
Ph.D. studies @uoaofficial and @ArchimedesUnit. Ex-research @MIT, @dtutweet, @athenaRICinfo, @studyatctu, @mpiMathSci. Ex-studies @UniofOxford, @upatras.
Zákynthos, Greece
Joined August 2020
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(1/4) 🚨 @NeurIPSConf 2025 Announcement Our paper “Teaching Transformers to Solve Combinatorial Problems through Efficient Trial & Error” will be presented in San Diego!
Paper: arxiv.org/abs/2509.22023
Yorgos Pantis retweeted
What if you could synthesize an LLM inference engine customized for your model and Mac, just in time?
Meet JITEngine: custom engines built with SkyDiscover for Apple Silicon, up to 2.7× faster than the fastest alternatives we tested.
jitengine.ai 🧵 (1/n)
Yorgos Pantis retweeted
1/10 🔮 Can a world model learn to make its own future more predictable?*
In Latent-Foresight, we jointly learn a compact latent representation of visual foundation model (VFM) features and a generative dynamics model that predicts their future.
Here's how 👇
🙏 (1/2) I suggest improving arXiv’s submission process by adding dedicated fields for authors’ institutional affiliations and funding sources, along with search filters for this information.
arXiv has received a 17.2 million investment from three leading philanthropic organizations. Thank you to Simons Foundation International, Siegel Family Endowment, and XTX Markets for your support of arXiv!
Read more: blog.arxiv.org/2026/09/23/ar…
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Yorgos Pantis retweeted
LLMs don’t need retraining to become more capable.
Our new architecture, Spotlight, gives AI models growing memory, allowing them to gain knowledge and capabilities without changing their weights.
Token generation takes constant work, no matter how much information is stored.
Yorgos Pantis retweeted
In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. 1/N
Yorgos Pantis retweeted
Can agents collaborate effectively? 👾👾👾
We recently saw that OpenAI released a breakthrough result for Navier-Stokes through a group of 10,000 agents working together and Anthropic testing cybersecurity vulnerabilities with communicating agents.
Do more agents help? If so, when and how? 🧐
(1/n) Communicating our findings in the thread! 🧵
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!
Yorgos Pantis retweeted
(1/n) 🏆 Congratulations to @ChristosTzamos and Christos Papadimitriou on receiving @ERC_Research Grants!
🔗 Discover more about the Archimedes: lnkd.in/dgdSy8uU
#ArtificialIntelligence #AI #Research #ERC #MachineLearning #ArchimedesUnit #AthenaResearchCenter
Yorgos Pantis retweeted
Really excited about this one!
msande319.stanford.edu/
LLMs keep getting bigger. Compute budgets, unfortunately, do not.
This fall @Stanford, we (@AnayMehrotra @gvelegkas and Amin Saberi) are teaching MS&E 319: Efficient Generative Language Models.
The course asks a simple question: given a modeling goal and a limited computational budget, how should we choose the training objective, model architecture, and inference algorithm?
We’ll cover some of the main ideas, and occasionally surprising tricks, that make large language models more efficient, including efficient pre-training, mixture-of-experts architectures, attention and KV-cache compression, quantization, speculative decoding, LoRA, RLHF, DPO, and distillation.
And no, “just buy more GPUs” will not be the only answer.
We’ll try to post all the lecture materials and recordings as the course unfolds.
web.stanford.edu/class/msand…
Yorgos Pantis retweeted
The Matroid Secretary Conjecture is True!
Imagine interviewing candidates in random order. You must accept or reject each candidate immediately, with no going back. How do you find the best one?
Yorgos Pantis retweeted
How should we make sequential decisions when maximizing reward isn’t enough—we also need each decision to be safe?
In our new work with my advisor @aldopacchiano, we study contextual bandits with high-probability constraints on realized cost.
