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2026-04-22
Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents
Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy memory, multi-step reasoning,…
Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of threat hunting: given a databas…
Revac: A Social Deduction Reasoning Agent
Social deduction games such as Mafia present a unique AI challenge: players must reason under uncertainty, interpret incomplete and intentionally misleading information, evaluate…
Beyond Rating: A Comprehensive Evaluation and Benchmark for AI Reviews
The rapid adoption of Large Language Models (LLMs) has spurred interest in automated peer review; however, progress is currently stifled by benchmarks that treat reviewing primari…
A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promis…
Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic
Reinforcement learning (RL) as post-training is crucial for enhancing the reasoning ability of large language models (LLMs) in coding and math. However, their capacity for visual…
From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimi…
SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models
Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insuffi…
Time Series Augmented Generation for Financial Applications
Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. Standard benchmarks often fai…
From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
Counterfactual explanations (CEs) provide an intuitive way to understand recommender systems by identifying minimal modifications to user-item interactions that alter recommendati…
Lost in Translation: Do LVLM Judges Generalize Across Languages?
Automatic evaluators such as reward models play a central role in the alignment and evaluation of large vision-language models (LVLMs). Despite their growing importance, these eva…
How Far Are Video Models from True Multimodal Reasoning?
Despite remarkable progress toward general-purpose video models, a critical question remains unanswered: how far are these models from achieving true multimodal reasoning? Existin…
EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation
Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has achieved significant advan…
Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.
BACKGROUND: Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LLMs) can transform ML model-pr…
APSevLM: Acute Pancreatitis Severity Language Model.
Approximately one-fifth of patients with acute pancreatitis (AP) develop severe forms, which are associated with high mortality rates, making early prediction of severity crucial…
2026-04-21
Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion
We present a systematic evaluation of large language model families -- spanning both proprietary cloud APIs and locally-hosted open-source models -- on two purpose-built benchmark…
ClawEnvKit: Automatic Environment Generation for Claw-Like Agents
Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale. We argue that what is needed is not just a da…
ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship
Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While recent works explore empathe…
Semantic Step Prediction: Multi-Step Latent Forecasting in LLM Reasoning Trajectories via Step Sampling
Semantic Tube Prediction (STP) leverages representation geometric to regularize LLM hidden-state trajectories toward locally linear geodesics during fine-tuning, thereby greatly i…
Multilingual Training and Evaluation Resources for Vision-Language Models
Vision Language Models (VLMs) achieved rapid progress in the recent years. However, despite their growth, VLMs development is heavily grounded on English, leading to two main limi…
On the Importance and Evaluation of Narrativity in Natural Language AI Explanations
Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. The integration of Natural L…
OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation
Recent advancements in audio-video joint generation models have demonstrated impressive capabilities in content creation. However, generating high-fidelity human-centric videos in…
Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients.
BACKGROUND: Clinical trial enrollment in oncology remains critically low, with fewer than 5% of eligible adults participating, in large part due to the complexity and labor intens…
A Comparative Evaluation of Three Large Language Models for Parent-Centered Questions About Anorexia Nervosa.
BACKGROUND: Large language models (LLMs) are increasingly used to obtain health information, including guidance on child and adolescent mental health. In anorexia nervosa (AN), wh…
2026-04-18
An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework.
The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare environments has created critical cybersecurity vulnerabilities that demand both accurate and in…
2026-04-17
QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies
Large language models have demonstrated strong performance on general-purpose programming tasks, yet their ability to generate executable algorithmic trading strategies remains un…
From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench
Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existing benchmarks primarily focus…
An Axiomatic Benchmark for Evaluation of Scientific Novelty Metrics
The rigorous evaluation of the novelty of a scientific paper is, even for human scientists, a challenging task. With the increasing interest in AI scientists and AI involvement in…
MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation
The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage design, offering a flex…
Context Over Content: Exposing Evaluation Faking in Automated Judges
The $\textit{LLM-as-a-judge}$ paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: that judges evaluate text s…
AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment
Artificial Intelligence is increasingly introduced into systems engineering activities, particularly within requirements engineering, where quality assessment and validation remai…
MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events
Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to support human overs…
Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABC
This paper introduces the first \emph{self-evolving} logic synthesis framework, which leverages Large Language Model (LLM) agents to autonomously improve the source code of \texts…
Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation
Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, building a trustworthy simulator…
RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. Although diffusion-b…
Applying natural language processing and large language models to clinical notes for phenotyping and diagnosing rare diseases: a systematic review.
