Search: “evaluation”

Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models

arXiv:2604.25416v1 Announce Type: new Abstract: Model-Based Reinforcement Learning distinguishes between physical dynamics models operating on proprioceptive inputs and latent dynamics models operatin

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Model Releases

BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling

DGX agent

arXiv:2604.24999v1 Announce Type: new Abstract: Thoracic Computed Tomography (CT) scans offer detailed insights into the intricate branching network of the airway tree, which is essential for understa

model-releasesarxiv-cs-cv

29 Apr 2026

CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic

arXiv:2604.24935v1 Announce Type: cross Abstract: The Controller Area Network (CAN) is a safety-critical in-vehicle communication protocol that lacks built-in security mechanisms, making intrusion det

CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation

arXiv:2604.25774v1 Announce Type: new Abstract: Accurate nutrient estimation from unstructured recipe text is an important yet challenging problem in dietary monitoring, due to ambiguous ingredient te

Contrast-Enhanced Gating in GRUs for Robust Low-Data Sequence Learning

arXiv:2402.09034v3 Announce Type: replace Abstract: Activation functions govern how recurrent networks regulate and transmit information across temporal dependencies. Despite advances in sequence mode

Tutorials

Contrastive Image-Metadata Pre-Training for Materials Transmission Electron Microscopy

arXiv:2604.24909v1 Announce Type: new Abstract: The vast majority of transmission electron microscopy (TEM) data never gets published and ends up on a backup drive until deleted to free up space. Thes

tutorialsarxiv-cs-lg

CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation

model-releasesarxiv-cs-cv

arXiv:2604.25376v1 Announce Type: new Abstract: Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data

Cross-Lingual Jailbreak Detection via Semantic Codebooks

arXiv:2604.25716v1 Announce Type: new Abstract: Safety mechanisms for large language models (LLMs) remain predominantly English-centric, creating systematic vulnerabilities in multilingual deployment.

Curriculum-guided multimodal representation learning enables generalizable prediction of nanomaterial-protein interactions

arXiv:2507.14245v2 Announce Type: replace Abstract: Nanomaterial-protein interactions (NPI) are pivotal to realizing the therapeutic and diagnostic potential of nanomaterials. Although AI promises to

DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms

arXiv:2604.25300v1 Announce Type: new Abstract: Deploying tiny object perception on edge platforms is challenging because practical systems must satisfy both strict compute budgets and end-to-end late

Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge

model-releasesarxiv-cs-cv

arXiv:2512.08323v2 Announce Type: replace Abstract: Teeth landmark detection is a key task in modern orthodontics, supporting advanced diagnosis, personalized treatment planning, and effective monitor

Agents

Diagnosis, Bad Planning & Reasoning. Treatment, SCOPE -- Planning for Hybrid Querying over Clinical Trial Data

arXiv:2604.25120v1 Announce Type: new Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through

agentsarxiv-cs-cl

Dictionary learning for Kernel EDMD

arXiv:2604.25572v1 Announce Type: cross Abstract: Studying nonlinear dynamical systems through their state space behavior can be challenging, and one possible alternative is to analyze them via their

DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference

arXiv:2510.19669v4 Announce Type: replace Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility

Diverse Image Priors for Black-box Data-free Knowledge Distillation

arXiv:2604.25794v1 Announce Type: cross Abstract: Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in

Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives

arXiv:2604.25423v1 Announce Type: new Abstract: Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial ex

Dont Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination

arXiv:2604.24978v1 Announce Type: new Abstract: Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We

Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation

arXiv:2602.13864v2 Announce Type: replace-cross Abstract: Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data impu

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

arXiv:2604.25885v1 Announce Type: cross Abstract: Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performa

Faithful Autoformalization via Roundtrip Verification and Repair

arXiv:2604.25031v1 Announce Type: new Abstract: When an LLM formalizes natural language, how do we know the output is faithful? We propose a roundtrip verification approach which does not require grou

Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models

arXiv:2604.25313v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) models frequently produce answers grounded in parametric memory rather than the retrieved context, undermining the

FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments

arXiv:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environme

FARM: Enhancing Molecular Representations with Functional Group Awareness

arXiv:2410.02082v4 Announce Type: replace Abstract: We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES,

Fast Geometric Embedding for Node Influence Maximization

arXiv:2506.07435v3 Announce Type: replace-cross Abstract: Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we

Frictive Policy Optimization for LLMs: Epistemic Intervention, Risk-Sensitive Control, and Reflective Alignment

arXiv:2604.25136v1 Announce Type: new Abstract: We propose Frictive Policy Optimization (FPO), a framework for learning language model policies that regulate not only what to say, but when and how to

safetyarxiv-cs-cl

Local Ai

From World-Gen to Quest-Line: A Dependency-Driven Prompt Pipeline for Coherent RPG Generation

arXiv:2604.25482v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for narrative generation, but their use in complex, multi-layered role-playing game (RPG) world

local-aiarxiv-cs-cl

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604.25853v1 Announce Type: new Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language

GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances

arXiv:2604.24766v1 Announce Type: new Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controll

GeoSearch: Augmenting Worldwide Geolocalization with Web-Scale Reverse Image Search and Image Matching

arXiv:2604.25390v1 Announce Type: cross Abstract: Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity.

Golden RPG: Confidence-Adaptive Region-Aware Noise for Compositional Text-to-Image Generation

model-releasesarxiv-cs-cv

arXiv:2604.25314v1 Announce Type: new Abstract: Compositional text-to-image (T2I) generation requires a model to honour multiple sub-prompts that describe distinct image regions. Recent work shows tha

Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs

arXiv:2412.08835v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message p

Image Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy

arXiv:2604.25464v1 Announce Type: new Abstract: Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key ch

Immediate Derivatives Suffice for Online Recurrent Adaptation

arXiv:2603.28750v3 Announce Type: replace Abstract: For three decades online recurrent learning has been assumed to require propagating a Jacobian tensor through the network's dynamics at O(n^4) per s

Improving LLM Predictions via Inter-Layer Structural Encoders

arXiv:2603.22665v2 Announce Type: replace Abstract: The standard practice in Large Language Models (LLMs) is to base predictions on final-layer representations. However, intermediate layers encode com

Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacity

arXiv:2604.24827v1 Announce Type: new Abstract: Closed-source frontier labs do not disclose parameter counts, and the standard alternative -- inference economics -- carries 2imes+ uncertainty from har

Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces

arXiv:2604.05310v2 Announce Type: replace Abstract: Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmissi

safetyarxiv-cs-ro

Interactive Episodic Memory with User Feedback

arXiv:2604.24893v1 Announce Type: new Abstract: In episodic memory with natural language queries (EM-NLQ), a user may ask a question (e.g., 'Where did I place the mug?') that requires searching a long

safetyarxiv-cs-cv

Applications

JaGuard: Position Error Correction of GNSS Jamming with Deep Temporal Graphs

arXiv:2509.14000v4 Announce Type: replace Abstract: Global Navigation Satellite Systems (GNSS) face growing disruption from intentional jamming, undermining critical infrastructure where precise posit

applicationsarxiv-cs-lg

Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model

arXiv:2604.25196v1 Announce Type: new Abstract: Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators

arXiv:2503.06778v3 Announce Type: replace Abstract: Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annot

Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs

arXiv:2604.25296v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown transformative potential in medical applications, yet their performance is hindered by conventional

safetyarxiv-cs-cl

Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference

arXiv:2604.24911v1 Announce Type: new Abstract: Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predicti

Local Ai

Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking

arXiv:2604.25405v1 Announce Type: new Abstract: Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by

local-aiarxiv-cs-cv

Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures

arXiv:2604.25817v1 Announce Type: new Abstract: Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to

Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling

arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only

minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation

arXiv:2604.24805v1 Announce Type: new Abstract: Modern machine learning optimizes for accuracy without explicitly accounting for internal computational cost, even though physical and biological system

Mitigating Coordinate Prediction Bias from Positional Encoding Failures

arXiv:2510.22102v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant cha

Named Entity Recognition of Historical Texts via Large Language Model

arXiv:2508.18090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. On