Who's Who of AI

Juan Diego Rodriguez

419 trust researcher @juand-r.bsky.social · 4,761 followers
AI research NLP & language
Why they matter

Researcher with public evidence across AI research, NLP & language.

AI signals
7
past 30d
Sources
7
distinct domains
Discussions
18
past 30d
Latest signal
6d ago
View every signal from Juan Diego Rodriguez →
CS PhD student at UT Austin in #NLP Interested in language, reasoning, semantics and cognitive science. One day we'll have more efficient, interpretable and robust models! Other interests: math, philosophy, cinema https://www.juandiego-rodriguez.com/
What they're sharing

Articles & links

news.berkeley.edu/2026/10/09/u... “AI tools in science could jeopardize what makes for a good scientist in the first place: staying close to data, being immersed in the material, and working as part of a team.”

Using AI for just 10 minutes erodes your ability to persist at hard things - Berkeley News news.berkeley.edu
AI Weekly's analysis →
  • A UC Berkeley-led study of 1,222 online participants found that short exposure to ChatGPT weakened persistence once the AI was withdrawn mid-task.
  • Across three randomized experiments on fraction problems and SAT reading, accuracy dropped sharply for the AI-assisted group after help was removed at problem 12.
  • The paper, 'AI Assistance Reduces Persistence and Hurts Independent Performance,' was presented at the 2026 Conference on Language Modeling.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 13 from the directory shared this · 7d ago
↻ Juan Diego Rodriguez reposted
@zoltanv.bsky.social

wow, the Huggingface guys made an annotated version of Magnifica Humanitas huggingface.co/spaces/socie...

The Annotated Encyclical - a Hugging Face Space by society-ethics huggingface.co
AI Weekly's analysis →
  • Hugging Face's society-ethics team added 105 scholarly AI ethics citations to Pope Leo XIV's May 2026 encyclical Magnifica Humanitas.
  • The encyclical declares technology 'is never neutral' and warns that 'never has humanity had such power over itself.'
  • The annotation project is open for community contributions linking academic AI ethics research to the papal document.
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↻ Juan Diego Rodriguez reposted
Mark Riedl @markriedl.bsky.social

I wrote a paper on some of the failure modes of LM-based AI that can lead to this arxiv.org/abs/2609.16189

Position: AI Is Not Ready for Strategic Conflicts arxiv.org
AI Weekly's analysis →
  • Mark Riedl and Glenn Matlin argue no LM-enabled wargame should inform planning, doctrine, policy, or crisis response without an auditable safety case.
  • The paper names five failure modes: decision laundering, adjudication opacity, role collapse, escalation-through-adjudication, and failure of strategic imagination.
  • Ordinary benchmarks cannot establish safety for these settings; wargames can expose failures as stress tests but are not themselves safety cases for consequential use.
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2) Characterizing Narrative Content in Web-scale LLM Pretraining Data, by @teagrjohnson.bsky.social, @elliottash.bsky.social, @andrewpiper.bsky.social, @mariaa.bsky.social arxiv.org/abs/2606.19468 Why: annotation and analysis of narrative features across the pretraining data (…

Characterizing Narrative Content in Web-scale LLM Pretraining Data arxiv.org
AI Weekly's analysis →
  • A new arXiv preprint introduces NarraBERT, a RoBERTa-based classifier, and applies it to 3 million passages from the 3-trillion-token Dolma corpus.
  • The framework operationalizes three narrative elements, agency, setting, and events, across 11 interpretable dimensions, trained on 400 annotated passages.
  • The authors report narrative qualities are unequally distributed across pretraining sources and topics in ways current curation practices do not measure.
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View on Bluesky · ♥ 4 ↻ 1 ↩ 1 · 3 from the directory shared this · 109d ago
↻ Juan Diego Rodriguez reposted
Frank Pasquale @frankpasquale.bsky.social

Those “who own our algorithms, who own our cable news networks . . . they and their platforms are engineering our emotions so they can profit off our pain. They are selling us conflict, and they’re calling it connection.” www.ft.com/content/63e8...

