Tootfinder

A Formal Framework for Fluency-based Multi-Reference Evaluation in Grammatical Error Correction
Eitan Klinger, Zihao Huang, Tran Minh Nguyen, Emma Jayeon Park, Yige Chen, Yang Gu, Qingyu Gao, Siliang Liu, Mengyang Qiu, Jungyeul Park
https://arxiv.org/abs/2510.06749

A Formal Framework for Fluency-based Multi-Reference Evaluation in Grammatical Error Correction
Evaluating grammatical error correction requires metrics that reflect the diversity of valid human corrections rather than privileging a single reference. Existing frameworks, largely edit-based and English-centric, rely on rigid alignments between system and reference edits, limiting their applicability in multilingual and generative settings. This paper introduces a formal framework for \textit{fluency-based multi-reference evaluation}, framing $n$-gram similarity as an aggregation problem ov…

Simulations of Globular Cluster Evolution with Multiple Stellar Populations
Mirek Giersz, Abbas Askar, Arkadiusz Hypki, Jongsuk Hong, Grzegorz Wiktorowicz, Lucas Hellstrom
https://arxiv.org/abs/2510.06942

Simulations of Globular Cluster Evolution with Multiple Stellar Populations
The formation of stars with light-element abundance variations in globular clusters and the subsequent dynamical evolution of these multiple populations remains an open question. One of the most widely discussed is the AGB scenario, in which chemically processed material from the envelopes of AGB stars mixes with re-accreted primordial gas flowing into the center of the cluster. Based on this scenario, more than two hundred MOCCA simulations of cluster evolution have been carried out, incorpora…

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
Yi Lu, Jianing Wang, Linsen Guo, Wei He, Hongyin Tang, Tao Gui, Xuanjing Huang, Xuezhi Cao, Wei Wang, Xunliang Cai
https://arxiv.org/abs/2510.08189

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models' ability to understand and respond to complex, long-horizon scenarios. To address this incomplete evaluation of Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to stimulate long-horizon reasoning beha…

NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions
Haolin Yang, Yuxing Long, Zhuoyuan Yu, Zihan Yang, Minghan Wang, Jiapeng Xu, Yihan Wang, Ziyan Yu, Wenzhe Cai, Lei Kang, Hao Dong
https://arxiv.org/abs/2510.08173

NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions
Instruction-following navigation is a key step toward embodied intelligence. Prior benchmarks mainly focus on semantic understanding but overlook systematically evaluating navigation agents' spatial perception and reasoning capabilities. In this work, we introduce the NavSpace benchmark, which contains six task categories and 1,228 trajectory-instruction pairs designed to probe the spatial intelligence of navigation agents. On this benchmark, we comprehensively evaluate 22 navigation agents, in…

Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts
Rochana Prih Hastuti, Rian Adam Rajagede, Mansour Al Ghanim, Mengxin Zheng, Qian Lou
https://arxiv.org/abs/2509.07755 …

Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts
As large language models (LLMs) adapted to sensitive domains such as medicine, their fluency raises safety risks, particularly regarding provenance and accountability. Watermarking embeds detectable patterns to mitigate these risks, yet its reliability in medical contexts remains untested. Existing benchmarks focus on detection-quality tradeoffs, overlooking factual risks under low-entropy settings often exploited by watermarking's reweighting strategy. We propose a medical-focused evaluation w…

SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models
Andong Deng, Taojiannan Yang, Shoubin Yu, Lincoln Spencer, Mohit Bansal, Chen Chen, Serena Yeung-Levy, Xiaohan Wang
https://arxiv.org/abs/2510.08559

SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models
Large Multimodal Models (LMMs) have achieved remarkable progress across various capabilities; however, complex video reasoning in the scientific domain remains a significant and challenging frontier. Current video benchmarks predominantly target general scenarios where perception/recognition is heavily relied on, while with relatively simple reasoning tasks, leading to saturation and thus failing to effectively evaluate advanced multimodal cognitive skills. To address this critical gap, we intr…

Evolution of spin excitations in superconducting La$_{2-x}$Ca$_{x}$CuO$_{4-\delta}$ from the underdoped to the heavily overdoped regime
S. Hameed, Y. Liu, M. Knauft, K. S. Rabinovich, G. Kim, G. Christiani, G. Logvenov, F. Yakhou-Harris, A. V. Boris, B. Keimer, M. Minola
https://arxiv.org/abs/2509.06680

Evolution of spin excitations in superconducting La$_{2-x}$Ca$_{x}$CuO$_{4-δ}$ from the underdoped to the heavily overdoped regime
We investigate high-energy spin excitations in hole-doped La$_{2-x}$Ca$_{x}$CuO$_{4-δ}$ films across a broad Ca doping range $x = 0.05-0.50$ using resonant inelastic x-ray scattering (RIXS). Polarization analysis and incident-photon energy detuning measurements confirm the persistence of collective paramagnon excitations up to $x = 0.50$. Consistent with previous studies on other cuprate families, we observe a pronounced crossover near $x = 0.15$, where paramagnon spectral weight is transferre…

Optimized SVR Framework for Electric Load Forecasting
Nishant Gadde, Yoshua Alexander, Sarvesh Parthasarthy, Arman Allidina
https://arxiv.org/abs/2510.06476 https://

Optimized SVR Framework for Electric Load Forecasting
Load forecasting has always been a challenge for grid operators due to the growing complexity of power systems. The increase in extreme weather and the need for energy from customers has led to load forecasting sometimes failing. This research presents a Support Vector Regression (SVR) framework for electric load forecasting that outperforms the industry standard. The SVR model demonstrates better accuracy across all evaluation metrics that are important for power system operations. The model h…

ProSEA: Problem Solving via Exploration Agents
William Nguyen, Vinh Luong, Christopher Nguyen
https://arxiv.org/abs/2510.07423 https://arxiv.org/pdf/2510.0…

ProSEA: Problem Solving via Exploration Agents
Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions, falling short of true collaboration or adaptive reasoning. We introduce ProSEA, a modular, general-purpose multi-agent framework designed for iterative problem solving through exploration and plan evolution. ProSEA features a hierarchical architecture in which a Manager Agent orchestrates domain-specialized Expert A…

OpenJAI-v1.0: An Open Thai Large Language Model
Pontakorn Trakuekul, Attapol T. Rutherford, Jullajak Karnjanaekarin, Narongkorn Panitsrisit, Sumana Sumanakul
https://arxiv.org/abs/2510.06847

OpenJAI-v1.0: An Open Thai Large Language Model
We introduce OpenJAI-v1.0, an open-source large language model for Thai and English, developed from the Qwen3-14B model. Our work focuses on boosting performance on practical tasks through carefully curated data across three key use cases: instruction following, long-context understanding, and tool use. Evaluation results show that OpenJAI-v1.0 improves on the capabilities of its base model and outperforms other leading open-source Thai models on a diverse suite of benchmarks, while avoiding ca…