A team of researchers from Allen Institute for Artificial Intelligence (Ai2), University of Washington and CMU introduce Fluid Benchmarking, an adaptive LLM evaluation method that replaces static accuracy with 2-parameter IRT ability estimation and Fisher-information–driven item selection. By asking only the…
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arXiv:2509.12507v1 Announce Type: cross Abstract: One of the main goals of robotics and intelligent agent research is to enable natural communication with humans in physically situated settings. While recent work has focused on verbal modes such as language and speech,…
arXiv:2504.17356v2 Announce Type: replace-cross Abstract: Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrapper, and embedded-based approaches, the reinforcement learning (RL)-based subspace exploration…
arXiv:2509.06308v2 Announce Type: replace-cross Abstract: This paper studies high-dimensional additive regression under the transfer learning framework, where one observes samples from a target population together with auxiliary samples from different but potentially related regression models. We first introduce a target-only…
arXiv:2507.20999v3 Announce Type: replace Abstract: Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought (CoT) reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning. While parameter-efficient fine-tuning (PEFT) helps reduce cost, most…
arXiv:2403.16393v2 Announce Type: replace-cross Abstract: The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thus, efficient error detection for LLMs…
arXiv:2509.12990v1 Announce Type: cross Abstract: In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To handle the challenges posed by subtle and infrequent mistakes, we propose a Dual-Stage Reweighted…
arXiv:2405.02358v3 Announce Type: replace Abstract: Time series data are ubiquitous across diverse real-world applications, making time series analysis critically important. Traditional approaches are largely task-specific, offering limited functionality and poor transferability. In recent years, foundation models have revolutionized NLP and…