OBJECTIVES: Patients with rare diseases often face long delays before receiving a diagnosis. Using electronic health records for automated phenotyping and diagnosis of rare diseas…
Evaluation of large language models with clinical guidance for vetting outpatient magnetic resonance imaging lumbar spine referrals.
ObjectivesAccurate triage of lumbar spine magnetic resonance imaging (MRI) referrals for sciatica is important for patient assessment, diagnosis and surgical planning. This study…
Dual perspectives on large language models in rheumatology: physician-rated quality and patient-centered usability of GPT-4o versus DeepSeek-V3.
OBJECTIVES: This study conducted an informatics system evaluation of two LLMs (GPT-4o and DeepSeek-V3) for patient education, combining clinician-rated quality with patient-percei…
2026-04-16
GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis
The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. However, evaluating these LLM-…
HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark
Existing agent-safety evaluation has focused mainly on externally induced risks. Yet agents may still enter unsafe trajectories under benign conditions. We study this complementar…
Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning
The rapid evolution of multimodal large models has revolutionized the simulation of diverse characters in speech dialogue systems, enabling a novel interactive paradigm. Character…
Correct Prediction, Wrong Steps? Consensus Reasoning Knowledge Graph for Robust Chain-of-Thought Synthesis
LLM reasoning traces suffer from complex flaws -- *Step Internal Flaws* (logical errors, hallucinations, etc.) and *Step-wise Flaws* (overthinking, underthinking), which vary by s…
TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, such as LLM training, remains…
MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment
User simulators are essential for the scalable training and evaluation of interactive AI systems. However, existing approaches often rely on shallow user profiling, struggle to ma…
MAny: Merge Anything for Multimodal Continual Instruction Tuning
Multimodal Continual Instruction Tuning (MCIT) is essential for sequential task adaptation of Multimodal Large Language Models (MLLMs) but is severely restricted by catastrophic f…
MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging
The potential of Multimodal Large Language Models (MLLMs) in domain of medical imaging raise the demands of systematic and rigorous evaluation frameworks that are aligned with the…
Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
Watermarking is becoming the default mechanism for AI content authentication, with governance policies and frameworks referencing it as infrastructure for content provenance. Yet…
ROSE: Retrieval-Oriented Segmentation Enhancement
Existing segmentation models based on multimodal large language models (MLLMs), such as LISA, often struggle with novel or emerging entities due to their inability to incorporate…
Seedance 2.0: Advancing Video Generation for World Complexity
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pr…
Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study.
BACKGROUND: Large language models use machine learning to produce natural language. These models have a range of potential applications in health care, such as patient education a…
Fine-Tuned Large Language Models for Automated Radiology Impression Generation: A Multicenter Evaluation.
Purpose To develop a fine-tuned large language model (Medical Imaging Report Assistant, MIRA) and evaluate its performance in generating radiology impressions from multicenter dat…
A Multi-AI Agent Framework for Interactive Neurosurgical Education and Evaluation: From Vignettes to Virtual Conversations.
