Enhancing Zero-shot Commonsense Reasoning by Integrating Visual Knowledge via Machine Imagination
arXiv:2603.05040v1 Announce Type: new Abstract: Recent advancements in zero-shot commonsense reasoning have empowered Pre-trained Language Models (PLMs) to acquire extensive commonsense knowledge without requiring task-specific fine-tuning. Despite this progress, these models frequently suffer from limitations caused by human reporting biases...
Jagarin: A Three-Layer Architecture for Hibernating Personal Duty Agents on Mobile
arXiv:2603.05069v1 Announce Type: new Abstract: Personal AI agents face a fundamental deployment paradox on mobile: persistent background execution drains battery and violates platform sandboxing policies, yet purely reactive agents miss time-sensitive obligations until the user remembers to ask. We present...
MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus
arXiv:2603.05129v1 Announce Type: new Abstract: Diagnosing hepatic diseases accurately and interpretably is critical, yet it remains challenging in real-world clinical settings. Existing AI approaches for clinical diagnosis often lack transparency, structured reasoning, and deployability. Recent efforts have leveraged large language...
CTRL-RAG: Contrastive Likelihood Reward Based Reinforcement Learning for Context-Faithful RAG Models
arXiv:2603.04406v1 Announce Type: new Abstract: With the growing use of Retrieval-Augmented Generation (RAG), training large language models (LLMs) for context-sensitive reasoning and faithfulness is increasingly important. Existing RAG-oriented reinforcement learning (RL) methods rely on external rewards that often fail to...
Multiclass Hate Speech Detection with RoBERTa-OTA: Integrating Transformer Attention and Graph Convolutional Networks
arXiv:2603.04414v1 Announce Type: new Abstract: Multiclass hate speech detection across demographic categories remains computationally challenging due to implicit targeting strategies and linguistic variability in social media content. Existing approaches rely solely on learned representations from training data, without explicitly incorporating...
Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models
arXiv:2603.04647v1 Announce Type: new Abstract: Retrieval augmented generation mitigates limitations of large language models in factual consistency and knowledge updating by introducing external knowledge. However, practical applications still suffer from semantic misalignment between retrieved results and generation objectives, as well...
iAgentBench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics
arXiv:2603.04656v1 Announce Type: new Abstract: With the emergence of search-enabled generative QA systems, users are increasingly turning to tools that browse, aggregate, and reconcile evidence across multiple sources on their behalf. Yet many widely used QA benchmarks remain answerable by...
Stacked from One: Multi-Scale Self-Injection for Context Window Extension
arXiv:2603.04759v1 Announce Type: new Abstract: The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data...
TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings
arXiv:2603.04772v1 Announce Type: new Abstract: Despite the exceptional reasoning capabilities of Multimodal Large Language Models (MLLMs), their adaptation into universal embedding models is significantly impeded by task conflict. To address this, we propose TSEmbed, a universal multimodal embedding framework that...
Autoscoring Anticlimax: A Meta-analytic Understanding of AI's Short-answer Shortcomings and Wording Weaknesses
arXiv:2603.04820v1 Announce Type: new Abstract: Automated short-answer scoring lags other LLM applications. We meta-analyze 890 culminating results across a systematic review of LLM short-answer scoring studies, modeling the traditional effect size of Quadratic Weighted Kappa (QWK) with mixed effects metaregression....
Machine Learning for Complex Systems Dynamics: Detecting Bifurcations in Dynamical Systems with Deep Neural Networks
arXiv:2603.04420v1 Announce Type: new Abstract: Critical transitions are the abrupt shifts between qualitatively different states of a system, and they are crucial to understanding tipping points in complex dynamical systems across ecology, climate science, and biology. Detecting these shifts typically...
On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks
arXiv:2603.04451v1 Announce Type: new Abstract: Inspired by measurement incompatibility and Bell-family inequalities in quantum mechanics, we propose the Non-Classical Network (NCnet), a simple classical neural architecture that stably exhibits non-classical statistical behaviors under typical and interpretable experimental setups. We find...
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...
Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation
arXiv:2603.04478v1 Announce Type: new Abstract: Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG...
Invariant Causal Routing for Governing Social Norms in Online Market Economies
arXiv:2603.04534v1 Announce Type: new Abstract: Social norms are stable behavioral patterns that emerge endogenously within economic systems through repeated interactions among agents. In online market economies, such norms -- like fair exposure, sustained participation, and balanced reinvestment -- are critical...
Why Do Neural Networks Forget: A Study of Collapse in Continual Learning
arXiv:2603.04580v1 Announce Type: new Abstract: Catastrophic forgetting is a major problem in continual learning, and lots of approaches arise to reduce it. However, most of them are evaluated through task accuracy, which ignores the internal model structure. Recent research suggests...
Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings
arXiv:2603.04692v1 Announce Type: new Abstract: Predictive modeling in engineering applications has long been dominated by bespoke models and small, siloed tabular datasets, limiting the applicability of large-scale learning approaches. Despite recent progress in tabular foundation models, the resulting synthetic training...
Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
arXiv:2603.04780v1 Announce Type: new Abstract: Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as enforcing specific indicator patterns for latents or restricting how they can interact with others. We...
Justices poised to adopt exceptions to federal criminal defendants’ appellate waivers
The Supreme Court heard oral argument on Tuesday in Hunter v. United States about what exceptions exist to federal defendants’ waivers of their right to appeal. The justices seemed poised […]The postJustices poised to adopt exceptions to federal criminal defendants’...
Birthright citizenship: the exceptions provide the rule
The battle over birthright citizenship is a battle over its exceptions. The 14th Amendment’s first sentence proudly proclaims that “[a]ll persons born . . . in the United States, and subject to the jurisdiction […]The postBirthright citizenship: the exceptions provide...
Syrian nationals urge Supreme Court to keep ruling in place allowing them to stay in the United States
A group of Syrian nationals urged the Supreme Court on Thursday to leave in place a ruling by a federal judge in New York City that allows them to remain […]The postSyrian nationals urge Supreme Court to keep ruling in...
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Elon Musk takes the stand in a San Francisco courtroom.
From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
arXiv:2603.03292v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval-Augmented Generation (RAG) mitigates these issues, existing methods...
Language Model Goal Selection Differs from Humans' in an Open-Ended Task
arXiv:2603.03295v1 Announce Type: cross Abstract: As large language models (LLMs) get integrated into human decision-making, they are increasingly choosing goals autonomously rather than only completing human-defined ones, assuming they will reflect human preferences. However, human-LLM similarity in goal selection remains...
Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs
arXiv:2603.03302v1 Announce Type: cross Abstract: Effective knowledge management is critical for preserving institutional expertise and improving the efficiency of workforce training in state transportation agencies. Traditional approaches, such as static documentation, classroom-based instruction, and informal mentorship, often lead to fragmented...
HumanLM: Simulating Users with State Alignment Beats Response Imitation
arXiv:2603.03303v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to simulate how specific users respond to a given context, enabling more user-centric applications that rely on user feedback. However, existing user simulators mostly imitate surface-level patterns and...
Quantum-Inspired Self-Attention in a Large Language Model
arXiv:2603.03318v1 Announce Type: cross Abstract: Recent advances in Natural Language Processing have been predominantly driven by transformer-based architectures, which rely heavily on self-attention mechanisms to model relationships between tokens in a sequence. Similarly, the field of Quantum Natural Language Processing,...
Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery
arXiv:2603.03322v1 Announce Type: cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rigorously evaluating an AI's capacity for knowledge discovery remains a critical challenge. Existing benchmarks predominantly rely on static...