Survey of Various Fuzzy and Uncertain Decision-Making Methods
arXiv:2603.15709v1 Announce Type: new Abstract: Decision-making in real applications is often affected by vagueness, incomplete information, heterogeneous data, and conflicting expert opinions. This survey reviews uncertainty-aware multi-criteria decision-making (MCDM) and organizes the field into a concise, task-oriented taxonomy. We summarize...
Parametric Social Identity Injection and Diversification in Public Opinion Simulation
arXiv:2603.16142v1 Announce Type: new Abstract: Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costly and slow human surveys. Despite their scalability, current LLM-based simulation methods fail to capture...
SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation
arXiv:2603.16219v1 Announce Type: new Abstract: Realizing personalized intelligence faces a core dilemma: sending user history to centralized large language models raises privacy concerns, while on-device small language models lack the reasoning capacity required for high-quality generation. Our pilot study shows...
How often do Answers Change? Estimating Recency Requirements in Question Answering
arXiv:2603.16544v1 Announce Type: new Abstract: Large language models (LLMs) often rely on outdated knowledge when answering time-sensitive questions, leading to confident yet incorrect responses. Without explicit signals indicating whether up-to-date information is required, models struggle to decide when to retrieve...
Discovering the Hidden Role of Gini Index In Prompt-based Classification
arXiv:2603.15654v1 Announce Type: new Abstract: In classification tasks, the long-tailed minority classes usually offer the predictions that are most important. Yet these classes consistently exhibit low accuracies, whereas a few high-performing classes dominate the game. We pursue a foundational understanding...
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...
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
The Public/Private Home
Families today are more private and more public than traditional family law doctrine ever envisioned. This Article reveals how many elements of family life, which the law often assumes will occur in public—work, school, social life—have moved into the private...
GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages
arXiv:2603.13793v1 Announce Type: new Abstract: Low resource languages present unique challenges for natural language processing due to the limited availability of digitized and well structured linguistic data. To address this gap, the GhanaNLP initiative has developed and curated 41,513 parallel...
Repetition Without Exclusivity: Scale Sensitivity of Referential Mechanisms in Child-Scale Language Models
arXiv:2603.13696v1 Announce Type: new Abstract: We present the first systematic evaluation of mutual exclusivity (ME) -- the bias to map novel words to novel referents -- in text-only language models trained on child-directed speech. We operationalise ME as referential suppression:...
QuarkMedBench: A Real-World Scenario Driven Benchmark for Evaluating Large Language Models
arXiv:2603.13691v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel on standardized medical exams, high scores often fail to translate to high-quality responses for real-world medical queries. Current evaluations rely heavily on multiple-choice questions, failing to capture the unstructured,...
The Phenomenology of Hallucinations
arXiv:2603.13911v1 Announce Type: new Abstract: We show that language models hallucinate not because they fail to detect uncertainty, but because of a failure to integrate it into output generation. Across architectures, uncertain inputs are reliably identified, occupying high-dimensional regions with...
Optimizing LLM Annotation of Classroom Discourse through Multi-Agent Orchestration
arXiv:2603.13353v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly positioned as scalable tools for annotating educational data, including classroom discourse, interaction logs, and qualitative learning artifacts. Their ability to rapidly summarize instructional interactions and assign rubric-aligned labels has...
ManiBench: A Benchmark for Testing Visual-Logic Drift and Syntactic Hallucinations in Manim Code Generation
arXiv:2603.13251v1 Announce Type: new Abstract: Traditional benchmarks like HumanEval and MBPP test logic and syntax effectively, but fail when code must produce dynamic, pedagogical visuals. We introduce ManiBench, a specialized benchmark evaluating LLM performance in generating Manim CE code, where...
State Algebra for Probabilistic Logic
arXiv:2603.13574v1 Announce Type: new Abstract: This paper presents a Probabilistic State Algebra as an extension of deterministic propositional logic, providing a computational framework for constructing Markov Random Fields (MRFs) through pure linear algebra. By mapping logical states to real-valued coordinates...
Projection-Free Evolution Strategies for Continuous Prompt Search
arXiv:2603.13786v1 Announce Type: new Abstract: Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effectiveness can be significantly hindered by the black-box nature and the inherent high-dimensionality of the...
