Extend the tournament-graph framework to handle noisy or probabilistic oracles

Develop an extension of the BlitzRank tournament graph framework that robustly handles noisy or probabilistic k-wise comparison oracles, including algorithms and analyses that account for error-prone edges and their asymmetric structural impact on transitive inference and SCC formation.

Background

The current framework assumes a deterministic oracle, but practical settings with LLMs or human judgments can introduce noise where erroneous edges may create or collapse cycles, potentially degrading performance.

The authors suggest ideas such as modeling edge confidence or weighting edges by transitive reach, but leave the design and theoretical treatment as a future direction.