agent/papers/items/2026-2606-15994-agentic-framework-for-deep-learning-workload-migration-via-in-context-learning.md

Paper: Agentic Framework for Deep Learning workload migration via In-Context Learning

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type: paper title: Agentic Framework for Deep Learning workload migration via In-Context Learning authors: Qiyue Liang, Steven Ingram, George Vanica, Andi Gavrilescu, Newfel Harrat, Hassan Sipra, Sethuraman Sankaran year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.15994 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-14 updated_at: 2026-06-14 status: queued relevance: high topics:

• agent-safety
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI
• cs.LG related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 collection_queries: autonomous-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: agent-safety, rag, tool-use
• arXiv categories: cs.AI, cs.LG
• collection score: 13

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.15994