Search: “research”

DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs

arXiv:2605.31427v1 Announce Type: new Abstract: Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to

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dgMARK: Decoding-Guided Watermarking for Diffusion Language Models

DGX agent

arXiv:2601.22985v2 Announce Type: replace Abstract: We propose dgMARK, a decoding-guided watermarking method for discrete diffusion language models (dLLMs). Unlike autoregressive models, dLLMs can gen

researcharxiv-cs-lg

1 Jun 2026

DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics

arXiv:2605.30538v1 Announce Type: new Abstract: Disasters are inevitable and increasingly costly, and effective response depends on querying structured tabular data: precise, information-dense records

Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability

arXiv:2605.30482v1 Announce Type: new Abstract: Machine learning is increasingly used in mathematical discovery, but in mathematics the desired output is often not a prediction itself, but an explicit

Discovering Differences in Strategic Behavior Between Humans and LLMs

arXiv:2602.10324v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their b

Discovering Thermodynamically Admissible Dissipation Potentials via Grammar-Based Symbolic Regression

arXiv:2605.31532v1 Announce Type: cross Abstract: Constitutive laws for inelastic materials must satisfy strict thermodynamic admissibility requirements, yet current data-driven approaches sacrifice i

DisPlace: Discriminative Place Projections for Multi-Reference Visual Place Recognition

arXiv:2605.30769v1 Announce Type: new Abstract: A key challenge in Visual Place Recognition (VPR) is matching query images against reference maps captured under diverse environmental conditions and vi

Divergence Decoding: Inference-Time Unlearning via Auxiliary Models

arXiv:2605.31293v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently memorize sensitive training data thereby creating significant privacy and copyright risks. Addressing these risk

Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models

arXiv:2605.30713v1 Announce Type: cross Abstract: Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their applicati

Domain Adaptation and Reasoning Frameworks in Language Models: A Controlled Experiment with Historical Cosmology

arXiv:2605.30415v1 Announce Type: cross Abstract: We investigate how domain adaptation reshapes explanatory behavior in language models using historical cosmology as a controlled setting. In Phase 1,

Don't be so Stief! Learning KV Cache low-rank approximation over the Stiefel manifold

arXiv:2601.21686v2 Announce Type: replace Abstract: Key-value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capaci

DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks

arXiv:2605.31065v1 Announce Type: cross Abstract: Non-terrestrial networks (NTNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling ubiquitous connectivity and massive c

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training

arXiv:2512.13996v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top-k routing imposes a rigid sparsit

Dynamical local Frechet curve regression in manifolds

arXiv:2505.05168v3 Announce Type: replace-cross Abstract: Under mild conditions, this paper derives a least-squares local linear Frechet curve predictor for response and regressor evaluated in a separ

Early Prediction of Future Behavioral Strategy from Process Traces

arXiv:2605.30550v1 Announce Type: new Abstract: Adaptive systems often need to make task-specific decisions about people from limited evidence: a tutor may need to anticipate how a learner will approa

EBuddy: a workflow orchestrator for industrial human-machine collaboration

arXiv:2603.28579v2 Announce Type: replace Abstract: This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets

researcharxiv-cs-ro

EEmo-Logic: A Unified Dataset and Multi-Stage Framework for Comprehensive Image-Evoked Emotion Assessment

arXiv:2602.01173v3 Announce Type: replace Abstract: Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowerin

Effective Biological Representation Learning by Masking Gene Expression

arXiv:2605.31562v1 Announce Type: new Abstract: RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many appli

Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation

arXiv:2605.30753v1 Announce Type: new Abstract: Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many

EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context

arXiv:2503.05846v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric train

Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models

arXiv:2603.19262v2 Announce Type: replace-cross Abstract: Large language models increasingly rely on inference-time procedures such as chain-of-thought reasoning, self-refinement, retrieval augmentati

Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization

arXiv:2605.30928v1 Announce Type: new Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward

researcharxiv-cs-ro

Evaluating using Mock Tool Calls to Quarantine Untrusted Prompt Inputs

arXiv:2605.30521v1 Announce Type: new Abstract: Large language models must frequently process untrusted inputs, such as judging an answer from another model or running tasks like spam and harm classif

Evidence for systematic semantic structure in individual phonemes

arXiv:2603.17306v3 Announce Type: replace Abstract: A foundational assumption in linguistics holds that sound-meaning relations are largely arbitrary. Here we show that this assumption fails at the le

