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新论文 · v1arXiv:2611.16357

Optimal Seat Movement Strategies Among Modular Bus Units

Shiyue Wang, Yuhao Liu, Zhibin Chen, Mingyang Pei, Changhong Lu

原文摘要

Movable seats enable passengers to transfer between coupled modular bus units while remaining seated, but require physical reconfiguration that existing modular transit models largely abstract away. We model seat reconfiguration as a colored generalized sliding-tile puzzle on the physical track graph, capturing simultaneous movements, seat indistinguishability within destination groups, and vertex, edge, and corner-following conflicts. A lexicographic objective minimizes makespan first and then the sum of costs (SoC), which penalizes movements and waiting at non-target positions. We develop an exact time-expanded mixed-integer linear programming formulation and a Boundary-Sequence Dynamic Programming (BSDP) algorithm. BSDP decomposes the problem at single-edge module interfaces and coordinates local schedules through compressed boundary-crossing sequences, provably preserving feasibility and optimal costs. On two- and three-module benchmarks, BSDP achieves mean per-instance speedups of 3.24x and 6.30x over Gurobi, respectively. In fixed-horizon tests with four to seven modules, both methods start from identical greedy schedules and receive a 300 s budget. BSDP obtains lower final SoC than Gurobi on 197 of 200 instances and ties on three, reducing mean final SoC by 17.93%-69.07% across the four size classes. Geometry sensitivity and ablation studies further clarify the contribution of boundary compression to computational performance. The framework links seat reconfiguration to the timing and operational cost of modular bus decoupling.

cs.CE · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12392

More Than Just Style: Benchmarking Personality Influence in Large Language Models

Yuhao Liu, Mengyu Wang, Xiao Chen, Lu Liu

原文摘要

In addition to the effects of explicit input prompts and instructions, large language model outputs are influenced by underlying behavioral preferences similar to human personality tendencies. These tendencies can affect how models interpret situations and make decisions. However, their influence is difficult to disentangle from the effects of model capabilities and instructions. Therefore, we introduce SPACE, a benchmark framework combining a controlled evaluation protocol with complementary datasets for personality verification, capability assessment, and hierarchical situational tasks. Controlled comparisons across guidance conditions help distinguish personality influence from measured capability changes and immediate instruction effects. Experimental results demonstrate that although prompting can shift generation style, models' internal personality tendencies are persistent and not readily overridden by opposing instructions. Furthermore, these underlying tendencies have an observable influence on LLM task performance, highlighting the need for greater attention to these characteristics and potential applications. Training code and SPACE benchmark specifications are available at https://github.com/Niklauseik/SPACE-training.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08731

eSharp: Diffusion-based Sharp Monocular Metric Event-to-Depth Estimation

Qiushi Dai, Aniket Dashpute, Yuhao Liu, Rishubh Parihar, Guha Balakrishnan, Ashok Veeraraghavan

原文摘要

Depth perception is fundamental for autonomous navigation, industrial inspection, and mixed-reality systems. It often operates under strict constraints on power and form factor. In such cases, monocular depth estimation (MDE) is often used. Even as recent advances continue to improve monocular depth quality, conventional frame-based MDE systems remain challenged by fast motion and high-dynamic-range scenes. This work introduces eSharp, an MDE framework that addresses these problems using an event camera. eSharp repurposes pretrained diffusion priors for event-only MDE and produces accurate metric depth with unparalleled sharpness on real data. Real-world event datasets provide only sparse LiDAR depth maps for ground-truth supervision, making dense prediction challenging. We address this limitation through cross-modal distillation, using LiDAR-calibrated dense pseudo-labels generated from a spatially co-registered frame camera to train eSharp. Experiments show that eSharp outperforms prior event-to-depth baselines in both metric-depth accuracy and spatial fidelity, establishing diffusion models as a promising foundation for event-driven 3D perception.

cs.CV · eess.IV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.03474

Otter: A Provably MEV-Resilient Automated Market Maker via Surplus Redistribution

