Search: “evaluation”

EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning

arXiv:2601.03471v3 Announce Type: replace-cross Abstract: Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effec

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Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning

DGX agent

arXiv:2605.26292v1 Announce Type: cross Abstract: Parameter-efficient adaptation of vision-language foundation models is crucial for precise multimodal understanding of biomedical images, yet existing

model-releasesarxiv-cs-cl

27 May 2026

Evidence Absence Is Not Evidence Insufficiency: Diagnosing NEI Construction Artifacts in Fact Verification

arXiv:2605.26663v1 Announce Type: new Abstract: Evidence absence is not evidence insufficiency, but fact verification benchmarks can make them observationally similar. The Not Enough Information (NEI)

researcharxiv-cs-cl

EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation

arXiv:2509.04310v4 Announce Type: replace Abstract: Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in extit{complex}, extit

agentsarxiv-cs-ai

Explainable Comparison of Feature-Based and Deep Learning Models for TROPOMI Methane Plume Screening

arXiv:2605.27236v1 Announce Type: new Abstract: Continuous and global detection of large methane emissions is a crucial step for global warming mitigation. Satellite observations, such as from S5P/TRO

researcharxiv-cs-lg

Exploring the robustness of TractOracle methods in RL-based tractography

arXiv:2507.11486v2 Announce Type: replace Abstract: Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approache

researcharxiv-cs-lg

FalAR: A Large-scale Speaker-Annotated European Portuguese Speech Corpus of Parliamentary Sessions

arXiv:2605.27062v1 Announce Type: new Abstract: State-of-the-art performance for Automatic Speech Recognition (ASR) largely depends on the availability of large-scale labeled corpora. This creates a d

safetyarxiv-cs-cl

FinHarness: An Inline Lifecycle Safety Harness for Finance LLM Agents

arXiv:2605.27333v1 Announce Type: new Abstract: Finance LLM agents must simultaneously block prompt-induced unauthorized actions and approve legitimate multi-step business workflows. However, boundary

safetyarxiv-cs-cl

Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints

arXiv:2603.17685v3 Announce Type: replace Abstract: Balancing policy expressiveness with the exploration-exploitation trade-off is a core challenge in online Reinforcement Learning (RL). While Stochas

FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis

arXiv:2605.26423v1 Announce Type: new Abstract: Task-based fMRI provides a direct readout of task-evoked neural dynamics, but it is expensive and difficult to acquire at scale, motivating rest-to-task

FTibSuite: A Comprehensive Resource Suite for Tibetan Vision-Language Modeling

arXiv:2605.26601v1 Announce Type: new Abstract: Vision-language models have progressed rapidly, but Tibetan remains a severely underserved low-resource language due to the lack of reproducible trainin

safetyarxiv-cs-cv

Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.26230v1 Announce Type: new Abstract: Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typica

Governed Evolution of Agent Runtimes through Executable Operational Cognition

arXiv:2605.27328v1 Announce Type: cross Abstract: Recent advances in agentic systems increasingly treat code as an executable operational substrate rather than as a disposable output artifact. Prior w

agentsarxiv-cs-ai

GraphDancer: Training LLMs to Explore and Reason over Graphs via Two-Stage Curriculum Post-Training

arXiv:2602.02518v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many real-world knowledge sources are organize

Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning

arXiv:2503.20507v4 Announce Type: replace-cross Abstract: Modern high-performance computing (HPC) environments rely on hybrid storage systems (HSS) that combine multiple storage devices with diverse l

agentsarxiv-cs-lg

Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs

arXiv:2605.26835v1 Announce Type: new Abstract: LLM-based multi-agent systems have been widely adopted for knowledge retrieval and report generation, synthesizing known information through web search

High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework

arXiv:2605.27113v1 Announce Type: cross Abstract: In recent years, financial institutions and firms have increasingly adopted synthetic data to address data scarcity and to generate counterfactual mar

HiSpec: Hierarchical Speculative Decoding for LLMs

arXiv:2510.01336v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates LLM inference by using a smaller draft model to speculate tokens that a larger target model verifies. Verific

Tutorials

How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning

arXiv:2605.27310v1 Announce Type: new Abstract: Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained geometry nee

tutorialsarxiv-cs-cv

How Reliable are LLMs for Reasoning on the Re-ranking task?

arXiv:2508.18444v2 Announce Type: replace-cross Abstract: With the improving semantic understanding capability of Large Language Models (LLMs), they exhibit a greater awareness and alignment with huma

safetyarxiv-cs-ai

Local Ai

Incremental Gauss-Newton Descent for Machine Learning

arXiv:2408.05560v2 Announce Type: replace Abstract: Stochastic gradient updates are widely used for their efficiency and scalability, but their effective step sizes can depend strongly on feature scal

local-aiarxiv-cs-lg

Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

arXiv:2605.26307v1 Announce Type: cross Abstract: Software-Defined Networking (SDN) provides flexible and programmable network management; however, its centralized control architecture remains highly

It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty

arXiv:2605.27288v1 Announce Type: cross Abstract: Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behav

safetyarxiv-cs-ai

Hardware

JetViT: Efficient High-Resolution Vision Transformer with Post-Training Attention Search

arXiv:2605.26636v1 Announce Type: cross Abstract: We introduce JetViT, a novel family of hybrid-architecture Vision Transformer (ViT) models that match the accuracy of state-of-the-art full-attention

hardwarearxiv-cs-ai

Joint Instance Segmentation and Geometric Attribute Regression for Roof Structures in Aerial Imagery

arXiv:2605.26370v1 Announce Type: new Abstract: We present a method for jointly predicting instance-level roof segment masks together with three continuous geometric attributes -- building height, roo

researcharxiv-cs-cv

Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation

arXiv:2511.14993v3 Announce Type: replace-cross Abstract: This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis.