DOVA: Deliberation-First Multi-Agent Orchestration for Autonomous Research Automation
arXiv:2603.13327v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated remarkable capabilities in tool use, reasoning, and code generation, yet single-agent systems exhibit fundamental limitations when confronted with complex research tasks demanding multi-source synthesis, adversarial verification, and personalized...
PA-Net: Precipitation-Adaptive Mixture-of-Experts for Long-Tail Rainfall Nowcasting
arXiv:2603.13818v1 Announce Type: new Abstract: Precipitation nowcasting is vital for flood warning, agricultural management, and emergency response, yet two bottlenecks persist: the prohibitive cost of modeling million-scale spatiotemporal tokens from multi-variate atmospheric fields, and the extreme long-tailed rainfall distribution where...
A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
arXiv:2603.13998v1 Announce Type: new Abstract: While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of observed gains largely unexplored. Consequently, it remains...
Multimodal Emotion Regression with Multi-Objective Optimization and VAD-Aware Audio Modeling for the 10th ABAW EMI Track
arXiv:2603.13760v1 Announce Type: new Abstract: We participated in the 10th ABAW Challenge, focusing on the Emotional Mimicry Intensity (EMI) Estimation track on the Hume-Vidmimic2 dataset. This task aims to predict six continuous emotion dimensions: Admiration, Amusement, Determination, Empathic Pain, Excitement,...
Steering at the Source: Style Modulation Heads for Robust Persona Control
arXiv:2603.13249v1 Announce Type: new Abstract: Activation steering offers a computationally efficient mechanism for controlling Large Language Models (LLMs) without fine-tuning. While effectively controlling target traits (e.g., persona), coherency degradation remains a major obstacle to safety and practical deployment. We hypothesize...
vla-eval: A Unified Evaluation Harness for Vision-Language-Action Models
arXiv:2603.13966v1 Announce Type: new Abstract: Vision Language Action VLA models are typically evaluated using per benchmark scripts maintained independently by each model repository, leading to duplicated code, dependency conflicts, and underspecified protocols. We present vla eval, an open source evaluation...
GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent
arXiv:2603.13875v1 Announce Type: new Abstract: Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-cache of past activations, which incurs substantial memory overhead. A desirable alternative is ompressive memory: read...
Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation
arXiv:2603.13891v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring. Across 19 LLMs and two experiments totaling more than 4 million annotation judgments,...
CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification
arXiv:2603.14078v1 Announce Type: new Abstract: Textual Emotion Classification (TEC) is one of the most difficult NLP tasks. State of the art approaches rely on Large language models (LLMs) and multi-model ensembles. In this study, we challenge the assumption that larger...
Mitigating Overthinking in Large Reasoning Language Models via Reasoning Path Deviation Monitoring
arXiv:2603.14251v1 Announce Type: new Abstract: Large Reasoning Language Models (LRLMs) demonstrate impressive capabilities on complex tasks by utilizing long Chain-of-Thought reasoning. However, they are prone to overthinking, which generates redundant reasoning steps that degrade both performance and efficiency. Recently, early-exit...
Motivation in Large Language Models
arXiv:2603.14347v1 Announce Type: new Abstract: Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We...
BiT-MCTS: A Theme-based Bidirectional MCTS Approach to Chinese Fiction Generation
arXiv:2603.14410v1 Announce Type: new Abstract: Generating long-form linear fiction from open-ended themes remains a major challenge for large language models, which frequently fail to guarantee global structure and narrative diversity when using premise-based or linear outlining approaches. We present BiT-MCTS,...
Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval
arXiv:2603.13235v1 Announce Type: new Abstract: Continual fine-tuning aims to adapt a pre-trained backbone to new tasks sequentially while preserving performance on earlier tasks whose data are no longer available. Existing approaches fall into two categories which include input- and parameter-adaptation....
Beyond Attention: True Adaptive World Models via Spherical Kernel Operator
arXiv:2603.13263v1 Announce Type: new Abstract: The pursuit of world model based artificial intelligence has predominantly relied on projecting high-dimensional observations into parameterized latent spaces, wherein transition dynamics are subsequently learned. However, this conventional paradigm is mathematically flawed: it merely displaces...