Search: “evaluation”

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats

arXiv:2606.04115v1 Announce Type: cross Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Do Transformers Need Three Projections? Systematic Study of QKV Variants

DGX agent

arXiv:2606.04032v1 Announce Type: cross Abstract: Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role

model-releasesarxiv-cs-ai

4 Jun 2026

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

arXiv:2606.04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in

Applications

Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset

arXiv:2606.03995v1 Announce Type: cross Abstract: Background: Alzheimer's disease (AD) affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition (NC), mild cognit

applicationsarxiv-cs-ai

Efficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage

arXiv:2603.16652v2 Announce Type: replace Abstract: Monitoring cavity-nesting wild bees and wasps is vital for biodiversity research and conservation. Layer trap nests (LTNs) are emerging as a valuabl

Local Ai

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation

arXiv:2606.04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundat

local-aiarxiv-cs-ai

Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling

arXiv:2606.05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algor

Entity Binding Failures in Speech LLM Reasoning: Diagnosis and Chain-of-Thought Intervention

arXiv:2606.04474v1 Announce Type: new Abstract: Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognit

researcharxiv-cs-cl

Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning

arXiv:2606.04492v1 Announce Type: new Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory

agentsarxiv-cs-lg

EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

arXiv:2606.04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et a

researcharxiv-cs-ai

Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline

arXiv:2606.04315v1 Announce Type: new Abstract: LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tun

agentsarxiv-cs-ai

Applications

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv:2606.04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early predict

applicationsarxiv-cs-lg

FLAGG: Flexible Autoregressive Graph Generation

arXiv:2606.05067v1 Announce Type: new Abstract: The Deep Graph Generation's panorama spans two extremes: one-shot and sequential models. The former generates nodes and edges jointly, while the latter

From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents

arXiv:2606.04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles,

agentsarxiv-cs-ai

From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents

arXiv:2606.04329v1 Announce Type: cross Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance. However, persistent memory

GeM-NR: Geometry-Aware Multi-View Editing for Nonrigid Scene Changes

arXiv:2606.05142v1 Announce Type: cross Abstract: Recent developments in multi-view image editing with generative models have brought us a step closer toward general 3D content generation and customiz

Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models

arXiv:2606.04922v1 Announce Type: cross Abstract: Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favo

Geospatial Foundation Models to Enable Progress on Sustainable Development Goals

arXiv:2505.24528v3 Announce Type: replace Abstract: Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and c

safetyarxiv-cs-cv

GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settings

arXiv:2606.04367v1 Announce Type: new Abstract: Interlinear glossed text (IGT) is the standard format for linguistic annotation in language documentation. Producing it manually, however, is often slow

researcharxiv-cs-cl

Gradient estimators for parameter inference in discrete stochastic kinetic models

arXiv:2604.02121v2 Announce Type: replace-cross Abstract: Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For determinis

GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards

arXiv:2606.04889v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). Howeve

safetyarxiv-cs-cl

Graph Set Transformer

arXiv:2606.05116v1 Announce Type: new Abstract: We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element pred

Gravity-Aware Hierarchical Routing for Lightweight SensorLLM on Human Activity Recognition

arXiv:2606.04019v1 Announce Type: cross Abstract: Recent studies on sensor-language alignment have shown that two-stage frameworks can improve the semantic modeling ability of wearable-sensor human ac

Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives

arXiv:2606.05018v1 Announce Type: new Abstract: Popular initiatives and referendums are central to Swiss democracy, yet the validation of handwritten signature lists remains a labor-intensive manual p

HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning

arXiv:2606.04825v1 Announce Type: new Abstract: Despite the importance of tactile sensing for reliable manipulation, most existing Vision-Language-Action (VLA) datasets remain vision-only, and those t

safetyarxiv-cs-ro

HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning

arXiv:2606.04345v1 Announce Type: cross Abstract: This paper presents HYolo, an intelligent IoT-based object detection framework that integrates hypergraph learning into the YOLO architecture. Traditi

researcharxiv-cs-ai

if you had 100k to invest in OpenAI and/or Anthropic IPOs which would you go for?

