Concept · 24 episode(s)

Context Management

← all concepts

Definition

Context management is the active engineering problem of deciding what goes into a model’s context window and what gets summarized, dropped, or retrieved on demand. It dominates real-world agent behavior more than most prompt-engineering write-ups admit.

Episodes covering this

  1. 194
    How a Robot Builds a Debugging Notebook It Can Read, Edit, and Hand to Another Robot
    ASPIRE: Agentic /Skills Discovery for Robotics
    Lu, Wu, Kou et al. · NVIDIA·24 min·Jul 02, 2026
  2. 192
    A 32B Open Model Matched Frontier Systems By Learning to Take Notes
    AutoMem: Automated Learning of Memory as a Cognitive Skill
    Wu, Zhu, Zhang et al. · Stanford University·22 min·Jul 02, 2026
  3. 180
    The Bug Where Smart Assistants Read a Fact and Still Forget It
    Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
    Patel · Vrin·24 min·Jun 29, 2026
  4. 169
    Why Better Bug Reports Can Make AI Coding Agents Worse
    SHERLOC: Structured Diagnostic Localization for Code Repair Agents
    Tamoyan, Narenthiran, Arakelyan et al. · NVIDIA / TU Darmstadt·24 min·Jun 24, 2026
  5. 166
    A Router That Beats the Frontier Models It Calls
    Sakana Fugu Technical Report
    Tang, Cetin, Xu et al. · Sakana AI·26 min·Jun 23, 2026
  6. 164
    The Summarizer That Quietly Deletes Your Agent's Safety Rules
    Governance Decay: How Context Compaction Silently Erases Safety Constraints in Long-Horizon LLM Agents
    Chen · Beijing Institute of Technology·28 min·Jun 23, 2026
  7. 154
    How a 7B Model Out-Investigates a 72B One by Choosing What to Look At
    Native Active Perception as Reasoning for Omni-Modal Understanding
    Xing, Xu, Wang et al. · The Chinese University of Hong Kong·21 min·Jun 18, 2026
  8. 149
    When Cornering a Chatbot Makes It Lie: J.P. Morgan's Case for 'Playing Dead'
    Is Your Agent Playing Dead? Deployed LLM Agents Exhibit Constraint-Evasive Fabrication and Thanatosis
    Rodríguez, Pozanco, Borrajo · J.P. Morgan AI Research·23 min·Jun 16, 2026
  9. 147
    Agents Fail at the Body, Not the Brain: A Self-Rewriting Scaffold That Lifts a 9B Model 44 Points
    HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
    Chen, Lu, Zhao et al. · ·30 min·Jun 15, 2026
  10. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
    HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
    Wang, Wang, Taylor et al. · University of California·24 min·Jun 12, 2026
  11. 132
    The Agent Failed — But Did the Instructions Deserve to Be Followed?
    SkillAxe: Sharpening LLM-Authored Agent Skills Through Evaluation-Guided Self-Refinement
    Gautam, Radhakrishna, Gulwani · Microsoft·30 min·Jun 11, 2026
  12. 131
    Why Autonomous Research Agents Forget Their Own Lessons, and Arbor's Fix
    Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
    Jin, Hu, Qiu et al. · Renmin University of China·33 min·Jun 11, 2026
  13. 130
    Why AI Agents Coordinate Better Through a Shared Board Than a Boss
    Decentralized Multi-Agent Systems with Shared Context
    Mao, Mirhoseini · Stanford University·34 min·Jun 11, 2026
  14. 125
    AI Coding Agents Run a Marathon, and Fewer Than One in Three Finish
    SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
    Desai, Hu, Cabezas et al. · Abundant·27 min·Jun 09, 2026
  15. 113
    What If a Prompt Injection Never Left? Attacks That Wait in Agent Memory
    What If Prompt Injection Never Left? Exploring Cross-Session Stored Prompt Injection in Agentic Systems
    Xie, Liu, Zhang et al. · Institute of Information Engineering·27 min·Jun 04, 2026
  16. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
    OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents
    Yang, Wu, Chen et al. · UIUC·24 min·Jun 03, 2026
  17. 086
    Why Frozen-Weight Agents Still Get Worse Over Time
    Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
    Zhu, Ro, Robertson et al. · The University of Texas at Austin·23 min·May 27, 2026
  18. 085
    Why Long-Context Models Might Need Compute, Not Capacity, Before Eviction
    Language Models Need Sleep
    Lee, McLeish, Goldstein et al. · Carnegie Mellon University·24 min·May 26, 2026
  19. 083
    Training the Translator: How a Small Communication Model Lets Agent Teams Outperform Themselves
    AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning
    Hu, Qian, Wang et al. · GSAI·24 min·May 26, 2026
  20. 082
    Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
    QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
    Xie, Lin, Wang et al. · The Ohio State University·31 min·May 26, 2026
  21. 051
    Why Parallel Sampling Plateaus, And What Evidence Graphs Do Instead
    Argus: Evidence Assembly for Scalable Deep Research Agents
    Zhang, Su, Chen et al. · MiroMind AI·22 min·May 18, 2026
  22. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
    History Anchors: How Prior Behavior Steers LLM Decisions Toward Unsafe Actions
    Salgado · Independent Researcher·23 min·May 15, 2026
  23. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
    Dynamic analysis enhances issue resolution
    Liu, Wang, Chen et al. · Sun Yat-sen University·21 min·May 02, 2026
  24. 002
    An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light
    End-to-end autonomous scientific discovery on a real optical platform
    Yang, Chen, Zhao et al. · Zhejiang University·29 min·May 01, 2026

Worth reading next

Papers we haven't done a deep dive on yet, but would recommend on this topic.