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DFKI-MLT at SemEval-2026 TASK 7: Steering Multilingual Models Towards Cultural Knowledge

arXiv:2605.23069v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across r

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Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation

DGX agent

arXiv:2605.22950v1 Announce Type: cross Abstract: Score matching is an alternative to maximum likelihood estimation when the normalizing constant is unknown or too costly to evaluate. However, vanilla

model-releasesarxiv-cs-lg

25 May 2026

Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring

arXiv:2605.23282v1 Announce Type: cross Abstract: Defocus deblurring in pathological microscopy remains challenging due to the spatially varying and locally discontinuous nature of optical blur induce

Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025

arXiv:2605.23094v1 Announce Type: cross Abstract: Generative augmentation is often proposed as a remedy for small medical-image datasets, but synthetic images are only useful when they improve downstr

DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods

arXiv:2605.23052v1 Announce Type: cross Abstract: We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our syst

DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving

arXiv:2605.23176v1 Announce Type: new Abstract: Spatiotemporal intelligence in autonomous driving (AD) requires an agent to integrate multi-view observations into a coherent scene representation, main

Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning

arXiv:2509.26383v5 Announce Type: replace-cross Abstract: Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs

Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries

arXiv:2404.05108v3 Announce Type: replace-cross Abstract: Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and lea

Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention

arXiv:2605.23451v1 Announce Type: new Abstract: Real-world image super-resolution aims to recover high-quality images from complex and unknown real-world degradations. However, existing generative Rea

Entropy Equivalence Testing

arXiv:2605.23225v1 Announce Type: cross Abstract: We introduce the problem of entropy equivalence testing for probability distributions, a relaxation of the well-studied closeness testing problem, whe

ETCHR: Editing To Clarify and Harness Reasoning

arXiv:2605.23897v1 Announce Type: cross Abstract: Multimodal Large Language Models have advanced visual reasoning, yet a purely textual chain of thought remains a bottleneck for questions that require

Evaluating Large Language Models in a Complex Hidden Role Game

arXiv:2605.22826v1 Announce Type: cross Abstract: Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments.

Evaluating Memory Structure in LLM Agents

arXiv:2602.11243v2 Announce Type: replace-cross Abstract: Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augm

Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking

arXiv:2605.23118v1 Announce Type: cross Abstract: Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-of

Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model

arXiv:2605.23790v1 Announce Type: new Abstract: Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting sa

FAST-ME: Foundation-aware Adaptive Stopping for Motion Estimation for Efficient IoT Video Analysis

arXiv:2605.23428v1 Announce Type: new Abstract: In modern multimedia systems, efficient video processing is critical, especially in resource-constrained environments such as IoT-based camera networks,

FastKernels: Benchmarking GPU Kernel Generation in Production

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

arXiv:2510.08945v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduc

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches

arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource const

FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral st

GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models

arXiv:2605.23238v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior i

GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory

arXiv:2602.12316v2 Announce Type: replace Abstract: Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely ev

HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval

arXiv:2605.23572v1 Announce Type: cross Abstract: In the competitive landscape of sponsored search, balancing retrieval quality with production latency is a critical challenge. While large retrieval m

Hierarchical Concept Geometry in Language Models Emerges from Word Co-occurrence

arXiv:2605.23821v1 Announce Type: new Abstract: We propose a distributional theory of how hypernymy -- the ``is-a'' relation between general and specific concepts -- is encoded geometrically in langua

How Far Are We from Generating Missing Modalities with Foundation Models?

arXiv:2506.03530v3 Announce Type: replace-cross Abstract: Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutio

How Hard is it to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness

arXiv:2605.23628v1 Announce Type: new Abstract: Multi-task benchmarks have become a central pillar of machine learning research, yet their growing influence has incentivised benchmark gaming -- strate

HTMuon: Improving Muon via Heavy-Tailed Spectral Correction

arXiv:2603.10067v2 Announce Type: replace-cross Abstract: Muon has recently shown promising results in LLM training. In this work, we study how to further improve Muon. We argue that Muon's orthogonal

Human-Centered Learning Mechanics: A Dynamical Framework for Entropy-Regulated Representation Learning

arXiv:2605.22940v1 Announce Type: cross Abstract: Deep learning is increasingly viewed as a dynamical process in parameter space, yet many existing theories still treat training as a closed optimizati

ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization

arXiv:2605.22885v1 Announce Type: new Abstract: Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data

Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems

arXiv:2605.23109v1 Announce Type: new Abstract: AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage t

IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction

arXiv:2605.23187v1 Announce Type: new Abstract: Existing object navigation benchmarks usually tell an embodied agent which object category to find, such as microwave or chair. Human-facing embodied AI

Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures

arXiv:2511.03882v2 Announce Type: replace-cross Abstract: Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether thi

Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most

arXiv:2605.22672v2 Announce Type: replace Abstract: We document inverse scaling in LLMs on forecasting problems whose underlying time series exhibit superlinear growth and tail risk of regime change,

It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt

arXiv:2605.23825v1 Announce Type: cross Abstract: It has generally been assumed that geopolitical bias in language models originates from the training data used during the pre-training phase. We teste

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking

arXiv:2603.24226v3 Announce Type: replace-cross Abstract: Scaling studies for industrial search, advertising, and recommendation have largely emphasized enlarging model capacity or refining architectu

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

arXiv:2605.23082v1 Announce Type: cross Abstract: Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as

L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark

arXiv:2605.22893v1 Announce Type: cross Abstract: We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to fost

Learning Safely Without Knowing the World:COMPASS-Hedge

arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing

LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding

arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg

Lipschitz Optimization for Formal Verification of Homographies

arXiv:2605.23203v1 Announce Type: cross Abstract: The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as he

LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics

arXiv:2601.18685v3 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Rese

LLM-driven design of physics-constrained constitutive models: two agents are better than one

arXiv:2605.23754v1 Announce Type: new Abstract: Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics

LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement

arXiv:2605.23174v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact measurement of physiological signals from facial videos, offering strong potential for remote hea

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models

arXiv:2605.23007v1 Announce Type: cross Abstract: We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorith

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

arXiv:2601.14652v5 Announce Type: replace Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliv

MedExpMem: Adapting Experience Memory for Differential Diagnosis

arXiv:2605.22872v1 Announce Type: cross Abstract: Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differenti

MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection

arXiv:2602.13480v2 Announce Type: cross Abstract: Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull de

Memorization Dynamics of Fill-in-the-Middle Pretraining

arXiv:2605.22981v1 Announce Type: cross Abstract: Fill-in-the-middle (FIM) is a pretraining objective widely used to equip causal language models with infilling ability, yet its effect on verbatim mem