Kate Sanders

@kesnet50.bsky.social

Researcher at Microsoft Copilot Tuning. Cal alum, Ph.D. @ JHU CLSP. #NLProc https://katesanders9.github.io/

This year's shared task allows you to submit for the retrieval track, generation track, or full RAG track on a challenging new collection of unedited ("raw") videos. Research Papers (Apr. 1) Shared Task (Apr. 20)

I will be at AAAI 2026 in Singapore next week! ✈️ I'm looking forward to seeing everyone's cool projects and discussing reasoning, post-training, and multimodality. Please reach out if you will be there and would like to connect.

When reading AI reasoning text (aka CoT), we (humans) form a narrative about the underlying computation process, which we take as a transparent explanation of model behavior. But what if our narratives are wrong? We measure that and find it usually is. Now on arXiv: arxiv.org/abs/2508.16599

Humans Perceive Wrong Narratives from AI Reasoning Texts

A new generation of AI models generates step-by-step reasoning text before producing an answer. This text appears to offer a human-readable window into their computation process, and is increasingly r...

arxiv.org

In our forthcoming paper, John Hummel and I ask what it would mean for a neural computing architecture such as a brain to implement a symbol system, and the related question of what makes it difficult for them to do so, with an eye toward the differences between humans, animals, and ANNs.

From Basic Affordances to Symbolic Thought: A Computational Phylogenesis of Biological Intelligence

What is it about human brains that allows us to reason symbolically whereas most other animals cannot? There is evidence that dynamic binding, the ability to combine neurons into groups on the fly, is...

arxiv.org

I'm recruiting MLEs @ #ACL2025! Reach out if you know folks interested in legal NLP, structured prediction, and full-time at a startup environment in NYC I'll also always chat about: • population-level inference on corpora • broad-coverage semantics • which café has the best Sachertorte in Vienna

Taking off for Vienna #ACL2025! 🇦🇹 Excited to talk with people about transparent reasoning, multimodality, and fact verification. Stop by our multimodal RAG workshop on Friday 🔥🔥🔥 Please reach out if you want to grab coffee!

MAGMaR Workshop@magmar-workshop.bsky.social · 2y ago

New Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR) to be held at @aclmeeting.bsky.social ACL in Vienna this summer. We have a new shared task that stumps most LLMs - including ones pretrained on our test collection. nlp.jhu.edu/magmar/

This New Yorker piece is the most hopeful I've felt about the world in a long time. I had no idea solar was booming like this. And if you live in the same world as me, dominated by oil & gas guys maintaining that solar and wind are inefficient gimmicks, you might not've known some of this either.

It took from the invention of the photovoltaic solar cell, in 1954, until 2022 for the world to install a terawatt of solar power; the second terawatt came just two years later, and the third will arrive either later this year or early next.
That’s because people are now putting up a gigawatt’s worth of solar panels, the rough equivalent of the power generated by one coal-fired plant, every fifteen hours. Solar power is now growing faster than any power source in history, and it is closely followed by wind power—which is really another form of energy from the sun, since it is differential heating of the earth that produces the wind that turns the turbines.
Last year, ninety-six per cent of the global demand for new electricity was met by renewables, and in the United States ninety-three per cent of new generating capacity came from solar, wind, and an ever-increasing variety of batteries to store that power.
lauren@lauren.rotatingsandwiches.com · last yr.

anti-doomer sentence of the day: "Globally, roughly a third more power is being generated from the sun this spring than last" www.newyorker.com/news/annals-...

🔈When LLMs solve tasks with a mid-to-low resource input or target language, their output quality is poor. We know that. But can we put our finger on what breaks inside the LLM? We introduce the 💥 translation barrier hypothesis 💥 for failed multilingual generation with LLMs. arxiv.org/abs/2506.22724

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