Evolutionary Algorithm for Reservoir Learning and Yielding

arXiv:2605.30372v1 Announce Type: cross Abstract: Reservoir computing, a type of recurrent neural network, is a promising approach for temporal learning as it separates dynamic processing from the tra

Extracting accent features in spoken Brazilian Portuguese without sociolinguistic labels

arXiv:2605.30457v1 Announce Type: cross Abstract: Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL)

Fine-Tuning Improves Information Conveyance in Language Models

arXiv:2605.30844v1 Announce Type: cross Abstract: Fine-tuning is often believed to reduce uncertainty and diversity in large language models, but existing analyses overlook output length, a key confou

Fixed Universal Transformers

arXiv:2605.31423v1 Announce Type: new Abstract: We introduce universal transformers: fixed transformers that can simulate any transformer in a given class via a suitable input embedding. Analogous to

FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction

arXiv:2605.31189v1 Announce Type: new Abstract: Tabular prediction in high-stakes domains requires models that are accurate, transparent, and robust to imperfect inputs. We propose FlagGAM, a rule-def

Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments

arXiv:2601.01075v2 Announce Type: replace-cross Abstract: Embodied systems experience the world as 'a symphony of flows': a combination of many continuous streams of sensory input coupled to self-moti

Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error

arXiv:2605.31438v1 Announce Type: new Abstract: Time series forecasting often requires learning nonlinear and time-delayed dependencies. A paradigmatic class of forecasting models are nonlinear vector

FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation

arXiv:2504.10564v3 Announce Type: replace-cross Abstract: We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates conti

Formalizing and falsifying causal pathways of rare events

arXiv:2605.31254v1 Announce Type: new Abstract: Building on recent formalizations of root cause analysis for rare events (``outliers'') in structural equation models, we propose a formal definition of

Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation

arXiv:2605.30893v1 Announce Type: new Abstract: Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, traini

Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks

arXiv:2605.31257v1 Announce Type: new Abstract: Fraud detection in payment networks relies on labels generated through heterogeneous and imperfect observation processes, yet existing approaches treat

Free energy Estimation on Any State Space

arXiv:2605.31063v1 Announce Type: cross Abstract: Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformation

From Leaky Thoughts to Private Reasoning: Controlling What LRMs Say to Themselves

arXiv:2602.24210v2 Announce Type: replace-cross Abstract: Large reasoning models (LRMs) produce reasoning traces (RTs) that often contain sensitive information. These leaky thoughts are difficult to c

From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift

arXiv:2605.31187v1 Announce Type: new Abstract: Detecting covariate shift is critical for building reliable vision systems. While most prior work focuses on improving robustness to shift, explicitly d

From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy

arXiv:2605.30371v1 Announce Type: cross Abstract: We address the issue of global convergence in stochastic continuous optimization. For that purpose, we formulate the Canonical Evolutionary Strategy (

Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning

arXiv:2605.30375v1 Announce Type: cross Abstract: High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraf

Functional Attention: From Pairwise Affinities to Functional Correspondences

arXiv:2605.31559v1 Announce Type: new Abstract: Learning mappings between infinite-dimensional function spaces, or operator learning, is essential for many machine learning applications. Although tran

GaMi: Geometry-Agnostic Material Identification via Cross-Modal Subtractive Disentanglement

arXiv:2605.30818v1 Announce Type: cross Abstract: Non-contact material identification enables adaptive interaction for embodied intelligence yet faces challenges from geometry-induced variations (e.g.

Generalizing Multi-Scale Time-Series Modeling with a Single Operator

arXiv:2605.31129v1 Announce Type: new Abstract: Multi-scale modeling has emerged as an effective design principle for time-series forecasting by capturing temporal dynamics at multiple resolutions. As

Generating and Refining Dynamic Evaluation Rubrics for LLM-as-a-Judge

arXiv:2605.30568v1 Announce Type: new Abstract: LLM-as-a-Judge is a scalable alternative to human evaluation, yet existing rubric-based methods rely on human-annotated data such as reference answers o

Generative Models and Statistical Validation

arXiv:2605.30453v1 Announce Type: cross Abstract: Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and den

GETA: Generalized Encrypted Traffic Analysis

arXiv:2605.31277v1 Announce Type: cross Abstract: Traditional traffic analysis is being fundamentally challenged by the rapid adoption of encryption, tunnelling, and privacy-preserving protocols, whic

Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings

arXiv:2605.31580v1 Announce Type: new Abstract: Transformer-based architectures have advanced sequence modeling in language and vision, yet general-purpose representation learning for heterogeneous mu

Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies

arXiv:2605.30361v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because the discret