Semantic Novelty at Scale: Narrative Shape Taxonomy and Readership Prediction in 28,606 Books
arXiv:2602.20647v1 Announce Type: new Abstract: I introduce semantic novelty--cosine distance between each paragraph's sentence embedding and the running centroid of all preceding paragraphs--as an information-theoretic measure of narrative structure at corpus scale. Applying it to 28,606 books in PG19 (pre-1920...
CAMEL: Confidence-Gated Reflection for Reward Modeling
arXiv:2602.20670v1 Announce Type: new Abstract: Reward models play a fundamental role in aligning large language models with human preferences. Existing methods predominantly follow two paradigms: scalar discriminative preference models, which are efficient but lack interpretability, and generative judging models, which...
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization
arXiv:2602.20743v1 Announce Type: new Abstract: Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually...
Overton Pluralistic Reinforcement Learning for Large Language Models
arXiv:2602.20759v1 Announce Type: new Abstract: Existing alignment paradigms remain limited in capturing the pluralistic nature of human values. Overton Pluralism addresses this gap by generating responses with diverse perspectives from a single query. This paper introduces OP-GRPO (Overton Pluralistic Group...
Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
arXiv:2602.20816v1 Announce Type: new Abstract: The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distributions. Traditional KL divergence tends to be dominated by the next tokens with the highest...
FinAnchor: Aligned Multi-Model Representations for Financial Prediction
arXiv:2602.20859v1 Announce Type: new Abstract: Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embeddings varies across tasks and time periods. In this paper, we...
Exa-PSD: a new Persian sentiment analysis dataset on Twitter
arXiv:2602.20892v1 Announce Type: new Abstract: Today, Social networks such as Twitter are the most widely used platforms for communication of people. Analyzing this data has useful information to recognize the opinion of people in tweets. Sentiment analysis plays a vital...
Linear Reasoning vs. Proof by Cases: Obstacles for Large Language Models in FOL Problem Solving
arXiv:2602.20973v1 Announce Type: new Abstract: To comprehensively evaluate the mathematical reasoning capabilities of Large Language Models (LLMs), researchers have introduced abundant mathematical reasoning datasets. However, most existing datasets primarily focus on linear reasoning, neglecting other parts such as proof by...
Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
arXiv:2602.21103v1 Announce Type: new Abstract: Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To...
MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
arXiv:2602.20423v1 Announce Type: cross Abstract: Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts. While vision-language models such as CLIP offer strong cross-modal representations, their potential for dense, text-guided medical image segmentation...
Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
arXiv:2602.20449v1 Announce Type: cross Abstract: Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties. However, protein language has key differences from natural language, such as...
GATES: Self-Distillation under Privileged Context with Consensus Gating
arXiv:2602.20574v1 Announce Type: cross Abstract: We study self-distillation in settings where supervision is unreliable: there are no ground truth labels, verifiable rewards, or external graders to evaluate answers. We focus on document-grounded question answering with asymmetric context, where a single...
Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
arXiv:2602.20994v1 Announce Type: cross Abstract: Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., volumes, counts, sizes, locations). In MRI studies of brain tumors, however, we often involve multi-parametric...
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
arXiv:2602.21009v1 Announce Type: cross Abstract: Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints. While summarizing history via interest centers offers a practical alternative,...
Tensor Network Generator-Enhanced Optimization for Traveling Salesman Problem
arXiv:2602.20175v1 Announce Type: new Abstract: We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a fundamental combinatorial optimization challenge. Our approach employs a tensor network Born machine based on automatically...
FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
arXiv:2602.20194v1 Announce Type: new Abstract: Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. We propose a federated framework for estimating a Continuous-Time Markov Chain (CTMC) hazard model of...
Model Merging in the Essential Subspace
arXiv:2602.20208v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model without additional training. Despite extensive research, task interference remains a major obstacle that often undermines...
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling
arXiv:2602.20210v1 Announce Type: new Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks across different modalities, including crystal structure prediction (CSP) and \emph{de novo} generation (DNG). While recent deep generative models have shown promising performance, they remain...
MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
arXiv:2602.20223v1 Announce Type: new Abstract: Recently, TabPFN has gained attention as a foundation model for tabular data. However, it struggles to integrate heterogeneous modalities such as images and text, which are common in domains like healthcare and marketing, thereby limiting...
Learning to Solve Complex Problems via Dataset Decomposition
arXiv:2602.20296v1 Announce Type: new Abstract: Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generation approach that...
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
arXiv:2602.20306v1 Announce Type: new Abstract: High-fidelity computational models of cardiac mechanics provide mechanistic insight into the heart function but are computationally prohibitive for routine clinical use. Surrogate models can accelerate simulations, but generalization across diverse anatomies is challenging, particularly in...
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
arXiv:2602.20307v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks...
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
arXiv:2602.20309v1 Announce Type: new Abstract: Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing compute and memory demands, especially as models scale to longer horizons and larger...
CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
arXiv:2602.20329v1 Announce Type: new Abstract: This work presents Causal Drift Generator (CaDrift), a time-dependent synthetic data generator framework based on Structural Causal Models (SCMs). The framework produces a virtually infinite combination of data streams with controlled shift events and time-dependent...
Emergent Manifold Separability during Reasoning in Large Language Models
arXiv:2602.20338v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting significantly improves reasoning in Large Language Models, yet the temporal dynamics of the underlying representation geometry remain poorly understood. We investigate these dynamics by applying Manifold Capacity Theory (MCT) to a compositional...
Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
arXiv:2602.20344v1 Announce Type: new Abstract: Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling costs such as molecular graph analysis. However,...
GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
arXiv:2602.20399v1 Announce Type: new Abstract: Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity training data. Pre-training on abundant off-the-shelf geometries offers a natural alternative, yet faces a fundamental...
GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
arXiv:2602.20427v1 Announce Type: new Abstract: Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional ones like exact solvers or heuristics with gradient-based search, they typically rely on...
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
arXiv:2602.20468v1 Announce Type: new Abstract: Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover,...
A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies
arXiv:2602.20527v1 Announce Type: new Abstract: Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) have advanced rapidly in recent years and have been successfully applied to e-learning environments like intelligent tutoring systems (ITSs). Despite great success, the broader application of DRL...