Cross-model generalization of findings

Determine whether the empirical findings reported for SERA using the Qwen 3-32B base model and GLM-4.5-Air/GLM-4.6 teachers generalize to other language model families when evaluated thoroughly.

Background

Experiments in the paper use Qwen 3-32B as the base model and GLM-4.5-Air/GLM-4.6 as teachers, with limited checks using Claude models.

The authors explicitly state uncertainty about whether their findings generalize across other base model families.

References

While we have some experiments with Claude 3.7 Sonnet and CLaude 4.0 Sonnet that hints at generalization of our method, we do not know whether our findings generalize to other model families when evaluated thoroughly.

— SERA: Soft-Verified Efficient Repository Agents  (2601.20789 - Shen et al., 28 Jan 2026) in Section 9 (Limitations), Model-specific results

First, although we cover dense Transformer, dense hybrid, and MoE architectures, all evaluated models belong to the Qwen family; whether the observed pruning patterns generalize to other model families remains to be studied.

— What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity  (2609.17515 - Zhang et al., 15 Sep 2026) in Limitations section

Absolute accuracies would change with a stronger reader; whether the relative gains hold across reader and encoder families is left to future work.

— Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion  (2609.19417 - Rakshit et al., 16 Sep 2026) in Limitations, subsection “Single component choices”

We therefore state cross-backbone generalization as untested rather than answer it with an underpowered arm.

— Phantom Gains: Auditing Self-Improvement Against a Measured Null  (2608.20290 - Xu et al., 20 Aug 2026) in Appendix, Section “Limitations, in full”

Fourth, all experiments use a single generative model (Qwen3-8B via Ollama in a capped, no-think configuration) and a single adjudicator (Llama 3). Generalization across model families is untested, and two consequences of these choices bear on the results: a capped, no-think configuration constrains reasoning depth and may compress differences between context qualities that a stronger generator would exploit, and the reviewed-correctness adjudicator is itself an unvalidated LLM, with no human agreement study performed against its labels.

— Auditable by Construction: An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance  (2608.20661 - Lunyakin, 21 Aug 2026) in Section 5.2, Limitations

It remains unclear whether the same findings generalize to substantially larger models or other model families.

— Efficient Reasoning Exploration via State-Conditioned Latent Steering with Progress Guidance  (2609.24066 - Zhang et al., 21 Sep 2026) in Section Limitations

And no significance test was run between the families, so the honest summary of this appendix is that the asymmetry, its conditioning, the restatement-trace collapse and the pipeline's lead over monolithic judging all reproduce on a second family, that single-note detection levels move with the family, and that a full cross-family comparison of the eight designs remains future work.

— LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It  (2608.31016 - Fox et al., 31 Aug 2026) in Appendix G, subsection “The two confounds, and what these runs cannot settle”

Several axes remain open. Loss-based selection across model families inherits the fit-to-text-versus-behaviour gap of \S\ref{sec:downstream} and still needs a downstream check.

— Post-Training Science for Supervised Fine-Tuning  (2609.01244 - O'Neill et al., 1 Sep 2026) in Conclusion, Section 7

Because a prior is plain text, one model's library could in principle be handed to another; such cross-model inheritance remains untested.

— SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams  (2609.02217 - Yan et al., 2 Sep 2026) in Section Conclusion

The ablations run three of the six conditions, so they cannot say whether the residue still sits in the partner notes on another model or at twenty episodes.

— Testing Interchangeability in LLM Agent Teams  (2609.05279 - Gao et al., 4 Sep 2026) in Section 4, subsection “Ablations: model, temperature, team age,” final paragraph