Tootfinder

Agent Network Protocol Technical White Paper
Gaowei Chang, Eidan Lin, Chengxuan Yuan, Rizhao Cai, Binbin Chen, Xuan Xie, Yin Zhang
https://arxiv.org/abs/2508.00007 https://

Agent Network Protocol Technical White Paper
With the development of large models and autonomous decision-making AI, agents are rapidly becoming the new entities of the internet, following mobile apps. However, existing internet infrastructure is primarily designed for human interaction, creating data silos, unfriendly interfaces, and high collaboration costs among agents, making it difficult to support the needs for large-scale agent interconnection and collaboration. The internet is undergoing a profound transformation, showing four cor…

Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs
Ken Tsui
https://arxiv.org/abs/2507.02778 https://

Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs
Although large language models (LLMs) have become transformative, they still make mistakes and can explore unproductive reasoning paths. Self-correction is an important capability for a trustworthy LLM, particularly an autoregressive LLM. While LLMs can identify error in user input, they exhibit a systematic 'Self-Correction Blind Spot' - failing to correct identical error in their own outputs. To systematically study this phenomenon, we introduce Self-Correction Bench, a systematic framework t…

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu, Chao-Han Huck Yang, Sung-Feng Huang, Chih-Kai Yang, Chee-En Yu, Chun-Wei Chen, Wei-Chih Chen, Chien-yu Huang, Yi-Cheng Lin, Yu-Xiang Lin, Chi-An Fu, Chun-Yi Kuan, Wenze Ren, Xuanjun Chen, Wei-Ping Huang, En-Pei Hu, Tzu-Quan Lin, Yuan-Kuei Wu, Kuan-Po Huang, Hsiao-Ying Huang, Huang-Cheng Chou, Kai-Wei Chang, Cheng-Han Chiang, Boris Ginsburg, Yu…

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following, without requiring task-specific audio instruction-tuning. Recent LALMs typically augment Large Language Models (LLMs) with auditory capabilities by training on large-scale, manually curated or LLM-synthesized audio-instruction datasets. However, these approaches have often suffered from the catastrophic forgetting of the LLM's original language abil…

CyberGym: Evaluating AI Agents' Cybersecurity Capabilities with Real-World Vulnerabilities at Scale
Zhun Wang, Tianneng Shi, Jingxuan He, Matthew Cai, Jialin Zhang, Dawn Song
https://arxiv.org/abs/2506.02548

CyberGym: Evaluating AI Agents' Cybersecurity Capabilities with Real-World Vulnerabilities at Scale
Large language model (LLM) agents are becoming increasingly skilled at handling cybersecurity tasks autonomously. Thoroughly assessing their cybersecurity capabilities is critical and urgent, given the high stakes in this domain. However, existing benchmarks fall short, often failing to capture real-world scenarios or being limited in scope. To address this gap, we introduce CyberGym, a large-scale and high-quality cybersecurity evaluation framework featuring 1,507 real-world vulnerabilities fo…

Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program
Xin Li, Li Ding, Qiao Lin, Zhen-Wei Yu
https://arxiv.org/abs/2509.02946

Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program
Demand response providers (DRPs) are intermediaries between the upper-level distribution system operator and the lower-level participants in demand response (DR) programs. Usually, DRPs act as leaders and determine electricity pricing strategies to maximize their economic revenue, while end-users adjust their power consumption following the pricing signals. However, this profit-seeking bi-level optimization model often neglects the satisfaction of end-users participating in DR programs. In addi…

This https://arxiv.org/abs/2502.15806 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos
Large Reasoning Models (LRMs) have significantly advanced beyond traditional Large Language Models (LLMs) with their exceptional logical reasoning capabilities, yet these improvements introduce heightened safety risks. When subjected to jailbreak attacks, their ability to generate more targeted and organized content can lead to greater harm. Although some studies claim that reasoning enables safer LRMs against existing LLM attacks, they overlook the inherent flaws within the reasoning process i…

IMA-Catcher: An IMpact-Aware Nonprehensile Catching Framework based on Combined Optimization and Learning
Francesco Tassi (Human-Robot Interfaces and Interaction Lab., Istituto Italiano di Tecnologia, Italy), Jianzhuang Zhao (Human-Robot Interfaces and Interaction Lab., Istituto Italiano di Tecnologia, Italy), Gustavo J. G. Lahr (Human-Robot Interfaces and Interaction Lab., Istituto Italiano di Tecnologia, Italy), Luna Gava (Event-Driven Perception for Robotics Lab, Istituto Italiano d…

IMA-Catcher: An IMpact-Aware Nonprehensile Catching Framework based on Combined Optimization and Learning
Robotic catching of flying objects typically generates high impact forces that might lead to task failure and potential hardware damages. This is accentuated when the object mass to robot payload ratio increases, given the strong inertial components characterizing this task. This paper aims to address this problem by proposing an implicitly impact-aware framework that accomplishes the catching task in both pre- and post-catching phases. In the first phase, a motion planner generates optimal tra…

I have the feeling that Ursula von der Leyen, in the trade discussions between the EU and the US, might has been heeding Napoleon's advice: "never interfere when your opponent is making a mistake."

The NEID Earth Twin Survey. III. Survey Performance After Three Years on Sky
Arvind F. Gupta, Evan Fitzmaurice, Suvrath Mahadevan, Paul Robertson, Jacob K. Luhn, Jason T. Wright, Sarah E. Logsdon, Daniel M. Krolikowski, Leonardo A. Paredes, Chad F. Bender, Mark R. Giovinazzi, Andrea S. Lin, Cullen H. Blake, Caleb I. Ca\~nas, Eric B. Ford, Samuel P. Halverson, Shubham Kanodia, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Arpita Roy, Christian Schwab, Gu{\dh}mundur Stef\'ans…

The NEID Earth Twin Survey. III. Survey Performance After Three Years on Sky
The NEID Earth Twin Survey (NETS) has been delivering a rich set of precise radial velocity (RV) measurements for 41 bright, nearby main sequence stars. Here, we describe the status of the survey after three years on sky and we present the full set of RV measurements and accompanying stellar activity indicators. We discuss intermediate survey diagnostics, including calibration of the known RV zero point offset introduced following the Contreras fire in 2022 and the identification of an undiagno…

IFEvalCode: Controlled Code Generation
Jian Yang, Wei Zhang, Shukai Liu, Linzheng Chai, Yingshui Tan, Jiaheng Liu, Ge Zhang, Wangchunshu Zhou, Guanglin Niu, Zhoujun Li, Binyuan Hui, Junyang Lin
https://arxiv.org/abs/2507.22462

IFEvalCode: Controlled Code Generation
Code large language models (Code LLMs) have made significant progress in code generation by translating natural language descriptions into functional code; however, real-world applications often demand stricter adherence to detailed requirements such as coding style, line count, and structural constraints, beyond mere correctness. To address this, the paper introduces forward and backward constraints generation to improve the instruction-following capabilities of Code LLMs in controlled code ge…