arXiv subjects

Roberto Garcia

Publications and source records attributed to Roberto Garcia.

8 recordsLinked to original sources

arXiv2026

Dynamics and geometry of the inner sub-parsec-scale jet in 3C 279 observed with the Event Horizon Telescope

The 2021 Event Horizon Telescope observations resolve the innermost jet region of the blazar 3C279 with unprecedented detail. The reconstructed images consistently reveal a compact core elongated nearly orthogonal to the large-scale jet axis. This rarely observed morphology recurs across multiple epochs and from 22-230 GHz and is therefore intrinsic rather than an imaging artifact. Geometric model fitting identifies several components with apparent speeds up to 10c, requiring bulk Lorentz factors greater than 10.3 and constraining viewing angles to extremely small values (smaller than one degree). Rest-frame brightness temperatures are systematically low (between 10^9 and 10^10 K), consistent with optically thin emission at 230 GHz. These results suggest that the jet bends toward the observer on sub-parsec scales, producing strong relativistic beaming. Possible drivers of the observed jet bending and temporal evolution include the jet's interaction with the interstellar medium, kink or Kelvin--Helmholtz instabilities, magnetic reconnection near the horizon, or binary-induced precession. However, the current temporal coverage of VLBI data remains insufficient to distinguish between these mechanisms. Continued multifrequency VLBI monitoring will be essential to constraining the dynamics and geometry of the jet base in 3C279.

astro-ph.GA↗

arXiv2026

MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers

Large language models (LLMs) store factual knowledge in their parameters. While recent work has shown that this knowledge resides in MLP layers, existing constructive and mechanistic interpretability models of fact-storage in LLMs fail to explain the surprising empirical phenomenon that they store facts at an information-theoretically optimal rate. In this work, we develop a theoretical account of this phenomenon. We develop the first Transformer-compatible fact-storing MLP closed-form construction that satisfies the following three properties empirically observed in LLMs: it (i) attains optimal fact storage scaling, (ii) handles arbitrary input/output geometries, and (iii) works inside Transformers. Key to our work is to analyze the decoding margin of MLPs, whereas prior work only studies MLP fact storage. Under isotropic embeddings, our construction achieves information-theoretically optimal storage capacity scaling and requires $10$-$104\times$ fewer parameters at matched fact count than prior constructions. For arbitrary key and value embeddings, we show that our construction attains the same storage capacity scaling, up to penalization factors depending on the embedding geometries. Moreover, we demonstrate that our constructed MLPs can be used within Transformer blocks for factual recall tasks at optimal capacity scaling, requiring $15$-$63\times$ fewer parameters at matched fact count than prior constructions. Finally, as a proof-of-concept, we show that fact-storing MLPs enable modular fact editing by swapping a Transformer's MLP with a new one.

cs.LG↗

arXiv2026

Broadband multiwavelength properties of the archetypal blazar 3C 279 during the 2017 Event Horizon Telescope campaign

The archetypal blazar 3C 279 hosts a prominent relativistic jet and exhibits strong broadband variability across the electromagnetic spectrum. In April 2017, the Event Horizon Telescope (EHT) observed 3C 279, alongside one of the most extensive quasi-simultaneous multiwavelength (MWL) campaigns ever conducted. With the aim of investigating the physical processes governing 3C 279, we analyzed individual observations and multiband light curves, and constructed a new quasi-simultaneous MWL spectrum. We also performed phenomenological modeling using the turbulent extreme multi-zone (TEMZ) model to constrain the fundamental physical properties of the source. The EHT observations reveal a clear flux increase in the innermost core between April 5 and 11, 2017. Over a broader timescale, radio measurements at longer wavelengths show concurrent enhancements in core flux and polarization around mid-April, coinciding with the ejection of a superluminal knot. Record UV-optical flares with strong polarization variability occurred in late March, followed by gamma-ray activity that declined before the end of the EHT observing period. During this interval, the source remained in a low X-ray state and showed no detectable VHE emission. The TEMZ modeling suggests that the broadband spectrum and variability of 3C 279 can be explained within a jet scenario in which turbulent plasma cells are compressed by a stationary conical shock. However, alternative interpretations, such as magnetic reconnection or a moving shock-in-jet event, remain plausible. This coordinated MWL campaign advances our understanding of the origin of jet and gamma-ray emission in 3C 279, while also providing a comprehensive publicly available dataset that will serve as a valuable reference for future studies.

