Stay tuned for keynote speakers announcements!

Opening Keynotes

Gain deep insights into their multi-agent AI architecture, observing how the system evaluates dynamic combat situations, formulates tactics, and executes maneuvers. The session will explicitly address the immense engineering complexities of bridging the gap from high-fidelity simulation to live flight operations. A technical leap akin to DeepMind's transition from virtual to physical agent control, but within the stringent demands of aviation safety and performance. Prepare for a technical deep-dive into real-life AI agent deployment at its most critical.

Master Stage

The first AI flying a production fighter jet

At Photoroom, building AI for creative tools means constantly experimenting and sometimes (pretty often actually) rebuilding from scratch. In this talk, Jon Almazan, Machine Learning Researcher at Photoroom, takes you behind the scenes of the team’s latest open-source text-to-image model, designed to push the limits of visual fidelity, controllability, and open reproducibility. He’ll share how the team iterated on training pipelines, data composition, and architectural tweaks to make the model both high-performing and publicly usable  and why open research remains essential for innovation in production environments. 

Expect a technical deep dive into what worked, what didn’t, and what it really means to build a new model when starting from zero.

Inside Photoroom’s Open-Source T2I model

As AI models scale, so does their hidden cost: CO₂.

While few teams have the tools or methodology to accurately measure the carbon impact of inference, new frameworks are emerging to make energy use visible and actionable.

In this session, Salesforce and Scaleway will share how they’re building the foundations for transparent and standardized measurement of AI’s environmental footprint:

Salesforce will present the AI Energy Score, a standardized method developed with Hugging Face to evaluate model energy efficiency.

Scaleway will show how it translates hardware and GPU utilization data into carbon metrics through its Environmental Footprint Calculator.

A practical deep dive for ML and infra engineers who want to make “energy-efficient AI” measurable, not mythical.

Beyond FLOPs: Engineering transparency into AI’s carbon footprint

Europe’s AI landscape is evolving fast, from open research labs to production-grade language models.

This session brings together Kyutai, the lab behind open research models like Moshi, and Indigo.ai, developers of Italy’s DistilBERTino and advanced conversational systems.

Together, they’ll discuss how European teams are translating cutting-edge research into usable, scalable AI products.

From lab to product with European LLMs

AI systems are evolving at breakneck speed: new architectures, model weights, and deployment frameworks appear weekly. But which innovations actually move the needle for developers?

Artificial Analysis will go beyond leaderboard hype to unpack the real technical trade-offs shaping today’s AI frontier: reasoning efficiency vs. latency, cost vs. intelligence, open vs. closed weights, and throughput vs. accuracy.

Drawing on months of live benchmarking across models, frameworks, and chips, they’ll share data-driven insights on how these forces interact and what it means for anyone building or scaling AI-powered systems in production.

Expect benchmarks, performance graphs, and a candid look at where the frontier actually stands.

Benchmarking the frontier: what really matters in the new AI stack

AI for video now spans two radically different architectures: 

Between them, broadcasters like France Télévisions must guarantee reliability, content provenance, and metadata integrity under production constraints.

This panel breaks down the engineering behind each layer. Inference pipelines, asynchronous rendering, edge vs cloud trade-offs, and how standards are reshaping video AI at scale.

A technical deep dive into how video AI systems are actually built, deployed, and trusted in production.

Central Room

From edge inference to generative pipelines: building video AI that works

As AI agents move from experimentation to production, the real challenge isn't building a single agent BUT orchestrating fleets of specialized agents that collaborate with each other and humans. Dust, is building the infrastructure layer for this new paradigm.

In this talk, you'll explore the practical architecture decisions behind production agentic systems: how to pragmatically combine RAG, MCP, and context windows; manage permissions across high-stakes and low-risk workflows; design multi-agent architectures where agents coordinate to solve complex tasks; and tackle the thorny problem of evaluation when traditional benchmarks don't capture operational reality. 

Discover Dust's learnings about enabling agent-to-agent-to-human collaboration at scale.

From single agents to agent fleets: lessons from building Dust's agentic platform

Inference speed is the bottleneck holding back the next generation of AI applications. Agentic workflows, real-time creation, and test-time compute scaling all demand much faster token generation at runtime.

This talk reveals Kog's path to 100x faster AI inference through systematic GPU optimization and novel Transformer architectural innovations that circumvent hardware constraints. When AI operates at 10,000 or 100,000 tokens per second, computers stop being static rule-execution engines and become smart, real-time adaptive platforms. Programming in natural language becomes practical. Complex games and applications generate themselves dynamically. Agents and Deep Research are instant.

Gaël, founder and CEO of Kog, will share the unique research insights and GPU engineering breakthroughs used to build the future of real-time AI computing

Real-time AI: building 100x faster inference

Giotto.ai achieved remarkable results on the 2025 Abstract and Reasoning Corpus (ARC-AGI) challenge, a benchmark designed to measure true reasoning capabilities in AI systems. In this presentation, we will walk through the core innovations that enabled this success - including test-time training, symmetry-based scoring, and automata-driven data augmentation. We’ll explain how these components interact to improve generalization and adaptability in reasoning LLMs, and what this means for the future of machine intelligence.

ARC-AGI a secret sauce for reasoning LLMs?

In this talk by Dr. Antoine Jardin, researcher at CNRS and CTO of Arlequin, we explore what it means for information to be true and authentic in the age of generative AI. Fake news and misinformation is an ever-increasing threat. Defining safety and authenticity requires deep understanding of how models are trained and developed, and even the smartest or safest of models may still lack the ability to differentiate truth from fiction. Using a combination of unsupervised learning and multi-modal inference, Arlequin helps cut through the noise and identify narratives without inferring bias from the analysis. We will explore how combining methodologies will lead to a safer, more auditable and understandable superintelligence.

Creativity Room

Inductive learning, AI safety, and superintelligence in the age of disinformation

Designing a printed circuit board (PCB) is a classic example of a complex combinatorial problem: hundreds of components need to be connected without overlaps, while minimizing path lengths and respecting physical constraints. The number of possible configurations quickly becomes astronomical, making traditional approaches inefficient.

In this 15-minute demo, InstaDeep will showcase how Reinforcement Learning (RL), combined with their MCP framework, can tackle PCB routing by learning to “play the game” of placing and connecting components. Beyond PCB design, this approach illustrates how RL can be applied to real-world optimization problems in industries such as logistics, infrastructure, and even biology.

Solving complex combinatorial challenges with Reinforcement Learning: the PCB routing demo

MCP has grown to be a key component for AI agents connecting AI with the world. 

We will dive into the latest development of the stack including MCP, ACP, and ChatGPT apps… Then explore best practices and tips on how to build, deploy and monetize an MCP server in production.

A deep dive into MCP and ChatGPT apps

Steeve Morin of ZML will walk through how to build inference systems that run efficiently across a variety of hardware — from edge devices to servers — without sacrificing latency, throughput, or reliability. He will dive into architectural decisions, hardware constraints, trade-offs between generality and specialization, and strategies to make inference flexible, portable, and performant. Expect real insights from his work at ZML and lessons learned from putting inference into production at scale.

Inference Everywhere: optimizing performance

Agenda 2025

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