How to Tell If Your Agent Used the Right Stuff
Many so-called “agent failures” are actually context failures in disguise. In this session, we’ll explore how to tell whether your agent truly saw and used the right context, using techniques like tracing and attribution, golden datasets for context-aware evaluation, and targeted probes to test retrieval quality.
Low-Resource Languages as Stress Tests for NLP Data
Low-resource languages expose weaknesses in NLP systems that are often hidden by benchmark data. Drawing on experience annotating fieldwork data, this talk shows how ambiguity and annotation decisions reveal fundamental data quality issues relevant to real-world NLP pipelines.
AI in the physical world: from observation to discovery
In 2026, AI is moving beyond digital tasks into the physical world. It increasingly interacts with instruments, experiments, and real-world data. Physicists stand at this frontier, using deep learning, LLMs, and agents to analyze nature itself. What have we learned about AI when it meets reality?
Towards Chunk-less RAG
Retrieval-Augmented Generation (RAG) systems rely on pre-chunked documents, tying retrieval to arbitrary boundaries. This talk explores an experimental approach that surfaces semantically relevant text spans, without chunking. We’ll share surprising findings and examine whether this technique points toward a viable chunk-free retrieval paradigm.