← All threads

Context Engineering for Agents

Techniques for constructing, pruning, and augmenting context windows to improve LLM agent performance and reliability.

43 papers · 3 months

Where this stands

The written synthesis of this thread is for subscribers. Subscribe.

How this thread developed

  1. 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.

  2. 6 further papers

    Sept 2026 · Zhejiang University, Apple

    Paper-derived rubrics improve AI generation of scientific research plans

    Demonstrates deriving prompts and evaluation rubrics from scientific papers for research-plan generation, advancing context augmentation for domain-specific agent tasks.

    released code

  3. Sept 2026 · Perplexity AI

    Benchmark tests retrieval for agent-written queries across 190 million web pages

    Introduces Q2D-Web benchmark for evaluating retrieval in agent-written queries, informing context window construction for agents.

  4. 2 further papers

    Sept 2026 · Rutgers University

    Kernel-managed memory personalizes multiple AI agents with shorter prompts

    Shows how kernel-controlled retrieval shapes context windows to reduce prompt length while personalizing agent behavior.

  5. 3 further papers

    Sept 2026 · Mohamed bin Zayed University of Artificial Intelligence, University of Science and Technology of China

    Deep research agents struggle with long, multimodal evidence chains

    Benchmark reveals that long, multimodal evidence chains stress context construction and reasoning.

    released code

  6. 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.

  7. 2 further papers

    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.

  8. 1 further paper

    Sept 2026 · Amazon.com

    Agentic document QA pays off mainly with stronger vision-language models

    Compares static and agentic pipelines for document QA, finding that agentic approaches only become competitive with stronger vision-language models, providing insights for context engineering.

  9. 2 further papers

    Sept 2026 · University of Southern California

    Short natural context can flip decision models to wrong answers

    Shows that short, fluent context additions can flip correct decisions, highlighting context fragility.

  10. 3 further papers

    Sept 2026 · RIKEN

    Execution Logs Can Mislead Visual Judges in Video-Generation Agents

    Shows that execution logs in the judge context can mislead visual verification, a reliability constraint for context construction.

  11. Sept 2026 · Rutgers University, Nanjing Tech University

    LLM pipelines can lose 40.5 accuracy points between stages

    Quantifies the decomposition tax across LLM pipeline stages and shows re-grounding later stages with the original problem reduces interface information loss.

  12. 2 further papers

    Oct 2026 · The University of Hong Kong, Huawei Research

    Parallel tile inspection helps models find details in large images

    Uses parallel tile inspection to manage visual context, improving detail detection in large images.

  13. 1 further paper

    Oct 2026 · Carnegie Mellon University, AWS AI Labs

    Models Learn to Allocate Context and Scale Inference More Effectively

    Introduces Hermes harness for context allocation, improving inference effectiveness.

  14. 1 further paper

    Oct 2026 · Adobe Research, University of Maryland College Park

    Voice agents lose document grounding as conversations and context grow

    Shows document grounding degrades with context length in voice agents, motivating better context management.

  15. 2 further papers

    Oct 2026 · KU Leuven, East China University of Science and Technology

    Persona effects in language models are predictable but not portable

    investigates whether persona prompt effects generalize across models, finding they are predictable within a model but not portable.

    released code

  16. Oct 2026 · Columbia University, Amazon Web Services

    General instructions outperform gold answers in on-policy context distillation

    Shows simple formatting instructions outperform gold answers for on-policy context distillation, improving context construction for agents.

16 of 41 papers shown