Decision-Sufficient State Representations: Measuring and Reducing Write-Time Regret
Quick summary
arXiv:2609.32805v2 Announce Type: replace Abstract: Long tasks produce more history than an LLM agent can hold in its context, and more than it uses reliably even when the history fits. A growing line of work therefore has agents carry a short written state instead: at every step a writer rewrites the state, and a reader acts from the state alone. Steps stay cheap, but anything the writer drops is lost before later decisions reveal that they need it. We quantify this loss and ask whether training can reduce it. Comparing the written state with the best state of the same size written in hindsig
Key takeaways
- arXiv:2609.32805v2 Announce Type: replace Abstract: Long tasks produce more history than an LLM agent can hold in its context, and more than it uses reliably even when the history fits.
- A growing line of work therefore has agents carry a short written state instead: at every step a writer rewrites the state, and a reader acts from the state alone.
- Steps stay cheap, but anything the writer drops is lost before later decisions reveal that they need it.
Why it matters
The importance of “Decision-Sufficient State Representations: Measuring and Reducing Write-Time Regret” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
Member comments