Learning to Predict, Discover, and Reason in High-Dimensional Discrete Event Sequences
arXiv:2603.16313v1 Announce Type: new Abstract: Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes (DTCs). These discrete events form complex temporal sequences that reflect the evolving health of the...
COGNAC at SemEval-2026 Task 5: LLM Ensembles for Human-Level Word Sense Plausibility Rating in Challenging Narratives
arXiv:2603.15897v1 Announce Type: new Abstract: We describe our system for SemEval-2026 Task 5, which requires rating the plausibility of given word senses of homonyms in short stories on a 5-point Likert scale. Systems are evaluated by the unweighted average of...
SEAHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Southeast Asia
arXiv:2603.16070v1 Announce Type: new Abstract: Hate speech detection relies heavily on linguistic resources, which are primarily available in high-resource languages such as English and Chinese, creating barriers for researchers and platforms developing tools for low-resource languages in Southeast Asia, where...
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
arXiv:2603.16105v1 Announce Type: new Abstract: Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches have been proposed, less emphasis has been placed on selecting the most suitable...
Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
arXiv:2603.16127v1 Announce Type: new Abstract: We investigate the role of learning rate scheduling in the large-scale pre-training of large language models, focusing on its influence on downstream performance after supervised fine-tuning (SFT). Decay-based learning rate schedulers are widely used to...
Social Simulacra in the Wild: AI Agent Communities on Moltbook
arXiv:2603.16128v1 Announce Type: new Abstract: As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online...
Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR
arXiv:2603.16184v1 Announce Type: new Abstract: We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B...
Attention-guided Evidence Grounding for Spoken Question Answering
arXiv:2603.16292v1 Announce Type: new Abstract: Spoken Question Answering (Spoken QA) presents a challenging cross-modal problem: effectively aligning acoustic queries with textual knowledge while avoiding the latency and error propagation inherent in cascaded ASR-based systems. In this paper, we introduce Attention-guided...
PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development
arXiv:2603.16354v1 Announce Type: new Abstract: We present PashtoCorp, a 1.25-billion-word corpus for Pashto, a language spoken by 60 million people that remains severely underrepresented in NLP. The corpus is assembled from 39 sources spanning seven HuggingFace datasets and 32 purpose-built...
Who Benchmarks the Benchmarks? A Case Study of LLM Evaluation in Icelandic
arXiv:2603.16406v1 Announce Type: new Abstract: This paper evaluates current Large Language Model (LLM) benchmarking for Icelandic, identifies problems, and calls for improved evaluation methods in low/medium-resource languages in particular. We show that benchmarks that include synthetic or machine-translated data that...
On the Emotion Understanding of Synthesized Speech
arXiv:2603.16483v1 Announce Type: new Abstract: Emotion is a core paralinguistic feature in voice interaction. It is widely believed that emotion understanding models learn fundamental representations that transfer to synthesized speech, making emotion understanding results a plausible reward or evaluation metric...
DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis
arXiv:2603.16546v1 Announce Type: new Abstract: Aspect-Based Sentiment Intensity Analysis (ABSIA) has garnered increasing attention, though research largely focuses on domain-specific, sentence-level settings. In contrast, document-level ABSIA--particularly in addressing complex tasks like extracting Aspect-Category-Opinion-Sentiment-Intensity (ACOSI) tuples--remains underexplored. In this work, we...
EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models
arXiv:2603.16553v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong cognitive intelligence (IQ), yet many real-world interactions also require emotional intelligence (EQ) to produce responses that are both factually reliable and emotionally appropriate. In settings such as emotional support,...
Characterizing Delusional Spirals through Human-LLM Chat Logs
arXiv:2603.16567v1 Announce Type: new Abstract: As large language models (LLMs) have proliferated, disturbing anecdotal reports of negative psychological effects, such as delusions, self-harm, and ``AI psychosis,'' have emerged in global media and legal discourse. However, it remains unclear how users...
