Search: “evaluation”

HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs

arXiv:2511.18760v2 Announce Type: replace Abstract: Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments.

Human

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Applications

Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

DGX agent

arXiv:2605.30834v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnera

applicationsarxiv-cs-ai

1 Jun 2026

Hardware

How Cosmos 3 Helps Physical AI Think Before It Acts

NVIDIA Cosmos 3 uses a mixture-of-transformers architecture that pairs a reasoning transformer with a generation transformer, enabling it to understand object interactions and spatial-temporal relatio

hardwarenvidia-blog

How Much Do LLMs Know About Chinese Zero Pronouns?

arXiv:2605.31056v1 Announce Type: new Abstract: Zero Pronouns (ZPs) are a pervasive linguistic phenomenon in pro-drop languages such as Chinese and have long posed a challenge for natural language pro

researcharxiv-cs-cl

HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning

arXiv:2605.31068v1 Announce Type: new Abstract: We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The pro

safetyarxiv-cs-cv

Hyperspectral Image Classification using Spectral-Spatial Mixer Network

arXiv:2511.15692v2 Announce Type: replace Abstract: This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification. The architecture inte

local-aiarxiv-cs-cv

HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds

arXiv:2510.15614v3 Announce Type: replace Abstract: Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effe

I compared 62 samplers and 16 schedulers for WAN 2.1 image generation and rated the image quality so you don't have to 😬

local-air-stablediffusion

A comprehensive comparison study of 62 different samplers and 16 schedulers used in WAN 2.1 image generation, with systematic quality ratings to help users understand which combinations produce the be

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

arXiv:2602.19049v2 Announce Type: replace Abstract: Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We

safetyarxiv-cs-cl

ImmigrationQA: A Source-Grounded Dataset and Small-Model Adaptation for U.S. Immigration Law

arXiv:2605.30589v1 Announce Type: cross Abstract: U.S. immigration law spans thousands of pages of official policy, federal regulations, and procedural guidance that change frequently and carry high s

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback

arXiv:2605.30478v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models using programmatically checkable signals such as unit-test outcomes, enab

IntAttention: A Fully Integer Attention Pipeline for Efficient Edge Inference

arXiv:2511.21513v2 Announce Type: replace Abstract: Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary m

Is the Last Layer Sufficient for Uncertainty Quantification?

arXiv:2605.30741v1 Announce Type: cross Abstract: Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Sever

Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble

arXiv:2605.30720v1 Announce Type: cross Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural

researcharxiv-cs-ai

Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task

arXiv:2605.31480v1 Announce Type: new Abstract: Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about se

researcharxiv-cs-cl

Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions

arXiv:2605.31586v1 Announce Type: cross Abstract: Grasping the semantics of rare constructions (form-meaning pairings) has been shown to be a challenging problem that has currently only been solved by

Learning-Based Navigation for Indoor Mobile Robots

model-releasesarxiv-cs-ro

arXiv:2605.30468v1 Announce Type: new Abstract: This paper presents a learning-based navigation framework for indoor mobile robots. The proposed method combines a supervised neural global planner, tra

Learning Generalizable Robot Policy with Human Demonstration Video as a Prompt

arXiv:2505.20795v2 Announce Type: replace Abstract: Recent robot learning methods commonly rely on imitation learning from massive robotic dataset collected with teleoperation. When facing a new task,

safetyarxiv-cs-ro

Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection

arXiv:2605.31155v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled

Learning Multi-Agent Coordination via Sheaf-ADMM

model-releasesarxiv-cs-lg

arXiv:2605.31005v1 Announce Type: new Abstract: We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by

Tutorials

Learning Parametric Nitrogen Fertilizer Response Curves Using Neuro Symbolic Regression

arXiv:2605.31276v1 Announce Type: new Abstract: Accurately modeling crop response to Nitrogen (N) fertilization is a fundamental challenge in precision agriculture, as it impacts both economic returns

tutorialsarxiv-cs-lg

Applications

Learning to Solve and Optimize by Evolving Code

arXiv:2605.31049v1 Announce Type: cross Abstract: Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems

applicationsarxiv-cs-ai

LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting

arXiv:2605.31376v1 Announce Type: cross Abstract: Autonomous robots in unknown indoor environments require both reliable collision avoidance and object-level understanding. Classical representations s

safetyarxiv-cs-cv

Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models

arXiv:2605.31158v1 Announce Type: new Abstract: Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time ga

local-aiarxiv-cs-cv

Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

arXiv:2605.31259v1 Announce Type: new Abstract: Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities. At the Spallation Neutron Source (SN

safetyarxiv-cs-lg

Linear Ordering Problem: Time for a Change

arXiv:2605.31051v1 Announce Type: cross Abstract: The Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem with important applications in areas such as economics, social c

LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services

arXiv:2603.04946v2 Announce Type: replace Abstract: In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage s

local-aiarxiv-cs-cl

MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning

arXiv:2605.30857v1 Announce Type: new Abstract: Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuni

MAECO-Lite: Modular Ontology for Dynamic Malware Analysis

arXiv:2605.31199v1 Announce Type: cross Abstract: Capturing dynamic malware behavior in a practical but still semantically precise manner remains a significant challenge in cyber threat intelligence.

researcharxiv-cs-ai

Agents

MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks

arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone

agentsarxiv-cs-ai

Mathematical Morphology in Machine Learning

arXiv:2605.30700v1 Announce Type: new Abstract: This work introduces mathematical morphology-an established visual computing theory-into machine learning to exploit shape and density aspects often ove

Applications

MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding

arXiv:2605.30794v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks. However, they re

applicationsarxiv-cs-ai

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com

Memory-Bound but Not Bandwidth-Limited: The Physical AI Inference Gap in Batch-1 LLM Decode

arXiv:2605.30571v1 Announce Type: cross Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM

Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances

arXiv:2603.26612v2 Announce Type: replace Abstract: Drones equipped with overhead manipulators offer unique capabilities for inspection, maintenance, and contact-based interaction. However, the motion

researcharxiv-cs-ro

Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching

arXiv:2411.00759v5 Announce Type: replace Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unl

Agents

MiniMax M3 imminent. Will be doing deep testing with it on my own coding agent and harness. Review coming soon.

MiniMax M3, an upcoming AI model, is expected to be released soon and will undergo comprehensive testing within a custom coding agent framework. A detailed technical review of the model's performance

agentsdair-ai--x

Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement

arXiv:2605.30437v1 Announce Type: new Abstract: Generative AI (GenAI) image editors, such as Nano Banana, produce visually compelling results for retouching tasks, enabling non-experts to edit images

Mixture of Concept Bottleneck Experts

arXiv:2602.02886v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs ty

researcharxiv-cs-ai

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials

model-releasesarxiv-cs-lg

arXiv:2605.30889v1 Announce Type: cross Abstract: Constructing production-quality machine-learned interatomic potentials (MLIPs) requires balancing accuracy, dynamical stability, and computational thr

Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?

arXiv:2605.31036v1 Announce Type: cross Abstract: Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms.

Industry

Moderna gets $50 million to develop mRNA Ebola vaccine against Bundibugyo

The Coalition for Epidemic Preparedness Innovations (CEPI) announced an expanded collaboration with Moderna, committing up to $50 million to support preclinical development and Phase 1 clinical testin

industryars-technica

Mollified Value Learning

arXiv:2602.23280v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains ch

MosaicLeaks:Privacy Risks in Querying-in-the-Open for Deep Research Agents

arXiv:2605.30727v1 Announce Type: new Abstract: Deep research agents increasingly combine private local documents with external tools like web retrieval, creating a privacy risk: an agent's external q

MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance

arXiv:2605.30925v1 Announce Type: new Abstract: Text-to-motion generation has progressed rapidly in recent years, offering an expressive interface for animation and human-computer interaction. However

Multimodal Fusion via Self-Consistent Task-Gradient Fields

arXiv:2410.15475v2 Announce Type: replace Abstract: Multimodal learning aims to preserve as much task-related information as possible from different inputs. However, current fusion designs often disto

Multivariate Distributional Reinforcement Learning Using Sliced Divergences

arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema

Neuro-Symbolic Predictive Process Monitoring

arXiv:2509.00834v2 Announce Type: replace Abstract: This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitori

local-aiarxiv-cs-ai