arXiv:2512.15662v1 Announce Type: new Abstract: Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions. However, most existing large language models (LLMs) decouple reasoning from verification: they either generate reasoning without explicit self-checking or rely on external verifiers to detect errors post hoc. The former lacks […]
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
arXiv:2512.15134v1 Announce Type: cross Abstract: A central goal of interpretability is to recover representations of causally relevant concepts from the activations of neural networks. The quality of these concept representations is typically evaluated in isolation, and under implicit independence assumptions that may not hold in practice. Thus, it is unclear whether common featurization methods – […]
When sufficiency is insufficient: the functional information bottleneck for identifying probabilistic neural representations
arXiv:2512.15671v1 Announce Type: new Abstract: The neural basis of probabilistic computations remains elusive, even amidst growing evidence that humans and other animals track their uncertainty. Recent work has proposed that probabilistic representations arise naturally in task-optimized neural networks trained without explicitly probabilistic inductive biases. However, prior work has lacked clear criteria for distinguishing probabilistic representations, […]
Sparse Autoencoders Make Audio Foundation Models more Explainable
arXiv:2509.24793v2 Announce Type: replace-cross Abstract: Audio pretrained models are widely employed to solve various tasks in speech processing, sound event detection, or music information retrieval. However, the representations learned by these models are unclear, and their analysis mainly restricts to linear probing of the hidden representations. In this work, we explore the use of Sparse […]
Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants
arXiv:2512.15712v1 Announce Type: new Abstract: Interpreting the internal activations of neural networks can produce more faithful explanations of their behavior, but is difficult due to the complex structure of activation space. Existing approaches to scalable interpretability use hand-designed agents that make and test hypotheses about how internal activations relate to external behavior. We propose to […]
Evaluating LLMs for Zeolite Synthesis Event Extraction (ZSEE): A Systematic Analysis of Prompting Strategies
arXiv:2512.15312v1 Announce Type: cross Abstract: Extracting structured information from zeolite synthesis experimental procedures is critical for materials discovery, yet existing methods have not systematically evaluated Large Language Models (LLMs) for this domain-specific task. This work addresses a fundamental question: what is the efficacy of different prompting strategies when applying LLMs to scientific information extraction? We […]
Tourists Profiling by Interest Analysis
arXiv:2512.14704v1 Announce Type: cross Abstract: With the recent digital revolution, analyzing of tourists’ behaviors and research fields associated with it have changed profoundly. It is now easier to examine behaviors of tourists using digital traces they leave during their travels. The studies conducted on diverse aspects of tourism focus on quantitative aspects of digital traces […]
SemShareKV: Efficient KVCache Sharing for Semantically Similar Prompts via Token-Level LSH Matching
arXiv:2509.24832v2 Announce Type: replace-cross Abstract: As large language models (LLMs) continue to scale, the memory footprint of key-value (KV) caches during inference has become a significant bottleneck. Existing approaches primarily focus on compressing KV caches within a single prompt or reusing shared prefixes or frequently ocurred text segments across prompts. However, such strategies are limited […]
Autonomous Source Knowledge Selection in Multi-Domain Adaptation
arXiv:2512.14710v1 Announce Type: cross Abstract: Unsupervised multi-domain adaptation plays a key role in transfer learning by leveraging acquired rich source information from multiple source domains to solve target task from an unlabeled target domain. However, multiple source domains often contain much redundant or unrelated information which can harm transfer performance, especially when in massive-source domain […]
From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model
arXiv:2512.05277v2 Announce Type: replace-cross Abstract: Temporal understanding in autonomous driving (AD) remains a significant challenge, even for recent state-of-the-art (SoTA) Vision-Language Models (VLMs). Prior work has introduced datasets and benchmarks aimed at improving temporal reasoning, but these have emphasized other video content, including sports, cooking, and movies. No existing benchmark focuses exclusively on the unique […]