Guy Van den Broeck @guyvdb
Professor of Computer Science at UCLA @UCLAComSci; working on Artificial Intelligence web.cs.ucla.edu/~guyvdb/ Los Angeles, CA Joined April 2008-
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Making Google's Paper Assistant Tool available to ICLR submitters: blog.iclr.cc/2026/09/10/mak…
Terence Tao just posted this really insightful thread which seems to have been written in reaction to the Navier Stokes announcement. Please give it a read. mathstodon.xyz/@tao/117237320…
A Probabilistic Interpretation of KV Cache Eviction 🙇♂️🙇♂️🙇♂️🙇♂️🙇♂️
“A Probabilistic Interpretation of KV Cache Eviction” Most KV eviction methods use top-k heuristics to decide what to keep, but this can heavily distort attention after tokens are removed. This paper reframes KV eviction as expectation estimation, then uses probabilistic
What happens if you interpret attention scores as probabilities? We can then use sampling techniques in KV cache eviction. Check out our new paper!
“A Probabilistic Interpretation of KV Cache Eviction” Most KV eviction methods use top-k heuristics to decide what to keep, but this can heavily distort attention after tokens are removed. This paper reframes KV eviction as expectation estimation, then uses probabilistic
“A Probabilistic Interpretation of KV Cache Eviction” Most KV eviction methods use top-k heuristics to decide what to keep, but this can heavily distort attention after tokens are removed. This paper reframes KV eviction as expectation estimation, then uses probabilistic sampling and importance weighting to correct for missing KV entries during decoding. This gives a principled bias-variance tradeoff that stays competitive with existing methods while being more robust. alphaxiv.org/abs/2608.28293
This might be a good time to mention my recent keynotes titled: "Agentic AI is Neurosymbolic AI"
I would have assumed it was fairly obvious, but in case it's not: a million-line codebase (also known as a "harness"), running at inference time, orchestrating thousands of calls to a neural network for any given task, is the exact definition of a "neurosymbolic architecture"
I would have assumed it was fairly obvious, but in case it's not: a million-line codebase (also known as a "harness"), running at inference time, orchestrating thousands of calls to a neural network for any given task, is the exact definition of a "neurosymbolic architecture"
🚨 2yr postdoc position on #reliable #constrained generation with #deep #generative #AI with applications to biology 🚨 to work with me and @WouterBoomsmaDK on #AI that can generate complex structures guaranteed to satisfy physical constraints 👇👇👇 elxw.fa.em3.oraclecloud.com/hcmUI/Candidat…
The ICLR 2027 CfP is up! iclr.cc/Conferences/20…
🍡Introducing a new constrained decoding approach for agentic LLMs: 1⃣guarantees successful tool calls with max_tokens=k (no truncation); 2⃣improves downstream performance with test time compute. Presented now at #ICML2026 poster session 1 #1912 by @jieyuzhao11.
MoE routing shouldn’t be a hard top-k guess. ProbMoE turns TopK routing into probabilistic inference over expert subsets, giving routers more informative gradients, more exploration, and better expert utilization. Because it’s probabilistic, it naturally extends to Dynamic-k, learning both which experts to use and how many each token needs. Moreover, ProbMoE is broadly applicable to TopK-routed MoEs, while keeping training efficient through sparse expert execution. Sparse compute. Smarter routing. 🚀
A Canonical Generalization of OBDD arxiv.org/abs/2604.05537 (news.ycombinator.com/item?id=477475…)
Congratulations @itisalex3 on acceptance to #ACL2026 🎉 If you are interested in KV cache compression, I highly recommend reading this paper.
What happens when we compress the KV cache of prompts with multiple instructions? 🤔 Existing compression methods can lead to some instructions being ignored. 🙀 We propose simple changes to KV cache eviction that fix this problem alongside other pitfalls to be aware of. 💯
Very excited to be leading this research direction, employing probabilistic programming to improve LLM inference for code generation.
When you prompt an LLM for code, you get one deterministic program. However, the LLM actually defines a distribution over many programs, and existing methods discard it‼️ PPoT uses this distribution to extract free performance and efficiency gains. 🧵👇
When you prompt an LLM for code, you get one deterministic program. However, the LLM actually defines a distribution over many programs, and existing methods discard it‼️ PPoT uses this distribution to extract free performance and efficiency gains. 🧵👇
We just implemented trai (try + AI) (github.com/binpash/trai), a Claude plugin that can help you isolate 🫷 changes done in your file system by tool calls (like pip install), and only commit them if they are intended ☺️. Try it out (pun intended) and share your feedback!
@trunghlt @IanLi1118 new tokens depend on already unmasked tokens, but they do not depend on each other when unmasking multiple tokens in a single step
We put probabilistic circuits into diffusion language models and got a big boost in reasoning performance!
One of the biggest promises of Diffusion LLMs is parallel generation: predicting multiple tokens at once to bypass the sequential bottleneck of autoregressive models. However, parallel generation comes with a price. For example: Should the sentence “He is from [MASK] [MASK]” be
Gautam Kamath @thegautamkamath
68K Followers 627 Following Assistant Prof of CS @NYU_Courant. Co-EiC @TmlrOrg. I lead @TheSalonML.
antonio vergari ⚔�... @tetraduzione
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123 Followers 249 Following si em veieu actiu a Twitter és que estic deixant alguna altra droga
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520 Followers 5K Following
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Percy Liang @percyliang
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Kevin Patrick Murphy @sirbayes
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Michael Black @Michael_J_Black
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64K Followers 659 Following University Distinguished Professor (Emeritus), Oregon State Univ.; Former President, AAAI; ArXiv Editor in Chief & Chair CS Section
Zachary Lipton @zacharylipton
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Ferenc Huszár @fhuszar
44K Followers 1K Following Founder & Supreme Leader of Technical Staff at Reasonable. Professor on leave from @Cambridge_CL. Alum of @Twitter, Magic Pony, @Balderton
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NeurIPS Conference @NeurIPSConf
164K Followers 41 Following Sydney Dec 6-12, 26, Paris and Atlanta. Tweets to this account are not monitored. Please send feedback to [email protected].
Kyunghyun Cho @kchonyc
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Lucas Beyer (bl16) @giffmana
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levent @__alpoge__
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Karel @KarelDoostrlnck
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816 Followers 3K Following Once introduced as “defender of the null hypothesis”. Founder. PhD in Evo Psych. Writing: @deepdebates
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456K Followers 373 Following Pi creature caretaker. Contact/faq: https://t.co/brZwdQfdif
Kimi.ai @Kimi_Moonshot
363K Followers 137 Following Built by Moonshot AI to empower everyone to be superhuman. PR: [email protected] DC: https://t.co/wBsBTn6fmE
Linxin Song @linxins2
520 Followers 738 Following PhD student @csatusc @nlp_usc | Intern @MSFTResearch | Prev. @Google @SFResearch | LLM/VLM/Robotics
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Julia Kempe @KempeLab
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Marianne Arriola @mariannearr
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Bharath Hariharan @BharathHarihar3
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346 Followers 403 Following PhD student @uclaNLP — ML / #NLProc / multilingual @AIatMeta, @GoogleResearch
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Cameron @just_cameron
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Yufei Tian @yufei_t
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21K Followers 8 Following An operating system by System76. Unleash your potential on secure, reliable open-source software.You might like
