Knowledge catalogue
arXiv:2607.15080v2 Announce Type: replace Abstract: This article explores the application of covariate balance diagnostics for detecting the presence of hidden confounding/model miss-specification in
arXiv:2609.13995v1 Announce Type: new Abstract: Debate over synthetic data in marketing research has polarized between claims that large language models (LLMs) make human respondents obsolete and call
arXiv:2609.13012v1 Announce Type: new Abstract: Vision-language models retain a substantial amount of pixel-decodable visual content in their visual key-value cache. We show, in our setting, that this
arXiv:2609.11431v1 Announce Type: new Abstract: Genetic Programming and its variants, such as grammatical evolution, are widely used in Symbolic Regression to derive mathematical expressions from mult
arXiv:2609.07603v1 Announce Type: new Abstract: Financial scenarios are diverse and complex, spanning varying data conditions, tool configurations, and workflows. Yet existing CUA, Computer-Using Agen
arXiv:2609.07785v1 Announce Type: new Abstract: An LLM-agent leaderboard invites a familiar inference: an agent ranked above another is the better agent. Public evaluation logs may not support that co
arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze
arXiv:2603.16848v2 Announce Type: replace Abstract: The ``LLM-as-a-judge'' paradigm has become a standard method for evaluating open-ended generation. To address the quadratic scalability costs of pai
arXiv:2608.30805v1 Announce Type: cross Abstract: Natural-language tasks can elicit different verdicts from protocol-following evaluators that receive the same declared information. We study aggregate
arXiv:2608.30980v1 Announce Type: cross Abstract: We study self-modeling: an LLM's ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a pr
arXiv:2601.17173v2 Announce Type: replace-cross Abstract: Question answering systems are typically evaluated on factual correctness, yet many real-world applications-such as education and career guida
arXiv:2608.26135v1 Announce Type: new Abstract: We present a modular data-science pipeline for estimating public sentiment towards individuals from fragmented, unstructured open-source intelligence (O
arXiv:2608.23758v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely cons
arXiv:2608.21860v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) reasoning has significantly enhanced the multi-step problem-solving capabilities of large language models (LLMs) by introducing
arXiv:2608.22103v1 Announce Type: new Abstract: As agents grow more capable and autonomous, their tendency to reward hack, satisfying a task's checks while violating its intent, becomes an increasingl
arXiv:2608.17971v1 Announce Type: new Abstract: The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical we
arXiv:2601.18899v3 Announce Type: replace-cross Abstract: Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking a f
arXiv:2601.21979v2 Announce Type: replace Abstract: Feature embeddings acquired from pretrained models are widely used in medical applications of deep learning to assess the characteristics of dataset
This post was made with AI. I tried to remove as much slop as possible and keep it straight to the point to save your time as I know how annoying AI slop posts can be, but I still wanted to retain all
arXiv:2608.12788v1 Announce Type: new Abstract: The rapid advancement of Auto-Research has surfaced a fundamental evaluation challenge: how can we measure the alignment, logical coherence, and evoluti
arXiv:2608.11727v1 Announce Type: new Abstract: When a coding agent obeys a rule, it may simply have been going to do that anyway. Existing instruction-following benchmarks cannot tell the difference:
arXiv:2501.17629v2 Announce Type: replace-cross Abstract: Several studies claim that large language models have passed the Turing Test and hence can 'think', yet none follow Turing's original instruct
arXiv:2608.07606v1 Announce Type: cross Abstract: Despite advances in 3D ultrasound, most percutaneous cardiac interventions still rely on 2D visualization, limiting depth perception and spatial under
arXiv:2608.07727v1 Announce Type: new Abstract: Dravidian languages, mainly Tamil, Telugu, Kannada, and Malayalam make up only a small part of the data used to train multilingual language models, so i
arXiv:2608.07812v1 Announce Type: new Abstract: A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations o
arXiv:2608.05726v1 Announce Type: new Abstract: Large Language Models (LLMs) are often used as evaluators of text quality, known as LLM-as-a-Judge, which can outperform conventional automatic evaluati
arXiv:2608.04646v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong performance on Theory of Mind (ToM) tests, prompting debate about the nature and validity of the
arXiv:2408.02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensiona
arXiv:2606.15974v2 Announce Type: replace Abstract: Despite the significant advancement of LLMs in conversation summarization, their evaluation remains limited by insufficient scenarios, input lengths
arXiv:2503.10647v2 Announce Type: replace Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dim
arXiv:2607.26922v1 Announce Type: new Abstract: Multi-agent LLM pipeline systems break down the task among multiple roles for better reasoning, but are benchmarked mainly with large-scale commercial m
arXiv:2607.23408v1 Announce Type: new Abstract: Expensive black-box optimization is ubiquitous in science and engineering, where function evaluations are costly and the evaluation budget is limited. T
arXiv:2607.10428v2 Announce Type: replace Abstract: Evaluating large language models (LLMs) as multi-turn conversational partners requires probing capabilities that single-turn benchmarks miss: person
arXiv:2607.20589v1 Announce Type: new Abstract: Persona simulation involves utilizing large language models (LLMs) to anticipate human choices or interactions based on specific characteristic informat
arXiv:2509.21028v4 Announce Type: replace Abstract: We introduce SciTrek, a synthetic question-answering dataset for assessing and improving long-context numerical reasoning in large language models (
arXiv:2607.12792v1 Announce Type: cross Abstract: Jailbreak-robustness research typically evaluates safety through generated responses using an LLM-as-judge approach. Such evaluations, however, are se
arXiv:2607.07957v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain
arXiv:2601.22588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this 'LLM-as-a-Judge' paradigm is costly, opaque,
arXiv:2607.05462v1 Announce Type: cross Abstract: As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse. We present BioSecBench-Refu
arXiv:2501.11086v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-t
arXiv:2607.04814v1 Announce Type: cross Abstract: Extending automatic speech recognition (ASR) to low-resource African languages is constrained by the prohibitive demands of data collection at scale.
arXiv:2607.02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, appl
arXiv:2607.04595v1 Announce Type: new Abstract: Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class. Single-cell pe
arXiv:2607.00399v1 Announce Type: new Abstract: End-to-end autonomous driving models often encounter performance bottlenecks, as training-time scaling leads to high computational costs and diminishing
arXiv:2607.00777v1 Announce Type: cross Abstract: Recognizing jazz standards from audio is a challenging form of tune-level music retrieval: different performances of the same standard may vary in tem
arXiv:2607.00019v1 Announce Type: cross Abstract: This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the
arXiv:2606.30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control
arXiv:2606.29685v1 Announce Type: new Abstract: How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations
arXiv:2510.00809v3 Announce Type: replace Abstract: While Time Series Foundation Models (TSFMs) excel in zero-shot tasks, their behavior under continual fine tuning is poorly understood. We present th
arXiv:2406.07147v3 Announce Type: replace-cross Abstract: This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cogniti
arXiv:2606.25436v1 Announce Type: cross Abstract: Dialogue systems based on large language models (LLMs) have advanced significantly in recent years. However, dialectal variation remains a major chall
arXiv:2606.25984v1 Announce Type: cross Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and
arXiv:2606.23959v1 Announce Type: new Abstract: Because mathematics is highly abstract, a single statement can take very different forms depending on what subfield it is framed in. There are many exam
arXiv:2606.23238v2 Announce Type: replace Abstract: Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction ov
arXiv:2603.02370v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have grown increasingly powerful in recent years, but can also exhibit harmful biases. Prior studies investigat
arXiv:2606.21622v1 Announce Type: cross Abstract: Person-level psychological assessment requires aggregating meaning across many messages from the same individual, a task that document-level training
arXiv:2606.11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent
arXiv:2606.12071v1 Announce Type: cross Abstract: LLMs are increasingly used to generate and judge scientific ideas. This makes novelty evaluation a central problem. Full idea evaluation is difficult
arXiv:2606.08791v1 Announce Type: cross Abstract: We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and outputs alone. Our main result is an exact decompos
arXiv:2606.07521v1 Announce Type: cross Abstract: This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 mo