Elaine Shi, Mengqian Zhang, Hao Chung, Yuhao Li

原文摘要

Miner extractable value (MEV) in automated market makers allows block builders to profit from transaction ordering and injected trades, imposing costs on users and contributing to builder centralization. We introduce Otter (Optimal Truthful Trading with Excess Redistribution), a two-asset batch AMM that achieves provable MEV resilience when the consensus layer provides censorship resilience and block space is uncongested. Otter makes truthful behavior a dominant strategy for both users and builders. Consequently, a builder cannot profit from strategic deviations, including reordering bids or injecting sybil bids, even when the builder is itself a trader with intrinsic value. Moreover, we show that Otter maximizes social welfare, in a strong sense, within a natural class of mechanisms satisfying the desired game-theoretic properties. To achieve these guarantees, we introduce a new paradigm called surplus redistribution, which provably prevents residual surplus from being captured as MEV by redirecting it to the broader community. Specifically, the pool's output tokens need not be distributed entirely among users in the current batch. Instead, residual surplus may be transferred to a smart contract governed by the decentralized community. The accumulated surplus can be used to benefit community members --- for example, by subsidizing transaction fees, rewarding liquidity providers, or returning assets to the pool. Our approach relies on the underlying consensus layer to provide censorship resilience. We motivate its necessity through an impossibility result showing that the desired game-theoretic guarantees become unattainable when the builder is additionally allowed to censor transactions. Thus, our results also provide a mathematically formal demonstration of how consensus-level security guarantees can fundamentally expand what is achievable at the application layer.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01715

EarlySelectKV: Anticipatory KV Selection via Cross-Layer Query Proxies

Yuhao Liu, Yuehao Li, Linghe Zhang, Gaobin Huang, Junran Li, Hongchang Wang, Haifang Jian

原文摘要

Query-conditioned sparse KV selection reduces long-context inference cost by attending to a subset of historical KV entries at each decoding step. Existing selectors must wait for the target-layer query before scoring candidates and generating indices, placing routing on the immediate pre-attention path. We introduce EarlySelectKV, a training-free transformation that uses proxy queries from earlier hidden states to precompute selected KV indices while retaining the real target-layer query for final attention. Tests with RocketKV, Quest, and Loki show no consistent pattern of quality degradation across the evaluated settings. In the primary RocketKV evaluation, ES-RocketKV differs by +0.018 points on LongBench, approximately -0.01 points on RULER, and +0.25 percentage points on representative NIAH/Passkey settings. In a TensorRT-LLM deployment with CPU-offloaded routing, ES-RocketKV improves end-to-end output throughput by up to 12.4% and reduces GPU-resident memory by 11.1% of the combined KV/K-summary pool. This makes routing timing an explicit optimization target alongside KV subset selection in sparse long-context decoding. Code is available at https://github.com/sea-with-sakura/EarlySelectKV.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01302

A Decomposition Theorem and a Simple Algorithm for Contraction Fixed Points

Xi Chen, Yuhao Li, Mihalis Yannakakis

原文摘要

We prove a decomposition theorem for the problem of computing an approximate fixed point of a contraction map $f\colon [0,1]^k \to [0,1]^k$ under the $\ell_{\infty}$-norm. After a preliminary version of the decomposition theorem appeared on arXiv [CLY26], Feodorov and Haslebacher [FH26] gave a striking application of the theorem, showing that it can be used to convert any $\operatorname{poly}(k,\log(1/\varepsilon))$-query algorithm with running time $(k\log(1/\varepsilon))^{O(k)}$ into an algorithm with running time $(k\log(1/\varepsilon))^{O(\sqrt{k})}$. They instantiated this framework with an $O(k^2\log(1/\varepsilon))$-query algorithm with the desired running time, thereby obtaining the first subexponential-time algorithm for contraction fixed points under the $\ell_{\infty}$-norm and, via well-known reductions, the first deterministic subexponential-time algorithm for simple stochastic games and mean-payoff games. In addition to the decomposition theorem, as our second main result in this paper, we show that the $O(k\log(1/\varepsilon))$-query algorithm of [CLY25] can be implemented in time $(k\log(1/\varepsilon))^{O(k)}$; our algorithm may be viewed as conceptually simpler compared to the algorithm of [FH26]. A key observation here is that the function constructed in [CLY25], whose fixed points are used as the next query points, turns out to be nonexpansive under the $\ell_{\infty}$-norm, so the brute-force procedure can be replaced by known algorithms for contraction fixed points.

