代码该放哪里？在双时序库存模拟中定位基础模型错误
Where Should the Code Go? Locating Foundation-Model Errors in Bitemporal Inventory Simulation
AI 导读
一篇 arXiv 论文在可精确求解的 lost-sales 库存环境中诊断基础模型错误来源，涵盖 36,672 个独立评分请求、12 个闭源与开源权重模型配置。
正文 · 原文
Abstract:Operational reports carry two clocks: when an event happened and when its report arrived; later reports can revise earlier ones. A foundation-model component that maintains the state supported by the reports visible at a cutoff, then rolls a supplied scenario forward, can be helped by exact code before it, after it or inside the call, or asked to reason harder. We ask which remedy removes which error, for which models, on an exactly solvable lost-sales inventory environment: 36,672 independently rescored requests over twelve configurations of closed and open-weight models, with a forced-answer-call interface as a control and protocols frozen before the calls. Weak models fail in separable ways. Canonical input removes report-selection error, exact continuation removes rollout error, neither repairs the other, and which error dominates differs by model; strong models are at ceiling on raw reports. Asking for more reasoning is no substitute and can depend on the interface: under a forced answer call high effort collapses GPT-5.4 mini and DeepSeek V4 Pro, which gain or hold under automatic choice; on its provider the forced call disables reasoning for DeepSeek V4 Flash; and at high effort gpt-oss exhausts its output budget. The contribution is a controlled component diagnostic, released with every request, and evidence that for some model and provider pairs the tool-calling interface confounds reasoning evaluation.
| Comments: | 22 pages. Accepted as a poster at the NeurIPS 2026 Workshop on Foundation Models for Temporal Systems (FMTS). Code and data: this https URL |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.14671 [cs.SE] |
| (or arXiv:2611.14671v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14671 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Durga Sandeep Saluru [view email]
[v1]
Tue, 10 Nov 2026 19:27:26 UTC (79 KB)
来源：arXiv cs.AI · arxiv.org