arXiv:2603.24448v1 Announce Type: cross Abstract: Current clinical decision support systems (CDSSs) typically base their predictions on correlation, not causation. In recent years, causal machine learning (ML) has emerged as a promising way to improve decision-making with CDSSs by offering interpretable, treatment-specific reasoning. However, existing research often emphasizes model development rather than designing clinician-facing interfaces. To […]
Bridging the Evaluation Gap: Standardized Benchmarks for Multi-Objective Search
arXiv:2603.24084v1 Announce Type: new Abstract: Empirical evaluation in multi-objective search (MOS) has historically suffered from fragmentation, relying on heterogeneous problem instances with incompatible objective definitions that make cross-study comparisons difficult. This standardization gap is further exacerbated by the realization that DIMACS road networks, a historical default benchmark for the field, exhibit highly correlated objectives that […]
Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents
arXiv:2603.24556v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs). Yet, its effectiveness fundamentally hinges on document chunking – an often-overlooked determinant of its quality. This paper presents an empirical study quantifying performance differences across four chunking strategies: fixed-size sliding window, recursive, breakpoint-based […]
FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
arXiv:2510.02578v5 Announce Type: replace Abstract: We present FLOWR.root, an SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation with joint potency and binding affinity prediction and confidence estimation. The model supports de novo generation, interaction- and pharmacophore-conditional sampling, fragment elaboration and replacement, and multi-endpoint affinity prediction (pIC50, pKi, pKd, pEC50). Training combines large-scale ligand libraries with […]
Evaluation of Large Language Models via Coupled Token Generation
arXiv:2502.01754v3 Announce Type: replace-cross Abstract: State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multiple times. In this work, we argue that the evaluation and ranking of large language models should control for the […]
Is normalized biomass really abundance? pitfalls, artifacts, and misconceptions in the field of size spectra analysis: a case for back-transformed spectra and standardized binning
arXiv:2602.01496v2 Announce Type: replace Abstract: The NBSS (normalized biomass size spectrum) is a common, intuitive approach for the study of natural ecosystems. However, very few studies have been dedicated to verifying possible flaws and paradoxes in this widely used method. Evident points of concern of the NBSS method are 1.) the loss of variability due […]
Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets
arXiv:2405.17573v3 Announce Type: replace-cross Abstract: We study Leaky ResNets, which interpolate between ResNets and Fully-Connected nets depending on an ‘effective depth’ hyper-parameter $tildeL$. In the infinite depth limit, we study ‘representation geodesics’ $A_p$: continuous paths in representation space (similar to NeuralODEs) from input $p=0$ to output $p=1$ that minimize the parameter norm of the network. […]
CAPTCHA Solving for Native GUI Agents: Automated Reasoning-Action Data Generation and Self-Corrective Training
arXiv:2603.23559v1 Announce Type: cross Abstract: GUI agents are rapidly shifting from multi-module pipelines to end-to-end, native vision-language models (VLMs) that perceive raw screenshots and directly interact with digital devices. Despite rapid progress on general GUI tasks, CAPTCHA solving remains a major challenge. On the other hand, although specialized CAPTCHA solving pipelines exist, they cannot handle […]
MedAidDialog: A Multilingual Multi-Turn Medical Dialogue Dataset for Accessible Healthcare
arXiv:2603.24132v1 Announce Type: cross Abstract: Conversational artificial intelligence has the potential to assist users in preliminary medical consultations, particularly in settings where access to healthcare professionals is limited. However, many existing medical dialogue systems operate in a single-turn question–answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. In this work, we […]
AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering
arXiv:2602.07906v4 Announce Type: replace-cross Abstract: Autonomous Machine Learning Engineering (MLE) requires agents to perform sustained, iterative optimization over long horizons. While recent LLM-based agents show promise, current prompt-based agents for MLE suffer from behavioral stagnation due to frozen parameters. Although Reinforcement Learning (RL) offers a remedy, applying it to MLE is hindered by prohibitive execution […]
Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents
arXiv:2603.23518v1 Announce Type: cross Abstract: General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. In contrast, instruction-tuned embedders can align embeddings with textual instructions yet cannot autonomously infer latent corpus structures, such as determining the optimal number of clusters. To address both limitations, we […]
Qworld: Question-Specific Evaluation Criteria for LLMs
arXiv:2603.23522v1 Announce Type: cross Abstract: Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question’s context. Binary scores and static rubrics fail to capture these context-dependent requirements. Existing methods define criteria at the dataset level or generate them in a single pass, which limits their ability to explore […]