zhiwen Yu2026-03-10💻 cs

The coordination between TSO and DSO in the context of energy transition - A review

This paper reviews and analyzes various coordination schemes between Transmission and Distribution System Operators (TSOs and DSOs) to effectively integrate Distributed Energy Resources, aiming to maintain system balance and prevent network congestion while overcoming technical and market challenges associated with the ongoing energy transition.

Hang Nguyen, Koen Kok, Trung Thai Tran, Phuong H. Nguyen2026-03-10💻 cs

Hierarchical Multi-Modal Planning for Fixed-Altitude Sparse Target Search and Sampling

This paper introduces HIMoS, a hierarchical multi-modal planning framework that enables Autonomous Underwater Vehicles to efficiently search for and sample sparse benthic targets like coral colonies at a fixed altitude by integrating a global topological route optimizer with a local differentiable belief propagation planner, thereby outperforming traditional exhaustive and adaptive sampling strategies in high-fidelity simulations.

Lingpeng Chen, Yuchen Zheng, Apple Pui-Yi Chui, Junfeng Wu, Ziyang Hong2026-03-10💻 cs

PRIME: Efficient Algorithm for Token Graph Routing Problem

The paper introduces PRIME, a two-stage iterative algorithm that combines pruned graph search with an Adaptive Sign Gradient Method to efficiently solve the Token Graph Routing Problem on blockchain platforms, significantly outperforming existing baselines like Uniswap in both execution price and computational speed.

Haotian Xu, Yuqing Zhu, Yuming Huang, Jing Tang2026-03-10💻 cs

The Complexity of Extending Storylines with Minimum Local Crossing Number

This paper investigates the computational complexity of extending fixed storyline layouts by inserting missing characters to minimize the local crossing number, proving the problem is W[1]-hard when parameterized by the number of inserted characters and active characters, but fixed-parameter tractable when parameterized by the sum of active characters and the local crossing number.

Alexander Dobler, Siddharth Gupta, Philipp Kindermann, Fabrizio Montecchiani, Martin Nöllenburg2026-03-10💻 cs

ERASE -- A Real-World Aligned Benchmark for Unlearning in Recommender Systems

This paper introduces ERASE, a large-scale benchmark designed to align machine unlearning evaluation with real-world recommender system constraints by covering diverse tasks, datasets, and algorithms to systematically analyze the effectiveness and robustness of current unlearning methods.

Pierre Lubitzsch, Maarten de Rijke, Sebastian Schelter2026-03-10💻 cs

PhaForce: Phase-Scheduled Visual-Force Policy Learning with Slow Planning and Fast Correction for Contact-Rich Manipulation

PhaForce is a phase-scheduled visuomotor policy that enhances contact-rich manipulation by coordinating a slow, vision-dominant diffusion planner with a fast, force-driven corrector to enable high-frequency, phase-aware residual corrections, achieving an 86% success rate and superior adaptability compared to existing baselines.

Mingxin Wang, Zhirun Yue, Renhao Lu, Yizhe Li, Zihan Wang, Guoping Pan, Kangkang Dong, Jun Cheng, Yi Cheng, Houde Liu2026-03-10💻 cs

Local-Global Prompt Learning via Sparse Optimal Transport

The paper proposes SOT-GLP, a novel few-shot adaptation method for vision-language models that employs shared sparse optimal transport to partition visual regions among class-specific local prompts while maintaining global alignment, thereby achieving state-of-the-art performance in both classification accuracy and out-of-distribution detection by preserving the native feature geometry.

Deniz Kizaro\u{g}lu, Ülku Tuncer Küçüktas, Emre Çakmakyurdu, Alptekin Temizel2026-03-10💻 cs

Eigenvalue Patterns and Participation Analysis of Symmetric Renewable Energy Power Systems

This paper addresses the scalability challenges of state-space analysis in large-scale renewable power systems by leveraging system symmetry to define distinct inner-group and group-grid eigenvalue patterns and introducing a novel group participation factor for stability analysis and targeted optimization.

Yao Qin, Yitong Li, Wei Wang, Shaoze Zhou, Zheng Wei, Jinjun Liu2026-03-10💻 cs

$\Delta$ VLA: Prior-Guided Vision-Language-Action Models via World Knowledge Variation

This paper introduces $\Delta$ VLA, a prior-guided framework that enhances robotic manipulation by modeling discrete world-knowledge variations relative to an explicit current state prior, rather than predicting absolute future states, thereby achieving state-of-the-art performance and efficiency through its novel components: the Prior-Guided World Knowledge Extractor, Latent World Variation Quantization, and Conditional Variation Attention.

