· ai
[Paper] Aggregating Diverse Cue Experts for AI-Generated Image Detection
The rapid emergence of image synthesis models poses challenges to the generalization of AI-generated image detectors. However, existing methods often rely on mo...
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- · ai
[Paper] Uncovering Political Bias in Large Language Models using Parliamentary Voting Records
As large language models (LLMs) become deeply embedded in digital platforms and decision-making systems, concerns about their political biases have grown. While...
- · ai
[Paper] On the use of graph models to achieve individual and group fairness
Machine Learning algorithms are ubiquitous in key decision-making contexts such as justice, healthcare and finance, which has spawned a great demand for fairnes...
- · ai
[Paper] Fast and explainable clustering in the Manhattan and Tanimoto distance
The CLASSIX algorithm is a fast and explainable approach to data clustering. In its original form, this algorithm exploits the sorting of the data points by the...
- · ai
[Paper] Pervasive Annotation Errors Break Text-to-SQL Benchmarks and Leaderboards
Researchers have proposed numerous text-to-SQL techniques to streamline data analytics and accelerate the development of database-driven applications. To compar...
- · ai
[Paper] Asymptotic Universal Alignment: A New Alignment Framework via Test-Time Scaling
Aligning large language models (LLMs) to serve users with heterogeneous and potentially conflicting preferences is a central challenge for personalized and trus...
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[Paper] Translating Light-Sheet Microscopy Images to Virtual H&E Using CycleGAN
Histopathology analysis relies on Hematoxylin and Eosin (H&E) staining, but fluorescence microscopy offers complementary information. Converting fluorescenc...
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[Paper] Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs
Retrieval-Augmented Generation for software engineering often relies on vector similarity search, which captures topical similarity but can fail on multi-hop ar...
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[Paper] Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often...
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[Paper] Adaptive Requesting in Decentralized Edge Networks via Non-Stationary Bandits
We study a decentralized collaborative requesting problem that aims to optimize the information freshness of time-sensitive clients in edge networks consisting ...
- · ai
[Paper] M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding
Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning, and recent advances...
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[Paper] Spatial Context Improves the Integration of Text with Remote Sensing for Mapping Environmental Variables
Recent developments in natural language processing highlight text as an emerging data source for ecology. Textual resources carry unique information that can be...