BACKGROUND AND OBJECTIVES: Traditional medical board examinations present clinical information in static vignettes with multiple-choices (MC), fundamentally different from how phy…
2026-04-15
Parallax: Why AI Agents That Think Must Never Act
Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots b…
Round-Trip Translation Reveals What Frontier Multilingual Benchmarks Miss
Multilingual benchmarks guide the development of frontier models. Yet multilingual evaluations reported by frontier models are structured similar to popular reasoning and knowledg…
QuarkMedSearch: A Long-Horizon Deep Search Agent for Exploring Medical Intelligence
As agentic foundation models continue to evolve, how to further improve their performance in vertical domains has become an important challenge. To this end, building upon Tongyi…
Do VLMs Truly "Read" Candlesticks? A Multi-Scale Benchmark for Visual Stock Price Forecasting
Vision-language models(VLMs) are increasingly applied to visual stock price forecasting, yet existing benchmarks inadequately evaluate their understanding of stock price in candle…
AISafetyBenchExplorer: A Metric-Aware Catalogue of AI Safety Benchmarks Reveals Fragmented Measurement and Weak Benchmark Governance
The rapid expansion of large language model (LLM) safety evaluation has produced a substantial benchmark ecosystem, but not a correspondingly coherent measurement ecosystem. We pr…
Growing Pains: Extensible and Efficient LLM Benchmarking Via Fixed Parameter Calibration
The rapid release of both language models and benchmarks makes it increasingly costly to evaluate every model on every dataset. In practice, models are often evaluated on differen…
Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks
Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies focus almost exclusively on the…
Fragile Reconstruction: Adversarial Vulnerability of Reconstruction-Based Detectors for Diffusion-Generated Images
Recently, detecting AI-generated images produced by diffusion-based models has attracted increasing attention due to their potential threat to safety. Among existing approaches, r…
VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model.
The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by c…
User Experience and Early Clinical Outcomes of a Mental Wellness Chatbot for Depression and Anxiety: Pilot Evaluation Mixed Methods Study.
BACKGROUND: Artificial intelligence-powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological support, yet little is known abou…
2026-04-14
UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents
Tool-use capability is a fundamental component of LLM agents, enabling them to interact with external systems through structured function calls. However, existing research exhibit…
General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks
Contemporary large language models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in specialized domains like mathematics and physics. However, their abi…
FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers. It becomes crucial to strengthen the correctness of t…
From Translation to Superset: Benchmark-Driven Evolution of a Production AI Agent from Rust to Python
Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We present a methodology for LLM-a…
RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents
The rapid adoption of Large Language Models (LLMs) in interactive systems has enabled the creation of dynamic, open-ended Role-Playing Agents (RPAs). However, evaluating these age…
ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents
GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long t…
PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints
We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents. However, the dynamics of mul…
METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models
Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented settin…
OmniShow: Unifying Multimodal Conditions for Human-Object Interaction Video Generation
In this work, we study Human-Object Interaction Video Generation (HOIVG), which aims to synthesize high-quality human-object interaction videos conditioned on text, reference imag…
LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation
Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks requiring precise object-level…
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medical image segmentation due to…
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge for foundation segmentation…
Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study.
BACKGROUND: Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine…
Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.
OBJECTIVE: To compare the performance of state-of-the-art Gemini and GPT models on ophthalmology board-style questions and examine variation by subspecialty, cognitive complexity,…
2026-04-11
2026-04-09
ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.
Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to…
A guide to using embedded ethics in human stem-cell-based embryo model research.
Human stem-cell-based embryo models (hSCBEMs) offer unprecedented opportunities for basic and translational research. However, the rapid pace of scientific developments in the fie…
2026-04-08
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in mu…
The Character Error Vector: Decomposable errors for page-level OCR evaluation
The Character Error Rate (CER) is a key metric for evaluating the quality of Optical Character Recognition (OCR). However, this metric assumes that text has been perfectly parsed,…
Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open-ended generation tasks most relevant…
Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents
Large language models are increasingly deployed as autonomous agents executing multi-step workflows in real-world software environments. However, existing agent benchmarks suffer…
A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
Estimating Conditional Average Treatment Effects (CATE) at the individual level is central to precision marketing, yet systematic benchmarking of uplift modeling methods at indust…
ACE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments
Existing Agent benchmarks suffer from two critical limitations: high environment interaction overhead (up to 41\% of total evaluation time) and imbalanced task horizon and difficu…
JUÁ - A Benchmark for Information Retrieval in Brazilian Legal Text Collections
Legal information retrieval in Portuguese remains difficult to evaluate systematically because available datasets differ widely in document type, query style, and relevance defini…
Action Images: End-to-End Policy Learning via Multiview Video Generation
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, exis…
Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning
Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals into editable TikZ code. While T…