Inner Loop
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Bilevel Autoresearch: Meta-Autoresearching Itself
Announce Type: replace Abstract: If autoresearch is itself a form of research, then autoresearch can be applied to research itself. We present Bilevel Autoresearch, a bilevel framework in which an outer autoresearch loop improves an inner autoresearch loop by reading its code and traces, identifying bottlenecks, and generating injectable Python search mechanisms at runtime. The inner loop optimizes task performance; the outer loop optimizes how the inner loop searches.
When Can Phasor-Domain Device Models Be Trusted for Electromechanical Stability Analysis of Grid-Forming Converter-Dominated Microgrids?
arXiv:2606.08082v1 Announce Type: new Abstract: Grid-forming (GFM) converter-dominated microgrids are often analyzed using reduced-order phasor-domain electromechanical GFM models, but the validity of these models is often taken for granted. Assuming ideal inner-loop tracking (IILT) of terminal-voltage references, these models neglect the inner-loop and filter dynamics at the electromagnetic-transient (EMT) timescale to simplify stability analysis. This paper argues that such neglected...
Feedback Linearization and Control of a Grid-Forming Power Converter in an Islanded Microgrid
Announce Type: new Abstract: In an islanded setting, grid-forming inverters must regulate their terminal voltage without support from an external grid, even though the load current depends directly on that voltage. The usual approach is a cascaded proportional--integral (PI) controller, built on a fast inner current loop and a slower outer voltage loop, with feedforward terms used to compensate dq rotational coupling. However, this compensation is only exact at the operating point where the...
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
Announce Type: new Abstract: Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities. Existing approaches are dominated by per-sample meta-learning, which scales poorly due to memory-intensive inner-loop optimization. The natural alternative -- feed-forward encoding -- typically introduces modality-specific assumptions, sacrificing the generality that makes learning with neural fields attractive.
DAGGER: Gradient-Free Construction of Transiently Amplifying Networks under Hard Connectivity Constraints
arXiv:2606.01227v1 Announce Type: new Abstract: Many networks not only support but also rely on transient non-normal amplification, an orders-of-magnitude increase in the activity of an otherwise stable system. Constructing such networks under hard sign/sparsity/diagonal constraints -- the regime relevant for biological connectomes and structured RNN initializations -- has so far required either gradient-based local search with thousands of inner-loop eigendecompositions or Schur-form direct...
Donald Trump Is Ready for Fight Night. So Are Donors
President Donald Trump is enthralled with the Ultimate Fighting Championship staging an event at the White House on his birthday this weekend—in effect his present to himself, since he came up with the idea. We have the details on both the fighting and the anticipated lobbying. Lobbying by the Octagon While the White House does not yet know exactly which celebrities might show up for the UFC on Sunday because they have not accepted their Ticketmaster email invitations, Trump’s aides tell...
Latent Structural Categorical Matrix Completion with Application to Quasispecies Analysis
Announce Type: cross Abstract: Matrix completion has been extensively studied for real-valued data, but existing methods are often limited in handling categorical variables. We propose LCMC, a double-loop optimization framework for categorical matrix completion via latent factorization based on a binary tensor representation. In this setting, each categorical entry is encoded as a one-hot vector along a third tensor mode, thereby preserving its discrete, non-ordinal nature.
Anthropic's open-source framework for AI-powered vulnerability discovery
A reference implementation for autonomous vulnerability discovery and remediation with Claude, based on our learnings from partnering with security teams at several organizations since launching Claude Mythos Preview. For a write up of these learnings along with best practices, see the accompanying blog post (also available in blog-post.md ). For a lightweight SDK-only walkthrough of the same recon → find → triage → report → patch loop, see the companion cookbook.
First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope
arXiv:2605.28916v2 Announce Type: replace-cross Abstract: We report a comparison of two state-of-the-art agentic AI systems, Claude Code (Anthropic) and Codex (OpenAI), tasked with autonomously executing a simple end-to-end gravitational wave data analysis pipeline on a shared computing infrastructure without human intervention. The pipeline comprises power spectral density estimation from raw Einstein Telescope simulated noise, geometric template bank generation, matched filter recovery of...
Greg Bovino Was the Star at a European Remigration Conference
On Saturday morning, as hundreds of far-right activists and lawmakers from across Europe gathered outside a conference center in the central Portuguese town of Figueira da Foz, a group of half a dozen men dressed in identical uniforms of khaki chinos, dark blue shirts, and sunglasses marched into the parking lot. On the lapels of their jackets, some wore the red and blue circular emblem of Patriot Front, the US white-supremacist group formed in the wake of the 2017 Unite the Right rally in...