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Measuring Creativity in the Age of Generative AI: Distinguishing Human and AI-Generated Creative Performance in Hiring and Talent Systems

arXiv:2604.19799v1 Announce Type: cross Abstract: Generative AI is rapidly transforming how organizations create value and evaluate talent. While large language models enhance baseline output quality,

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Measuring the Machine: Evaluating Generative AI as Pluralist Sociotechical Systems

DGX agent

arXiv:2604.20545v1 Announce Type: new Abstract: In measurement theory, instruments do not simply record reality; they help constitute what is observed. The same holds for generative AI evaluation: ben

model-releasesarxiv-cs-ai

23 Apr 2026

Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy

arXiv:2601.02989v2 Announce Type: replace Abstract: Large language models (LLMs), despite strong performance on complex mathematical problems, exhibit systematic limitations in counting tasks. This is

Mechanistic Interpretability Tool for AI Weather Models

arXiv:2604.20467v1 Announce Type: cross Abstract: Artificial Intelligence (AI) weather models are improving rapidly, and their forecasts are already competitive with long-established traditional Numer

researcharxiv-cs-lg

MedSkillAudit: A Domain-Specific Audit Framework for Medical Research Agent Skills

arXiv:2604.20441v1 Announce Type: new Abstract: Background: Agent skills are increasingly deployed as modular, reusable capability units in AI agent systems. Medical research agent skills require safe

safetyarxiv-cs-ai

Membership Inference for Contrastive Pre-training Models with Text-only PII Queries

arXiv:2603.14222v2 Announce Type: replace-cross Abstract: Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet the

safetyarxiv-cs-ai

Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs

arXiv:2601.02931v2 Announce Type: replace Abstract: Autoregressive LLMs perform well on relational tasks that require linking entities via relational words (e.g., father/son, friend), but it is unclea

safetyarxiv-cs-cl

Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data

arXiv:2604.20261v1 Announce Type: new Abstract: Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable

agentsarxiv-cs-ai

Applications

Meta Additive Model: Interpretable Sparse Learning With Auto Weighting

arXiv:2604.20111v1 Announce Type: cross Abstract: Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretabilit

applicationsarxiv-cs-ai

Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models

arXiv:2604.20148v1 Announce Type: cross Abstract: Can small language models achieve strong tool-use performance without complex adaptation mechanisms? This paper investigates this question through Met

MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management

arXiv:2601.11505v2 Announce Type: replace-cross Abstract: Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D manage

MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment

arXiv:2604.20685v1 Announce Type: new Abstract: Aligning large language models (LLMs) to desirable human values requires balancing multiple, potentially conflicting objectives such as helpfulness, tru

MIRROR: A Hierarchical Benchmark for Metacognitive Calibration in Large Language Models

arXiv:2604.19809v1 Announce Type: new Abstract: We introduce MIRROR, a benchmark comprising eight experiments across four metacognitive levels that evaluates whether large language models can use self

MirrorBench: Evaluating Self-centric Intelligence in MLLMs by Introducing a Mirror

arXiv:2604.14785v2 Announce Type: replace Abstract: Recent progress in Multimodal Large Language Models (MLLMs) has demonstrated remarkable advances in perception and reasoning, suggesting their poten

Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation

model-releasesarxiv-cs-cv

arXiv:2604.20366v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability.

Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering

arXiv:2604.16756v2 Announce Type: replace-cross Abstract: Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.

MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design

model-releasesarxiv-cs-lg

arXiv:2412.14590v2 Announce Type: replace Abstract: Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions sti

MLG-Stereo: ViT Based Stereo Matching with Multi-Stage Local-Global Enhancement

model-releasesarxiv-cs-cv

arXiv:2604.20393v1 Announce Type: new Abstract: With the development of deep learning, ViT-based stereo matching methods have made significant progress due to their remarkable robustness and zero-shot

MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings

arXiv:2604.19902v1 Announce Type: cross Abstract: We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM

MOA: Multi-Objective Alignment for Role-Playing Agents

arXiv:2512.09756v2 Announce Type: replace Abstract: Role-playing agents (RPAs) require balancing multiple objectives, such as instruction following, persona consistency, and stylistic fidelity, which

safetyarxiv-cs-cl

Model Capability Assessment and Safeguards for Biological Weaponization

arXiv:2604.19811v1 Announce Type: cross Abstract: AI leaders and safety reports increasingly warn that advances in model reasoning may enable biological misuse, including by low-expertise users, while

Model Internal Sleuthing: Finding Lexical Identity and Inflectional Features in Modern Language Models

arXiv:2506.02132v5 Announce Type: replace Abstract: Large transformer-based language models dominate modern NLP, yet our understanding of how they encode linguistic information relies primarily on stu

Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design

arXiv:2604.20254v1 Announce Type: new Abstract: Text-guided molecular design is a key capability for AI-driven drug discovery, yet it remains challenging to map sequential natural-language instruction

agentsarxiv-cs-ai

MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation

arXiv:2604.20468v1 Announce Type: cross Abstract: Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However

