arXiv:2607. 20768v1 Announce Type: cross Abstract: Majority voting over LLMs is widely assumed to benefit from diversity, and diversity measures are used to choose which models to combine.
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arXiv:2608. 04618v1 Announce Type: new Abstract: Heterogeneous language-model ensembles expand the space of candidate responses, yet they lack a principled criterion for when a newly generated answer should supersede an already supported one.
The study evaluates how sixteen language models from ten families generate diverse formulations of a psychotherapeutic case, finding an average semantic diversity of 1.69 distinct formulations versus 1.43 for a single-model baseline. It introduces the Vendi Score to quantify diversity and defines a per-model dissent metric to identify the most divergent voice within an ensemble. The analysis shows that model identity significantly influences dissent, but this effect varies across model pairs and panel compositions, indicating that ensemble dispersion is a measurable property rather than an assumed one.
arXiv:2608. 06940v1 Announce Type: new Abstract: LLM judge panels are a standard evaluation tool, but prior work reports highly correlated panel errors: nine judges provide roughly the effective information of two independent ones, and aggregation closes only a small fraction of the gap.
arXiv:2607. 05806v1 Announce Type: new Abstract: Training data for machine learning is routinely collected by a selection process the model never sees: loans are observed only when granted, outcomes only when a test was ordered.
The paper investigates whether stacking multiple defenses around large language models (LLMs) truly compounds security. Using the Adversary Access‑Tier Model (AATM) and a cost‑tiering system, the authors analyze a seven‑layer defense stack and find that failure correlations between layers are consistently positive, meaning the residual attack success is higher than the multiplicative prediction. Despite high coverage and low false refusals, the stack’s performance is largely driven by common architectural causes rather than diverse, independent defenses.
arXiv:2608. 16190v1 Announce Type: cross Abstract: Trusted monitoring has a cheap, trusted model score a stronger untrusted model's actions, and a diverse ensemble of them beats a single stronger monitor at matched cost.