research

Context Training with Active Information Seeking

arXiv:2605.13050v1 Announce Type: cross Abstract: Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or nich

Continual Harness: Online Adaptation for Self-Improving Foundation Agents [R]

DGX agent

Continual Harness proposes an approach to online adaptation for foundation agents that moves beyond traditional gradient-based retraining by introducing a dual-agent architecture (Teacher/Student) wit

researchr-machinelearning

14 May 2026

Continual Learning with Multilingual Foundation Model

arXiv:2605.13415v1 Announce Type: cross Abstract: This paper presents a multi-stage framework for detecting reclaimed slurs in multilingual social media discourse. It addresses the challenge of identi

Convergent Differential Privacy Analysis for General Federated Learning

arXiv:2408.15621v3 Announce Type: replace Abstract: The powerful cooperation of federated learning (FL) and differential privacy~(DP) provides a promising paradigm for the large-scale private clients.

Coordinating Multiple Conditions for Trajectory-Controlled Human Motion Generation

arXiv:2605.13729v1 Announce Type: cross Abstract: Trajectory-controlled human motion generation aims to synthesize realistic human motions conditioned on both textual descriptions and spatial trajecto

Coreset-Induced Conditional Velocity Flow Matching

arXiv:2605.12951v1 Announce Type: cross Abstract: We propose Coreset-Induced Conditional Velocity Flow Matching (CCVFM), a generative model that augments hierarchical rectified flow with a data-inform

Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models

arXiv:2605.12519v1 Announce Type: cross Abstract: Training language models to produce both correct answers and sound reasoning remains an open challenge. Reinforcement learning with verifiable rewards

Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems

arXiv:2605.13174v1 Announce Type: cross Abstract: A likelihood-free transport filtering method is proposed based on the couplings between state and observation variables. By exploiting a block-triangu

CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation

arXiv:2605.12938v1 Announce Type: cross Abstract: Camera-conditioned video generation requires positional encoding that remains reliable under changes in camera motion, lens configuration, and scene s

Cross Modality Image Translation In Medical Imaging Using Generative Frameworks

arXiv:2605.13686v1 Announce Type: cross Abstract: Medical image-to-image (I2I) translation enables virtual scanning, i.e. the synthesis of a target imaging modality from a source one without additiona

Cycle-resolved Cephalopod-Inspired Pulsed-Jet Robot With High-Volume Expulsion and Drag-Reduced Gliding

arXiv:2605.05875v2 Announce Type: replace Abstract: Cephalopod pulsed-jet locomotion is not a single isolated expulsion event, but a coordinated cycle involving jet expulsion, passive gliding, and man

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning

arXiv:2605.12906v1 Announce Type: cross Abstract: Data selection during supervised fine-tuning (SFT) can critically change the behavior of large language models (LLMs). Although existing work has stud

Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning

arXiv:2603.10305v3 Announce Type: replace Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information acro

Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation

arXiv:2605.12952v1 Announce Type: new Abstract: Grad-ECLIP is published at ICML 2024 and represents a new Transformer interpretation technical route (intermediate features-based). First, this paper de

Deep Delta Learning

arXiv:2601.00417v3 Announce Type: replace-cross Abstract: Transformer residual streams evolve by additive accumulation: each layer appends a feature update to a shared hidden state, but has no direct

DeepFilters: Scattering-Aware Pupil Engineering with Learned Digital Filter Reconstruction for Extended Depth of Field Microscopy

arXiv:2605.13619v1 Announce Type: cross Abstract: Extended depth of field microscopy encodes axial information into a single acquisition through engineered point spread functions, but conventional and

Delightful Exploration

arXiv:2605.13287v1 Announce Type: cross Abstract: Most exploration algorithms search broadly until uncertainty is resolved. When the action space is too large to resolve within budget, practitioners d

Deontic Argumentation

arXiv:2509.25781v2 Announce Type: replace Abstract: We address the issue of defining a semantics for deontic argumentation that supports weak permission. Some recent results show that grounded semanti

Design of Magnetic Continuum Robots with Tunable Force Response Using Rotational Ring Pairs

arXiv:2605.13613v1 Announce Type: new Abstract: In this paper, we discuss a novel continuum robot design that enables the online tuning of the magnetic response at its tip. The proposed method allows

Differences in Text Generated by Diffusion and Autoregressive Language Models

arXiv:2605.12522v1 Announce Type: cross Abstract: Diffusion language models (DLMs) are promising alternatives to autoregressive language models (ARMs), yet the intrinsic differences in their generated

Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptions

arXiv:2511.01303v2 Announce Type: replace-cross Abstract: We consider the problem of constructing differentially private nonparametric confidence intervals (CIs) for an arbitrary quantity using resamp

Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration

arXiv:2602.08920v2 Announce Type: replace Abstract: Uncertainty calibration in pre-trained transformers is critical for their reliable deployment in risk-sensitive applications. Yet, most existing pre

DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport

arXiv:2605.12939v1 Announce Type: new Abstract: Recent diffusion- and flow-based VTON methods achieve strong results with pretrained generative models, but their reliance on multi-step sampling incurs

DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning

arXiv:2605.13708v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secur

Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels

arXiv:2605.12805v1 Announce Type: cross Abstract: MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity

Discrete Stochastic Localization for Non-autoregressive Generation

arXiv:2605.12836v1 Announce Type: new Abstract: Continuous diffusion is a natural framework for non-autoregressive generation but has generally lagged behind masked discrete diffusion models (MDMs) on

Diversity of Extensions in Abstract Argumentation

arXiv:2605.13332v1 Announce Type: new Abstract: Argumentation is an important topic of AI for modelling and reasoning about arguments. In abstract argumentation, we consider directed graphs, so-called

Do Activation Verbalization Methods Convey Privileged Information?

arXiv:2509.13316v4 Announce Type: replace-cross Abstract: Recent interpretability methods have proposed to translate LLM internal representations into natural language descriptions using a second verb

Does language matter for spoken word classification? A multilingual generative meta-learning approach

arXiv:2605.13084v1 Announce Type: cross Abstract: Meta-learning has been shown to have better performance than supervised learning for few-shot monolingual spoken word classification. However, the met

DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning

arXiv:2605.13418v1 Announce Type: new Abstract: Differentially private optimization suffers from a fundamental geometric mismatch: deep networks have highly anisotropic loss landscapes, yet DP-SGD inj

Dual-Pathway Circuits of Object Hallucination in Vision-Language Models

arXiv:2605.13156v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in bridging visual perception and natural language understanding, enabling a wid

Dynamics Computation of Soft-Rigid Hybrid-Link System and Its Application to Motion Analysis of an Athlete Wearing Sport Prosthesis

arXiv:2605.13192v1 Announce Type: new Abstract: This paper presents a motion analysis framework for an athlete wearing sport-specific flexible prosthesis based on the soft-rigid hybrid-link system. Su

DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments

arXiv:2605.12897v1 Announce Type: new Abstract: DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environm

Early Data Exposure Improves Robustness to Subsequent Fine-Tuning

arXiv:2605.12705v1 Announce Type: new Abstract: How can we train models whose post-trained capabilities survive subsequent fine-tuning? Rather than focusing on downstream interventions to mitigate for

Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation

arXiv:2605.13122v1 Announce Type: new Abstract: Instruction-based image editing (IIE) models have recently demonstrated strong capability in modifying specific image regions according to natural langu

ENSEMBITS: an alphabet of protein conformational ensembles

arXiv:2605.13789v1 Announce Type: cross Abstract: Protein structure tokenizers (PSTs) are workhorses in protein language modeling, function prediction, and evolutionary analysis. However, existing PST

Exemplar-Free Continual Learning for State Space Models

arXiv:2505.18604v3 Announce Type: replace Abstract: State-Space Models (SSMs) excel at capturing long-range dependencies with structured recurrence, making them well-suited for sequence modeling. Howe

Exploring how EFL students talk to and through AI to develop texts

arXiv:2605.12523v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) introduces new considerations for English as a foreign language (EFL) writing pedagogy. This study explores ho

Exploring Human-Robot Collaboration: Analysis of Interaction Modalities in Challenging Tasks

arXiv:2605.13380v1 Announce Type: new Abstract: This work compares three interaction modalities for human-robot collaboration: passive, reactive, and proactive. We studied 18 participants assembling a

Exploring the holographic entropy cone via reinforcement learning

arXiv:2601.19979v2 Announce Type: replace-cross Abstract: We develop a reinforcement learning algorithm to study the holographic entropy cone. Given a target entropy vector, our algorithm searches for

Feature Learning Dynamics in Infinite-Depth Neural Networks

arXiv:2512.21075v2 Announce Type: replace-cross Abstract: Deep neural networks have achieved remarkable success in practice, yet a mechanistic understanding of how features evolve during training rema

FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression

arXiv:2605.12704v1 Announce Type: cross Abstract: A fundamental challenge in symbolic regression (SR) is efficiently recovering complex mathematical expressions from observational data. Although this

Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit

arXiv:2409.02708v2 Announce Type: replace Abstract: Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the avai

Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning

arXiv:2604.00001v2 Announce Type: replace-cross Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but exist

Flow Matching with Uncertainty Quantification and Guidance

arXiv:2602.10326v2 Announce Type: replace Abstract: Despite the remarkable success of sampling-based generative models such as flow matching, they can still produce samples of inconsistent or degraded

Follow the Mean: Reference-Guided Flow Matching [R]

researchr-machinelearning

This paper presents a method for controllable generation in flow matching models by adapting them through reference examples. The key insight is that the velocity field in flow matching is determined

FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection

arXiv:2605.12826v1 Announce Type: cross Abstract: The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital im

From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows

arXiv:2603.05093v2 Announce Type: replace-cross Abstract: Feature attributions often hide a critical modeling choice: they explain a prediction along a counterfactual path from a reference state to an