More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems

arXiv:2604.19827v1 Announce Type: cross Abstract: Software engineering faces a fundamental challenge: multi-agent AI systems fail in ways that defy explanation by traditional theories. While individua

agentsarxiv-cs-ai

MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation

model-releasesarxiv-cs-cv

arXiv:2505.18823v2 Announce Type: replace Abstract: Accurate medical image segmentation allows for the precise delineation of anatomical structures and pathological regions, which is essential for tre

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards

arXiv:2506.16658v2 Announce Type: replace-cross Abstract: Multi-armed bandit (MAB) is a widely adopted framework for sequential decision-making under uncertainty. Traditional bandit algorithms rely so

Multi-Objective Reinforcement Learning for Generating Covalent Inhibitor Candidates

arXiv:2604.20019v1 Announce Type: new Abstract: Rational design of covalent inhibitors requires simultaneously optimizing multiple properties, such as binding affinity, target selectivity, or electrop

Multi-Perspective Evidence Synthesis and Reasoning for Unsupervised Multimodal Entity Linking

arXiv:2604.20283v1 Announce Type: new Abstract: Multimodal Entity Linking (MEL) is a fundamental task in data management that maps ambiguous mentions with diverse modalities to the multimodal entities

Multi-Timescale Model Predictive Control for Slow-Fast Systems

arXiv:2511.14311v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse app

researcharxiv-cs-ro

Mythos and the Unverified Cage: Z3-Based Pre-Deployment Verification for Frontier-Model Sandbox Infrastructure

arXiv:2604.20496v1 Announce Type: cross Abstract: The April 2026 Claude Mythos sandbox escape exposed a critical weakness in frontier AI containment: the infrastructure surrounding advanced models rem

NanoCockpit: Performance-optimized Application Framework for AI-based Autonomous Nanorobotics

arXiv:2601.07476v2 Announce Type: replace Abstract: Autonomous nano-drones, powered by vision-based tiny machine learning (TinyML) models, are a novel technology gaining momentum thanks to their broad

agentsarxiv-cs-ro

Near-Future Policy Optimization

arXiv:2604.20733v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a core post-training recipe. Introducing suitable off-policy trajectories into on-polic

Neural Bandit Based Optimal LLM Selection for a Pipeline of Subtasks

arXiv:2508.09958v3 Announce Type: replace Abstract: As large language models (LLMs) become increasingly popular, there is a growing need to predict which out of a set of LLMs will yield a successful a

agentsarxiv-cs-cl

Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the sphere

arXiv:2604.19846v1 Announce Type: cross Abstract: IceCube is a cubic-kilometer-scale neutrino detector located at the geographic South Pole. A precise directional reconstruction of IceCube neutrinos i

NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering

arXiv:2602.15353v2 Announce Type: replace-cross Abstract: Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowled

safetyarxiv-cs-ai

'Newspaper Eat' Means 'Not Tasty': A Taxonomy and Benchmark for Coded Language in Real-World Chinese Online Reviews

model-releasesarxiv-cs-cl

arXiv:2601.19932v2 Announce Type: replace Abstract: Coded language is an important part of human communication. It refers to cases where users intentionally encode meaning so that the surface text dif

Applications

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets

arXiv:2508.14098v2 Announce Type: replace-cross Abstract: Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical

applicationsarxiv-cs-ai

Normalizing Flows with Iterative Denoising

arXiv:2604.20041v1 Announce Type: cross Abstract: Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have s

Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames

arXiv:2604.20454v1 Announce Type: new Abstract: Metaphors are powerful framing devices, yet their source domains alone do not fully explain the specific associations they evoke. We argue that the inte

Object Referring-Guided Scanpath Prediction with Perception-Enhanced Vision-Language Models

arXiv:2604.20361v1 Announce Type: new Abstract: Object Referring-guided Scanpath Prediction (ORSP) aims to predict the human attention scanpath when they search for a specific target object in a visua

researcharxiv-cs-cv

Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning

arXiv:2604.20627v1 Announce Type: new Abstract: The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. G

OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads

arXiv:2508.08822v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication wo

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model

arXiv:2604.20806v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level mul

On Bayesian Softmax-Gated Mixture-of-Experts Models

model-releasesarxiv-cs-lg

arXiv:2604.20551v1 Announce Type: cross Abstract: Mixture-of-experts models provide a flexible framework for learning complex probabilistic input-output relationships by combining multiple expert mode

Applications

On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence

arXiv:2604.19800v1 Announce Type: cross Abstract: This paper presents a detailed study of how graph neural networks can be used on edge intelligent meters in a microgrid to forecast photovoltaic power

applicationsarxiv-cs-ai

On the definition and importance of interpretability in scientific machine learning

arXiv:2505.13510v3 Announce Type: replace Abstract: Though neural networks trained on large datasets have been successfully used to describe and predict many physical phenomena, there is a sense among

researcharxiv-cs-lg

On the Existence of Universal Simulators of Attention

arXiv:2506.18739v2 Announce Type: replace-cross Abstract: Previous work on the learnability of transformers extemdash focused primarily on examining their ability to approximate specific algorithmic p