ShiftMem is a research system, not a released foundation model. This card documents the provider-hosted language models used inside the bounded strategy review layer of the completed Protocol-v2 inventory experiment.
| Label | Provider | Model identifier | Formal role |
|---|---|---|---|
| DeepSeek | SiliconFlow | deepseek-ai/DeepSeek-V3.2 |
Core model A |
| MiniMax | SiliconFlow | MiniMaxAI/MiniMax-M2.5 |
Core model B |
The identifiers reproduce the provider configuration used during the July 2026 experiment. Provider-side implementations may change without a new repository commit, so future runs should not assume byte-identical model behavior from the name alone.
The model receives public realized inventory history, the active bounded
strategy, trigger evidence, and method-specific memory context. It may propose
only forecast_window, safety_stock_multiplier, and lead_time_buffer, plus
cited memory IDs, confidence, and a short reason.
The model cannot:
- issue the daily inventory order;
- change the deterministic controller or supplier;
- change its review interval or cooldown;
- access hidden regime identity, future outcomes, or Oracle context;
- directly assign memory validity or lifecycle state.
Schema-invalid output receives one correction attempt. A second failure keeps the previous valid strategy and is included in both reliability and business outcomes.
The completed held-out matrix contains 160 cells across two methods, two models, eight scenarios, and five paired environment seeds. The primary non-stable population contains 70 ShiftMem-versus-VectorMemory pairs.
The predeclared overall result does not support ShiftMem superiority: the mean 30-day oracle-relative cost-gap difference is +45.44, with 95% interval [-2.72, 93.60] and Wilcoxon p=0.203. Positive values are unfavorable to ShiftMem. A post-hoc clustered mean sensitivity is also unfavorable, while the two models show materially different method-effect directions.
Across the complete matrix, 27.1% of cell-recorded attempts had a parse failure and 12.9% of reviews retained the prior strategy through fallback. Failure rates were not balanced across the two models. The evaluation therefore measures the deployed combination of memory, model compliance, provider behavior, and fallback logic—not an isolated memory algorithm.
- controlled research on conditional agent memory;
- reproducibility and auditability studies;
- analysis of model-dependent behavior under synthetic nonstationarity;
- testing bounded LLM strategy-review architectures.
- autonomous production inventory control;
- claims of model or memory superiority outside the tested matrix;
- safety-critical or financially consequential deployment;
- evaluation of general reasoning ability;
- inference about provider models not explicitly tested here.
The experiment uses synthetic single-item environments, one provider, fixed method order, five environment seeds per scenario, and no independent LLM-output replications. The so-called Oracle is a parameter-aware base-stock heuristic, not a proof of globally optimal control. See the post-Test audit for the complete claim scope.
Exact provider/model IDs, dependency versions, source commits, configuration hashes, and evidence hashes are recorded in the three derived Protocol-v2 JSON artifacts. Network-free verification uses:
.venv\Scripts\python.exe scripts/finalize_formal_results.py --verify