Tootfinder

När han sade: ”Ryska krigsskepp, dra åt helvete!” var han övertygad om att han skulle dö.
Men Roman Hrybov överlevde. Framför allt överlevde hans ord.
Läs Anna-Lena Lauréns och Daniel Costantinis intervju med Hrybov. https://www.dn.se/varlden…

Ukraina: Han bad det ryska krigsskeppet att dra åt helvete
När han sade: ”Ryska krigsskepp, dra åt helvete!” var han övertygad om att han skulle dö. Men Roman Hrybov överlevde. Framför allt överlevde hans ord.

DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces
Yuan Xu, Shaowen Xiang, Yizhi Song, Ruoting Sun, Xin Tong
https://arxiv.org/abs/2509.13444

DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces
Large Language Models are reshaping task automation, yet remain limited in complex, multi-step real-world tasks that require aligning with vague user intent and enabling dynamic user override. From a formative study with 12 participants, we found that end-users actively seek to shape generative interfaces rather than relying on one-shot outputs. To address this, we introduce the human-agent co-generation paradigm, materialized in DuetUI. This LLM-empowered system unfolds alongside task progress…

@… @… That’s one of the reasons I don’t use an LLM!
I did try it once, for coding. It lied to me. So I didn’t use it again.
People keep saying “it’s like an intern”. If an intern repeatedly lies to your face, they are bad at the on…

ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
Xingshan Zeng, Weiwen Liu, Lingzhi Wang, Liangyou Li, Fei Mi, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu
https://arxiv.org/abs/2508.12685

ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions, often involving complex function calls and dynamic user-agent exchanges. Existing simulation-based data generation methods for such scenarios rely heavily on costly autoregressive interactions between multiple LLM agents, thereby limiting real-world performance of agentic tasks. In this paper, we propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, for constructi…

us_roads: United States roads (2000)
The road networks of the 50 US States and the District of Columbia based on UA Census 2000 TIGER/Line Files. Edges are stretches of road and vertices are intersections of roads. The data sets were assembled by Dominik Schultes. The 'merged' network contains all the states merged together.
This network has 64892 nodes and 76809 edges.
Tags: Transportation, Roads, Unweighted

LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology
Renan Souza, Timothy Poteet, Brian Etz, Daniel Rosendo, Amal Gueroudji, Woong Shin, Prasanna Balaprakash, Rafael Ferreira da Silva
https://arxiv.org/abs/2509.13978

LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology
Modern scientific discovery increasingly relies on workflows that process data across the Edge, Cloud, and High Performance Computing (HPC) continuum. Comprehensive and in-depth analyses of these data are critical for hypothesis validation, anomaly detection, reproducibility, and impactful findings. Although workflow provenance techniques support such analyses, at large scale, the provenance data become complex and difficult to analyze. Existing systems depend on custom scripts, structured quer…

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
Xuhe Gong, Hengbo Zhao, Xiao Fu, Jingchen Lian, Qifan Yang, Ran Li, Ruijuan Xiao, Tao Zhang, Hong Li
https://arxiv.org/abs/2508.11989

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic potentials, trained on data from first-principles calculations, offer a powerful alternative by efficiently approximating the complex three-dimensional potential energy surface with near-DFT accuracy. In this work, we develop a general-purpose machine learning interat…

Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming
Vasileios Saketos, Sebastian Kaltenbach, Sergey Litvinov, Petros Koumoutsakos
https://arxiv.org/abs/2508.11703

Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming
Algorithmic discovery has traditionally relied on human ingenuity and extensive experimentation. Here we investigate whether a prominent scientific computing algorithm, the Kalman Filter, can be discovered through an automated, data-driven, evolutionary process that relies on Cartesian Genetic Programming (CGP) and Large Language Models (LLM). We evaluate the contributions of both modalities (CGP and LLM) in discovering the Kalman filter under varying conditions. Our results demonstrate that ou…

Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning
Shambhavi Krishna, Atharva Naik, Chaitali Agarwal, Sudharshan Govindan, Taesung Lee, Haw-Shiuan Chang
https://arxiv.org/abs/2509.13624

Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning
Large language models are increasingly deployed across diverse applications. This often includes tasks LLMs have not encountered during training. This implies that enumerating and obtaining the high-quality training data for all tasks is infeasible. Thus, we often need to rely on transfer learning using datasets with different characteristics, and anticipate out-of-distribution requests. Motivated by this practical need, we propose an analysis framework, building a transfer learning matrix and …

Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
Jianfeng Zhu, Julina Maharjan, Xinyu Li, Karin G. Coifman, Ruoming Jin
https://arxiv.org/abs/2509.13244

Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
Large Language Models (LLMs) are increasingly deployed in roles requiring nuanced psychological understanding, such as emotional support agents, counselors, and decision-making assistants. However, their ability to interpret human personality traits, a critical aspect of such applications, remains unexplored, particularly in ecologically valid conversational settings. While prior work has simulated LLM "personas" using discrete Big Five labels on social media data, the alignment of LLMs with co…