On ALFWorld with GPT-5.4 executor, JITMEM uses 9.8K input tokens, 0.87K output tokens, and 11.6 steps per task; relative to JITMEM-base, the paper reports reductions of 10.1%, 13.0%, and 12.1%, while all memory methods add input context but generally reduce output tokens and steps versus no memory.

On ALFWorld with GPT-5.4 executor, JITMEM uses 9.8K input tokens, 0.87K output tokens, and 11.6 steps per task; relative to JITMEM-base, the paper reports reductions of 10.1%, 13.0%, and 12.1%, while all memory methods add input context but generally reduce output tokens and steps versus no memory. · CiteArk

Measurement results

0/24 assessed

0 supported0 disputed or conflicting24 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Reported	Latest observation	Evidence judgment
9 thousand tokens per task	—	Not assessed
1.4 thousand tokens per task	—	Not assessed
17.8 steps per task	—	Not assessed
19.7 thousand tokens per task	—	Not assessed
1.26 thousand tokens per task	—	Not assessed
16.2 steps per task	—	Not assessed
22.4 thousand tokens per task	—	Not assessed
1.36 thousand tokens per task	—	Not assessed
16.9 steps per task	—	Not assessed
10.9 thousand tokens per task	—	Not assessed
1 thousand tokens per task	—	Not assessed
13.2 steps per task	—	Not assessed

On ALFWorld with GPT-5.4 executor, JITMEM uses 9.8K input tokens, 0.87K output tokens, and 11.6 steps per task; relative to JITMEM-base, the paper reports reductions of 10.1%, 13.0%, and 12.1%, while all memory methods add input context but generally reduce output tokens and steps versus no memory.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results