KARMA: Karma-Aligned Reward Model Adaptation

arXiv:2605.26738v1 Announce Type: new Abstract: Human communication depends on implicit social signals where effectiveness is shaped by tone, context, and conversational norms rather than semantic con

safetyarxiv-cs-cl

LaRe: Latent Refocusing for Multimodal Reasoning

model-releasesarxiv-cs-cl

arXiv:2511.02360v4 Announce Type: replace-cross Abstract: Chain of Thought (CoT) reasoning enhances logical performance by decomposing complex tasks, yet its multimodal extension faces a trade-off. Th

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection

model-releasesarxiv-cs-lg

arXiv:2505.21285v4 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effecti

LearnedCache: An eBPF-Integrated Perceptron-Based Eviction Policy for the Linux Page Cache

arXiv:2605.26168v1 Announce Type: cross Abstract: Linux is the foundation of the digital age, accounting for the majority of the cloud and mobile OS markets. Any device that runs Linux uses the Linux

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming

arXiv:2605.26828v1 Announce Type: new Abstract: Learning from Demonstration~(LfD) should capture not only how a task is executed, but also its high-level task structure that explains the demonstrated

agentsarxiv-cs-ro

Learning GUI Grounding with Spatial Reasoning from Visual Feedback

arXiv:2509.21552v2 Announce Type: replace-cross Abstract: Graphical User Interface (GUI) grounding is commonly framed as a coordinate prediction task -- given a natural language instruction, generate

agentsarxiv-cs-cl

Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data

arXiv:2605.26271v1 Announce Type: cross Abstract: We study a nonlinear factor model in which observed responses depend on low-rank latent factors through an unknown monotone link function. This settin

researcharxiv-cs-lg

Left-Right Symmetry Breaking in CLIP-style Vision-Language Models Trained on Synthetic Spatial-Relation Data

arXiv:2601.12809v2 Announce Type: replace-cross Abstract: Spatial understanding remains a key challenge in vision-language models. Yet it is still unclear whether such understanding is truly acquired,

Lessons from Penetration Tests on Large-Scale Agent Systems

arXiv:2605.27042v1 Announce Type: cross Abstract: As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of

agentsarxiv-cs-ai

Tutorials

Leveraging Text-to-Image Diffusion Models for Unsupervised Visual Object Tracking

arXiv:2605.26933v1 Announce Type: new Abstract: Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotatio

tutorialsarxiv-cs-cv

Applications

Leveraging Visual Signals for Robust Token-Level Uncertainty in Vision-Language Generation

arXiv:2605.27136v1 Announce Type: new Abstract: Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deployment.

applicationsarxiv-cs-cv

LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring

model-releasesarxiv-cs-cl

arXiv:2605.27088v1 Announce Type: new Abstract: Aligning LLMs for math tutoring typically requires RL-based training with multi-GPU infrastructure. We investigate whether training-free prompt optimiza

LLMs versus the Halting Problem: Characterizing Program Termination Reasoning

arXiv:2601.18987v5 Announce Type: replace-cross Abstract: Determining whether a program terminates is a central problem in computer science. Turing's Halting Problem established termination as undecid

Local Ai

Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences

arXiv:2605.26756v1 Announce Type: new Abstract: Diffusion models can unintentionally memorize training samples, raising concerns about privacy and copyright. While recent methods can detect memorizati

local-aiarxiv-cs-lg

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

arXiv:2605.27365v1 Announce Type: cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into

LongCat-Video-Avatar 1.5 Technical Report

model-releasesarxiv-cs-cv

arXiv:2605.26486v1 Announce Type: new Abstract: Despite advances in audio-driven video generation, achieving commercial-grade stability remains challenging. We present LongCat-Video-Avatar 1.5, an upg

LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models

arXiv:2605.27254v1 Announce Type: cross Abstract: Selecting which instances to label is a key challenge in low-label tabular learning. For recent Tabular Foundation Models such as TabPFN, context sele

Applications

LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes

arXiv:2601.15283v2 Announce Type: replace Abstract: We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative

applicationsarxiv-cs-cv

Measuring Prediction Uncertainty in Neural Cellular Automata

arXiv:2605.26726v1 Announce Type: cross Abstract: Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when

safetyarxiv-cs-ai

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability

arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recover

MemFail: Stress-Testing Failure Modes of LLM Memory Systems

arXiv:2605.26667v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empiric

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph St