Episode 35: Human Mobility and International Law - EJIL: The Podcast!
Understanding the Regulation of the Use of Artificial Intelligence Under International Law
The development of artificial intelligence (AI) has revolutionized various aspects of human life, from the economic sector to the government system. While it brings significant benefits, AI also poses legal and ethical risks that have not been fully addressed in...
When to Think Fast and Slow? AMOR: Entropy-Based Metacognitive Gate for Dynamic SSM-Attention Switching
arXiv:2602.13215v1 Announce Type: new Abstract: Transformers allocate uniform computation to every position, regardless of difficulty. State Space Models (SSMs) offer efficient alternatives but struggle with precise information retrieval over a long horizon. Inspired by dual-process theories of cognition (Kahneman, 2011),...
Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation
arXiv:2602.13263v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often degrade on accented speech because acoustic-phonetic and prosodic shifts induce a mismatch to training data, making labeled accent adaptation costly. However, common pseudo-label selection heuristics are largely text-centric (e.g.,...
ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmatics
arXiv:2602.13870v1 Announce Type: new Abstract: The growing importance of culturally-aware natural language processing systems has led to an increasing demand for resources that capture sociopragmatic phenomena across diverse languages. Nevertheless, Arabic-language resources for politeness detection remain under-explored, despite the rich...
InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing
arXiv:2602.18985v1 Announce Type: new Abstract: Infrared radiation computing underpins advances in climate science, remote sensing and spectroscopy but remains constrained by manual workflows. We introduce InfEngine, an autonomous intelligent computational engine designed to drive a paradigm shift from human-led orchestration...
ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Disease
arXiv:2602.19298v1 Announce Type: new Abstract: Evaluating personalized, sequential treatment strategies for Alzheimer's disease (AD) using clinical trials is often impractical due to long disease horizons and substantial inter-patient heterogeneity. To address these constraints, we present the Alzheimer's Learning Platform for...
ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference
arXiv:2602.23681v1 Announce Type: new Abstract: The paradigm of large language model (LLM) reasoning is shifting from parameter scaling to test-time compute scaling, yet many existing approaches still rely on uniform brute-force sampling (for example, fixed best-of-N or self-consistency) that is...
RUMAD: Reinforcement-Unifying Multi-Agent Debate
arXiv:2602.23864v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus formation, and computational efficiency. Static topology methods lack adaptability to task complexity variations, while external...
DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths
arXiv:2603.00309v1 Announce Type: new Abstract: The increasingly popular agentic AI paradigm promises to harness the power of multiple, general-purpose large language model (LLM) agents to collaboratively complete complex tasks. While many agentic AI systems utilize predefined workflows or agent roles...
NeuroHex: Highly-Efficient Hex Coordinate System for Creating World Models to Enable Adaptive AI
arXiv:2603.00376v1 Announce Type: new Abstract: \textit{NeuroHex} is a hexagonal coordinate system designed to support highly efficient world models and reference frames for online adaptive AI systems. Inspired by the hexadirectional firing structure of grid cells in the human brain, NeuroHex...
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
arXiv:2603.00599v1 Announce Type: new Abstract: Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Networks (HGNNs) have become the dominant solution for learning on hypergraphs. Traditional HGNNs are...
A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities
arXiv:2603.02540v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans. This...
Design Behaviour Codes (DBCs): A Taxonomy-Driven Layered Governance Benchmark for Large Language Models
arXiv:2603.04837v1 Announce Type: new Abstract: We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control behavioral governance layer, the MDBC (Madan DBC) system, applied at inference time to large language...
Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation
arXiv:2603.04423v1 Announce Type: new Abstract: VHF radio miscommunication remains a major safety risk in maritime operations, with human factors accounting for over 58% of recorded incidents in Europe between 2014 and 2023. Despite decades of operational use, VHF radio communications...
Flowers: A Warp Drive for Neural PDE Solvers
arXiv:2603.04430v1 Announce Type: new Abstract: We introduce Flowers, a neural architecture for learning PDE solution operators built entirely from multihead warps. Aside from pointwise channel mixing and a multiscale scaffold, Flowers use no Fourier multipliers, no dot-product attention, and no...
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
arXiv:2603.04458v1 Announce Type: new Abstract: Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their...
Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
arXiv:2603.04477v1 Announce Type: new Abstract: Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive means of monitoring human gait and posture. We present an activity classification system based on a circular dilated convolutional neural network (CDCNN) that...
An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs
arXiv:2603.04545v1 Announce Type: new Abstract: Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expensive and vary in complexity, as each involves a different number of...
TTSR: Test-Time Self-Reflection for Continual Reasoning Improvement
arXiv:2603.03297v1 Announce Type: cross Abstract: Test-time Training enables model adaptation using only test questions and offers a promising paradigm for improving the reasoning ability of large language models (LLMs). However, it faces two major challenges: test questions are often highly...
Controllable and explainable personality sliders for LLMs at inference time
arXiv:2603.03326v1 Announce Type: cross Abstract: Aligning Large Language Models (LLMs) with specific personas typically relies on expensive and monolithic Supervised Fine-Tuning (SFT) or RLHF. While effective, these methods require training distinct models for every target personality profile. Inference-time activation steering...
Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)
arXiv:2603.03309v1 Announce Type: new Abstract: Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items with sparse metadata. This research proposes an innovative hybrid framework that leverages Large Language Models...
Entropic-Time Inference: Self-Organizing Large Language Model Decoding Beyond Attention
arXiv:2603.03310v1 Announce Type: new Abstract: Modern large language model (LLM) inference engines optimize throughput and latency under fixed decoding rules, treating generation as a linear progression in token time. We propose a fundamentally different paradigm: entropic\-time inference, where decoding is...
RADAR: Learning to Route with Asymmetry-aware DistAnce Representations
arXiv:2603.03388v1 Announce Type: new Abstract: Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-world scenarios. A core challenge is encoding the relational features in asymmetric distance...
Towards Improved Sentence Representations using Token Graphs
arXiv:2603.03389v1 Announce Type: new Abstract: Obtaining a single-vector representation from a Large Language Model's (LLM) token-level outputs is a critical step for nearly all sentence-level tasks. However, standard pooling methods like mean or max aggregation treat tokens as an independent...
When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators
arXiv:2603.03491v1 Announce Type: new Abstract: Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottleneck. However, their reliance on emerging non-volatile memory devices introduces device-level non-idealities-such as write...
mlx-snn: Spiking Neural Networks on Apple Silicon via MLX
arXiv:2603.03529v1 Announce Type: new Abstract: We introduce mlx-snn, the first spiking neural network (SNN) library built natively on Apple's MLX framework. As SNN research grows rapidly, all major libraries -- snnTorch, Norse, SpikingJelly, Lava -- target PyTorch or custom backends,...
Adaptive Sensing of Continuous Physical Systems for Machine Learning
arXiv:2603.03650v1 Announce Type: new Abstract: Physical dynamical systems can be viewed as natural information processors: their systems preserve, transform, and disperse input information. This perspective motivates learning not only from data generated by such systems, but also how to measure...
Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling
arXiv:2603.03662v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful framework for processing graph-structured data. However, conventional GNNs and their variants are inherently limited by the homophily assumption, leading to degradation in performance on heterophilic graphs....
ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation
arXiv:2603.02945v1 Announce Type: new Abstract: Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, especially when they are trained on different objectives, often leads to significant...