Search: “models”

Here's a cool piece of LLM lore: the original scaling laws were wrong due to a bug, which probably led to a lot of wasted compute on oversized undertrained models 🫣 (and that was before we even starte

toolsswyx--x

4 Jul 2026

Human

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Agents

A Convex Obstacle Avoidance Formulation

DGX agent

arXiv:2512.13836v2 Announce Type: replace-cross Abstract: Autonomous driving requires reliable collision avoidance in dynamic environments. Nonlinear Model Predictive Controllers (NMPCs) are suitable

agentsarxiv-cs-ro

3 Jul 2026

A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention

arXiv:2601.04509v2 Announce Type: replace Abstract: Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard

A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification

arXiv:2607.01974v1 Announce Type: cross Abstract: This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to

Actual causality in fault trees

arXiv:2607.01840v1 Announce Type: new Abstract: Fault trees are a widely used as effective risk models for complex systems, answering the question 'what can go wrong?', especially through minimal cut

Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning

arXiv:2607.02205v1 Announce Type: new Abstract: Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonl

safetyarxiv-cs-ro

Agent Runs now available in the Vercel MCP and CLI

Vercel has announced the availability of Agent Runs in both the Vercel MCP (Model Context Protocol) and CLI (Command Line Interface), expanding developer capabilities for AI-powered automation and dep

agentsvercel-blog

Aggregation with Exponential Weights is Optimal in Expectation

arXiv:2607.02247v1 Announce Type: cross Abstract: The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss

Auto-FL-Research: Agentic Search for Federated Learning Algorithms

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local

Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring

arXiv:2607.02121v1 Announce Type: cross Abstract: As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Gu

CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

arXiv:2607.02262v1 Announce Type: new Abstract: Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are pr

local-aiarxiv-cs-cl

Hardware

DeadPool: Resilient LLM Training with Hot-Swapping via Zero-Overhead Checkpoint

arXiv:2607.01646v1 Announce Type: new Abstract: State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures acro

hardwarearxiv-cs-lg

DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents

arXiv:2607.01557v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios. People's individual personalities and concerns require tailored

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis

arXiv:2607.01800v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete se

researcharxiv-cs-cl

Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts

arXiv:2607.01852v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside the

ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues

arXiv:2607.01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics

arXiv:2603.13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundr

Geometry-Aware R-Structured Kolmogorov-Arnold Networks

arXiv:2607.01449v1 Announce Type: new Abstract: We propose a novel hybrid neural architecture, the Geometry-aware R-Structured Kolmogorov-Arnold Network (GRS-KAN), which integrates V.L.Rvachev's R-fun

Tools

had alot of fun at @aiDotEngineer - nerding out about Codexmaxxing, meeting old friends and making new ones congratulations @swyx and team f…

This post references attendance at an @aiDotEngineer event where the author engaged in discussions about 'Codexmaxxing' (likely optimizing use of AI code models), reconnected with existing contacts, a

toolsswyx--x

Tutorials

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size

arXiv:2607.01487v1 Announce Type: new Abstract: We propose a scaling law that takes into account model size and training data while explicitly splitting the latter into training steps and batch size (

tutorialsarxiv-cs-lg

Human Supervisor Workload Prediction: Lag Horizon Selection

arXiv:2505.15939v2 Announce Type: replace Abstract: Teleoperation systems must be aware of the human's workload during missions to maintain operator performance. Prior work employed wearable physiolog

researcharxiv-cs-ro

Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow

arXiv:2606.20101v2 Announce Type: replace-cross Abstract: Audio editing aims to modify specific content in an existing audio clip according to a natural language instruction while preserving the remai

Hybrid quantum-classical neural network for sentiment analysis

arXiv:2607.01943v1 Announce Type: new Abstract: Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learnin

Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

arXiv:2607.01237v1 Announce Type: cross Abstract: Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high d

kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail

arXiv:2607.02072v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing g

Tutorials

Learning Consumer Preferences from Bundle Sales Data

arXiv:2209.04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data. Product bundling is a widely use

tutorialsarxiv-cs-lg

Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning

arXiv:2503.21796v2 Announce Type: replace-cross Abstract: Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-s

MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

arXiv:2607.01539v1 Announce Type: cross Abstract: This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can act as a credible outer-loop optimizer for classification model selection. W

agentsarxiv-cs-lg

Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing

arXiv:2607.01978v1 Announce Type: new Abstract: Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) wi

NeoMap: Training-free Novel-View Synthesis from Single Images and Videos

arXiv:2607.01962v1 Announce Type: cross Abstract: We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assum

Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization

arXiv:2607.01972v1 Announce Type: cross Abstract: Large language models (LLMs) are often asked to produce JSON conforming to a fixed schema, powering information extraction, tool calling, agentic plan

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604.02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typicall

Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

arXiv:2607.02142v1 Announce Type: cross Abstract: Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities. It is one of the

Prompt Coverage Adequacy

arXiv:2607.02057v1 Announce Type: cross Abstract: In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software

Rank-Then-Act: Reward-Free Control from Frame-Order Progress

arXiv:2607.01897v1 Announce Type: cross Abstract: We introduce Rank-Then-Act (RTA), a framework for learning control policies from expert video demonstrations without environment rewards. RTA trains a

Rethinking Post-Hoc Calibration in Semantic Segmentation

arXiv:2607.01902v1 Announce Type: cross Abstract: Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead do

safetyarxiv-cs-lg

Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning

arXiv:2607.01511v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, mo

SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces

arXiv:2607.02345v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents increasingly automate software engineering tasks through reusable skills, natural-language instruction documen

Spin-Weighted Spherical Harmonics Enable Complete and Scalable E(3)-Equivariant Networks

arXiv:2607.01408v1 Announce Type: cross Abstract: E(3)-equivariant networks are promising for 3D atomistic system modeling, yet their scalability is limited by the O(L^6) complexity of the Clebsch-Gor

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking

arXiv:2607.02087v1 Announce Type: new Abstract: Hierarchical state-space models (HSSMs) offer a promising approach to long-horizon prediction by segmenting sequences into temporal chunks. However, the

The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

arXiv:2607.02416v1 Announce Type: new Abstract: Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models

researcharxiv-cs-cl

Using AI to improve cancer immunotherapy outcomes, via training from transcriptomes of 10,000 tumor samples, 33 cancer types @NatureMedicine…

Researchers used machine learning trained on transcriptomic data from 10,000 tumor samples across 33 cancer types to develop AI models that predict and improve outcomes in cancer immunotherapy. The ap

researchyann-lecun--x

We are pleased to present our latest research at #ICML2026, “Bridging Spherical Black-Box Optimizers” https://arxiv.org/abs/2606.25761 When …

We are pleased to present our latest research at #ICML2026, “Bridging Spherical Black-Box Optimizers” https://arxiv.org/abs/2606.25761 When optimizing through simulators, external APIs, or in reinforc

researchdavid-ha--x

You are going to end up running hallucinated code

You are going to end up running hallucinated code ~60% Fable cost cut by transparently turning the code into an image and having the model OCR it. WILD idea. also hilarious. https://github.com/teamcho

agentsjerry-liu--x

Applications

A Synthetic-Driven Vision System for Assembly Step Recognition

arXiv:2607.00129v1 Announce Type: new Abstract: Quality control in industrial assembly is essential, and real-time monitoring of the assembly process is crucial for preventing costly defects and ensur

applicationsarxiv-cs-cv

Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization

arXiv:2607.00531v1 Announce Type: cross Abstract: Scientific reasoning is an increasingly important capability of large language models, yet improving the robustness and efficiency of training such re

BaRA: BFS-and-Reflection Web Data Collection Agent

arXiv:2607.00007v1 Announce Type: cross Abstract: Large language model (LLM)-based web agents reduce manual scripting for web data collection, yet on live websites, they often miss relevant pages, ret

Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework

arXiv:2607.01034v1 Announce Type: cross Abstract: Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their cap