Context Engineering for Agents
Techniques for constructing, pruning, and augmenting context windows to improve LLM agent performance and reliability.
23 papers · 3 months
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June 2026
Pruning context to recent tool calls improves agent reliability and efficiency
Pruning context to recent tool calls improves reliability and efficiency, comparing four context engineering strategies.
June 2026
An agentic framework that fills in missing context for real-world image generation
Agentic framework that fills in missing context for real-world image generation via Context-Aware Planning.
14 further papers
Sept 2026 · University of California San Diego
Coding agents retrieve better by targeting missing evidence sets
Proposes MSS-Complement retrieval that targets missing evidence, directly improving context construction for coding agents.
1 further paper
Sept 2026 · Southern University of Science and Technology, Peking University
Selective attention calls, guided by model's own state, speed long-context inference
ODA uses a trained recall head to selectively invoke global attention during long-context inference, directly optimizing context access efficiency.
Sept 2026 · NLPR&MAIS, Institute of Automation, Chinese Academy of Sciences
Bounded visual workspaces improve multimodal agents’ accuracy and efficiency
Bounded visual workspace via ledger storage reduces context length and improves accuracy.
Sept 2026 · Carnegie Mellon University, Bosch Center for AI
Dropping Rather Than Rewriting Context Cuts Long-Horizon Agent Costs
Proposes a context-compaction method that truncates rather than rewrites, reducing errors and costs in long-horizon agent tasks.
released code
Sept 2026 · Shanghai Jiao Tong University, Theseus Labs
Restructuring agent environments improves performance on noisy, evolving tasks
Introduces Env-Rethink, which restructures agent environment information to improve performance on noisy tasks.
Sept 2026 · TierFlow Team, Renmin University of China
Agents Can Drop Old Reasoning After Externalizing Task State
Shows that agents can drop old reasoning after externalizing task state using online compression, reducing token use and improving accuracy on WorkBuddyBench.
8 of 23 papers shown