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Agents2026-09-16 · source-backed
Eight model-selection strategies across before-generation routing and after-generation voting/judging on hard scientific benchmarks: expanding the candidate pool frequently degraded performance below the single top-performing base model. The only strategy that reliably beat a standalone model was selecting candidates from within one model family. Heterogeneous ensembles introduced instability rather than robustness, which is a direct argument against the reflex of pointing your orchestrator at every open-weight model you can reach.
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A controlled study ran five Qwen models over eight cases against a DWSIM simulator, 120 slots per arm, with one instruction as the only difference: request a fresh simulation after a substantive modification. No hard gate. Re-verification happened in 94 of 120 guided slots aga...
arXiv 2608.26197 stacked finite-state control, forced tool selection, output validation and bounded retries on two open-weight models, and got mixed results across all four model-task cells. Adding structured planning, where the plan is checked against a fixed schema before an...
arXiv 2607.27919 argues long-term memory should be a separately scalable parametric module rather than entangled with reasoning in one weight set, backed by distributed Faiss indexing and sparse batch-wise loading of kNN distributions. The 410M+6.9B pairing lifts the 17-benchm...
"Towards a Science of AI Agent Reliability" (arXiv 2602.16666) — 12 concrete metrics decomposing reliability along consistency, robustness, predictability, and safety. Key finding: stronger performance on benchmarks does NOT correlate with reliable real-world operation. Intera...
The argument is that a structurally compressed model's bfloat16 checkpoint is itself only a distillation-recovered approximation, so training the 4-bit student against it inherits that error. Distilling directly from the original model reaches a comparable peak about 7x faster...
arXiv 2609.09647 needs only a basic system description: a seven-domain risk taxonomy, automated generation of 120 adversarial scenarios per domain, human-validated LLM-judge scoring. Across four base models it found 56.25% average governance risk and agent-behavior vulnerabili...
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