NERDs – Entity-centered long-term memory for LLM agents
Long-running agents struggle to attend to relevant information as context grows, and eventually hit the wall when the context window fills up. NERDs (Networked Entity Representation Documents) are Wikipedia-style entity pages that LLM agents build for themselves by reading a large corpus chunk-by-chunk. Instead of reprocessing the full text at query time, a downstream agent searches and reasons over these entity documents. The idea comes from a pattern that keeps showing up: brains, human cognition, knowledge bases, and transformer internals all organize complex information around entities…
In plain words
NERDs is a system that helps long-running AI agents manage large amounts of information by creating Wikipedia-style entity pages from text corpora. Instead of searching through entire documents repeatedly, agents build and maintain these entity documents during an initial reading phase, then query them for relevant information. This approach reduces token usage by approximately 90% compared to processing full context while maintaining performance on tasks involving character relationships, plot tracking, and settings across large texts.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Long-running agents struggle to attend to relevant information as context grows, and eventually hit the wall when the context window fills up. NERDs (Networked Entity Representation Documents) are Wikipedia-style entity pages that LLM agents build for themselves by reading a large corpus chunk-by-chunk. Instead of reprocessing the full text at query time, a downstream agent searches and reasons over these entity documents. The idea comes from a pattern that keeps showing up: brains, human cognition, knowledge bases, and transformer internals all organize complex information around entities and their relationships. NERDs apply that principle as a preprocessing step for long-context understanding. We tested on NovelQA (86 novels, avg 200K+ tokens). On entity-tracking questions (characters, relationships, plot, settings) NERDs match full-context performance while using ~90% fewer tokens per question, and token usage stays flat regardless of document length. To highlight the methods limitation, we also tested it on counting tasks and locating specific passages (which aren't entity-centered) where it did not preform as well. nerdviewer.com lets you browse all the entity docs we generated across the 86 novels. Click through them like a fan-wiki. It's a good way to build intuition for what the agent produces. Paper: https://www.techrxiv.org/users/1021468/articles/1381483-thin...
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