Arash Asgari, Huan Wu, Amirreza Naziri, Mojtaba Kolahdouzi, Laleh Seyyed-Kalantari. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Despite significant advances in diffusion models, achieving precise and composable image editing without task-specific training remains a challenge. Existing approaches often rely on iterative optimization or linear latent operations, which are slow, brittle, and prone to attribute entanglement (e.g., editing "lipstick" inadvertently alters skin tone). We introduce SphereEdit, a training-free framework that leverages the spherical geometry of diffusion embeddings and token aware cross-attention to enable interpretable, fine-grained control. We represent semantic attributes as unit vector directions in the denoiser’s prediction space and show that antipodal symmetry (‘old’ is approximately the negation of ‘young’) naturally supports bidirectional edits, while approximate orthogonality enables clean composition through spherical coefficient. At inference, these directions modulate cross-attention activations, producing spatially localized edits without optimization or fine-tuning. SphereEdit achieves sharper, more disentangled edits than prior baselines, while remaining plug-and-play and applicable across diverse image editing tasks. The code is available at https://github.com/sala-kon/SphereEdit

Large language models (LLMs) struggle to navigate culturally specific communication norms, limiting their effectiveness in global contexts.We focus on Persian taarof, a social norm in Iranian interactions, which is a sophisticated system of ritual politeness that emphasizes deference, modesty, and indirectness, yet remains absent from existing cultural benchmarks.We introduce TAAROFBENCH, the first benchmark for evaluating LLM understanding of taarof, comprising 450 role-play scenarios covering 12 common social interaction topics, validated by native speakers.Our evaluation of five frontier LLMs reveals substantial gaps in cultural competence, with accuracy rates 40-48% below native speakers when taarof is culturally appropriate.Performance varies between interaction topics, improves with Persian-language prompts, and exhibits gender-based asymmetries.We also show that responses rated "polite" by standard metrics often violate taarof norms, indicating the limitations of Western politeness frameworks.Through supervised fine-tuning and Direct Preference Optimization, we achieve 21.8% and 42.3% improvement in model alignment with cultural expectations.Our human study with 33 participants (11 native Persian, 11 heritage, and 11 non-Iranian speakers) forms baselines in varying degrees of familiarity with Persian norms.This work lays the foundation for developing diverse and culturally aware LLMs, enabling applications that better navigate complex social interactions.1

BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for real-time surveillance, prediction, forecasting, and early warning of respiratory disease. To this end, we selected southern African countries and Canadian provinces, along with COVID-19 and influenza as our case studies. METHODOLOGY: Six different datasets were collected for different provinces of Canada: number of influenza cases, number of COVID-19 cases, Google Trends, Reddit posts, satellite air quality data, and weather data. Moreover, five different data sources were collected for southern African countries whose COVID-19 number of cases were significantly correlated with each other: number of COVID-19 infections, Google Trends, Wiki Trends, Google News, and satellite air quality data. For each infectious disease, i.e. COVID-19 and Influenza for Canada and COVID-19 for southern African countries, data was processed, scaled, and fed into the deep learning model which included four layers, namely, a Convolutional Neural Network (CNN), a Graph Neural Network (GNN), a Gated Recurrent Unit (GRU), and a linear Neural Network (NN). Hyperparameters were optimized to provide an accurate 56-day-ahead prediction of the number of cases. RESULT: The accuracy of our models in real-time surveillance, prediction, forecasting, and early warning of respiratory diseases are evaluated against state-of-the-art models, through Root Mean Square Error (RMSE), coefficient of determination (R2-score), and correlation coefficient. Our model improves R2-score, RMSE, and correlation by up to 55.98 %, 39.71 %, and 44.47 % for 56 days-ahead COVID-19 prediction in Ontario, 34.87 %, 25.52 %, 50.91 % for 8 weeks-ahead influenza prediction in Quebec, and 51.04 %, 32.04 %, and 28.74 % for 56 days-ahead COVID-19 prediction in South Africa, respectively. CONCLUSION: This work presents a framework that automatically collects data from unconventional sources, and builds an early warning system for COVID-19 and influenza outbreaks. The result is extremely helpful to policy-makers and health officials for preparedness and rapid response against future outbreaks.

Simulation is a widely used approach for evaluating system performance, robustness, and potential issues during design and testing. Large Language Models (LLMs) have recently shown strong potential in autonomous agent systems, including negotiation tasks—a core aspect of commerce. This paper evaluates LLM-based autonomous negotiator agents (LANAs) in a buyer-seller bargaining game to assess their decision-making and reasoning. We simulate interactions between agents embodying contrasting social behaviors: (a) Cunning vs. Kind, and (b) Greedy vs. Generous. By analyzing both the game outcomes and the agents’ internal reasoning, we find that LLMs can effectively simulate distinct social behaviors in both dialogue and decision-making. Our results offer insights into how social traits affect negotiation dynamics, emphasizing the importance of clear policy design to ensure fairness and reliability in LANA-based systems.