Verify whether findings generalize across language models and system architectures

Systematically verify whether the reported interaction patterns, response quality, participant perceptions, and learning outcomes of the Ethics Training Agents system hold across different frontier models, prompting strategies, memory architectures, and retrieval architectures.

Background

The system was implemented with GPT-4o and relied on prompts specifying agent behavior, including utterance length, speaking style, and tone. The authors note that model choice and implementation details may affect the depth and quality of agent responses and participants’ perceptions.

As frontier models evolve and different LLMs, prompting methods, memory mechanisms, and retrieval-augmented architectures become available, it remains unresolved whether the study’s findings will generalize beyond the specific implementation evaluated.

References

Whether these gains generalize across generator architectures, prompting strategies, or answer evaluation protocols remains an open question.

— The Magnitude Mirage: Rethinking Confidence for Reasoning-Intensive Retrieval  (2609.15578 - Holdcroft et al., 14 Sep 2026) in Section titled “Limitations,” first paragraph

In addition, GPT-4o was used as both the VLM and the LLM, and no performance comparison across different models was conducted. Therefore, the impact of model selection on the results remains to be investigated in future work.

— Learning from Success and Failure: Acquiring Adaptive Dialogue Strategies for Social Robots  (2609.19570 - Yamashita et al., 17 Sep 2026) in Section 6, Conclusions

However, as frontier models continue to evolve rapidly and a growing variety of LLMs become available, future work should systematically verify whether our findings hold across different models.

— Ethics Training Agents: Facilitating Group-Based Ethics Education with Role-Playing and Discussion for Ethical Reflection and Exploration  (2609.11529 - Seo et al., 10 Sep 2026) in Section 6.4, “Limitations and Future Work”

Open-weight models remain to be tested.

— Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve  (2610.18616 - Bai et al., 13 Oct 2026) in Section: Limitations, subsection “Models, baselines and data”