Enhancing Action and Ingredient Modeling for Semantically Grounded Recipe Generation
arXiv:2602.15862v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap,...
NeuroSleep: Neuromorphic Event-Driven Single-Channel EEG Sleep Staging for Edge-Efficient Sensing
arXiv:2602.15888v1 Announce Type: cross Abstract: Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often computationally prohibitive under tight energy budgets. To address this bottleneck,...
Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
arXiv:2602.17001v1 Announce Type: new Abstract: Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries from massive temporal records. However, existing Text-to-SQL methods are not designed for continuous morphological intents such...
IntentCUA: Learning Intent-level Representations for Skill Abstraction and Multi-Agent Planning in Computer-Use Agents
arXiv:2602.17049v1 Announce Type: new Abstract: Computer-use agents operate over long horizons under noisy perception, multi-window contexts, evolving environment states. Existing approaches, from RL-based planners to trajectory retrieval, often drift from user intent and repeatedly solve routine subproblems, leading to error...
When Semantic Overlap Is Not Enough: Cross-Lingual Euphemism Transfer Between Turkish and English
arXiv:2602.16957v1 Announce Type: new Abstract: Euphemisms substitute socially sensitive expressions, often softening or reframing meaning, and their reliance on cultural and pragmatic context complicates modeling across languages. In this study, we investigate how cross-lingual equivalence influences transfer in multilingual euphemism...
Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
arXiv:2602.17287v1 Announce Type: new Abstract: Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-token prediction training strategy, while widely adopted in practice, may lead to...
Evaluating Extremely Low-Resource Machine Translation: A Comparative Study of ChrF++ and BLEU Metrics
arXiv:2602.17425v1 Announce Type: new Abstract: Evaluating machine translation (MT) quality in extremely low-resource language (ELRL) scenarios poses unique challenges, as widely used metrics such as BLEU, effective in high-resource settings, often misrepresent quality in data-scarce contexts. This work presents a...
What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor
arXiv:2602.16842v1 Announce Type: new Abstract: We study the offline data-driven newsvendor problem with censored demand data. In contrast to prior works where demand is fully observed, we consider the setting where demand is censored at the inventory level and only...
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
arXiv:2602.16954v1 Announce Type: new Abstract: We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation...
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing...
A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
arXiv:2602.15834v1 Announce Type: new Abstract: We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an...
Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds
arXiv:2602.16015v1 Announce Type: new Abstract: Conformal prediction provides distribution-free coverage guaranties for regression; yet existing methods assume Euclidean output spaces and produce prediction regions that are poorly calibrated when responses lie on Riemannian manifolds. We propose \emph{adaptive geodesic conformal prediction},...
Feature-based morphological analysis of shape graph data
arXiv:2602.16120v1 Announce Type: new Abstract: This paper introduces and demonstrates a computational pipeline for the statistical analysis of shape graph datasets, namely geometric networks embedded in 2D or 3D spaces. Unlike traditional abstract graphs, our purpose is not only to...
Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
arXiv:2602.16145v1 Announce Type: new Abstract: There are a number of existing studies analysing the convergence behaviour of graph neural networks on large random graphs. Unfortunately, the majority of these studies do not model correlations between node features, which would naturally...
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
arXiv:2602.16147v1 Announce Type: new Abstract: Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features for cross-subject transfer than temporal waveforms. Through correlation analyses across...
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably...
Avey-B
arXiv:2602.15814v1 Announce Type: new Abstract: Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures....
Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics
arXiv:2602.15253v1 Announce Type: new Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first...
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated...
Approximation Theory for Lipschitz Continuous Transformers
arXiv:2602.15503v1 Announce Type: new Abstract: Stability and robustness are critical for deploying Transformers in safety-sensitive settings. A principled way to enforce such behavior is to constrain the model's Lipschitz constant. However, approximation-theoretic guarantees for architectures that explicitly preserve Lipschitz continuity...
Indian AI lab Sarvam’s new models are a major bet on the viability of open source AI
The new lineup includes 30-billion- and 105-billion-parameter models; a text-to-speech model; a speech-to-text model; and a vision model to parse documents.
From Scarcity to Scale: A Release-Level Analysis of the Pashto Common Voice Dataset
arXiv:2602.14062v1 Announce Type: new Abstract: Large, openly licensed speech datasets are essential for building automatic speech recognition (ASR) systems, yet many widely spoken languages remain underrepresented in public resources. Pashto, spoken by more than 60 million people, has historically lacked...
GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
arXiv:2602.14077v1 Announce Type: new Abstract: Inference-time scaling (ITS) in latent reasoning models typically introduces stochasticity through heuristic perturbations, such as dropout or fixed Gaussian noise. While these methods increase trajectory diversity, their exploration behavior is not explicitly modeled and can...
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
arXiv:2602.13264v1 Announce Type: new Abstract: In the critical task of making generative models trustworthy and robust, methods for Uncertainty Quantification (UQ) have begun to show encouraging potential. However, many of these methods rely on rigid heuristics that fail to generalize...
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...
Cart before the Horse? BSH Hausgeräte v Electrolux and Exclusive Jurisdiction over Patent Validity
In a much-anticipated judgment, the Grand Chamber of the CJEU in BSH Hausgeräte GmbH v Electrolux AP reshaped the landscape of cross-border patent litigation in the EU. The case concerned the interpretation of Article 24(4) of Regulation 1215/2012 (Brussels Ia),...
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...
Review of Hanna Schebesta and Kai Purnhagen, EU Food Law, Oxford, Oxford University Press, 2024, 432 pp, hb, £110.00
Anyone interested in food system reform should acknowledge the importance of EU law and learn to recognise its strengths and weaknesses, so as to fully harness its transformative potential. This is no easy task, for EU food law is a...