WMIP: A Working-Memory-Inspired Programmable Probe of Cumulative State Tracking in Large Language Models

Dengzhe Hou, Lingyu Jiang, Deng Li, Zirui Li, Fangzhou Lin, Kazunori D Yamada

WMIP: A Working-Memory-Inspired Programmable Probe of Cumulative State Tracking in Large Language Models: 3 upvotes on Hugging Face Daily Papers, #13 of 21 papers on 2026-11-12. Day-by-day upvote history.

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It had 3 upvotes by its first day, 3 upvotes by the next day, 3 upvotes by day 3, 3 upvotes by day 7, and has 3 now. It ranked #99 of 133 papers in its week and #274 of 340 in its month. Its code repository has 1 GitHub stars.

Human working memory lets people maintain and update information. Large language models (LLMs) face a related demand when tracking updates within a single prompt without writing out intermediate states, yet existing tests differ in their constraints on written steps. We introduce a working-memory-inspired probe of cumulative state tracking with program-computed answers, 3 to 100 updates, and final-state, intermediate-state, or multi-target queries. It compares protocols on identical items. Across 18 LLMs, accuracy is 84.9% on single-step arithmetic but 31.7% on core items requiring one number after three to seven updates. From 50 updates on, no model exceeds 10% accuracy. Core accuracy reaches 88.0% under a larger budget and instructions to write steps and place the answer on the last line, mainly due to the instruction changes rather than the budget. Core accuracy also correlates positively with public Berkeley Function Calling Leaderboard scores. These findings show that strong single-step arithmetic does not ensure reliable cumulative tracking and that output protocols substantially change measured accuracy. We release the generator, items, scoring code, and responses for reproducible comparisons across depths, query types, and protocols.

Paper page on Hugging Face · arXiv

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