Kostis Kaffes

PACMI Workshop @PacmiWorkshop · Jul 13

📢Important update: The PACMI'26 submission deadline has been extended to July 18, 2026 (AoE)! Thank you to everyone who has already submitted, we look forward to seeing more great work! #SOSP #MLForSystems
sites.google.com/corp/view/pacm…

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DAPLab Verified account @DAP__Lab · Dec 3, 2025

🚀 Excited to share that DAP Lab has 6 papers accepted at #NeurIPS2025 — covering multi-agent reasoning, LLM caching, persona risks, system tuning via LLM agents, simulation-first agent training, and RL theory 👇

🔍Check them out if you are at #NeurIPS2025! We’d love feedback, discussions, and potential collaborations. Paper list here:

• Multi-agent Markov Entanglement (Shuze Chen, Tianyi Peng) — Spotlight + winner of INFORMS JFIG & 2nd place in George Nicholson Student Paper Competition 🏆

• Tail-Optimized Caching for LLM Inference (Wenxin Zhang, Yueying Li, Ciamac C. Moallemi, Tianyi Peng) — improving LLM inference efficiency 👏

• LLM Generated Persona Is a Promise With a Catch (Ang Li, Haozhe Chen, Hongseok Namkoong, Tianyi Peng) — a position paper reflecting on strengths & caveats of LLM-derived personas 👩‍👩‍👦‍👦

• LLM Agents for Always-On Operating System Tuning (Georgios Liargkovas, Vahab Jabrayilov, Hubertus Franke, Kostis Kaffes) — leveraging LLMs for live OS tuning, showing better performance than classical ML tuning.🔧

• RAISE: Reliable Agent Improvement via Simulated Experience (Sahar Omidi Shayegan, Joshua Meyer, Victor Shih, Sebastian Sosa, Tianyi Peng, Kostis Kaffes, Eugene Wu, Andi Partovi, Mehdi Jamei) — simulation-first AI-agent training framework 🔄.

• Q-learning with Posterior Sampling (Priyank Agrawal, Shipra Agrawal, Azmat Azati) — a new RL algorithm achieving near-optimal theory guarantees in tabular episodic MDPs 🎯

#MachineLearning #AI #LLM #Systems #MultiAgent #NeurIPS

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DAPLab Verified account @DAP__Lab · Oct 14, 2025

This week, we are presenting a slate of new research at SOSP workshops, spanning agentic infrastructure and self-tuning kernels. Our work lays the foundation for a future agentic infrastructure that will enable the safe, reliable and efficient operation of LLM agents in real-world environments. Highlights below and all papers available on our website.

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