Three Concrete Challenges and Two Hopes for the Safety of Unsupervised Elicitation
arXiv:2602.20400v1 Announce Type: new Abstract: To steer language models towards truthful outputs on tasks which are beyond human capability, previous work has suggested training models on easy tasks to steer them on harder ones (easy-to-hard generalization), or using unsupervised training...
Imputation of Unknown Missingness in Sparse Electronic Health Records
arXiv:2602.20442v1 Announce Type: new Abstract: Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and contain missing data due to various challenges...
Nonparametric Teaching of Attention Learners
arXiv:2602.20461v1 Announce Type: new Abstract: Attention learners, neural networks built on the attention mechanism, e.g., transformers, excel at learning the implicit relationships that relate sequences to their corresponding properties, e.g., mapping a given sequence of tokens to the probability of...
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,...
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
arXiv:2602.20530v1 Announce Type: new Abstract: Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular emotions in controlled laboratory settings. Real-world human emotional experiences,...
Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
arXiv:2602.20578v1 Announce Type: new Abstract: We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer from suboptimal regret and limited feedback guarantees. Our main contribution is a new structural result...
SCOTUStoday for Wednesday, February 25: SCOTUS and the State of the Union
Another day, another live blog. Join us to discuss the possible announcement of opinions this morning beginning at 9:30 a.m. EST.The postSCOTUStoday for Wednesday, February 25: SCOTUS and the State of the Unionappeared first onSCOTUSblog.
TriTopic: Tri-Modal Graph-Based Topic Modeling with Iterative Refinement and Archetypes
arXiv:2602.19079v1 Announce Type: new Abstract: Topic modeling extracts latent themes from large text collections, but leading approaches like BERTopic face critical limitations: stochastic instability, loss of lexical precision ("Embedding Blur"), and reliance on a single data perspective. We present TriTopic,...
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models
arXiv:2602.19111v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, especially LoRA, are widely used for adapting pre-trained models to downstream tasks due to their computational and storage efficiency. However, in the context of LoRA and its variants, the potential of...
AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
arXiv:2602.19127v1 Announce Type: new Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop reasoning, which requires models to engage in deliberate thinking and multi-step interaction, serves as a...
A Dataset for Named Entity Recognition and Relation Extraction from Art-historical Image Descriptions
arXiv:2602.19133v1 Announce Type: new Abstract: This paper introduces FRAME (Fine-grained Recognition of Art-historical Metadata and Entities), a manually annotated dataset of art-historical image descriptions for Named Entity Recognition (NER) and Relation Extraction (RE). Descriptions were collected from museum catalogs, auction...
Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
arXiv:2602.19317v1 Announce Type: new Abstract: Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context. Existing state-of-the-art methods primarily rely on retrieval-augmented generation (RAG) solutions that construct personal context by...
PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification
arXiv:2602.19333v1 Announce Type: new Abstract: This research introduces the first large-scale, well-balanced Persian social media text classification dataset, specifically designed to address the lack of comprehensive resources in this domain. The dataset comprises 36,000 posts across nine categories (Economic, Artistic,...
Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation
arXiv:2602.19543v1 Announce Type: new Abstract: Knowledge hypergraphs surpass traditional binary knowledge graphs by encapsulating complex $n$-ary atomic facts, providing a more comprehensive paradigm for semantic representation. However, constructing high-quality hypergraphs remains challenging due to the \textit{scenario gap}: generic extractors struggle...
Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining
arXiv:2602.19548v1 Announce Type: new Abstract: One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. Despite the immense diversity of web content, existing open-source datasets predominantly apply a single fixed extractor to all...
Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework
arXiv:2602.19549v1 Announce Type: new Abstract: Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current multimodal retrieval applications. The state-of-the-art multi-vector paradigm excels in performance but suffers from prohibitive...
Support Vector Data Description for Radar Target Detection
arXiv:2602.18486v1 Announce Type: new Abstract: Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled...
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
arXiv:2602.18521v1 Announce Type: new Abstract: Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that...
Audio-Visual Continual Test-Time Adaptation without Forgetting
arXiv:2602.18528v1 Announce Type: new Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to...
Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment
arXiv:2602.18572v1 Announce Type: new Abstract: Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency...
Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
arXiv:2602.18581v1 Announce Type: new Abstract: Despite their apparent diversity, modern machine learning methods can be reduced to a remarkably simple core principle: learning is achieved by continuously optimizing parameters to minimize or maximize a scalar objective function. This paradigm has...
GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry
arXiv:2602.18584v1 Announce Type: new Abstract: Targeted data selection has emerged as a crucial paradigm for efficient instruction tuning, aiming to identify a small yet influential subset of training examples for a specific target task. In practice, influence is often measured...
MapTab: Can MLLMs Master Constrained Route Planning?
arXiv:2602.18600v1 Announce Type: new Abstract: Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing benchmarks remain insufficient for rigorously assessing their constrained reasoning capabilities. To bridge this gap, we introduce MapTab,...
Non-Interfering Weight Fields: Treating Model Parameters as a Continuously Extensible Function
arXiv:2602.18628v1 Announce Type: new Abstract: Large language models store all learned knowledge in a single, fixed weight vector. Teaching a model new capabilities requires modifying those same weights, inevitably degrading previously acquired knowledge. This fundamental limitation, known as catastrophic forgetting,...
Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models
arXiv:2602.18639v1 Announce Type: new Abstract: World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-level reconstruction. However, recent latent predictive architectures (JEPAs), including the DINO world model (DINO-WM), display a...
Adaptive Time Series Reasoning via Segment Selection
arXiv:2602.18645v1 Announce Type: new Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full series or appear in a few short intervals, so the model...
Information-Guided Noise Allocation for Efficient Diffusion Training
arXiv:2602.18647v1 Announce Type: new Abstract: Training diffusion models typically relies on manually tuned noise schedules, which can waste computation on weakly informative noise regions and limit transfer across datasets, resolutions, and representations. We revisit noise schedule allocation through an information-theoretic...
Communication-Efficient Personalized Adaptation via Federated-Local Model Merging
arXiv:2602.18658v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods, such as LoRA, offer a practical way to adapt large vision and language models to client tasks. However, this becomes particularly challenging under task-level heterogeneity in federated deployments. In this regime, personalization...
Transformers for dynamical systems learn transfer operators in-context
arXiv:2602.18679v1 Announce Type: new Abstract: Large-scale foundation models for scientific machine learning adapt to physical settings unseen during training, such as zero-shot transfer between turbulent scales. This phenomenon, in-context learning, challenges conventional understanding of learning and adaptation in physical systems....
In-Context Planning with Latent Temporal Abstractions
arXiv:2602.18694v1 Announce Type: new Abstract: Planning-based reinforcement learning for continuous control is bottlenecked by two practical issues: planning at primitive time scales leads to prohibitive branching and long horizons, while real environments are frequently partially observable and exhibit regime shifts...