research

On the impact of retrieved content representations in RAG Pipelines

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

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On the regularization of Wasserstein GANs

DGX agent

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

researcharxiv-cs-lg

1 Jun 2026

Open and closed models are on different exponentials

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

researchinterconnects

OpenSTBench: Beyond Semantic Evaluation for Speech Translation

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

Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression

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

ParalESN: Enabling parallel information processing in Reservoir Computing

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

PEEK: Picking Essential frames via Efficient Knowledge distillation

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

Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI

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

Physics-Informed Coarsening for Multigrid Graph Neural Surrogates

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

Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics

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

researcharxiv-cs-ro

PINNs Failure Modes are Overfitting

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

Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward

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

PolSAR Image Classification using a Hybrid Complex-Valued Network (HybridCVNet)

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

Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning

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

Ponder This Challenge - June 2026 - The Superhero Team Movies

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

researchibm-research

Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference

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

Position: Evaluation of ECG Representations Must Be Fixed

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

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential

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

Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach

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

Post-Training Neural Network Pruning using Graph Curvature

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

ProofWala: A Framework for Multilingual Proof Data Synthesis and Theorem-Proving

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

Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators

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

Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow

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

Quantifying Error Propagation and Model Collapse in Diffusion Models

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

Real-time multilingual ASR using rolling buffers and monolingual models [P]

researchr-machinelearning

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

Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma

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

Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation

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

Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks

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

Refining Word-Based Grammatical Error Annotation for L2 Korean

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

Regret-Based Federated Causal Discovery with Unknown Interventions

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 joins IBM Research

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

researchibm-research

Residual Reservoir Memory Networks

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

Rethinking Sparse Mixture of Experts from a Unified Perspective

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

Retriever Portfolios: A Principled Approach to Adaptive RAG

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

Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't

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

Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners

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

SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling

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

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference

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

Scalable Bayesian Inference for Nonlinear Conservation Laws

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

Scalable Inference-Time Annealing with Surrogate Likelihood Estimators

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

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms

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

ScaleMAP: Preserving Local Density and Neighborhood Structure in Low-Dimensional Embeddings

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

Scaling Higher-Order Graph Learning with Maximal Clique Complexes

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

SCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks

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

Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment

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

Seeing Fast and Slow: Bimodal 3D Scene Graphs for Open-set Tasks

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

researcharxiv-cs-ro

Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates

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

Self-Reflective Generation at Test Time

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