arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
arXiv:2608.29308v1 Announce Type: cross Abstract: In metric social choice, each voter ranks a set of $m$ candidates by her distance to them in an unknown metric space. The cost of a candidate is its...
The paper investigates proportional representation in a temporal voting setting where a single candidate is chosen each round and voters submit ranked preferences that may evolve over time. It extends classic proportionality axioms—justified representation (JR), proportional JR (PJR), extended JR (EJR), and proportionality for solid coalitions (PSC)—to accommodate various ways of determining which top-ranked candidates are considered approved, ranging from a fixed common cutoff to individual, round‑specific cutoffs. The authors analyze which axioms can be guaranteed under different informational assumptions about future rounds, showing that while EJR is unattainable, JR, PJR, and PSC can be achieved with a fixed cutoff if all preferences are known in advance; varying cutoffs reduce guarantees, yet PJR can still be achieved efficiently for groups that agree in every round, and PSC can be satisfied without future knowledge. They also demonstrate that checking these axioms is often coNP‑complete, though some stronger axioms may be easier to verify.
The paper studies how to predict the winner of a district‑based election using as few queries to individual votes as possible. It introduces an adaptive randomized algorithm that, for any number of candidates and any error tolerance, correctly predicts the overall winner with probability at least 1‑δ using ≥O(1/ε² log m/δ log 1/δ) queries. This improves previous results for two candidates and balanced districts, and nearly matches a proven lower bound for constant‑size candidate sets.
The paper proposes a transparent, user‑configurable rule for selecting arguments in deliberative polls, replacing opaque learned rankers. It formalises argument selection over bipolar justification sets, introduces seven civic recommender criteria, and presents a one‑hop reversed endorsement flow rule that meets them. Experiments on 17,000 simulated runs show the rule performs comparably to random on coverage but outperforms other methods on endorsement mass and robustness under adversarial pressure.
The paper announces an improved upper bound on the distortion of randomized voting rules in metric social choice, reducing it from the previous range of $[2.1126,2.5]$ to $2.1441$. It introduces the concept of random-size stable lotteries, proves their existence, and derives the new bound via a potential argument. The proofs were generated with GPT-5.6-Sol and subsequently verified and simplified by the author.
arXiv:2610.06892v1 Announce Type: cross Abstract: Learning from human preference data is the dominant route to aligning language models with human values. In linear social choice, where rewards are l...
arXiv:2503. 01985v2 Announce Type: replace-cross Abstract: In the classic committee election setting each voter approves a subset of candidates and the goal is to select $k$ winners based on these preferences.
The paper investigates the outer diversity of Condorcet domains, which are sets of rankings ensuring a transitive majority relation for elections with an odd number of votes from the domain. It defines outer diversity as the expected swap distance from a random vote to the nearest domain vote and provides numerical analyses for maximal domains with few candidates. The authors also derive asymptotic behaviors for several special domains, yielding theoretical insights into this measure.
arXiv:2610.23193v1 Announce Type: cross Abstract: Motivated by applications in reinforcement learning from human feedback, we consider a setting where a given collection of rankings needs to be compr...
arXiv:2606. 00291v1 Announce Type: cross Abstract: In RLHF, each training example contains a prompt $x$ and two candidate responses $y,y'$, and annotators provide pairwise preferences between these responses.
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.