Search: “evaluation”

arXiv:2604.09067v1 Announce Type: new Abstract: Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is

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Hardware

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

DGX agent

arXiv:2604.09107v1 Announce Type: cross Abstract: Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computationa

hardwarearxiv-cs-ai

13 Apr 2026

Tutorials

(That doesn't mean that there are no risks associated with the financing methods used to build data centers, of course, but the driving argu…

tutorialsethan-mollick--x

Ethan Mollick discusses the economic and financial risks surrounding data center construction, distinguishing between the risks inherent in financing methods and the broader arguments driving investme

The Fast Lane Hypothesis: Von Economo Neurons Implement a Biological Speed-Accuracy Tradeoff

arXiv:2604.09229v1 Announce Type: cross Abstract: Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species

researcharxiv-cs-ai

The model is not the agent. The harness is. You need to read this recent study, and a blog post from @hwchase17 ... (links below). It will r…

model-releasesharrison-chase--x

The model is not the agent. The harness is. You need to read this recent study, and a blog post from @hwchase17 ... (links below). It will resonate deeply. This diagram from a recent paper captures so

The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?

model-releasesarxiv-cs-cl

arXiv:2601.07220v3 Announce Type: replace Abstract: Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This sur

The Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs

arXiv:2601.01580v2 Announce Type: replace-cross Abstract: Self-reflection capabilities emerge in Large Language Models after RL post-training, with multi-turn RL achieving substantial gains over SFT c

TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference

arXiv:2604.09220v1 Announce Type: new Abstract: Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruct

Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models

arXiv:2603.01400v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pru

researcharxiv-cs-cv

Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments

arXiv:2604.09038v1 Announce Type: cross Abstract: Robust geo-localization in changing environmental conditions is critical for long-term aerial autonomy. While visual place recognition (VPR) models pe

Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models

arXiv:2604.08815v1 Announce Type: new Abstract: Medical vision-language models (VLMs) show strong performance on radiology tasks but often produce fluent yet weakly grounded conclusions due to over-re

safetyarxiv-cs-cv

UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models

arXiv:2604.02241v2 Announce Type: replace Abstract: Embodied visual tracking is crucial for Unmanned Aerial Vehicles (UAVs) executing complex real-world tasks. In dynamic urban scenarios with complex

Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining

arXiv:2504.12758v3 Announce Type: replace-cross Abstract: In this paper, we show that an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) wireless system with appropriate analog combining co

researcharxiv-cs-lg

VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering

arXiv:2604.08549v1 Announce Type: cross Abstract: We introduce VerifAI, an open-source expert system for biomedical question answering that integrates retrieval-augmented generation (RAG) with a novel

ViSAGE @ NTIRE 2026 Challenge on Video Saliency Prediction

arXiv:2604.08613v1 Announce Type: new Abstract: In this report, we present our champion solution for the NTIRE 2026 Challenge on Video Saliency Prediction held in conjunction with CVPR 2026. To exploi

VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning

arXiv:2604.09508v1 Announce Type: cross Abstract: Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex qu

VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning

arXiv:2604.08639v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep learning models in safety critical applications, yet no consensus exists on which UQ m

WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

arXiv:2604.08558v1 Announce Type: cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically

researcharxiv-cs-ai

We wrote a build-from-scratch Python book on the 98% of production AI systems that isn't the model call [P]

applicationsr-machinelearning

This Reddit post from r/MachineLearning announces a build-from-scratch Python book focused on the engineering infrastructure surrounding AI systems — the components beyond the model call itself, such

We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent t…

model-releasesjerry-liu--x

We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent that works with real-world files. ParseBench is a benchmark t

WildDet3D: Scaling Promptable 3D Detection in the Wild

arXiv:2604.08626v1 Announce Type: new Abstract: Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--

applicationsarxiv-cs-cv

Tutorials

WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

arXiv:2604.08958v1 Announce Type: cross Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to

tutorialsarxiv-cs-ai

XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers

arXiv:2604.09489v1 Announce Type: cross Abstract: Model poisoning attacks pose a significant security threat to Federated Learning (FL). Most existing model poisoning attacks rely on collusion, requir

Tools

Your AI isn't failing because of 'bad chunking.' 🛑 It’s failing because of a vibe mentality. Our Head of DevRel @RoieSchwabco explains why …

Your AI isn't failing because of 'bad chunking.' 🛑 It’s failing because of a vibe mentality. Our Head of DevRel @RoieSchwabco explains why anecdotal testing is the #1 roadblock for AI devs: ✅ Determin

toolspinecone--x

You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector

arXiv:2603.15757v2 Announce Type: replace-cross Abstract: What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from

gemma 4 going insane

This r/ollama thread discusses users experiencing erratic and broken behavior when running Google's Gemma 4 models locally via Ollama, including issues such as strange and unrelated responses, as well

model-releasesr-ollama

Agents

harness engineering > prompt engineering prompts get you 80% there the last 20% — tool routing, memory retrieval, retry logic, evals — thats…

