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arXiv:2607. 10139v1 Announce Type: cross Abstract: Selecting the correct answer from a pool of candidate reasoning chains is the engine of test-time scaling, yet the standard selectors each carry a cost: self-consistency inherits the errors of the single model it resamples, and trained reward models need labeled data and transfer poorly off-distribution.
The paper proposes treating an ensemble of language models as a jury rather than an electorate, where each model’s reliability is inferred from patterns of agreement and disagreement without using labels. Using Expectation Maximisation, the authors estimate each expert’s latent reliability and derive a log‑odds weighted linear scoring rule. On a 1560‑question benchmark, this label‑free approach matches the performance of label‑fitted weights and surpasses the best single model and other label‑free alternatives.
arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work...
The paper introduces Bayesian backward reasoning as a label‑free anchor for multi‑agent collective decision‑making. By constructing reverse posteriors from explicit likelihoods, the authors obtain differently factorized approximations of the underlying posterior, reducing shared errors among agents. Using Jensen‑Shannon divergence to rank agents, they propose three aggregation strategies—hard selection (MinJS), soft reweighting (FwdJS), and log‑linear fusion (LogLin)—which consistently outperform baseline methods on the DDXPlus benchmark across five LLM backbones, especially when agents disagree.
arXiv:2607. 02104v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise -- to rank responses, select models, or triage papers.
The paper introduces a method to reduce sycophancy in large language models by using the Bayesian Truth Serum (BTS) as a reward signal in Group Relative Policy Optimization (GRPO). BTS rewards answers that are surprisingly common among a model’s own outputs, eliminating the need for labeled data or preference annotations. Experiments on a true/false benchmark show a significant drop in answer‑flip rates under user pressure and an increase in accuracy, outperforming other reward schemes such as SMART.