arXiv:2510.25775v1 Announce Type: new Abstract: Contemporary chess engines offer precise yet opaque evaluations, typically expressed as centipawn scores. While effective for decision-making, these outputs obscure the underlying contributions of individual pieces or patterns. In this paper, we explore adapting SHAP (SHapley Additive exPlanations) to the domain of chess analysis, aiming to attribute a chess engines […]
Optimizing Mirror-Image Peptide Sequence Design for Data Storage via Peptide Bond Cleavage Prediction
arXiv:2510.25814v1 Announce Type: new Abstract: Traditional non-biological storage media, such as hard drives, face limitations in both storage density and lifespan due to the rapid growth of data in the big data era. Mirror-image peptides composed of D-amino acids have emerged as a promising biological storage medium due to their high storage density, structural stability, […]
WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
arXiv:2510.25960v1 Announce Type: cross Abstract: In this paper, we present a framework that uses acoustic side- channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior […]
Symbolically Scaffolded Play: Designing Role-Sensitive Prompts for Generative NPC Dialogue
arXiv:2510.25820v1 Announce Type: new Abstract: Large Language Models (LLMs) promise to transform interactive games by enabling non-player characters (NPCs) to sustain unscripted dialogue. Yet it remains unclear whether constrained prompts actually improve player experience. We investigate this question through The Interview, a voice-based detective game powered by GPT-4o. A within-subjects usability study ($N=10$) compared high-constraint […]
PORTool: Tool-Use LLM Training with Rewarded Tree
arXiv:2510.26020v1 Announce Type: cross Abstract: Current tool-use large language models (LLMs) are trained on static datasets, enabling them to interact with external tools and perform multi-step, tool-integrated reasoning, which produces tool-call trajectories. However, these models imitate how a query is resolved in a generic tool-call routine, thereby failing to explore possible solutions and demonstrating limited […]
An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0
arXiv:2510.25813v1 Announce Type: new Abstract: We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, […]
Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism
arXiv:2510.26083v1 Announce Type: cross Abstract: Specialized Generalist Models (SGMs) aim to preserve broad capabilities while achieving expert-level performance in target domains. However, traditional LLM structures including Transformer, Linear Attention, and hybrid models do not employ specialized memory mechanism guided by task information. In this paper, we present Nirvana, an SGM with specialized memory mechanism, linear […]
Discovering Interpretable Biological Concepts in Single-cell RNA-seq Foundation Models
arXiv:2510.25807v1 Announce Type: new Abstract: Single-cell RNA-seq foundation models achieve strong performance on downstream tasks but remain black boxes, limiting their utility for biological discovery. Recent work has shown that sparse dictionary learning can extract concepts from deep learning models, with promising applications in biomedical imaging and protein models. However, interpreting biological concepts remains challenging, […]
MV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction
arXiv:2510.26151v1 Announce Type: cross Abstract: Large annotated datasets are essential for training robust Computer-Aided Diagnosis (CAD) models for breast cancer detection or risk prediction. However, acquiring such datasets with fine-detailed annotation is both costly and time-consuming. Vision-Language Models (VLMs), such as CLIP, which are pre-trained on large image-text pairs, offer a promising solution by enhancing […]
Integrated Multi-omics Reveals MEF2C as a Direct Regulator of Microglial Immune and Synaptic Programs
arXiv:2510.25780v1 Announce Type: new Abstract: Background: Patients carrying MEF2C haploinsufficiency develop a recognizable neurodevelopmental syndrome featuring intellectual disability, treatment-resistant seizures, and autism spectrum behaviors. While MEF2C’s critical roles in cardiac development and neuronal function are well-established, its specific transcriptional operations within microglia (the brain’s resident immune cells) have remained surprisingly undefined. This knowledge gap is […]