📄 arxiv.org/abs/2608.26755
Yorgos Pantis retweeted
7/8 Paper link (with lots of other findings): arxiv.org/abs/2607.21716 Work done with great collaborators @pranavn1008, @depen_morwani, @KempnerInst and my fantastic advisors @CPehlevan, @ShamKakade6, @alex_damian_ (who is now taking grad students at MIT!)
Yorgos Pantis retweeted
Latent reasoning promises greater expressivity and efficiency, but existing methods have struggled to match explicit CoT at scale.
LOTUS takes a step forward: it matches explicit CoT at the 3B-parameter scale while reasoning entirely in continuous space and runs faster!!
🚀New paper on Looped Transformers!
Latent reasoning is fast, but struggles to match CoT-level accuracy at scale. Can looped Transformers give us both?
We find: yes! A looped padded backbone turns out to be a surprisingly simple recipe that works -- It gives latent thoughts a parallel workspace that can be supervised similarly to explicit CoT. 🧵
Yorgos Pantis retweeted
📢Greeks in AI 2026 starts tomorrow in Aθens🇬🇷
Are you ready?
🤖3 days of keynotes, posters, and panels bringing the global Greek AI community together🎉.
Get the full picture👇
greeksin.ai
🌐Posting from the symposium? Use #GreeksInAI so we can all follow along.
Yorgos Pantis retweeted
🏛️ Unveiling a new way to converse with antiquity with the "Predicting the Past" skill for Google @antigravity.
This agentic AI partner expands historical research by bridging Gemini’s reasoning with our specialized models, Aeneas and Ithaca.
🏛️ We’re unveiling a new way to converse with the ancient world.
By grounding Gemini directly in our expert models Aeneas and Ithaca, our Predicting the Past Skill in Google @antigravity lets historians study Greek and Latin texts using plain English. 🧵
Yorgos Pantis retweeted
Transformers are sequence models, but much of in-context learning theory considers prompts of independent examples / tokens.
We asked: what changes when ICL prompts are actually sequential?
I’ll be presenting this at #ICML2026 HiLD Workshop!
Yorgos Pantis retweeted
I’m at ICML 🇰🇷 presenting a 🔦 spotlight 🔦 poster today. Check it out if you want to know how process rewards help RLVR go beyond the base model support when outcome rewards alone can’t.
📆 Thu. 5-6:45 pm. Hall A. Poster # 1307.
Yorgos Pantis retweeted
A question on synthetic data generation: If we want a language model to solve k-step arithmetic problems (such as a+b*c-d=?), with operands from 1 to 100, which training distribution should we use?
A. Uniform distribution: Sample these k operands uniformly from 1 to 100
B. Power law: randomly shuffle 1-100 and impose an artificial power law. Sample these k operands according to this power law.
⚡Our ICML 2026 (spotlight) paper shows: Option B is better! Surprisingly, the same idea extends far beyond this simple example to many reasoning tasks that require implicit composition of multiple atomic skills, including multi-hop QAs and synthetic GSM problems.
📄Paper: arxiv.org/abs/2604.22951
📝Blog: zixuan-wang-dlt.github.io/po…
Yorgos Pantis retweeted
We're making a (epigraphic) habit out of this... 😉 Introducing the Predicting the Past Skill in @antigravity: grounding Gemini directly in our specialised Aeneas and Ithaca models to study Greek + Latin inscriptions interactively.
With @iassael & Zoi Tsangalidou @GoogleDeepMind
🏛️ We’re unveiling a new way to converse with the ancient world.
By grounding Gemini directly in our expert models Aeneas and Ithaca, our Predicting the Past Skill in Google @antigravity lets historians study Greek and Latin texts using plain English. 🧵
Check out our work on building EBMs for molecular graphs!
What’s after discrete diffusion? 💎🧬
Graph Energy Matching (GEM): molecular graphs sampled via transport-aligned discrete proposals.
SOTA among graph EBMs. Matches/surpasses diffusion/flow baselines at generation. Supercharges inference-time design!
michalbalcerak.ai/graph-ener…































