- A dependently-typed calculus of event telicity and culminativity
Pavel Kovalev, Carlo Angiuli · Jun 8, 2025
We present a dependently-typed cross-linguistic framework for analyzing the telicity and culminativity of events, accompanied by examples of using our framework to model English sentences.
- DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Man Ho Lam, Ming Wang, Yuxuan Wan, Junliang Liu · Jun 6, 2025
However, existing front-end UI code generation benchmarks have the following limitations: (1) While framework-based development becomes predominant in modern front-end programming, current benchmarks fail to incorporate mainstream developme
- Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models
Yingqi Hu, Zhuo Zhang, Jingyuan Zhang, Jinghua Wang, Qifan Wang · Jun 6, 2025
These findings highlight concrete privacy risks in FedLLMs and establish a benchmark and evaluation framework for future research on privacy-preserving federated learning.
- Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
Cheonbok Park, Jeonghoon Kim, Joosung Lee, Sanghwan Bae, Jaegul Choo · Jun 6, 2025
Reinforcement learning with verifiable reward (RLVR) has been instrumental in eliciting strong reasoning capabilities from large language models (LLMs) via long chains of thought (CoT).
- When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
Zhishang Xiang, Chuanjie Wu, Qinggang Zhang, Shengyuan Chen, Zijin Hong · Jun 6, 2025
To address this, we propose GraphRAG-Bench, a comprehensive benchmark designed to evaluate GraphRAG models onboth hierarchical knowledge retrieval and deep contextual reasoning.
- Voice Impression Control in Zero-Shot TTS
Kenichi Fujita, Shota Horiguchi, Yusuke Ijima · Jun 6, 2025
The results of both objective and subjective evaluations have demonstrated our method's effectiveness in impression control.
- Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
Yifan Sun, Jingyan Shen, Yibin Wang, Tianyu Chen, Zhendong Wang · Jun 5, 2025
Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities.
- Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement
Chenyu Lin, Yilin Wen, Du Su, Hexiang Tan, Fei Sun · Jun 5, 2025
Retrieval-augmented generation (RAG) improves performance on knowledge-intensive tasks but can be derailed by wrong, irrelevant, or conflicting retrieved text, causing models to rely on inaccurate evidence and cascade errors.
- Sensory-Motor Control with Large Language Models via Iterative Policy Refinement
Jônata Tyska Carvalho, Stefano Nolfi · Jun 5, 2025
We propose a method that enables large language models (LLMs) to control embodied agents through the generation of control policies that directly map continuous observation vectors to continuous action vectors.
- "Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation
Amin Seffo, Aladin Djuhera, Masataro Asai, Holger Boche · Jun 4, 2025
Web Browsing
Recent advancements in large language models (LLMs) have spurred interest in robotic navigation that incorporates complex spatial, mathematical, and conditional constraints from natural language into the planning problem.
- Watermarking Degrades Alignment in Language Models: Analysis and Mitigation
Apurv Verma, NhatHai Phan, Shubhendu Trivedi · Jun 4, 2025
In practice, sampling as few as two to four candidates largely restores unwatermarked alignment performance in truthfulness, safety, and helpfulness, without hurting watermark detection.
- Toward Beginner-Friendly LLMs for Language Learning: Controlling Difficulty in Conversation
Meiqing Jin, Liam Dugan, Chris Callison-Burch · Jun 4, 2025
We further introduce a new token-level evaluation metric, Token Miss Rate (TMR), that quantifies the proportion of incomprehensible tokens per utterance and correlates strongly with human judgments.
- HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
Zhaolu Kang, Junhao Gong, Jiaxu Yan, Wanke Xia, Yian Wang · Jun 4, 2025
Expert Verification
However, current benchmarks for evaluating MLLMs primarily emphasize general knowledge and vertical step-by-step reasoning typical of STEM disciplines, while overlooking the distinct needs and potential of the Humanities and Social Sciences
- EuroGEST: Investigating gender stereotypes in multilingual language models
Jacqueline Rowe, Mateusz Klimaszewski, Liane Guillou, Shannon Vallor, Alexandra Birch · Jun 4, 2025
Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric.
- Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
Xiaoying Zhang, Yipeng Zhang, Hao Sun, Kaituo Feng, Chaochao Lu · Jun 3, 2025
Critique Edit
Recent advances in reinforcement learning (RL) using numerical rewards have significantly enhanced the complex reasoning capabilities of large language models (LLMs).
- Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs
Nguyen-Khang Le, Quan Minh Bui, Minh Ngoc Nguyen, Hiep Nguyen, Trung Vo · Jun 3, 2025
Web Browsing
Web applications are critical to modern software ecosystems, yet ensuring their reliability remains challenging due to the complexity and dynamic nature of web interfaces.
- Esoteric Language Models: Bridging Autoregressive and Masked Diffusion LLMs
Subham Sekhar Sahoo, Zhihan Yang, Yash Akhauri, Johnna Liu, Deepansha Singh · Jun 2, 2025
Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation.
- iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu · Jun 2, 2025
Long Horizon
Detailed experiments demonstrate the consistent improvement delivered by iQUEST across four benchmark datasets and four LLMs.
- Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian Fürrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Muñoz-Gil · Jun 2, 2025
We benchmark the model over different experiments, analyzing the method's accuracy across varying qubit counts and circuit depths, showcasing the ability of the method to outperform existing approaches in gate counts and under noisy conditi
