cs papers | Gist.Science

Decentralised Federated Learning Framework for Privacy-Preserving Medical Insurance and Sustainable Rural Economic Development in Tamil Nadu

This paper proposes and rigorously evaluates TW-DP-FedAvg, a privacy-preserving federated learning framework tailored for rural Tamil Nadu's medical insurance sector, demonstrating that while differential privacy incurs a measurable accuracy cost compared to centralized training, the approach remains statistically equivalent and viable under India's new data protection regulations.

Janarthanam S, Raja Sarath Kumar Boddu, Vivekanadam B2026-07-15

💻 computer science

Mothership and multi-drone routing for spatial marine management

This paper introduces a novel mothership and multi-drone routing problem for spatial marine management, proposing a metaheuristic solution that achieves near-optimal results with significantly reduced computation time compared to exact methods, thereby enabling practical-scale coordination for applications like coral reef restoration.

Ryu B. Lippmann, Kate J. Helmstedt, Paul Corry2026-07-15

💻 computer science

Meta-PPO: Lightweight Meta-Control for Online Joint Hyperparameter Adaptation in Proximal Policy Optimization

This paper introduces Meta-PPO, a lightweight meta-control framework that dynamically adjusts PPO hyperparameters online based on training statistics, demonstrating improved performance, efficiency, and robustness across multiple continuous-control benchmarks compared to standard PPO.

Guinan Cai, Jiayu Yao2026-07-15

💻 computer science

Human-Centered Learning Mechanics for Reliable Smart Textiles: An Explainable and Frugal AI Framework for Separating Physiological Anomalies from Sensor Drift and Degradation

This paper presents HCLM-SmartTex, an explainable and frugal AI framework that distinguishes genuine physiological anomalies from smart-textile sensor degradation and artifacts by optimizing for human-centered decision costs rather than raw accuracy, utilizing a synthetic simulator for training and achieving high reliability with minimal computational footprint.

Kim Duc TRAN, Guillaume Tartare, Ludovic Koehl, Xianyi Zeng, Anthony Fleury, Kim Phuc Tran2026-07-15

💻 computer science

HPG-Rec: Hybrid Popularity-Guided Recommendation with Large Language Models

This paper introduces HPG-Rec, a novel, training-free framework that effectively addresses data sparsity and the semantic gap in recommender systems by combining controlled LLM candidate generation, rank-aware semantic scoring, dual popularity modeling, and MLP-based fusion to achieve significant performance improvements over state-of-the-art baselines.

Harshad Kubade, Tausif Diwan2026-07-15

💻 computer science

An Ontology-Based Classification Framework for Situation Extraction from Concrete Pouring Radio Logs

This study proposes a two-layer ontology-based framework that classifies complex concrete pouring radio logs into seven semantic axes to derive hazard signals, control actions, and situation states, demonstrating high agreement rates in rule-based extraction while identifying limitations in local LLM approaches for future validation.

Young-Jun Park2026-07-15

💻 computer science

Language Identification (LID) in Micro-Texts: An Ultra-Lightweight Geometric Approach.

This paper introduces an ultra-lightweight geometric method for language identification in micro-texts that encodes character frequencies into compact Euclidean spaces, demonstrating superior robustness against data sparsity and historical variations compared to traditional baselines while offering significant computational efficiency for edge computing.

Oscar Rendón-Aldaraca, Héctor Fraire-Huacuja, Ana María Mendoza-Martínez, José Ángel Mendoza-Sierra2026-07-15

💻 computer science

Contradiction-Aware Strategy Synthesis in Agent Skills via Loop Engineering: A Controlled Empirical Comparison with Conventional Search

This paper demonstrates that a contradiction-aware, loop-engineered AI agent skill strictly outperforms conventional web search in generating high-stakes, decision-grade study-abroad strategies, achieving 100% validity and comprehensive output coverage compared to the baseline's 0% validity and fragmented results.

Piyush Omanwar2026-07-15

💻 computer science

Detection First, Grading Second: A Secondary Analysis of Stage-Specific Errors in Two-Stage Colon CAD

This position-style secondary analysis argues that colon CAD systems should adopt a detection-first, grading-second workflow and be evaluated using stage-specific metrics, demonstrating through reanalysis of existing data that such an approach better aligns with clinical pathology workflows and reveals that the majority of grading errors are clinically less severe adjacent-grade swaps while highlighting the critical impact of disease prevalence on positive predictive value.

Sulav Dahal, Bipul Bhattarai2026-07-15

💻 computer science

Are You Measuring the Failure Subspace, or Its Ceiling?

This paper demonstrates that the widely cited low-dimensional nature of failure subspaces in test-time adaptation is an algebraic artifact of specific gradient definitions constrained by the classifier's row space, rather than an intrinsic property, as alternative unconstrained demands reveal high-dimensional, width-sensitive structures where most energy remains confined to the classifier's row space.

Sungmin Ryu2026-07-15

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