Tokenization Tradeoffs in Structured EHR Foundation Models
arXiv:2603.15644v1 Announce Type: new Abstract: Foundation models for structured electronic health records (EHRs) are pretrained on longitudinal sequences of timestamped clinical events to learn adaptable patient representations. Tokenization -- how these timelines are converted into discrete model inputs -- determines...
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
arXiv:2603.15645v1 Announce Type: new Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features,...
Alternating Reinforcement Learning with Contextual Rubric Rewards
arXiv:2603.15646v1 Announce Type: new Abstract: Reinforcement Learning with Rubric Rewards (RLRR) is a framework that extends conventional reinforcement learning from human feedback (RLHF) and verifiable rewards (RLVR) by replacing scalar preference signals with structured, multi-dimensional, contextual rubric-based evaluations. However, existing...
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
arXiv:2603.15651v1 Announce Type: new Abstract: The early prediction of sepsis in intensive care unit (ICU) patients is crucial for improving survival rates. However, the development of accurate predictive models is hampered by data fragmentation across healthcare institutions and the complex,...
Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking
arXiv:2603.15655v1 Announce Type: new Abstract: In decentralized Multi-Agent Reinforcement Learning (MARL), steganographic collusion -- where agents develop private protocols to evade monitoring -- presents a critical AI safety threat. Existing defenses, limited to behavioral or reward layers, fail to detect...
Transition Flow Matching
arXiv:2603.15689v1 Announce Type: new Abstract: Mainstream flow matching methods typically focus on learning the local velocity field, which inherently requires multiple integration steps during generation. In contrast, Mean Velocity Flow models establish a relationship between the local velocity field and...
Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences
arXiv:2603.15713v1 Announce Type: new Abstract: Industrial financial systems operate on temporal event sequences such as transactions, user actions, and system logs. While recent research emphasizes representation learning and large language models, production systems continue to rely heavily on handcrafted statistical...
Meta-TTRL: A Metacognitive Framework for Self-Improving Test-Time Reinforcement Learning in Unified Multimodal Models
arXiv:2603.15724v1 Announce Type: new Abstract: Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling strategies that produce only instance-level improvements, limiting the ability to learn from prior inferences and...
Longitudinal Risk Prediction in Mammography with Privileged History Distillation
arXiv:2603.15814v1 Announce Type: new Abstract: Breast cancer remains a leading cause of cancer-related mortality worldwide. Longitudinal mammography risk prediction models improve multi-year breast cancer risk prediction based on prior screening exams. However, in real-world clinical practice, longitudinal histories are often...
When Stability Fails: Hidden Failure Modes Of LLMS in Data-Constrained Scientific Decision-Making
arXiv:2603.15840v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as decision-support tools in data-constrained scientific workflows, where correctness and validity are critical. However, evaluation practices often emphasize stability or reproducibility across repeated runs. While these properties are...
Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations
arXiv:2603.15867v1 Announce Type: new Abstract: The massive use of Machine Learning (ML) tools in industry comes with critical challenges, such as the lack of explainable models and the use of black-box algorithms. We address this issue by applying Optimal Transport...
Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
arXiv:2603.15871v1 Announce Type: new Abstract: Following the pivotal success of learning strategies to win at tasks, solely by interacting with an environment without any supervision, agents have gained the ability to make sequential decisions in complex MDPs. Yet, reinforcement learning...
Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions
arXiv:2603.15907v1 Announce Type: new Abstract: Game theory provides the gold standard for analyzing adversarial engagements, offering strong optimality guarantees. However, these guarantees often become brittle when assumptions such as perfect information are violated. Reinforcement learning (RL), by contrast, is adaptive...
Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
arXiv:2603.15926v1 Announce Type: new Abstract: Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical...
GASP: Guided Asymmetric Self-Play For Coding LLMs
arXiv:2603.15957v1 Announce Type: new Abstract: Asymmetric self-play has emerged as a promising paradigm for post-training large language models, where a teacher continually generates questions for a student to solve at the edge of the student's learnability. Although these methods promise...
Deriving Hyperparameter Scaling Laws via Modern Optimization Theory
arXiv:2603.15958v1 Announce Type: new Abstract: Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on...