Tootfinder

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
Jugal Garg, Eklavya Sharma, Xiaowei Wu
https://arxiv.org/abs/2510.10335 https://arxiv.org…

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
We consider the problem of allocating $m$ indivisible chores among $n$ agents with possibly different weights, aiming for a solution that is both fair and efficient. Specifically, we focus on the classic fairness notion of proportionality and efficiency notion of Pareto-optimality. Since proportional allocations may not always exist in this setting, we allow the use of subsidies (monetary compensation to agents) to ensure agents are proportionally-satisfied, and aim to minimize the total subsid…

When Can Communication Lead to Efficiency?
Itai Arieli, Yakov Babichenko, Atulya Jain, Rann Smorodinsky
https://arxiv.org/abs/2510.12508 https://arxiv.org/…

When Can Communication Lead to Efficiency?
We study games with incomplete information and characterize when a feasible outcome is Pareto efficient. We show that any outcome with excessive randomization over actions is inefficient. Generically, efficiency requires that the total number of actions taken across states be strictly less than the sum of the number of players and states. We then examine the efficiency of equilibrium outcomes in communication models. Generically, a cheap talk outcome is efficient only if it is pure. When the se…

Chinese ModernBERT with Whole-Word Masking
Zeyu Zhao, Ningtao Wang, Xing Fu, Yu Cheng
https://arxiv.org/abs/2510.12285 https://arxiv.org/pdf/2510.12285

Chinese ModernBERT with Whole-Word Masking
Encoder-only Transformers have advanced along three axes -- architecture, data, and systems -- yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvements have not fully transferred to Chinese, where tokenization and morphology differ markedly from English. We introduce Chinese ModernBERT, a from-scratch Chinese encoder that couples: (i) a hardware-aware 32k BPE vocabulary tailored to frequent Chinese affixes/compounds, lowering the embedding budget; (ii) whole-word …

SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
Dachuan Shi, Abedelkadir Asi, Keying Li, Xiangchi Yuan, Leyan Pan, Wenke Lee, Wen Xiao
https://arxiv.org/abs/2510.05069

SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency. Despite this promise, latent reasoning still faces two challenges, especially in training-free settings: 1) purely latent reasoning broadens the search distribution by maintaining multiple implicit …

Pareto-optimal Tradeoffs Between Communication and Computation with Flexible Gradient Tracking
Yan Huang, Jinming Xu, Li Chai, Jiming Chen, Karl H. Johansson
https://arxiv.org/abs/2509.18129

Achieving Pareto Optimality in Games via Single-bit Feedback
Seref Taha Kiremitci, Ahmed Said Donmez, Muhammed O. Sayin
https://arxiv.org/abs/2509.25921 https://

Achieving Pareto Optimality in Games via Single-bit Feedback
Efficient coordination in multi-agent systems often incurs high communication overhead or slow convergence rates, making scalable welfare optimization difficult. We propose Single-Bit Coordination Dynamics for Pareto-Efficient Outcomes (SBC-PE), a decentralized learning algorithm requiring only a single-bit satisfaction signal per agent each round. Despite this extreme efficiency, SBC-PE guarantees convergence to the exact optimal solution in arbitrary finite games. We establish explicit regret…