A steady reading cost, however long the history.
LongMemEval asks questions about long chat histories between a person and an assistant. As the history grew to forty sessions, the reading behind each of Duplex’s answers levelled off, and where a fact had changed, it answered with the current one.
LongMemEval, by Wu et al., ICLR 2025. The benchmark →
Internal benchmarks. Every figure on this page comes from tests Vareon ran itself and has not been independently verified. Read the disclaimer
- 96%*Current answers where a fact had changedThe new fact, not the old one
- 3.7×*Less reading per question than reading the whole historyAt forty sessions of history
- 84%*Accuracy at forty sessions of history
* Figures from Vareon’s internal benchmarks, not independently verified. Read the disclaimer
Early in the history, a fact is stated: the team uses vendor A.*
How it was run.
- Benchmark
- LongMemEval, by Wu et al. (ICLR 2025), from the cleaned public release.
- The work
- Answer a question about a long chat history between a person and an assistant.
- Histories
- The sessions holding the answer, mixed with unrelated sessions from a pool of 19,195, at 2, 5, 10, 20 and 40 sessions.
- Scale
- 50 questions at five history sizes: 250 answers for every approach.
- Models
- GPT-5.5 read what each approach supplied and wrote every answer; a separate model judged them.
What it means for your work.
Long-running work stays affordable.
From twenty to forty sessions the history doubled, and the cost of each answer stayed at $0.032.*
Answers follow what changed.
Where a fact had changed, Duplex answered with the current one 96% of the time.*
It answers, rather than holding back.
Duplex declined 5 of the 250 questions, and answered the rest.*
See it on your own work.
From your own assistant to every agent your company runs, a pilot puts Duplex on your own work, with the measures agreed before it begins.