23 citations · 58 across the 52 of their papers we have counts for
14 papers · 1 filter
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards
Xin Gao, Yifan Yang, Xin Li +13
Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code mod…
OTel: Open Telco AI Datasets, Benchmarks, and Models
Farbod Tavakkoli, Gregory Diamos, Kenneth Church +19
We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safety/abstention, together with…
Contribution-Aware Bandwidth Allocation for Multimodal Split Learning
Iason Ofeidis, Leandros Tassiulas
Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, because a client holding several s…
Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
Jialin Chen, Aosong Feng, Harshit Verma +7
Financial markets are characterized by extreme non-stationarity, low signal-to-noise ratios, and strong dependence on external information such as news, company fundamentals, and m…
Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
Nikolaos Nakis, Panagiotis Promponas, Konstantinos Tsirkas +4
Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, community detection, and related…
CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2
The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mecha…
Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Seyed Mohammad Azimi-Abarghouyi, Mehdi Bennis, Leandros Tassiulas
Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating agents with local data, local computation, and partial system visibility. Once…
A Family of Open Time-Series Foundation Models for the Radio Access Network
Ioannis Panitsas, Leandros Tassiulas
The Radio Access Network (RAN) is evolving into a programmable and disaggregated infrastructure that increasingly relies on AI-native algorithms for optimization and closed-loop co…
Benchmark for Assessing Olfactory Perception of Large Language Models
Eftychia Makri, Nikolaos Nakis, Laura Sisson +4
Here we introduce the Olfactory Perception (OP) benchmark, designed to assess the capability of large language models (LLMs) to reason about smell. The benchmark contains 1,010 que…
Rate-Fidelity Tradeoffs in All-Photonic and Memory-Equipped Quantum Switches
Panagiotis Promponas, Leonardo Bacciottini, Paul Polakos +3
Quantum entanglement switches are a key building block for early quantum networks, and a central design question is whether near-term devices should use only flying photons or also…
LitBench: A Graph-Centric Large Language Model Benchmarking Tool For Literature Tasks
Andreas Varvarigos, Ali Maatouk, Jiasheng Zhang +4
While large language models (LLMs) have become the de facto framework for literature-related tasks, they still struggle to function as domain-specific literature agents due to thei…
HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation
Hiren Madhu, Ngoc Bui, Ali Maatouk +6
Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However,…
Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings
Yidong Jiang, Junrong Chen, Eftychia Makri +7
With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures. However, existing benc…
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
Lige Zhang, Ali Maatouk, Jialin Chen +3
Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information…