As is tradition, we are pleased to invite you to the 2026 edition of our Annual Workshop with the Scientific Committee : a two-day event taking place on July Tuesday 2 and Friday 3 in Palaiseau, featuring scientific highlights, surveys, elevator pitches, and posters from our scientific community.
Provisional programme
|
|
| Coffee reception | 9:00/9:30  |
| Opening by Sébastien Tixeuil (Sorbonne University) | 9:30/9:35 |
| Scientific focus and achievements + associated activities by Daniel Kofman (Telecom Paris) | 9:35/9:45 |
« Quantum 2.0 @ LINCS », Survey by Ludovic Noirie (Nokia), (15′)The 2nd quantum revolution (Quantum 2.0) is about manipulating quantum superposition, quantum entanglement and individual particles (intrinsic probabilistic behavior of quantum systems). Quantum 2.0 technologies that are emerging are quantum computing, quantum communications (including quantum key distribution systems), quantum networks and quantum sensing. In this presentation, we will show how the LINCS addresses this research domain, with a focus on quantum network activities.
In their initial conception, quantum support vector machines (SVM) leverage multiple sophisticated subroutines as well as quantum RAM to perform supervised learning. Two of these requirements present issues. First, the subroutine of HHL to invert matrices requires a well structured kernel matrix, which depends on the dataset and data embedding. Second, quantum RAM is a controversial tool, and may not be |
9:45/10:30 |
Coffee break |
10:30/11:00Â |
PhD Elevator PitchAntoine Lunven (Inria), Baptiste Corban (Inria), Bo Pan (SU), Capucine Barré (Sorbonne University / SystemX), Hanaa Tabet-Aoul (Nokia), Jules Sintes (Inria), Julien Cardinal (Inria), Lorenzo DI Filippo (Sorbonne University), Luis Muñecas Tomás (Nokia / Inria), Ngoc Nguyen (Nokia), Yue Yu (Telecom SudParis) |
11:00/12:00
|
Lunch buffet-style in the inner garden |
12:OO/14:00Â |
« NTN @ LINCS », Survey by François Baccelli (Inria / Telecom Paris), (15′)This talk will survey the work of LINCS on non terrestrial networks. It will cover research activities, platform developments, industrial projects, and interactions with governments and regulatory agencie.
Low earth orbit (LEO) satellites are emerging as a key theme, providing ubiquitous connectivity for upcoming 6G networks.
In this work we consider a dynamical cellular communication network in which mobile base stations are modeled as a homogeneous Poisson point process on 2D plane. Each base station moves at a constant speed in a random direction. A typical user connects to the nearest base station and it experiences variable signal and interference powers depending on the distance of all the stations. Along the motion of the stations, the user swaps its serving station, and such an event is called a handover. We are interested in the performance evaluation of the system under some classical and tropical metrics of interest at different time of events, inducing handovers, maximal proximity of serving station, nearest interferer at closest or farthest distance with respect to the user or at any typical time epoch. A comparison study of quality of service and Shannon capacity at these epochs is also provided, among the recurrence of such “good” or “bad” scenarios. We can make an analogy with seasons based on the fluctuations of signal and interference power. Strong or mild signal or interference power correspond to different seasons of Shannon capacity along the evolution of the system. This is a joint work with François Baccelli. |
14:00/14:45 |
| Invited talk by Leandros Tassiulas (Yale University): « Enabling Distributed Intelligent Services at the Network Edge », 30’
In this talk we present our recent results  addressing a variety of challenges in delivering intelligent services at the network edge. We first introduce a multimodal federated learning framework for on-device wireless jamming detection that combines signal representations with network-level measurements, achieving improved accuracy while reducing communication overhead and preserving privacy. We then present a collaborative inference architecture for LLMs that dynamically partitions computation between edge devices and servers, significantly improving system throughput under resource constraints. To support domain-specific intelligence, we discuss the development of telecommunications-focused LLMs that outperform general-purpose models on specialized tasks. We further explore a multimodal retrieval framework that aligns time-series data with textual context, enabling more effective cross-modal reasoning and prediction. Finally, we highlight emerging geometric approaches to LLM design that leverage non-Euclidean representations to better capture semantic structure. |
14:45/15:15 |
Poster session / Refreshment Break |
15:15/16:00Â |
« Refining Classical Compression and Understanding Modern Compression: Locality, Representations, and Transformer Mechanisms @ LINCS », Survey by Aslan Tchamkerten (Telecom Paris) – (15’)
|
16:00/16:45 |
| Other topics highlight by Alonso Silva (Nokia): « Causal inference by LLM agents », (15′)
Despite remarkable advances in the field, LLMs remain unreliable in distinguishing causation from correlation. Recent results from the Corr2Cause dataset benchmark reveal that state-of-the-art LLMs at the time — such as GPT-4 (F1 score: 29.08) — only marginally outperform random baselines (Random Uniform, F1 score: 20.38), indicating limited capacity of generalization. To tackle this limitation, we propose a novel structured approach: rather than directly answering causal queries, we provide the model with the capability to structure its thinking by calling a tool to build a structured knowledge graph, systematically encoding the provided correlational premises, to answer the causal queries. This intermediate representation significantly enhances the model’s causal capabilities. Experiments on the test subset of the Corr2Cause dataset benchmark with Qwen3-32B model (reasoning model) show substantial gains over standard direct prompting methods, improving F1 scores from 32.71 to 48.26 (over 47.5% relative increase), along with notable improvements in precision and recall. These results underscore the effectiveness of providing the model with the capability to structure its thinking and highlight its promising potential for broader generalization across diverse causal inference tasks. |
