Multi-Objective Coverage via Constraint Active Search
arXiv:2602.15595v1 Announce Type: new Abstract: In this paper, we formulate the new multi-objective coverage (MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cover the feasible multi-objective space. This problem is...
Certified Per-Instance Unlearning Using Individual Sensitivity Bounds
arXiv:2602.15602v1 Announce Type: new Abstract: Certified machine unlearning can be achieved via noise injection leading to differential privacy guarantees, where noise is calibrated to worst-case sensitivity. Such conservative calibration often results in performance degradation, limiting practical applicability. In this work,...
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Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans
arXiv:2602.14100v1 Announce Type: new Abstract: Whether neural networks can serve as cognitive models of morphological learning remains an open question. Recent work has shown that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans...
A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing
arXiv:2602.14158v1 Announce Type: new Abstract: Large language models (LLMs) show promise for healthcare question answering, but clinical use is limited by weak verification, insufficient evidence grounding, and unreliable confidence signalling. We propose a multi-agent medical QA framework that combines complementary...
Knowing When Not to Answer: Abstention-Aware Scientific Reasoning
arXiv:2602.14189v1 Announce Type: new Abstract: Large language models are increasingly used to answer and verify scientific claims, yet existing evaluations typically assume that a model must always produce a definitive answer. In scientific settings, however, unsupported or uncertain conclusions can...
Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization
arXiv:2602.13398v1 Announce Type: new Abstract: Designing cryoprotectant agent (CPA) cocktails for vitrification is challenging because formulations must be concentrated enough to suppress ice formation yet non-toxic enough to preserve cell viability. This tradeoff creates a large, multi-objective design space in...
Comparing Classifiers: A Case Study Using PyCM
arXiv:2602.13482v1 Announce Type: new Abstract: Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, demonstrating its utility in conducting deep-dive evaluations of multi-class...
Finding Highly Interpretable Prompt-Specific Circuits in Language Models
arXiv:2602.13483v1 Announce Type: new Abstract: Understanding the internal circuits that language models use to solve tasks remains a central challenge in mechanistic interpretability. Most prior work identifies circuits at the task level by averaging across many prompts, implicitly assuming a...
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v1 Announce Type: new Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal...
Singular Vectors of Attention Heads Align with Features
arXiv:2602.13524v1 Announce Type: new Abstract: Identifying feature representations in language models is a central task in mechanistic interpretability. Several recent studies have made an implicit assumption that feature representations can be inferred in some cases from singular vectors of attention...
Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks
arXiv:2602.13746v1 Announce Type: new Abstract: Industrial thermal power systems have coupled performance variables with hierarchical order of importance, making their simultaneous optimization computationally challenging or infeasible. This barrier limits the integrated and computationally scaleable operation optimization of industrial thermal power...
On Representation Redundancy in Large-Scale Instruction Tuning Data Selection
arXiv:2602.13773v1 Announce Type: new Abstract: Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high-quality datasets can outperform those trained on much larger but noisy or low-quality corpora,...
MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction
arXiv:2602.13791v1 Announce Type: new Abstract: Predicting transcriptional responses to unseen genetic perturbations is essential for understanding gene regulation and prioritizing large-scale perturbation experiments. Existing approaches either rely on static, potentially incomplete knowledge graphs, or prompt language models for functionally similar...
Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
arXiv:2602.13802v1 Announce Type: new Abstract: Time series forecasting has long been dominated by model-centric approaches that formulate prediction as a single-pass mapping from historical observations to future values. Despite recent progress, such formulations often struggle in complex and evolving settings,...
AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
arXiv:2602.13807v1 Announce Type: new Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings. However, most existing methods frame anomaly detection as a purely discriminative...
Testing For Distribution Shifts with Conditional Conformal Test Martingales
arXiv:2602.13848v1 Announce Type: new Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting. Existing CTM detectors construct test martingales by continually growing a reference set...
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
arXiv:2602.13921v1 Announce Type: new Abstract: Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. Standard Large Language Modles (LLMs) are often unsuitable for this task due to context window...
Assessing States’ Obligations under the UN Guiding Principles on Business and Human Rights Post-Brexit
Private economic actors wield unprecedented influence over the enjoyment of human rights, yet legal systems remain uneven in their regulation of corporate responsibility. Against this backdrop, this article examines a largely underexplored post-Brexit trajectory, the regulatory divergence in the implementation...
A Not Too Collaborative Constitution? Collaboration as Constitutional Value Versus Model
Constitutional scholarship in recent years has seen a proliferation of ‘isms’ – or the rise of constitutional ideas ‘with adjectives’. Beneath the current trend toward ‘adjectival constitutionalism’ also lie different understandings of constitutionalism as a topic, model, mode of change...