Yijie Zhu, Jie He, Rui Shao, Kaishen Yuan, Tao Tan, Xiaochen Yuan, Zitong Yu2026-03-10💻 cs

Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation

This paper introduces UniDiffDA, a unified analytical framework that decomposes diffusion-based data augmentation into three core components to enable a systematic, fair benchmarking of diverse methods across low-data classification tasks, ultimately offering practical design insights and reproducible code.

Zekun Li, Yinghuan Shi, Yang Gao, Dong Xu2026-03-10💻 cs

M $^3$ -ACE: Rectifying Visual Perception in Multimodal Math Reasoning via Multi-Agentic Context Engineering

The paper proposes M3-ACE, a multi-agentic context engineering framework that rectifies inaccurate visual perception in multimodal math reasoning by decoupling perception from reasoning and employing collaborative agents with specialized tools to dynamically refine visual evidence, thereby achieving state-of-the-art performance on benchmarks like MathVision.

Peijin Xie, Zhen Xu, Bingquan Liu, Baoxun Wang2026-03-10💻 cs

Designing Value-Based Platforms: Architectural Strategies Derived from the Digital Markets Act

This paper analyzes the technical implications of the Digital Markets Act (DMA) through qualitative methods to propose eight high-level design strategies and 15 specific tactics for building value-based platform architectures that ensure fairness, contestability, and user choice.

Fabian Stiehle, Markus Funke, Patricia Lago, Ingo Weber2026-03-10💻 cs

This Looks Distinctly Like That: Grounding Interpretable Recognition in Stiefel Geometry against Neural Collapse

This paper introduces Adaptive Manifold Prototypes (AMP), a framework that leverages Stiefel manifold optimization to represent class prototypes as orthonormal bases, thereby preventing prototype collapse caused by Neural Collapse while achieving state-of-the-art accuracy and improved causal faithfulness in fine-grained recognition.

Junhao Jia, Jiaqi Wang, Yunyou Liu, Haodong Jing, Yueyi Wu, Xian Wu, Yefeng Zheng2026-03-10💻 cs

The biased interaction game: Its dynamics and application in modelling social systems

This paper presents the biased interaction game as a versatile modeling tool for social systems, demonstrating how boundedly rational interactions under scarcity and bias naturally generate emergent hierarchies, inequality, and non-linear stability patterns while offering a framework to evaluate competing wealth redistribution philosophies like social welfare and universal basic income.

Phil Mercy, Martin Neil2026-03-10💻 cs

Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

This paper presents a communication-free multi-UAV coordination framework that leverages onboard anisotropic 3D LiDAR for simultaneous SLAM, obstacle detection, and neighbor tracking, enabling safe and scalable navigation in complex, GNSS-denied environments like dense forests.

Manuel Boldrer, Michal Kamler, Afzal Ahmad, Martin Saska2026-03-10💻 cs

Structure from Rank: Rank-Order Coding as a Bridge from Sequence to Structure

This paper proposes a rank-order based neural network inspired by the STG-LIFG-PMC pathway that demonstrates how rank-order coding effectively bridges acoustic input to abstract structure, enabling robust sequence reconstruction, global novelty detection, and proto-syntactic generalization through context-sensitive sensorimotor representations.

Xiaodan Chen, Alexandre Pitti, Mathias Quoy, Nancy Chen2026-03-10💻 cs

MoMaStage: Skill-State Graph Guided Planning and Closed-Loop Execution for Long-Horizon Indoor Mobile Manipulation

MoMaStage is a structured vision-language framework that enables robust long-horizon indoor mobile manipulation by guiding task planning through a topology-aware Skill-State Graph and ensuring execution reliability via a closed-loop mechanism that triggers semantic replanning upon detecting physical deviations, all without requiring explicit scene mapping.

Chenxu Li, Zixuan Chen, Yetao Li, Jiapeng Xu, Hongyu Ding, Jieqi Shi, Jing Huo, Yang Gao2026-03-10💻 cs

Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma

This study presents a rectified flow-based AI model that generates realistic post-treatment brain MRIs from pre-radiotherapy priors and dose maps for glioma patients, achieving high structural fidelity and significantly faster inference than diffusion models to support adaptive treatment planning.

Selena Huisman, Nordin Belkacemi, Vera Keil, Joost Verhoeff, Szabolcs David2026-03-10💻 cs

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