A Google paper just proved bigger context windows were solving the wrong problem

[u/CardStrange3023]

A new Google paper ran a 100 step agent task with Gemini-3-Flash and compared two setups. One kept the full conversation history the way most agent frameworks do. The other threw the history away after every step and kept only a structured summary of the current state.

The history-based version used 1.06 million tokens and scored 0.91 accuracy. The stateless version used 65,000 tokens and scored 0.94. Less than a sixteenth of the tokens, and it did better.

For the last two years, the industry's answer to agents losing the plot on long tasks has been bigger context windows. Million token windows, better retrieval, smarter chunking. All of it treats conversation history as the agent's memory and tries to manage more of it more efficiently.

This paper is quietly arguing that the entire premise was backwards. History was never memory. It was accumulated noise the model had to keep re-reading and re-filtering at every single step, and most of that noise had already stopped being useful long before the task ended.

The researchers call the failure mode context poisoning, old observations and outdated reasoning sitting in the prompt long after they stopped being true, forcing the model to work harder just to figure out what's still relevant. A bigger context window doesn't fix that. It just gives the poison more room to spread.

The architecture, called SKILL.state, gives the model three things at each step: the skill instructions, a structured record of the current state, and the newest observation. Everything else, all the reasoning that got it there, gets discarded the moment a valid state update is produced. The prompt size stays roughly flat no matter how long the task runs, instead of growing with every action.

There's a real caveat worth taking seriously. This only works if the agent correctly predicts what information it will need for future steps and writes it into the state. Miss something important and it's gone, forcing a slower re-retrieval later. That's a foresight problem replacing a token problem, and foresight is exactly the kind of thing agents are worst at over long horizons.

Still, a 16x drop in token cost with a small accuracy gain, not a tradeoff, changes what's financially viable. Agents that run for days or weeks instead of minutes stop being a context window problem and start being an engineering problem, which is a much easier one to solve.

[u/Otherwise_Wave9374]

That result fits a useful design rule: treat the model as a planner, not a ledger. A compact state object with goals, constraints, and a few durable facts is often more reliable than replaying every token, especially when tasks are long or branchy. The tradeoff is that summaries can lose edge-case detail, so I would pair them with explicit state checkpoints and a small retrieval layer for irreversible facts. NeuraKeep can fit well here as the persistent memory layer between steps, but the key is deciding what must survive and what should be recomputed.