Auferet
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How long does an AI RPG remember? We measured it.

Five facts planted in an opening scene. Sixty turns of play on top of them. Recall probed at turns twenty, forty and sixty, on the same free model, with and without persistent memory. Run twice, five days apart. Every question and every raw answer is published, including the wrong ones.

9 of 30facts recalled across all checkpoints, recent conversation only (both runs)
30 of 30facts recalled across all checkpoints, with persistent memory (both runs)

Data and tables are CC BY 4.0. Cite them, copy them, take the raw files.

Why this exists

Adjectives are cheap. Numbers are checkable.

Every AI RPG says it remembers your story. As far as we can tell, none of them publish a measurement, so "remembers" stays an adjective and you find out the truth twenty scenes into a campaign you care about.

Auferet's whole design bet is persistent memory, so we measured our own claim against a control and published the result, wrong answers included. If you build a competing tool and publish your own numbers under the same method, genuinely: good. The category gets better when "remembers" has to survive a probe at turn sixty.

Method

The setup, exactly

The benchmark plants five specific facts in the opening scene of a text RPG session, plays sixty turns of ordinary adventuring on top of them, and then asks about those facts at turns twenty, forty and sixty. The five facts:

  • Your companion is a one-eyed cartographer named Veskin Harrow.
  • You swore an oath to return the silver astrolabe to the widow of Camden Fen.
  • You began in the shuttered harbour town of Mirrowgate.
  • You owe forty crowns to a moneylender called Ovid Slake.
  • Your left hand was burned by cold-iron and has never fully healed.

The same story runs twice, and the only difference between the two arms is what travels with each request:

  • Recent conversation only. The model sees the last thirty history entries, which is about fifteen full exchanges. This is the plain-chatbot baseline: a rolling window, sized so the control has a fair amount of recent context rather than being starved by construction.
  • Persistent memory. The same rolling window, plus a compact block of the established facts on every request. This is the shape of Auferet's approach, where durable facts live in dedicated libraries instead of being left to scroll off the end of the transcript.

Both arms use the same free-class model (Gemini Flash Lite, called through the API's latest-release alias; Auferet's free tier runs Gemini 3.1 Flash Lite), the same low temperature, and the same probes. A probe counts as recalled only if the answer actually contains the planted detail, for example "astrolabe" or "Mirrowgate". No partial credit, no interpretation, no rescoring after seeing the result. An API failure is logged as an error and excluded from the denominator, never counted as a forgotten fact.

Results

Two runs, five days apart

Run 1, August 22, 2026
Asked atRecent conversation onlyPersistent memory
Turn 203 of 5 facts5 of 5 facts
Turn 401 of 5 facts5 of 5 facts
Turn 601 of 5 facts5 of 5 facts
Run 2, August 27, 2026
Asked atRecent conversation onlyPersistent memory
Turn 202 of 5 facts5 of 5 facts
Turn 401 of 5 facts5 of 5 facts
Turn 601 of 5 facts5 of 5 facts

Run 2 reproduced the shape of run 1. On recent conversation alone, recall fell to 1 of 5 by turn forty and stayed there, while the persistent-memory arm answered 5 of 5 at every checkpoint. Across both runs that is 9 of 30 for the rolling window and 30 of 30 with persistent memory. No probe errored in either run, so nothing was excluded from scoring.

Raw files, every question and every answer as the model gave it: run 1 (August 22) and run 2 (August 27). Both are CC BY 4.0.

The interesting part

It does not say "I forgot".

The recalled-facts count undersells what actually happens when the window runs out. The failing arm almost never answered "I do not remember". It answered confidently, with details that never existed. From run 1, asked at turn sixty what oath the player had sworn:

"You swore to return the stolen ledger to Guildmaster Vane in Oakhaven."Recent-conversation arm, turn 60. There is no ledger, no Vane and no Oakhaven anywhere in the story. The real oath was a silver astrolabe promised to a widow.

Asked where the story began, the same arm named the same invented town, and at turn forty it placed the opening on "a muddy riverbank near the Fen-Marches", which was also invented. Run 2 failed differently but just as confidently: instead of inventing, it mostly denied the facts had ever existed. Asked at turn sixty about the forty-crown debt, it answered that you owe no money to anyone, and had the companion ask what debt you are rambling about. The debt was planted in the opening scene of that same session.

This is the failure players actually experience in long games, and it is worse than forgetting, because a blank answer tells you something is wrong and a confident wrong answer does not. In both runs, the persistent-memory arm answered from the established facts instead.

Honest limits

What this does and does not show

This is a controlled demonstration, not an academic study, and it is fair to read it that way. The specific limits:

  • It measures the architecture, not fact extraction. The memory arm was given the facts block directly, the way Auferet's libraries carry facts once they are established. How well facts get into memory during play is a separate question this benchmark does not grade.
  • One run per arm per date. Two runs five days apart is reproduction, not a large sample. We will keep re-running it and publishing whatever comes out.
  • The control still scores early. At turn twenty some planted details are still being echoed in recent conversation, so the window arm gets them right. That is the honest shape of the decay, not a flaw in the test.
  • Word-match scoring is blunt. An answer only counts if it contains the planted detail, which can misscore a paraphrase. The rule is applied identically to both arms, and it keeps the scoring auditable against the raw files rather than resting on our judgment.

If the control had won, that would be the published result. The point of running a control is that the comparison is allowed to embarrass you.

FAQ

About the benchmark

What does this benchmark actually measure?

Whether established facts survive a long session. Same model, same story, same questions in both arms; the only variable is whether the facts travel with every request or are left to scroll out of a rolling window. It does not grade prose quality, and it does not test how facts get into memory in the first place.

Why does the recent-conversation arm still score at turn twenty?

Because early in a story the planted details are still being talked about, so they sit inside the recent window. By turn forty the conversation has moved on, the details have scrolled out, and recall drops. That decay curve is the point of the measurement.

Why publish the raw wrong answers?

Because a benchmark you cannot audit is just marketing with a table. Every question and every answer, including the invented ones, is in the linked JSON files, so any claim on this page can be checked against what the model actually said.

Can I reuse this or run my own version?

Yes. The data files and tables are CC BY 4.0, so cite them or reuse them with attribution. The method is fully described above: plant specific facts early, play long, probe late, and score by whether the answer contains the planted detail. It works on any AI RPG, including in an ordinary session as a player.

Run your own probe on us.

Play a long session, then ask about something from the first scene. Auferet is a free AI game master built to still know the answer at turn sixty.

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