Harrison Chase, co-founder of LangChain, argues that while prompt engineering is foundational and can get you 80% of the way to a functional AI system, the remaining 20% of engineering effort involves

agentsharrison-chase--x

It isn't at the level of the Big Three models when you poke at it, but a very solid start.

applicationsethan-mollick--x

Ethan Mollick shares an assessment of an AI model (likely a newer or smaller model) that, while not matching the performance of the leading frontier models (likely referring to top offerings from Open

Tools

MiniMax M2.7 is now on Together AI. Trained by letting it run its own RL loop, resulting in the highest open-source score on MLE Bench Lite.

MiniMax M2.7 is now available on Together AI, offering a powerful open-source model trained using a self-directed reinforcement learning loop. This training methodology enabled the model to achieve th

toolstogether-ai--x

Industry

Openclaw real costs: self hosting vs managed hosting vs API fees

This Reddit post examines the true total cost of running OpenClaw, an open-source AI agent framework, across different deployment models. While OpenClaw itself is free and open-source, hosting costs r

industryr-chatgpt

Industry

Update: ChatGPT pointed me toward ADHD, and today I got officially diagnosed

A Reddit user on r/ChatGPT shares a follow-up post recounting how interactions with ChatGPT led them to recognize potential signs of ADHD in themselves, which ultimately prompted them to seek a profes

industryr-chatgpt

11 Apr 2026

A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset

model-releasesarxiv-cs-lg

arXiv:2604.06227v1 Announce Type: new Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developi

A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction

arXiv:2604.06207v1 Announce Type: cross Abstract: This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), ai

applicationsarxiv-cs-ai

A First Guess is Rarely the Final Answer: Learning to Search in the Travelling Salesperson Problem

arXiv:2604.06940v1 Announce Type: cross Abstract: Most neural solvers for the Traveling Salesperson Problem (TSP) are trained to output a single solution, even though practitioners rarely stop there:

Hardware

A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures

arXiv:2602.03604v3 Announce Type: replace-cross Abstract: We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs

hardwarearxiv-cs-ai

A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech

arXiv:2604.06327v1 Announce Type: cross Abstract: Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual

A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP

arXiv:2604.06650v1 Announce Type: cross Abstract: Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at

A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring

arXiv:2604.07395v1 Announce Type: cross Abstract: Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single-shot manner: a model proposes an act

safetyarxiv-cs-cv

Tutorials

A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation

arXiv:2604.06365v1 Announce Type: cross Abstract: Arabic medical text generation is increasingly needed to help users interpret symptoms and access general health guidance in their native language. Ne

tutorialsarxiv-cs-ai

A-SLIP: Acoustic Sensing for Continuous In-hand Slip Estimation

arXiv:2604.08528v1 Announce Type: new Abstract: Reliable in-hand manipulation requires accurate real-time estimation of slip between a gripper and a grasped object. Existing tactile sensing approaches

researcharxiv-cs-ro

A systematic framework for generating novel experimental hypotheses from language models

arXiv:2408.05086v3 Announce Type: replace Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broa

safetyarxiv-cs-cl

Ace Step 1.5 XL ComfyUI automation workflow without lama for generating random tags using qwen, generate song and then give it a rating by using waveform analysis

model-releasesr-stablediffusion

This is a ComfyUI automation workflow for the ACE-Step 1.5 XL music generation model that uses Qwen (a language model text encoder) to randomly generate music tags/captions without requiring LAMA, ...

Agents

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

arXiv:2604.08545v1 Announce Type: new Abstract: The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a pro

agentsarxiv-cs-cv

Active Statistical Inference

arXiv:2403.03208v3 Announce Type: replace-cross Abstract: Inspired by the concept of active learning, we propose active inferencenicode{x2013}a methodology for statistical inference with machine-l

researcharxiv-cs-lg

Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation

arXiv:2604.07665v1 Announce Type: new Abstract: Self-supervised monocular depth estimation (MDE) has received increasing interests in the last few years. The objects in the scene, including the object

Adaptive Symmetrization of the KL Divergence

arXiv:2511.11159v2 Announce Type: replace Abstract: Many tasks in machine learning can be described as or reduced to learning a probability distribution given a finite set of samples. A common approac

researcharxiv-cs-lg

Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest

model-releasesarxiv-cs-cl

arXiv:2604.08525v1 Announce Type: cross Abstract: Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginni

AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling

model-releasesarxiv-cs-lg

arXiv:2604.06475v1 Announce Type: new Abstract: Deep Learning Reduced Order Models (ROMs) are becoming increasingly popular as surrogate models for parametric partial differential equations (PDEs) due