Andrea de Varda

Postdoc at MIT BCS, interested in language(s) and thought in humans and LMs

[Andrea de Varda @devarda_a]

New preprint! 🤖🧠
The cost of thinking is similar between large reasoning models and humans
👉 https://t.co/0G6ay4NQc5
w/ Ferdinando D'Elia, @AndrewLampinen, and @ev_fedorenko (1/6)

[Pramod RT/ಪ್ರಮೋದ್ ರಾ ತಾ @PramodRT9]

Causal reasoning lies at the core of human intelligence but few studies have tested its neural basis. In a new study, we find that despite its importance, causal reasoning is implemented not in a single unified brain system, but rather in separate domain-specific regions.

[Pengrui Han (Barry) @pengrui_han]

Had a wonderful time giving a guest lecture at Caltech on the cognitive architecture of LLMs, and more broadly, how we can use AI models as model organisms to study intelligence! :)

Many thanks to Prof. @CFCamerer for the invitation!

We discussed the emergent modularity in LLMs, what the similarities between human brains and LLMs might tell us about general principles of intelligence, and how AI models could help us study cognition.

Really enjoyed the discussions and questions! :)

Slides: https://t.co/6s6sut0CD5

(And special thanks to @devarda_a for letting me steal some of his beautiful slides! :))

[Thomas Hikaru Clark @thomashikaru]

If you've noticed a distinct dialect emerging in frontier AI models and struggled to understand what they're talking about, you're not crazy!

We turned to psycholinguistics to investigate what makes the writing style so weird, and why this might be happening

🚨New preprint! 🧵

[Andrew Lampinen @AndrewLampinen]

New blog post! I wrote about why I think it's useful to use metaphorical (and anthropomorphic) terms to describe AI. I start by describing the role of metaphor in language and cognition, and how metaphors can help us to efficiently understand relations and make predictions 1/2

[Andrea de Varda @devarda_a]

@p_song1 @statnews @ev_fedorenko @roger_p_levy Thank you!! 🙏

[Andrea de Varda @devarda_a]

Thrilled to be one of @statnews's 2026 #STATWunderkinds for my work on language processing and reasoning in 🧠 and 🤖

Big thanks to my current/previous advisors @ev_fedorenko @roger_p_levy Marco Marelli and everyone I get to work with!

[Andrea de Varda @devarda_a]

@tallinzen @statnews @ev_fedorenko @roger_p_levy Finally a reasonable plural 🤌

[Andrea de Varda @devarda_a]

@tallinzen @statnews @ev_fedorenko @roger_p_levy Ha, for a second I panicked that "advisors" had an irregular plural that I never learned 😅 Thanks, will take it up with STAT!

[Andrea de Varda @devarda_a]

@ArvidGollwitzer @statnews @ev_fedorenko @roger_p_levy Thank you!! :))

[Andrea de Varda @devarda_a]

@pengrui_han @statnews @ev_fedorenko @roger_p_levy Thanks Pengrui!! :))

[Andrea de Varda @devarda_a]

@D2Chattoway @statnews @ev_fedorenko @roger_p_levy Thanks so much! 🙏

[Andrea de Varda @devarda_a]

@dsuseendar @statnews @ev_fedorenko @roger_p_levy Thank you! :)

[Andrea de Varda @devarda_a]

@fportillolo @statnews @ev_fedorenko @roger_p_levy Thank you! :)

[Andrea de Varda @devarda_a]

@Logic_Cognition @statnews @ev_fedorenko @roger_p_levy Thanks Jakub!

[Andrea de Varda @devarda_a]

@siyuansong_ @statnews @ev_fedorenko @roger_p_levy Thank you!!

[Andrea de Varda @devarda_a]

@YangTanColl @statnews Thank you, very grateful to be part of ICoN! 🙏

[McGovern Institute @mcgovernmit]

Congrats to @devarda_a, named one of the 2026 #STATWunderkinds by @statnews!

Andrea is doing groundbreaking work in @ev_fedorenko + @roger_p_levy labs to understand how brains and machines process language.

Learn more: https://t.co/9OtM52SYQD

@mitbrainandcog @ScienceMIT

[Shi Gu @gushilab]

Why do neural networks become modular? Our paper is now out in Nature Machine Intelligence! @NaturePortfolio We show that multi-task learning promotes modularity under limited capacity and task demands help explain features of brain architecture in addition to the spatial constraints.

Great thanks to all the co-authors. @WHYWYH007 @yifannnwu @DaniSBassett @marcelomattar

https://t.co/uqN91o2P5Y

[Andrea de Varda @devarda_a]

Check out CogGym, led by @LanceYing42! 258 experiments from 100 cogsci papers (!!) in one format. Main takeaway: models are getting more human-like, but way slower than improvements on math and coding

[Lance Ying @LanceYing42]

Have we built machines that think like humans? 🧠🤖

In a new preprint, we propose CogGym, which compares AI and human judgments across 258 commonsense-reasoning experiments from 100 cognitive-science papers.

Paper: https://t.co/Hezlcxqpiz
Platform: https://t.co/HRBtLXTt1K

[Yevgeni Berzak @whylikethis_]

📄👀 Check out our new survey on Eye Tracking and NLP, where we reflect on past work in this space, and outline a roadmap for what's next.
Not needless to say - it's fully human written. The humans are Lena Jäger, David Reich, Omer Shubi and me. https://t.co/iQ5IxcJRu8