Knowledge catalogue
arXiv:2605.31296v1 Announce Type: cross Abstract: Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regio
arXiv:2605.31027v1 Announce Type: new Abstract: We propose a novel neural network architecture, termed Multi-Scale Separable Fourier Neural Networks (MS-SFNN), for the accurate and efficient solution
arXiv:2605.30925v1 Announce Type: new Abstract: Text-to-motion generation has progressed rapidly in recent years, offering an expressive interface for animation and human-computer interaction. However
arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, t
arXiv:2410.15475v2 Announce Type: replace Abstract: Multimodal learning aims to preserve as much task-related information as possible from different inputs. However, current fusion designs often disto
arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema
arXiv:2508.16687v2 Announce Type: replace Abstract: Traditional embeddings represent datapoints as vectors, which makes similarity easy to compute but limits how well they capture hierarchies and comp
arXiv:2605.31309v1 Announce Type: new Abstract: We survey Lyapunov-based techniques for the finite-time analysis of stochastic iterative algorithms, also known as stochastic approximation (SA) algorit
arXiv:2511.17380v2 Announce Type: replace Abstract: Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motiva
arXiv:2605.30778v1 Announce Type: new Abstract: Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchica
arXiv:2605.06235v2 Announce Type: replace-cross Abstract: Retrieval benchmarks are increasingly saturating, but we argue that efficient search is far from a solved problem. We identify a class of quer
arXiv:2605.31090v1 Announce Type: cross Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labe
arXiv:2605.30790v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) supplements a language model's input with retrieved documents, yet most RAG pipelines inherit retrieval component
arXiv:1709.08894v3 Announce Type: replace-cross Abstract: Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unl
This article analyzes the diverging performance trajectories between open-source and closed-source AI models, suggesting they follow different exponential growth curves rather than converging paths. T
arXiv:2605.30792v1 Announce Type: cross Abstract: Speech translation systems increasingly span speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline translation, and streamin
arXiv:2512.00919v2 Announce Type: replace-cross Abstract: We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regres
arXiv:2601.22296v2 Announce Type: replace-cross Abstract: Reservoir Computing (RC) has established itself as an efficient paradigm for temporal processing. However, its scalability remains severely co
arXiv:2605.31029v1 Announce Type: new Abstract: Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioni
arXiv:2605.31275v1 Announce Type: cross Abstract: Artificial Intelligence (AI) agents personalize their responses by tailoring explanations to users' backgrounds, interests, and prior interactions, re
arXiv:2605.31013v1 Announce Type: new Abstract: Learning-based surrogates for partial differential equations have recently matched the accuracy of classical solvers while achieving orders-of-magnitude
arXiv:2605.30503v1 Announce Type: new Abstract: Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned
arXiv:2605.30910v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) are a common class of machine learning-based partial differential equation (PDE) solvers which train a network
arXiv:2605.30824v1 Announce Type: new Abstract: Deep research tasks require LLMs to plan what to investigate, retrieve evidence, and synthesize long-form answers across multiple branches of inquiry. E
arXiv:2605.31137v1 Announce Type: new Abstract: Recently, convolutional neural networks (CNNs) have become popular for image classification due to their effectiveness in computer vision tasks. Now, re
arXiv:2605.31115v1 Announce Type: new Abstract: Dual-hand action segmentation, densely predicting actions for both hands from untrimmed videos, is essential for understanding complex bimanual activiti
IBM Research's June 2026 Ponder This Challenge presents a mathematical or logical puzzle involving superhero team movies. The challenge likely requires solvers to work through constraints and combinat
arXiv:2509.25269v3 Announce Type: replace-cross Abstract: In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval w
arXiv:2602.17531v2 Announce Type: replace-cross Abstract: This position paper argues that current benchmarking practice in 12-lead ECG representation learning must be fixed to ensure progress is relia
arXiv:2506.03779v2 Announce Type: replace-cross Abstract: Quantum kernel functions built using quantum-mechanical principles and have emerged as a centerpiece of quantum machine learning. The initial
arXiv:2511.04393v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as 'agents' for decision-making (DM) in interactive and dynamic environments. Yet, since they
arXiv:2601.16366v2 Announce Type: replace Abstract: This paper provides a fresh view of the neural network (NN) pruning problem through the lens of graph theory. To achieve effective pruning, we aim t
arXiv:2502.04671v3 Announce Type: replace Abstract: Neural approaches to theorem proving require robust infrastructure for interfacing with interactive theorem provers (ITPs), extracting structured pr
arXiv:2602.11216v2 Announce Type: replace Abstract: Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observa
arXiv:2605.30400v1 Announce Type: new Abstract: We present a protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. It outlines how we generate the associations, v
arXiv:2602.16601v2 Announce Type: replace-cross Abstract: Machine learning models are increasingly trained or fine-tuned on synthetic data. Recursively training on such data has been observed to signi
This research discusses a technique for enabling real-time multilingual automatic speech recognition (ASR) on edge devices by dynamically switching between compact monolingual models rather than using
arXiv:2603.08385v2 Announce Type: replace-cross Abstract: Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are monitored
arXiv:2605.31094v1 Announce Type: cross Abstract: The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on
arXiv:2508.11911v2 Announce Type: replace-cross Abstract: We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies laten
arXiv:2605.30545v1 Announce Type: new Abstract: Korean grammatical error correction (K-GEC) presents a structural mismatch between word-based evaluation and the morpheme-level locus of many learner er
arXiv:2512.23626v2 Announce Type: replace Abstract: Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data.
Renowned mathematician Subhash Khot has joined IBM Research, bringing his expertise to the organization's research initiatives. Khot is known for his work in theoretical computer science and computati
arXiv:2508.09925v3 Announce Type: replace-cross Abstract: We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservo
arXiv:2503.22996v3 Announce Type: replace Abstract: Sparse Mixture of Experts (SMoE) models scale the capacity of models while maintaining constant computational overhead. SMoE methods fall into two c
arXiv:2605.31176v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems typically rely on a single retriever and a single set of hyperparameters, despite facing highly heterogeneo
arXiv:2605.30523v1 Announce Type: cross Abstract: Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizati
arXiv:2602.21620v2 Announce Type: replace-cross Abstract: We study the discrete Bertrand pricing game with a non-increasing demand function. The game has n ge 2 players who simultaneously choose price
arXiv:2510.05115v3 Announce Type: replace Abstract: Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the generation of executable solver code from natural la
arXiv:2602.00942v3 Announce Type: replace Abstract: Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central cha
arXiv:2605.31127v1 Announce Type: new Abstract: Nonlinear conservation laws are at the heart of many of the most important dynamical systems in science and engineering. In practical applications, such
arXiv:2605.31498v1 Announce Type: new Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generat
arXiv:2601.22943v2 Announce Type: replace Abstract: Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characte
arXiv:2605.30597v1 Announce Type: new Abstract: Nonlinear dimensionality-reduction methods such as UMAP and PaCMAP adaptively normalize local distances during graph construction, erasing neighborhood
arXiv:2605.31373v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressi
arXiv:2605.31433v1 Announce Type: new Abstract: Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dep
arXiv:2605.30638v1 Announce Type: cross Abstract: We introduce Score Broadcast and Decorrelation (SBD), a principled framework for broadcast-based credit assignment for general families of differentia
arXiv:2605.31067v1 Announce Type: new Abstract: Open-set task execution can significantly benefit from seamlessly switching between coarse and fine scene representations depending on the context and t
arXiv:2605.30722v1 Announce Type: new Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalisin
arXiv:2510.02919v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly solve complex reasoning tasks via long chain-of-thought, but their forward-only autoregressive generation