If you've built anything with an LLM agent — a coding assistant, a browser agent, a research bot — you've probably watched it get worse the longer a task runs. It starts sharp, then a few dozen tool calls in, it starts repeating itself, forgets a constraint you gave it three turns ago, or burns through your token budget doing something it already tried and failed at.
The obvious fix everyone reaches for is a bigger context window. A recent paper out of Fudan University, GenericAgent, argues that's the wrong instinct entirely — and backs it up with numbers that are hard to ignore.
The real problem isn't length, it's density
The paper's core claim: agent performance isn't governed by how much context you can fit, it's governed by how much of that context is actually decision-relevant at any given moment. Stuffing more tokens into the window doesn't help if most of them are noise, and the paper cites three concrete failure modes:
Positional bias — information buried in the middle of a long context is harder for the model to retrieve than information at the start or end (the well-known "lost in the middle" effect).