Gary Marcus discusses investment strategy between potential OpenAI and Anthropic IPOs, likely weighing factors such as the companies' technological capabilities, market positioning, business models, a

safetygary-marcus--x

In-Context Graphical Inference

arXiv:2606.05042v1 Announce Type: cross Abstract: Marginal inference in discrete graphical models forces a choice between exactness and scalability: exact algorithms are intractable for high-treewidth

safetyarxiv-cs-cl

InstantRetouch: Efficient and High-Fidelity Instruction-Guided Image Retouching with Bilateral Space

model-releasesarxiv-cs-cv

arXiv:2606.05071v1 Announce Type: new Abstract: Language-guided photo retouching aims to adjust color and tone while preserving geometry and texture. Recently, diffusion-based retouching shows a super

Tutorials

J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization

arXiv:2510.09953v3 Announce Type: replace Abstract: Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, whereas AI-based models automate th

tutorialsarxiv-cs-cv

KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models

arXiv:2606.04180v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems. While these models are ty

L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI

arXiv:2606.04419v1 Announce Type: cross Abstract: MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam

researcharxiv-cs-ai

Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification

arXiv:2511.06331v2 Announce Type: replace Abstract: Detailed structural and species information on individual tree level is increasingly important to support precision forestry, biodiversity conservat

Learning Control-Affine Reduced-Order Models via Autoencoders

arXiv:2606.05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (

researcharxiv-cs-lg

Learning Empirically Admissible Neural Heuristics for Combinatorial Search

arXiv:2606.04860v1 Announce Type: cross Abstract: Finding optimal solution paths for combinatorial puzzles like the Rubik's Cube, sliding tile puzzles, and Lights Out remains a classical challenge in

Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use

model-releasesarxiv-cs-cl

arXiv:2603.03205v2 Announce Type: replace Abstract: Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon ac

Literature-Guided Minimax Optimization of Virtual Epilepsy Neurostimulation

arXiv:2606.04339v1 Announce Type: new Abstract: Computational models of epilepsy promise patient-specific treatment design, but most optimization workflows still search for parameters that perform wel

LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling

arXiv:2606.04438v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) and looped architectures scale models along two orthogonal axes, namely parameter capacity and effective depth. However, main

M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking

arXiv:2606.04829v1 Announce Type: new Abstract: Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstr

safetyarxiv-cs-ro

MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution

arXiv:2606.05068v1 Announce Type: new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because

Tutorials

Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models

arXiv:2606.04326v1 Announce Type: cross Abstract: Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from i

tutorialsarxiv-cs-ai

MENTOR: A Metacognition-Driven Self-Evolution Framework for Uncovering and Mitigating Implicit Domain Risks in LLMs

arXiv:2511.07107v3 Announce Type: replace Abstract: Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address im

MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers

arXiv:2606.04366v1 Announce Type: new Abstract: Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of l

Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

arXiv:2606.04191v1 Announce Type: cross Abstract: We describe our approach to the CTF4Science Lorenz challenge, a benchmark that mixes short-horizon forecasting, long-time distribution matching, and t

More about the guarantee and eligibility criteria: https://devin.ai/guarantee

Devin is an AI software engineer product that likely includes a performance guarantee and specific eligibility requirements for users, with details available on Cognition AI's official guarantee page.

agentscognition-ai--x

MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models

arXiv:2606.04349v1 Announce Type: cross Abstract: Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution het

Tutorials

Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis

arXiv:2606.04414v1 Announce Type: new Abstract: Multi-view cardiac magnetic resonance (CMR) imaging provides complementary anatomical information and is widely used for noninvasive disease assessment.

tutorialsarxiv-cs-cv

Applications

Multi-Camera AR Guidance System for Surgical Instrument Handling and Assembly: Investigating Workload and Efficiency

arXiv:2606.04992v1 Announce Type: new Abstract: The handling and assembly of instruments during surgery imposes high cognitive demands on scrub nurses, particularly when instruments are unfamiliar. We

applicationsarxiv-cs-cv