16:45/17:00 |
Transfer to Paris |
17:15Â |
Dinner cocktail at Rooftop Nijinsky, Théâtre du Châtelet , Paris
|
19:00 |
Friday, July 3, 2025 |
|
Coffee reception |
9:00/9:30 |
« Cybersecurity of Networks and Networked Systems @ LINCS » Survey by Francesca Bassi (SystemX), (15’)
Vehicle-to-everything (V2X) communication enables connected vehicles to share sensor data, extending awareness beyond the individual field of view. This cooperation, however, exposes participants to the risk of receiving unreliable data, which is mitigated by misbehaviour detection mechanisms. A representative threat is the ghost object: a vehicle whose presence is announced in V2X messages but that does not physically exist on the road. Ghost objects may originate from deliberate attacks, e.g., a malicious participant claiming priority at an intersection, but also from perception faults on the sender’s side, such as partial occlusions causing duplicate tracks. Since misbehaviour detection relies on cross-checking observations from multiple participants, a ghost attack can be exposed only if honest, sensor-equipped vehicles actually cover its alleged location. Evaluating how often real traffic satisfies this condition is challenging: hand-crafted scenes lack statistical significance, while large-scale simulations obscure the conditions leading to detection failure.
We address this challenge by using a learned generative traffic model to synthesize large ensembles of realistic scenes (1,000 per experimental condition) over highway and intersection layouts. Through explicit sensor and occlusion modeling, we estimate the probability that a ghost object is observed by too few equipped vehicles to be reliably contradicted, thus remaining effectively undetected. We analyze how this probability varies with road topology, traffic density, environmental occlusion, and sensor penetration rate, under both awareness-only messaging (CAMs) and cooperative perception sharing (CPMs). We show that the dominant risk factor is the local visibility structure shaped by static obstructions preventing line-of-sight between equipped vehicles and the ghost location, and that the vulnerability transition shifts with penetration rate. Our analysis shows how generative traffic models may help to achieve a finer characterization of the performance of misbehaviour detection algorithms and ultimately a better understanding of the underlying phenomena.
Distributed Denial-of-Service (DDoS) attacks continue to threaten modern networks and critical services. To address this challenge, this talk presents SMART, a reinforcement learning-based approach for adaptive DDoS mitigation in Software-Defined Networks (SDN).SMART combines SDN programmability with a scalable decision-making architecture that can adapt to changing network conditions while preserving the Quality of Service (QoS) of legitimate users. By introducing a projection mechanism and a modular neural network design, the approach reduces complexity and remains effective as the network grows, without requiring retraining.Experimental results show significant reductions in latency and improvements in throughput under both network- and application-layer DDoS attacks, while reducing neural network complexity by up to 65%. These results demonstrate how scalable reinforcement learning can enhance the resilience and programmability of future networks. |
9:30/10:15 |
| Runway Postdoc Startup program at Cornell Tech as a model for cultivating deep-tech startups and a future collaboration, by Israel Cidon (Cornell Tech) |
10:15/10:30 |
Coffee break |
10:30/11:00Â |
PhD Elevator PitchAlex Pierron (Telecom SudParis), Alexandre Lalle (Nokia), Ashok Krishnan (Inria), Hakim Ouedrago (Telecom SudParis), Mandar Datar (Telecom SudParis), Paul Rax (Inria), Santiago Tabarez (Telecom SudParis), Shu LI (Inria), Timothee Mac Garry (Telecom Paris), Tengfei An (Sorbonne University), Ufuk Bombar (Sorbonne University) |
11:00/12:00
|
Lunch buffet style in the inner garden |
12:00/14:00Â |
| Invited talk by Nina Taft (Google), «Navigating the GenAI Privacy Frontier: New Threats and Opportunities »,  (30’)
Generative AI brings profound privacy shifts, introducing both critical challenges and new opportunities. This talk will survey these issues, ranging from system-level privacy to novel solutions that assist users. First, we examine how core system requirements—such as PII extraction, data deletion, and minimization—are evolving and straining traditional architectures. Second, we discuss leveraging LLMs to map and characterize the landscape of questions users ask about privacy and security on social media when seeking help. Finally, we address the emerging need for AI agents to make autonomous privacy decisions on behalf of users, demonstrating how to build scalable privacy personas capable of predicting individual choices as well as simulating survey cohorts. |
14:00/14:30 |
« Network, Cloud and AI Convergence, Edge Computing @ LINCS », Survey by Daniel Kofman
|
14:30/15:15 |
| Poster session / Refreshment Break | 15:15/16:00 |
| Public Comment by the LINCS Scientific Committee | 16:00/16:30 |
| Workshop Closing | 16:30/17:00 |