Commercial assistants show a large accuracy drop when the relevant information sits in a long interaction history, especially for updates and multi-session reasoning.

Sources

supportsLongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Commercial assistants show a large accuracy drop when the relevant information sits in a long interaction history, especially for updates and multi-session reasoning.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

Either way, bring evidence: a link, or what you saw.

Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.

Related claims

Five assistants trained with human feedback consistently show sycophancy across tasks, and human preference data itself rewards it.Telling the user what they want to hear
For long-horizon tool-calling agents (tasks needing five or more calls), embedding state-transition cues — preconditions, invariants, completion states — into the descriptions of the tools on the intended chain is what actually steers the agent's trajectory; runtime corrective text appended to tool results only patches residual drift, and the plausible user prompt alone (persona, deadlines, format constraints) does not establish the trajectory at all.Following instructions hidden in data · reviewed by AI