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AI YOU Town: Make Friends and Money with Your Digital Twin
The AI YOU framework uses Bayesian updating and prompting to continuously update a user's personality profile across 22 dimensions for digital twin applications.
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Comparing Socio-technical Design Principles with Guidelines for Human-centered AI
This paper compares human-centered AI guidelines with traditional socio-technical design principles to propose updated heuristics for AI system integration.
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Knowing You at First Glance: Inferring Apparent Personality from Faces
A new study explores methods for AI agents to infer personality traits from facial images to improve human-robot interaction.
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PHITSBench: an execution-scored benchmark for AI-assisted PHITS radiation-transport input generation using natural language
PHITSBench is a new execution-scored benchmark designed to evaluate AI performance in generating inputs for the PHITS radiation-transport simulation code.
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SupplyNetPy: An Open-Source Python Library for High-Fidelity Modeling and Simulation of Arbitrary Supply Chain and Inventory Networks
The open-source Python library SupplyNetPy enables high-fidelity modeling and discrete-event simulation of complex supply chain and inventory networks.
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Feedback-Coupled Memory Systems in Continuous Time
This research formalizes the Feedback-Coupled Memory Systems architecture in continuous time, defining agent updates through decentralized economic principles.
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Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification
A multi-scale vision transformer approach is detailed for identifying multiple plant species in high-resolution photographs using limited training data.
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Multi-Turn On-Policy Distillation with Prefix Replay
The proposed Replayed-Prefix On-Policy Distillation method aims to reduce the computational costs associated with training agentic LLMs by using off-environment data.
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Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence
Researchers developed an explainable AI model using LiDAR data to improve the accuracy of satellite ground station siting by better accounting for local terrain obstructions.
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Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
This research proposes a model for optimizing power and channel performance in magnetic inductive cellular networks used in underground environments.
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WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays
WrAFT is a modular automated writing evaluation system designed to provide scoring and feedback for argumentative essays using various large language models.
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Towards a Bridge Layer Between Bibliographic and Formalized Mathematical Knowledge
Researchers have developed a relational database bridge to connect bibliographic metadata with formalized mathematical proof libraries.
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From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation
This paper proposes using the Toulmin model of argumentation to structure and interpret machine learning predictions for retinal diagnostics.
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Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
A new multi-agent framework automates the generation of verifiable rules for classifying chemical reactions to improve computer-assisted synthesis planning.
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Decision Making Needs Uncertainty Quantification [Lecture Notes]
These lecture notes examine the theoretical relationship between uncertainty quantification and effective decision-making in autonomous agents.
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Enabling Agents to Communicate Entirely in Latent Space
Researchers have introduced a communication framework that allows AI agents to exchange information directly through latent states rather than relying on natural language tokens.
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DemoPSD: Disagreement-Modulated Policy Self-Distillation
A new self-distillation method for large language models addresses overfitting and exploration issues caused by dense token-level supervision from teacher models.
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HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization
The HABIB_TAZ system uses synthetic training and multi-objective optimization to help language models separate formal logic from real-world content biases.
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CMSL: Constructive Multi-Sequence Learning for Recommendation Systems
Constructive Multi-Sequence Learning improves recommendation systems by moving beyond the monolithic chronological sequence approach used in traditional models.
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Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Pipette is a new simulation platform and benchmark created to improve the training and data efficiency of robotics systems used in biomedical laboratory environments.
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FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving
The FAST framework improves parallel reinforcement learning for autonomous driving by mitigating the straggler effect during environment sampling.
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Unsupervised Evaluation of Deep Audio Embeddings for Music Structure Analysis
This study evaluates the performance of various pre-trained audio models for music structure analysis without the need for supervised annotations.
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Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
A longitudinal study of over three million learning interactions reveals that while generative AI reduces time spent on math problems, it may negatively impact long-term knowledge retention.
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RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos
RealityBridge addresses the simulation-to-reality gap in autonomous driving by improving the fidelity of editable 3D Gaussian Splatting scenes.