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Notes on context management
11 notes
Mutable Transcripts: Mitigating Context Pollution through Editable Conversation State
The preprint proposes allowing users to revise prior conversation turns through natural-language edit requests, treating the transcript as an editable representation of conversational state rather than an immutable…
CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
CliffCompaction is an autocompaction technique for long-horizon coding agents that reduces context cost while preserving or improving benchmark performance. It only truncates or drops content rather than rephrasing it…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
A research paper suggests a new approach to managing AI agents' reasoning context, which includes conversation histories, large prompts, and tool definitions. The authors argue that this should be treated as a…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The paper proposes a new approach for managing AI agent context, arguing that it's not just a storage-and-retrieval problem but a lifecycle issue involving deciding what to remember, structuring it, choosing the right…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The article proposes a new discipline called Agentic Context Management (ACM) to address issues faced by AI agents in managing their reasoning context, such as conversation histories and large prompts. The authors…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The paper discusses Agentic Context Management (ACM), a new approach to managing the context in which AI agents operate. It argues that current methods for managing an agent's context, such as storing and retrieving…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The paper presents Agentic Context Management (ACM), a solution to the issue of AI agents being overwhelmed by their own accumulating history and growing token cost in production settings. The authors argue that…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system that aims to improve the efficiency of open-domain information seeking for information-seeking agents. It struggles with tracking task progress as interaction histories grow and…
Self-GC: Self-Governing Context for Long-Horizon LLM Agents
A research paper titled 'Self-GC: Self-Governing Context for Long-Horizon LLM Agents' presents a system called Self-GC that aims to manage the lifecycle of context objects for long-horizon language models (LLMs). The…
ACE: Pluggable Adaptive Context Elasticizer across Agents
Researchers propose a new module called Adaptive Context Elasticizer (ACE) to address the challenges posed by growing trajectory lengths in large language models (LLMs) with fixed context windows. ACE is designed to be…
ECHO: Prune to act, trace to learn with selective turn memory in agentic RL
Researchers propose ECHO, a selective turn-memory framework to address limitations of existing context management methods in long-horizon language agents. ECHO compresses completed environment turns into compact memory…
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