Claude Code

Claude Code now reads AGENTS.md if there is no Claude.md

hackernews · 원본 ID 49760187

Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design

Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programmatic oracle. We introduce a continual adaptation framework in which a frozen frontier model operates professional design software through more than 230 tools, while an external procedural memory of natural-language skills accumulates and refines reusable design procedures from experience. The memory widens by acquiring procedures for recu...

arxiv · 원본 ID http://arxiv.org/abs/2609.22086v1

Cross-sector generalization of accident-process role classification in occupational accident narratives

Occupational accident narratives contain valuable information about work situations, unfavourable conditions, accident events, and their consequences. Automatically structuring these narratives can facilitate large-scale accident analysis and support occupational risk prevention. However, the terminology and writing styles used to describe accidents vary considerably across sectors and organisations, raising questions about the ability of automated coding systems to generalize beyond their training domain. In this...

arxiv · 원본 ID http://arxiv.org/abs/2609.22081v1

Rotating Neutron Star Migrations as a Standardized Test for 3+1 Numerical Relativity

When simulating dynamically unstable neutron stars, truncation errors can introduce perturbations that drive the stellar configuration either toward gravitational collapse into a black hole or toward migration to a dynamically stable, lower-density configuration. This mechanism has become a standard benchmark for validating general-relativistic hydrodynamics codes in the case of spherically symmetric neutron-star models. In this work, we extend the analysis to the more general case of uniformly rotating neutron st...

arxiv · 원본 ID http://arxiv.org/abs/2609.22080v1

APort Vault: Benchmarking AI Agent Payment Authorization with the Open Agent Passport

APort Vault is a benchmark for payment authorization in tool-using AI agents. It replays 4,371 attacks written by humans against a live payment agent during a public capture-the-flag event, across 14 models from 8 labs, five policy configurations and two replay tracks, with and without a deterministic pre-action check implementing the Open Agent Passport (OAP) specification. 225,964 evaluations completed. We report five distinct events per evaluation, because collapsing them is how an agent benchmark produces a nu...

arxiv · 원본 ID http://arxiv.org/abs/2609.22076v1

The AGN Channel in 3D: Scattering Belts and the Importance of Eccentricity in the Black Hole Population

Active galactic nuclei (AGN) are a promising origin for observed gravitational wave mergers. Current population synthesis models are limited to 1D N-body or Monte Carlo methods which rely on statistical approaches to resolving dynamical scatterings. We present three-dimensional hybrid $N$-body simulations of a population of black holes (BHs) surrounding an AGN using a new code in development, AGNBI, where close interactions are directly simulated for both single and binary objects. Our results show binary formatio...

arxiv · 원본 ID http://arxiv.org/abs/2609.22074v1

CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better scale RL environments, we present CodeMidas, an agentic pipeline that turns implemented functionality in existing codebases into executable RL environments using source code as its only task-specific...

arxiv · 원본 ID http://arxiv.org/abs/2609.22068v1

Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise

Graph Convolutional Networks (GCNs) are highly sensitive to label noise, since corrupted supervision can propagate through the graph and degrade learned node representations. This work proposes PCC+GCN, a hybrid framework that uses Particle Competition and Cooperation (PCC) as a graph-based label-refinement stage before GCN training. PCC identifies suspicious labeled nodes through particle domination dynamics and determines whether their labels should be preserved, removed, or reassigned before GCN training. The f...

arxiv · 원본 ID http://arxiv.org/abs/2609.22053v1

How Researchers Use and Verify AI Coding Assistants: Tasks and Validation Practices in Scientific Programming

Generative AI has entered research programming, yet there is little evidence about which tasks researchers hand to it or how they decide whether its code is correct. We draw on 527 free-text responses to a 2025 survey of researchers who write code, most of them at U.S. universities. In each response, a researcher recounts a single task from their own work, the way they used an AI tool for it, and what they did to assess the result. We coded the task and the evaluation strategies reported, and related both to progr...

arxiv · 원본 ID http://arxiv.org/abs/2609.22049v1

Auditing bipartite motif interpretations: a worked example with conservation checks and open-path decomposition

Motif profiles of bipartite agent-object networks, such as tourist-site visits and customer-item transactions, are read as evidence about structural roles and about differences between networks, often without asking what the two degree sequences already fix. In a simple bipartite graph the induced k-fan count on one node type is a sum of degree combinations, so it has zero variance under a null that preserves both degree sequences. We apply this known result to a reconstructed tourism rating network of 17 tourists...

arxiv · 원본 ID http://arxiv.org/abs/2609.22014v1

RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both, interleaved rather than stacked end to end. We study hybrid CUAs that autonomously decide when to explore an interface, implement software, and run and visually verify their artifacts. We introduce RecreationWorld, a five-platform framework built around recreation: given a running reference, an agent must discover its behavior and buil...

arxiv · 원본 ID http://arxiv.org/abs/2609.22000v1

DietrichGebert/ponytail

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.

github · 원본 ID 1266797999