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Daehyeok Kim

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#machine learning Preprint Sep 2026

Leto: Fast In-Place Recovery for LLM Training on Surviving Hardware

Hardware-operable failures (HOFs) interrupt large language model (LLM) training but permit recovery on the same hardware without reset, repair, or replacement. Existing recovery systems nevertheless reload checkpoints, recompute lost progress, and rebuild process state, idling GPUs that could otherwise continue trainin...

Geon-Woo Kim, Joonseok Kim, Daehyeok Kim · 0 citations
#artificial intelligence Preprint Dec 2025

Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search

Vulcan is proposed, a framework that identifies LLM-friendly interfaces that isolate core decision logic from the rest of the implementation and demonstrates up to 4.9% higher savings for spot-VM scheduling, up to 2% lower miss ratios for cache eviction, and up to 14% higher application performance for tiered-memory sy...

Rohit Dwivedula, Divyanshu Saxena, Sujay Yadalam et al. · 1 citation
Book Open access Aug 2026

PacketExpress: Fully Exploiting Large MTUs for Internet Traffic in Private Networks

PacketExpress is presented, a packet processing architecture that enables networks to leverage large MTUs for Internet traffic without requiring modifications to neighboring networks and introduces F-PMTUD, which determines the path MTU within a single round-trip time without relying on ICMP.

Junghan Yoon, Youngmin Choi, Juyoung Park et al. · 1 citation

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