cs.CC · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36635

Tight Characterizations of AMM Mechanism Design under Weak Efficiency

Chengkai Zhang, Lirong Xia, Elaine Shi, Mengqian Zhang, Qishen Han, Yuhao Li

原文摘要

Miner extractable value (MEV) poses a persistent challenge for blockchain ecosystems. It arises from the fact that a block builder can exploit its unilateral control over transaction inclusion and ordering to extract profit, potentially at users' expense. Recent work has explored consensus-aided mechanism design to mitigate MEV. This approach combines generic consensus-layer guarantees with application-layer mechanisms that use these guarantees to address application-specific MEV problems. Prior work exemplifies this approach for automated market makers (AMMs). When the consensus layer offers fair sequencing, which orders transactions according to their arrival times, their proposed mechanism achieves incentive compatibility (IC) and weak local efficiency (wLE). Informally, IC rules out profitable strategic deviations by users and builders. Meanwhile, wLE ensures that after execution, any order willing to trade at the initial price cannot benefit from further trade with the pool at the ending marginal price. In this work, we resolve the main open question left by prior works: whether IC and wLE can be achieved under plain consensus (e.g., current Ethereum) without fair sequencing. We prove that no deterministic, anonymous batch AMM can achieve both properties. The impossibility holds even when the consensus layer guarantees censorship resilience and block space is unlimited, establishing the first separation between censorship resilience and fair sequencing in the context of decentralized mechanism design. Our impossibility proof combines three strategic capabilities: creating Sybil identities, over-reporting budgets, and misreporting values. We further show the tightness of our impossibility result: removing any one from the strategy space restores feasibility under censorship resilience. Specifically, we construct a mechanism achieving IC and wLE for each restricted setting.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36629

Sybil-Proofness of Generalized Adaptive Clinching and Implications for Blockchain Mechanism Design

Chengkai Zhang, Yuhao Li, Elaine Shi, Mengqian Zhang, Lirong Xia, Qishen Han

原文摘要

The adaptive clinching auction of Dobzinski, Lavi, and Nisan (FOCS'08) is a cornerstone of mechanism design with budget-constrained bidders, and it has since inspired a broad range of extensions to substantially richer market settings. Adaptive clinching enjoys compelling guarantees: it is Pareto optimal, and truthful bidding is a dominant strategy whenever budgets cannot be overreported. In many real-world environments, however, identities are cheap to create, making resilience to Sybil attacks an additional requirement. Whether the guarantees of adaptive clinching survive when a bidder can split its budget across fake identities has remained open. We introduce a generalized framework for adaptive clinching and prove that it satisfies Sybil-robust incentive compatibility: a bidder cannot benefit from splitting into any finite number of identities with arbitrary value reports, provided their aggregate reported budget does not exceed the bidder's enforceable true budget. Sybil resilience thus joins the list of ideal properties of adaptive clinching. Our work is motivated by practical problems in decentralized finance, where automated market makers (AMMs) are among the most widely used applications. We design a new AMM mechanism that extends classical adaptive clinching in two ways: it supports a two-sided market and protects against two-sided strategies, and it allows the supply function to depend on the auction state. Its one-sided special case is captured by our generalized adaptive clinching framework, and our proof techniques show that the full two-sided mechanism simultaneously satisfies Sybil-robust incentive compatibility and a relaxed notion of Pareto optimality. We argue that these guarantees are essentially the best possible via impossibility results. More broadly, our work reveals new connections between classical algorithmic game theory and modern decentralized blockchains.

cs.GT · econ.TH

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1731ACL Anthology · ACL 2026

Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming

Yuhao Li Beijing University of Posts and Telecommunications, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang Beijing University of Posts and Telecommunications, Huazheng Wang, Zirui Zhuang Beijing University of Posts and Telecommunications 等 11 位作者

原文摘要

Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects—complexity, readability, and correctness—and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22% accuracy improvement over baseline approaches. Code is available at Github.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1864ACL Anthology · ACL 2026

AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters

Hao Luo Wuhan University, Xiao Yan Wuhan University, Xinyan Li, Qiming Zeng Wuhan University, Yuhao Lin, Shanshan Feng Wuhan University and The Agency for Science, Technology and Research, Hao Wang Wuhan University, Jiawei Jiang Wuhan University

原文摘要

Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by generating detailed Chain-of-Thought (CoT) reasoning. However, they tend to apply a uniform, computation-intensive deep reasoning strategy to all problems, leading to unnecessary overhead on simple tasks. This significantly hinders their efficiency in real-world applications. While existing methods have improved reasoning efficiency to some extent, they still face critical challenges such as conflicting objectives, limited adaptability. To address these limitations, we propose AdaMix, an adaptive reasoning framework via decoupled optimization. To mitigate optimization conflicts, AdaMix first constructs two specialized adapters: an efficiency-oriented short adapter and an accuracy-oriented long adapter. It then incorporates a difficulty-aware routing model that assesses problem complexity to predict a reasoning intensity coefficient. This coefficient is used to dynamically interpolate a mixed adapter from the two base adapters, enabling fine-grained reasoning control. Our experiment demonstrates that our AdaMix reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets, thus indicating a favorable accuracy-efficiency trade-off.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表