Tootfinder

Do LLMs Align with My Task? Evaluating Text-to-SQL via Dataset Alignment
Davood Rafiei, Morgan Lindsay Heisler, Weiwei Zhang, Mohammadreza Pourreza, Yong Zhang
https://arxiv.org/abs/2510.04919

Do LLMs Align with My Task? Evaluating Text-to-SQL via Dataset Alignment
Supervised Fine-Tuning (SFT) is an effective method for adapting Large Language Models (LLMs) on downstream tasks. However, variability in training data can hinder a model's ability to generalize across domains. This paper studies the problem of dataset alignment for Natural Language to SQL (NL2SQL or text to SQL), examining how well SFT training data matches the structural characteristics of target queries and how this alignment impacts model performance. We hypothesize that alignment can be a…

Align Your Query: Representation Alignment for Multimodality Medical Object Detection
Ara Seo, Bryan Sangwoo Kim, Hyungjin Chung, Jong Chul Ye
https://arxiv.org/abs/2510.02789 h…

Align Your Query: Representation Alignment for Multimodality Medical Object Detection
Medical object detection suffers when a single detector is trained on mixed medical modalities (e.g., CXR, CT, MRI) due to heterogeneous statistics and disjoint representation spaces. To address this challenge, we turn to representation alignment, an approach that has proven effective for bringing features from different sources into a shared space. Specifically, we target the representations of DETR-style object queries and propose a simple, detector-agnostic framework to align them with modal…

https://www.alxnow.com/2025/09/05/alexandria-city-high-school-students-stage-walkout-protesting-gun-violence-in-schools/

Alexandria City High School students stage walkout protesting gun violence in schools | ALXnow
Hundreds of Alexandria City High School students held a walkout on Friday, Sept. 5, demonstrating againt gun violence in schools. The walkout at ACHS's King Street and Minnie Howard campuses started at 11:30 a.m., and lasted for less than a half hour. The demonstrations are part of a national walkout, were organized by Titans Demand

The Death Star of the Desert: Inside Allegiant Stadium https://raiderramble.com/2025/09/06/the-death-star-of-the-desert-inside-allegiant-stadium/

Scientists find new way to fight Alzheimer’s | Courthouse News Service
https://www.courthousenews.com/scientists-find-new-way-to-fight-alzheimers/

Superposition disentanglement of neural representations reveals hidden alignment
Andr\'e Longon, David Klindt, Meenakshi Khosla
https://arxiv.org/abs/2510.03186 https://

Superposition disentanglement of neural representations reveals hidden alignment
The superposition hypothesis states that a single neuron within a population may participate in the representation of multiple features in order for the population to represent more features than the number of neurons. In neuroscience and AI, representational alignment metrics measure the extent to which different deep neural networks (DNNs) or brains represent similar information. In this work, we explore a critical question: \textit{does superposition interact with alignment metrics in any un…

Reward Model Routing in Alignment
Xinle Wu, Yao Lu
https://arxiv.org/abs/2510.02850 https://arxiv.org/pdf/2510.02850

Reward Model Routing in Alignment
Reinforcement learning from human or AI feedback (RLHF / RLAIF) has become the standard paradigm for aligning large language models (LLMs). However, most pipelines rely on a single reward model (RM), limiting alignment quality and risking overfitting. Recent work explores RM routing--dynamically selecting an RM from a candidate pool to exploit complementary strengths while maintaining $O(1)$ RM calls--but existing methods suffer from cold-start and insufficient exploration. We propose BayesianR…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Alison Goldfrapp:
🎵 Magma
#AlisonGoldfrapp
https://open.spotify.com/track/6f4jVwLib6C3Qu2FEmsosM

From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models
Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2510.05095

From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models
Large reasoning models (LRMs) generate intermediate reasoning traces before producing final answers, yielding strong gains on multi-step and mathematical tasks. Yet aligning LRMs with human preferences, a crucial prerequisite for model deployment, remains underexplored. The statistically correct objective for preference alignment requires marginalizing over reasoning traces, but this computation is intractable in practice. A common workaround optimizes a single sampled trajectory, which introdu…

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Tien Phat Nguyen, Vu Minh Ngo, Tung Nguyen, Linh Van Ngo, Duc Anh Nguyen, Sang Dinh, Trung Le
https://arxiv.org/abs/2510.02788

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Cross-lingual topic modeling aims to uncover shared semantic themes across languages. Several methods have been proposed to address this problem, leveraging both traditional and neural approaches. While previous methods have achieved some improvements in topic diversity, they often struggle to ensure high topic coherence and consistent alignment across languages. We propose XTRA (Cross-Lingual Topic Modeling with Topic and Representation Alignments), a novel framework that unifies Bag-of-Words …