Tootfinder

Three paragraphs, from three different hotel reviews.
Can you tell which, if any, were AI‑generated?
🔸“The hotel is in a great location for everything. Lots of places to eat and drink. The hotel itself is always abuzz. The tavern located on the ground floor is definitely a must. Food, service, prices and atmosphere were great.”
🔸“A good hotel, though the room had the proportions of a well-appointed lift. Slept well, shower was excellent, staff were friendly. Breakfast was bus…

Improved Approximation Guarantees for Groupwise Maximin Share Fairness
Georgios Amanatidis, Anna Korfiati, Evangelos Markakis, Christodoulos Santorinaios
https://arxiv.org/abs/2606.04731 https://arxiv.org/pdf/2606.04731 https://arxiv.org/html/2606.04731
arXiv:2606.04731v1 Announce Type: new
Abstract: We study the problem of fairly allocating a set of indivisible goods to a set of $n$ agents with additive valuation functions. We focus on the very demanding notion of \textit{groupwise maximin share fairness} (GMMS), which requires that each agent $i$ receives value comparable to their maximin share, where the latter is computed \textit{with respect to any subset of agents that contains $i$}. We show that it is possible to compute $(\phi-1)$-approximate GMMS allocations in polynomial time, where $\phi \approx 1.618$ is the golden ratio). This improves on the previously known guarantee of $4/7$ of Chaudhury et al. [SICOMP; 2021] and Amanatidis et al. [TCS; 2020]. We propose a simple algorithm that maintains the same main properties as the Draft-and-Eliminate algorithm of Amanatidis et al. [TCS, 2020] and we improve on the approximation guarantee analysis by carefully bounding the relevant value within any subinstance induced by the restriction of our allocation to a subset of agents. Our analysis is asymptotically tight for algorithms that share these properties and has the additional benefit of giving improved guarantees for restricted settings; in particular, when the agents agree on the top $n$ goods or when the number of agents is small. To illustrate the challenges of going beyond the guarantees of our algorithm, we also present a variant with an improved approximation of $(\sqrt{10}-1)/3 \approx 0.72$ for the case of three agents. To achieve this improvement we partially characterize the maximin share guarantees of short picking sequences for a small number of goods.
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Looks interesting. "The study, titled “Law Professors Prefer AI Over Peer Answers,” was conducted with 16 law professors across U.S. law schools and tested whether large language models could serve as effective tutors for contract law courses.In a blind evaluation of nearly 3,000 anonymized comparisons, professors rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups."

AI Outperforms Law Professors in Stanford Law Study | Stanford Law School
A groundbreaking study led by Stanford Law School Professor Julian Nyarko reveals that law professors overwhelmingly prefer AI-generated answers to st

"Politicians, business leaders, and educators often say that people should learn to program because the jobs of the future will require it. However, as Benjamin Doxtdator pointed out, many of those claims are built on shaky ground. Even if they were true, education shouldn’t prepare people for the jobs of the future: it should give them the power to decide what kinds of jobs there are and to ensure that those jobs are worth doing."

Non-obvious Manipulability in the Additively Separable Group Activity Selection Problem
Maria Fomenko (Gran Sasso Science Institute), Giovanna Varricchio (University of Calabria)
https://arxiv.org/abs/2606.05048 https://arxiv.org/pdf/2606.05048 https://arxiv.org/html/2606.05048
arXiv:2606.05048v1 Announce Type: new
Abstract: In this work, we study the additively separable Group Activity Selection Problem (AS-GASP) in an imperfect information setting, where agents have private preferences over activities and weights over other agents. Our goal is to design mechanisms that assign agents to activities based on their declared preferences and weights, with the objective of maximizing social welfare while ensuring truthful reporting. We, therefore, focus on the notion of non-obvious manipulability (NOM), a form of resilience to manipulation. We first investigate the relationship between NOM and social welfare optimality. In this regard, our main result shows that, when preferences and weights are arbitrary or non-negative, any optimal mechanism is non-obviously manipulable. In contrast, when either preferences or weights are binary, we show that optimality and NOM may be incompatible. We then turn to computational aspects. While it is known that computing an optimal outcome for the AS-GASP is NP-hard even in restricted settings, we establish a strong inapproximability result showing that no polynomial-time algorithm can guarantee a bounded approximation ratio when preferences and weights may take arbitrary values. In turn, when preferences are non-negative, we show that a bounded approximation is possible, and we present two asymptotically optimal approximation mechanisms that are also guaranteed to satisfy NOM.
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GNStor: Design of GPU-Native High-Performance Remote All-Flash Array
Shushu Yi, Wenbo Wu, Guoci Chen, Junrong Zhu, Shengwen Liang, Mao Bo, Chenying Huan, Chen Tian, Jie Zhang
https://arxiv.org/abs/2606.04908 https://arxiv.org/pdf/2606.04908 https://arxiv.org/html/2606.04908
arXiv:2606.04908v1 Announce Type: new
Abstract: GPU has become the leading computing device for a wide range of data-intensive applications, which tightly collaborates with remote all-flash array (AFA) to accommodate ever-expanding datasets, facilitate multi-client data sharing, and guarantee fault tolerance. Although GPU is the center of computation, all I/O processes in existing GPU-AFA systems are still CPU-centric. CPU orchestrates remote I/O requests and executes a centralized AFA engine to take charge of AFA-level functionalities (e.g., access control and metadata persistence). This design disparity suffers from substantial CPU-GPU interaction overhead and I/O traffic amplification, compromising end-to-end I/O performance.
In this work, we present \emph{GNStor}, a GPU-native AFA system that enables GPU to directly access remote AFA without CPU intervention in the I/O path, thereby fully exploiting the performance of AFA. Specifically, GNStor first proposes a GPU-centric NVMe over RDMA (NoR) software stack (named \emph{GNoR}), paving a fast path for GPUs to directly initiate NoR I/O requests to SSDs within remote AFA. GNoR employs an atomic-operation-based I/O orchestration design and follows the single-instruction-multiple-thread (SIMT) execution model of GPU, fully exploiting the massive parallelism of GPU architectures. To facilitate essential AFA functionalities in a CPU-bypass I/O path, GNStor further designs \emph{deEngine}, a decentralized AFA engine that seamlessly decomposes and integrates AFA-level tasks into each SSD firmware, thereby achieving efficient AFA access at low cost. Evaluation results show that GNStor achieves 3.2$\times$ higher I/O throughput and reduces application execution time by 31.1\%, compared to state-of-the-art AFA systems.
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RE: https://c.im/@cdarwin/116858479856697478
The masqueraders and pretenders parade around in grand performative acts of love of God and country ...
while willfully betraying both
—the rest of us are going to have to fight to hold on to our nation
and…

