Tootfinder

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
Shenzhe Zhu, Jiao Sun, Yi Nian, Tobin South, Alex Pentland, Jiaxin Pei
https://arxiv.org/abs/2506.00073

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consum…

Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
Genta Indra Winata, David Anugraha, Emmy Liu, Alham Fikri Aji, Shou-Yi Hung, Aditya Parashar, Patrick Amadeus Irawan, Ruochen Zhang, Zheng-Xin Yong, Jan Christian Blaise Cruz, Niklas Muennighoff, Seungone Kim, Hanyang Zhao, Sudipta Kar, Kezia Erina Suryoraharjo, M. Farid Adilazuarda, En-Shiun Annie Lee, Ayu Purwarianti, Derry Tanti Wijaya, Monojit Choudhury

Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency…

Optimal Dispersion Under Asynchrony
Debasish Pattanayak, Ajay D. Kshemkalyani, Manish Kumar, Anisur Rahaman Molla, Gokarna Sharma
https://arxiv.org/abs/2507.01298

Optimal Dispersion Under Asynchrony
We study the dispersion problem in anonymous port-labeled graphs: $k \leq n$ mobile agents, each with a unique ID and initially located arbitrarily on the nodes of an $n$-node graph with maximum degree $Δ$, must autonomously relocate so that no node hosts more than one agent. Dispersion serves as a fundamental task in distributed computing of mobile agents, and its complexity stems from key challenges in local coordination under anonymity and limited memory. The goal is to minimize both the …

Stromausfall in Saporischschja nach ukrainischem Angriff
Im gesamten von Russland kontrollierten Teil der ukrainischen Region Saporischschja ist laut Berichten russischer Nachrichtenagenturen der Strom ausgefallen. Grund seien Angriffe ukrainischer Truppen. "Durch den Beschuss der ukrainischen Streitkräfte wurden im nordwestlichen Teil der Region Saporischschja Hochspannungsanlagen beschädigt", meldete die Agentur RIA unter B…
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Ukraine-Liveblog: ++ Ukraine-Verbündete beraten über weitere Hilfen ++
Auf Einladung Deutschlands und Großbritanniens kommt am Mittwoch die sogenannte Ramstein-Gruppe zusammen, um über weitere Hilfen für die Ukraine zu beraten - die USA nehmen nicht teil. Moskau fordert die Anerkennung der eroberten Gebiete.

#Blakes7 Series D, Episode 01 - Rescue
SLAVE: Yes, Master.
DORIAN: Analyze exterior disturbance.
SLAVE: It is an earthquake, Master.
AVON: Cause?
[Slave does not answer. Avon points his gun at Dorian.]
DORIAN: Cause?

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

This https://arxiv.org/abs/2505.24718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Reinforcing Video Reasoning with Focused Thinking
Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks. However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rewarding fails to account for partially correct answers, resulting in high reward variance and inefficient learning. In this paper, we propos…

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
Sana Ebrahimi, Mohsen Dehghankar, Abolfazl Asudeh
https://arxiv.org/abs/2505.24239 ht…

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
While multi-agent LLM systems show strong capabilities in various domains, they are highly vulnerable to adversarial and low-performing agents. To resolve this issue, in this paper, we introduce a general and adversary-resistant multi-agent LLM framework based on credibility scoring. We model the collaborative query-answering process as an iterative game, where the agents communicate and contribute to a final system output. Our system associates a credibility score that is used when aggregating…

⭐️Trump, Bashed the Federalist Society
(Not Corrupt Enough),
Asserts Autonomy on Judge Picks
⭐️Felon growns increasingly angry at court rulings blocking parts of his agenda,
including by judges he appointed
https://www.

Trump, Bashing the Federalist Society, Asserts Autonomy on Judge Picks
The president has grown increasingly angry at court rulings blocking parts of his agenda, including by judges he appointed.

CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Chen Xiong, Pin-Yu Chen, Tsung-Yi Ho
https://arxiv.org/abs/2506.00781 ht…

CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This …

This https://arxiv.org/abs/2205.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approximability Landscape of Welfare Maximization within Fair Allocations
Fair allocation of indivisible goods studies allocating $m$ goods among $n$ agents in a fair manner. While fairness is a fundamental requirement in many real-world applications, it often conflicts with (economic) efficiency. This raises a natural and important question: How can we identify the most welfare-efficient allocation among all fair allocations? This paper answers from the perspective of computational complexity. Specifically, we study the problem of maximizing utilitarian social welfa…