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版本更新 · v2arXiv:2608.15247

Improved Metric Distortion Bounds for Deterministic Weighted-Tournament Voting Rules

Jianan Lin, Chenhao Wang

原文摘要

We study deterministic weighted-tournament voting rules, also known as C2 rules, which use only the fraction of voters who prefer one candidate to another for each pair of candidates. Voters and candidates lie in a common but unknown metric space, and voters rank candidates by distance. The goal is to select a candidate minimizing the sum of distances to the voters, and distortion measures the worst-case approximation ratio relative to an optimal candidate. Charikar et al.~[EC 2025] introduced the Unblanketed Set rule with distortion at most $3.9312$ and proved a lower bound of $3.1128$ for deterministic C2 rules. We extend their rule to the Path-Unblanketed Set rule, which runs in polynomial time and achieves distortion at most $1+2\sqrt{2}\approx3.8284$. The rule uses paths between candidates to combine pairwise comparisons without increasing the approximation loss with the path length. With computer assistance, we prove a better upper bound of $3.3346$ for five or six candidates and a general lower bound of $3.1828$ using a construction with 17 candidates.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14952

JoyFrontBench: Evaluating Front-End Coding Agents through Goal-Oriented Acceptance Contracts

Xiaoyi Qiao, Yicheng Wang, Jianan Lin, Xiaoyang Ji, Dongyi Lv, Yidan Huang, Shi Suo, Shiying Fan 等 10 位作者

原文摘要

Front-end coding agents are expected to produce applications that fulfill user goals, yet high task scores can coexist with failures in essential workflows. Evaluating these agents therefore requires an explicit distinction between partial implementation and application success. We introduce JoyFrontBench, a benchmark that makes this distinction through goal-oriented acceptance contracts. Each contract identifies core tasks and mandatory criteria before generation: a core task passes only when all mandatory criteria are satisfied and a shared completion threshold is met, while application success requires every core task to pass on the same operational artifact within a single run. Derived from practitioner requirements, JoyFrontBench comprises 100 front-end development problems, 764 tasks, and 2,596 atomic criteria, with reviewed annotations linking user goals to core tasks and evidence requirements. The evaluation framework combines evidence-grounded judgments with deterministic record auditing and fixed scoring rules, preserving graded task assessment alongside explicit core-task and application-level acceptance. We evaluate five coding models under two agent harnesses, OpenHands and OpenCode, on the 70-problem test set; the remaining 30 problems form a public set for validation and diagnostic analyses. Under OpenHands, the mean Average Task Score (ATS) across models is 87.69%, while the mean Application Success Rate (ASR) is 51.52%. Further analyses reveal changes in model rankings across harnesses and variation in application success across repeated runs. These findings highlight the limitations of aggregate task scores in characterizing application success and motivate explicit assessment of core-goal fulfillment alongside graded task performance.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.29646

顺序 Phragmén 规则保证 2-近似核心稳定性

Sequential Phragmén Guarantees 2-Approximate Core Stability

Jianan Lin, Chenhao Wang

AI 速读 基于摘要

作者证明经典多项式时间投票规则 sequential Phragmén 在认可偏好下总能选出 2-稳定（2-核心）的委员会，并说明该界是紧的。

cs.GT

arXiv ↗PDF ↗
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We study approximately stable committee selection under approval preferences, following Cheng et al.~[EC~2019] and Jiang, Munagala, and Wang~[STOC~2020]. In an approval-based committee election, $n$ voters each approve a subset of the candidates, and a committee of size $k$ is selected. A size-$k$ committee is $λ$-stable if, for every nonempty subset $T$ of candidates, the number of voters who strictly prefer $T$ is less than $λ$ times the proportional share $\frac{n|T|}{k}$. We consider sequential Phragmén, a classical polynomial-time voting rule, and prove that it always returns a $2$-stable committee. This implies that the $2$-core is always nonempty and improves upon the $3.651$-stability guarantee of Gao, Sun, and Vondrák~[EC~2026]. The guarantee also extends to weighted voters and is tight even for unweighted elections: for every $1\leλ<2$, sequential Phragmén can return a committee that is not $λ$-stable. Compared with the exact-core algorithm developed in concurrent work by Becker, Greger, and Peters~[arXiv~2026], sequential Phragmén is simpler to implement, and our proof of its $2$-stability is elementary.

版本更新 · v2arXiv:2611.07482

SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable Rewards

Yichen Sun, Qirui Liu, Yan Wang, Jianan Lin, Linbo Jiang, Shiyu Wang, Zhibo Wang, Zhixuan Chu

原文摘要

Large Reasoning Models (LRMs) have achieved impressive performance via explicit Chain-of-Thought (CoT) reasoning, yet this process introduces critical safety risks. We formalize LRM safety objectives as requiring non-malicious reasoning and answers, safety consistency, and explicit intent identification. We argue that existing alignment methods suffer from poor adversarial generalization, a significant ``safety tax'' on reasoning, and unfaithful coarse-grained rewards, primarily due to their neglect of the LRM's intrinsic reasoning capabilities for safety. To address these, we propose \textbf{SAVeR$^2$}, a novel \textbf{S}afety \textbf{A}lignment method via \textbf{R}ewards with \textbf{R}easoning capability. SAVeR$^2$ decomposes the safety objective into four fine-grained verifiable reward signals and utilizes a difficulty-aware data selection strategy to stabilize training. Crucially, we introduce a reasoning-preserving gradient projection mechanism that analytically resolves conflicts between safety and reasoning gradients, effectively eliminating the safety tax. Extensive experiments on models ranging from 1.5B to 671B demonstrate that SAVeR$^2$ achieves superior safety performance and significantly lower over-refusal rates across multiple benchmarks without compromising general reasoning capabilities.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36613

Optimality and Impossibility in Multidimensional Strategyproof Facility Location

Jianan Lin

原文摘要

We study facility location in multidimensional normed spaces with the objective of minimizing social cost, the sum of agents' distances to their nearest facility. A deterministic mechanism selects $k$ facility locations based on the reports of $n$ agents and is strategyproof (SP) if no agent can reduce her distance by misreporting her location. We establish optimal approximation ratios and impossibility results. For a single facility in the Euclidean plane, we prove that the optimal approximation ratios among deterministic SP mechanisms are exactly those obtained by Goel and Hann-Caruthers [SCW 2023] for the coordinate-wise median (CM): $\frac{\sqrt{2(n^2+1)}}{n+1}$ for odd $n\ge3$ and $\sqrt{2}$ for even $n\ge4$. We remove the anonymity assumption from their optimality result. However, CM is not always optimal in non-Euclidean spaces: we give an SP mechanism that minimizes social cost exactly in $\ell_\infty^d$, whereas CM has a tight uniform approximation ratio of $2$ already in the plane. We then consider two facilities in $\ell_p$ spaces with $1<p<\infty$ and prove two impossibility results for every $n>2$. In the plane, no deterministic SP mechanism has a finite approximation ratio. In higher dimensions, the same impossibility holds for anonymous deterministic SP mechanisms. Finally, a reduction using distant agents and a limiting argument extends both impossibility results to every $k>2$ with $n>k$.

cs.GT

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arXiv ↗PDF ↗