- FML-bench: Benchmarking Machine Learning Agents for Scientific Research
Qiran Zou, Hou Hei Lam, Wenhao Zhao, Yiming Tang, Tingting Chen · Oct 12, 2025
Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments.
- Mapping Semantic & Syntactic Relationships with Geometric Rotation
Michael Freenor, Lauren Alvarez · Oct 10, 2025
Demonstrations
Understanding how language and embedding models encode semantic relationships is fundamental to model interpretability.
- The Speech-LLM Takes It All: A Truly Fully End-to-End Spoken Dialogue State Tracking Approach
Nizar El Ghazal, Antoine Caubrière, Valentin Vielzeuf · Oct 10, 2025
This paper presents a comparative study of context management strategies for end-to-end Spoken Dialog State Tracking using Speech-LLMs.
- Chlorophyll-a Mapping and Prediction in the Mar Menor Lagoon Using C2RCC-Processed Sentinel 2 Imagery
Antonio Martínez-Ibarra, Aurora González-Vidal, Adrián Cánovas-Rodríguez, Antonio F. Skarmeta · Oct 10, 2025
The Mar Menor, Europe's largest hypersaline coastal lagoon, located in southeastern Spain, has undergone severe eutrophication crises, with devastating impacts on biodiversity and water quality.
- Verifying Chain-of-Thought Reasoning via Its Computational Graph
Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray, Nicola Cancedda · Oct 10, 2025
Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a computation fails.
- Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
Yumin Choi, Dongki Kim, Jinheon Baek, Sung Ju Hwang · Oct 10, 2025
To tackle this problem, we then propose the Multimodal Prompt Optimizer (MPO), a unified framework that not only performs the joint optimization of multimodal prompts through alignment-preserving updates but also guides the selection proces
- FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
Yan Wang, Keyi Wang, Shanshan Yang, Jaisal Patel, Jeff Zhao · Oct 10, 2025
We introduce FinAuditing, a taxonomy-aligned, structure-aware benchmark built from real XBRL filings.
- Lossless Vocabulary Reduction for Auto-Regressive Language Models
Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida, Shin'ya Yamaguchi, Tomoya Ohba · Oct 9, 2025
Tokenization -- the process of decomposing a given text into a sequence of subwords called tokens -- is one of the key components in the development of language models.
- Everything is Plausible: Investigating the Impact of LLM Rationales on Human Notions of Plausibility
Shramay Palta, Peter Rankel, Sarah Wiegreffe, Rachel Rudinger · Oct 9, 2025
We investigate the degree to which human plausibility judgments of multiple-choice commonsense benchmark answers are subject to influence by (im)plausibility arguments for or against an answer, in particular, using rationales generated by L
- PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing
Anthony Hughes, Vasisht Duddu, N. Asokan, Nikolaos Aletras, Ning Ma · Oct 8, 2025
Language models (LMs) may memorize personally identifiable information (PII) from training data, enabling adversaries to extract it during inference.
- EconCausal: A Context-Aware Causal Reasoning Benchmark for Large Language Models in Social Science
Donggyu Lee, Hyeok Yun, Meeyoung Cha, Sungwon Park, Sangyoon Park · Oct 8, 2025
To address this, we introduce EconCausal, a large-scale benchmark comprising 10,490 context-annotated causal triplets extracted from 2,595 high-quality empirical studies published in top-tier economics and finance journals.
- Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned Image Recovery
Didrik Bergström, Deniz Gündüz, Onur Günlü · Oct 8, 2025
We consider image transmission via deep joint source-channel coding (DeepJSCC) over multi-hop additive white Gaussian noise (AWGN) channels by training a DeepJSCC encoder-decoder pair with a pre-trained deep hash distillation (DHD) module t
- Peeking inside the Black-Box: Reinforcement Learning for Explainable and Accurate Relation Extraction
Xinyu Guo, Zhengliang Shi, Minglai Yang, Mahdi Rahimi, Mihai Surdeanu · Oct 7, 2025
Finally, human evaluation shows that our best model generates relational keywords closely aligned with gold labels, increasing human explanation quality ratings by 54% (relative).
- Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis
Sedat Dogan, Nina Dethlefs, Debarati Chakraborty · Oct 7, 2025
We benchmark interpretable baselines (XGBoost, MLP) against end-to-end deep models (BERT, InceptionV3, CLIP) across early observation windows from 30 to 420 minutes.
- Improving Discrete Diffusion Unmasking Policies Beyond Explicit Reference Policies
Chunsan Hong, Seonho An, Min-Soo Kim, Jong Chul Ye · Oct 7, 2025
Empirically, across four benchmarks, our learned policy consistently outperforms max-confidence: for example, on SUDOKU, where unmasking order is critical, it yields a 20.1% gain over random and a 11.2% gain over max-confidence.
- AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Mingdai Yang, Nurendra Choudhary, Jiangshu Du, Edward W. Huang, Philip S. Yu · Oct 7, 2025
Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-existent items and full-catalog ranking.
- Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty
Yao Xiao, Jung-jae Kim, Roy Ka-wei Lee, Lidong Bing · Oct 7, 2025
Pairwise Preference
Self-play preference optimization has emerged as a prominent paradigm for aligning large language models (LLMs).
- Slm-mux: Orchestrating small language models for reasoning
Chenyu Wang, Zishen Wan, Hao Kang, Emma Chen, Zhiqiang Xie · Oct 6, 2025
Additional experiments show that the core principle of SLM-MUX extends to open-ended generation tasks (e.g., HumanEval) and benefits other model classes, including frontier LLMs and domain-specific fine-tuned SLMs.
- SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests
Punya Syon Pandey, Hai Son Le, Devansh Bhardwaj, Rada Mihalcea, Zhijing Jin · Oct 6, 2025
Critique Edit
Yet, existing safety benchmarks rarely test vulnerabilities in domains such as political manipulation, propaganda and disinformation generation, or surveillance and information control.
- Multilingual Routing in Mixture-of-Experts
Lucas Bandarkar, Chenyuan Yang, Mohsen Fayyaz, Junlin Hu, Nanyun Peng · Oct 6, 2025
These 1-2% gains are remarkably consistent across two evaluation tasks, three models, and 15+ languages, especially given that these simple interventions override routers of extensively trained, state-of-the-art LLMs.