A Unified Framework for Uniform-Price Resource Allocation Mechanisms
Ioannis Caragiannis, Dimitris Fotakis, Stratis Skoulakis
https://arxiv.org/abs/2606.06151 https://arxiv.org/pdf/2606.06151 https://arxiv.org/html/2606.06151
arXiv:2606.06151v1 Announce Type: new
Abstract: Mechanisms for allocating a divisible resource among strategic agents have been widely studied. The prominent paradigm is the proportional (Kelly) mechanism, which elicits a scalar bid per agent, allocates the resource proportionally, and charges payments equal to the bids. Follow-up mechanisms improve social welfare, but sacrifice simplicity by introducing complex allocation rules or unintuitive payments.
We introduce a unified framework for designing simple resource allocation mechanisms with proportional-style allocations and uniform pricing. Our framework yields a family of mechanisms that interpolate between the Kelly mechanism and the first-price auction. These mechanisms strictly improve upon Kelly's efficiency guarantees, even achieving full efficiency in equilibrium, while also providing revenue guarantees relative to the VCG mechanism.
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True patriots should want all Americans to vote,
They should oppose would-be dictators,
They should yield to the Constitution,
and they should demand a nation that is offered to everyone equally.
Actual followers of Jesus should defend the vulnerable,
They should give comfort to the sick, food to the hungry, welcome to the immigrant, and love to the least among us.
And while the masqueraders and pretenders parade around in grand performative acts of lo…

Americans Are Exhausted By The Right's Pretend Patriotism and Phony Faith
Life is exhausting these days for lots of us, and I think I’ve figured out why.

Welfare Maximization in Bilateral Trade: Improved Approximation Guarantees Beyond the Fixed Price Barrier
Shahar Dobzinski, Ariel Shaulker
https://arxiv.org/abs/2606.04890 https://arxiv.org/pdf/2606.04890 https://arxiv.org/html/2606.04890
arXiv:2606.04890v1 Announce Type: new
Abstract: We study the setting of welfare maximization in bilateral trade, where the values of both the buyer and the seller are drawn from independent distributions. Our goal is to maximize social welfare. In this setting, fixed price mechanisms have been extensively studied. In a fixed price mechanism, there is a price $p$ that depends only on the distributions of the buyer and the seller. Trade occurs if and only if the buyer's value is at least $p$ and the seller's value is at most $p$. A long line of work has culminated in determining almost exactly the approximation ratios achievable by fixed price mechanisms: there exists a fixed price mechanism that obtains at least a $0.72$ fraction of the social welfare, but no fixed price mechanism can guarantee more than a $0.7381$ fraction of it [Cai and Wu, STOC'23; Liu, Ren, and Wang, STOC'23]. No other incentive-compatible mechanism is known to beat the performance of fixed-price mechanisms in this setting.
This paper shows how to achieve a larger fraction of the optimal welfare with other classes of mechanisms. Specifically, we study the buyer-offering mechanism with a reserve price. In this mechanism, the buyer observes its value and makes a take-it-or-leave-it offer to the seller, where the offer is at least the reserve price. Beyond its simplicity, this natural mechanism is attractive because the seller always has a dominant strategy: accept the offer if its value is at most the offer, and otherwise reject it. We show that there always exists a reserve price that guarantees a $0.746$ fraction of the social welfare. This not only improves upon the best previously known approximation guarantee for the problem, but also demonstrates that fixed-price mechanisms are not optimal in this setting.
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