ft.com View on Bluesky →

An incomplete list: 1) Explaining Attention with Program Synthesis, by Amiri Hayes, @belindazli.bsky.social and arxiv.org/abs/2606.19317 Why: LMs can generate code that can explain/replace attention heads! 🤯 (Program synthesis 🤝 Interpretability)

[2606.19317] Explaining Attention with Program Synthesis arxiv.org
AI Weekly's analysis →
  • Under 1,000 LM-generated programs reproduce attention patterns in GPT-2, TinyLlama-1.1B and Llama-3B with above 75% average IoU on TinyStories.
  • Replacing 25% of attention heads with the synthesized programs raised average perplexity by only 16% while keeping downstream question-answering performance.
  • The pipeline computes a head's attention matrices, prompts a pretrained LM to write Python that reproduces them, then re-ranks by held-out accuracy.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 1 · 3 from the directory shared this · 109d ago
↻ Juan Diego Rodriguez reposted
Eugene Vinitsky @eugenevinitsky.bsky.social

Does physical modeling matter or should we just do data-driven stuff? I feel like this paper characterizes things perfectly: arxiv.org/abs/2410.23179. Physics-based modeling is always helpful but the relative gain closes at scale

Does equivariance matter at scale? arxiv.org
AI Weekly's analysis →
  • In rigid-body interaction experiments with transformers, equivariant models beat non-equivariant ones at every tested compute budget.
  • Non-equivariant networks trained with data augmentation can close the data-efficiency gap, but only given sufficient training epochs.
  • Optimal compute allocation between model size and training steps differs significantly between equivariant and non-equivariant architectures.
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Their own posts

Recent commentary

Tip on reviewing ARR papers in 2026: - Check the references FIRST. I wasted 40 minutes reviewing a paper before realizing the references were LLM-generated.

View on Bluesky · ♥ 31 ↻ 12 ↩ 5 · 117d ago

Turns out GPT Sol is much less annoying than Claude

View on Bluesky · ♥ 24 ↻ 2 ↩ 2 · 40d ago

So much academic writing is bland and uninspiring. And LLMs will make it worse. What your favorite examples of *good* writing?

View on Bluesky · ♥ 13 ↻ 1 ↩ 6 · 63d ago

Excited to teach my first ML class tomorrow!

View on Bluesky · ♥ 22 ↻ 0 ↩ 2 · 53d ago

If we over-rely on LLMs for science, I fear we will remain stuck in the convex hull of what already exists. Is anyone studying this?

View on Bluesky · ♥ 16 ↻ 0 ↩ 5 · 62d ago

I'm so tired of seeing LLM-generated reviews. They just wasting all our time. If this continues, "peer review" will mean nothing

View on Bluesky · ♥ 11 ↻ 1 ↩ 3 · 115d ago

It was a pleasure to moderate the panel at the ACL 2026 SRW: "The next big questions in NLP: What should students work on?" Huge thanks to our panelists, @boydgraber.bsky.social, @igurevych.bsky.social, Preslav Nakov and Yulia Tsvetkov for their thoughtful answers and helpful advice! Takeaways 👇

View on Bluesky · ♥ 12 ↻ 2 ↩ 1 · 89d ago

I wish I could just go to an island, no internet, no deadlines, no AI. Just a library, a coffeeshop, and people to collaborate with.

View on Bluesky · ♥ 11 ↻ 0 ↩ 2 · 69d ago

Claude keeps making the stupidest mistakes, mistakes no person would make. The problem with AI is it’s so hard to form a mental model to predict or understand its failures, the way we do with other people.

View on Bluesky · ♥ 12 ↻ 0 ↩ 1 · 58d ago

I asked GPT 5.6 Sol to solve the Collatz conjecture. Why is it searching the NOAA fisheries website??

View on Bluesky · ♥ 4 ↻ 0 ↩ 5 · 84d ago
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