astro-ph.HE↗

arXiv2026

From Embedded Properties to Trait Nodes: A Design Method for Identifying Reusable Metadata in Property Graph Schemas

Property-graph schemas often contain descriptive properties that recur across heterogeneous nodes and edges, yet schema designers lack a clear method for deciding whether such properties should remain embedded or be treated as reusable metadata structures. This paper addresses this design-stage problem within a 5GNF-oriented modeling perspective by proposing a method for identifying metadata candidates based on five criteria: cross-element occurrence, conceptual independence, lossless externalization, reuse potential, and governance relevance. The method classifies properties into trait candidates, embedded properties, and borderline cases using a rule-based decision workflow. The approach is illustrated using a running example from a library domain and examined through an illustrative validation involving participant-based classification tasks in two schema contexts. The results show that recurrence alone is not a sufficient basis for externalization and that metadata-candidate identification requires semantic interpretation beyond frequency. The main contribution of the paper is methodological: it provides a more explicit and systematic basis for deciding when descriptive properties should be modeled as reusable metadata in property-graph schemas.

cs.DB↗

arXiv2025

Constructing Efficient Fact-Storing MLPs for Transformers

The success of large language models (LLMs) can be attributed in part to their ability to efficiently store factual knowledge as key-value mappings within their MLP parameters. Recent work has proposed explicit weight constructions to build such fact-storing MLPs, providing an improved understanding of LLM fact storage mechanisms. In this paper, we introduce an MLP construction framework that improves over previous constructions in three areas: it 1) works for all but a measure-zero set of feasible input-output pairs, 2) achieves asymptotically optimal parameter efficiency matching information-theoretic bounds for some embeddings, and 3) maintains usability within Transformers for factual recall. Through our improvements, we 1) discover a metric on value embeddings that characterizes facts-per-parameter scaling for both constructed and gradient-descent-trained MLPs, 2) identify a simple encoder-decoder mechanism that empirically matches gradient-descent MLP facts-per-parameter asymptotics across all the inputs and outputs we test, and 3) uncover a fundamental tradeoff between an MLP's fact-storage capacity and its usability within Transformers. Finally, we demonstrate a proof-of-concept application of fact-storing MLPs: modular fact editing on one-layer Transformers by \textit{replacing entire MLPs at once}.

cs.LG↗

arXiv2025

Clio-X: AWeb3 Solution for Privacy-Preserving AI Access to Digital Archives

As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.

cs.CR↗

arXiv2025

Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters

Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from limitations in modern Transformers. These include reliance on sparse activations, incompatibility with attention layers, and the use of costly neuron masking techniques. To address these issues, we propose the Adaptive Rank Allocation framework and introduce the Rank and Neuron Allocator (RaNA) adapter. RaNA adapters leverage rank adapters, which operate on linear layers by applying both low-rank matrix decompositions and adaptive masking to efficiently allocate compute without depending on activation sparsity. This enables RaNA to be generally applied to MLPs and linear components of attention modules, while eliminating the need for expensive maskers found in neuron-adaptive methods. Notably, when compared to neuron adapters, RaNA improves perplexity by up to 7 points and increases accuracy by up to 8 percentage-points when reducing FLOPs by $\sim$44% in state-of-the-art Transformer architectures. These results position RaNA as a robust solution for improving inference efficiency in modern Transformer architectures.

cs.LG↗

arXiv2021

Constraints on black-hole charges with the 2017 EHT observations of M87*

Our understanding of strong gravity near supermassive compact objects has recently improved thanks to the measurements made by the Event Horizon Telescope (EHT). We use here the M87* shadow size to infer constraints on the physical charges of a large variety of nonrotating or rotating black holes. For example, we show that the quality of the measurements is already sufficient to rule out that M87* is a highly charged dilaton black hole. Similarly, when considering black holes with two physical and independent charges, we are able to exclude considerable regions of the space of parameters for the doubly-charged dilaton and the Sen black holes.

gr-qc↗