arXiv subjects

Marc Bara

Publications and source records attributed to Marc Bara.

8 recordsLinked to original sources

arXiv2026

Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence

Multi-agent AI systems improve inference by spawning agents and synthesizing reports. But another agent is not another observation: apparently independent reports may descend from the same evidence, and genuinely independent evidence can produce nearly identical reports. We formalize this as an epistemic Sybil problem. A report Z is an epistemic Sybil extension relative to reports R when I(Theta; Z | R) = 0. No report-only aggregator can generally distinguish replication from independent corroboration: identical reports can warrant different posteriors under unobserved ancestry. A Gaussian shared-root model shows common ancestry does not imply complete redundancy. Repeated extraction adds information toward a source-level ceiling, and correlated extraction errors, which a shared base model can induce among independent agents, lower that ceiling further. We test these predictions with more than 20,000 controlled LLM-agent report and extraction calls on synthetic evidentiary documents. Holding one evidence root fixed while report multiplicity rises from 1 to 32 collapses naive posterior coverage from 0.940 to 0.263. Holding report count fixed while evidence-root multiplicity rises from 1 to 16 closes the gap, and the aggregators are statistically indistinguishable at k = 16. The agent's replicate extraction errors are correlated (gamma_cal = 0.719, estimated out of sample), and a correlated-extraction aggregator restores calibration accordingly. A controlled manipulation isolates representation similarity from evidential ancestry. It changes a report-space deduplication mechanism's mean inferred cluster count by 1.425 (95% CI [1.363, 1.485]), whereas a fourfold change in true ancestry changes it by only 0.040 ([-0.045, 0.120]). Collective inference should therefore track evidential ancestry and dependence, not agent or report multiplicity or similarity.

cs.AI↗

arXiv2026

HAIF: A Human-AI Integration Framework for Hybrid Team Operations

The rapid deployment of generative AI, copilots, and agentic systems in knowledge work has created an operational gap: no existing framework addresses how to organize daily work in teams where AI agents perform substantive, delegated tasks alongside humans. Agile, DevOps, MLOps, and AI governance frameworks each cover adjacent concerns but none models the hybrid team as a coherent delivery unit. This paper proposes the Human-AI Integration Framework (HAIF): a protocol-based, scalable operational system built around four core principles, a formal delegation decision model, tiered autonomy with quantifiable transition criteria, and feedback mechanisms designed to integrate into existing Agile and Kanban workflows without requiring additional roles for small teams. The framework is developed following a Design Science Research methodology. HAIF explicitly addresses the central adoption paradox: the more capable AI becomes, the harder it is to justify the oversight the framework demands-and yet the greater the consequences of not providing it. The paper includes domain-specific validation checklists, adaptation guidance for non-software environments, and an examination of the framework's structural limitations-including the increasingly common pattern of continuous human-AI co-production that challenges the discrete delegation model. The framework is tool-agnostic and designed for iterative adoption. Empirical validation is identified as future work.

cs.SE↗

arXiv2025

Moravec's Paradox and Restrepo's Model: Limits of AGI Automation in Growth

This note extends Restrepo (2025)'s model of economic growth under AGI by incorporating Moravec's Paradox -the observation that tasks requiring sensorimotor skills remain computationally expensive relative to cognitive tasks. We partition the task space into cognitive and physical components with differential automation costs, allowing infinite costs for some physical bottlenecks. Our key result shows that when physical tasks constitute economic bottlenecks with sufficiently high (or infinite) computational requirements, the labor share of income converges to a positive constant in the finite-compute regime (rather than zero). This fundamentally alters the distributional implications of AGI while preserving the growth dynamics for cognitive-intensive economies.

econ.GN↗

arXiv2025

PromptChain: A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets

We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. Our design includes: (1) a comprehensive metadata schema for cross-model compatibility, (2) a stake-weighted validation mechanism to align incentives, and (3) a token economy that rewards contributors proportionally to their impact. The proposed architecture demonstrates how decentralized systems could potentially match centralized alternatives in efficiency while providing superior ownership guarantees and censorship resistance through blockchain-anchored provenance tracking. By decoupling prompts from specific AI models or outputs, this work establishes the foundation for an open ecosystem of human-AI collaboration in the Web3 era, representing the first systematic treatment of prompts as standalone digital assets with dedicated decentralized infrastructure.

cs.CR↗

arXiv2025

Unexplored Opportunities for Automatic Differentiation in Astrophysics

We present a systematic analysis of automatic differentiation (AD) applications in astrophysics, identifying domains where gradient-based optimization could provide significant computational advantages. Building on our previous work with GRAF (Gradient-based Radar Ambiguity Functions), which discovered optimal radar waveforms achieving 4x computational speedup by exploring the trade-off space between conflicting objectives, we extend this discovery-oriented approach to astrophysical parameter spaces. While AD has been successfully implemented in several areas including gravitational wave parameter estimation and exoplanet atmospheric retrieval, we identify nine astrophysical domains where, to our knowledge, gradient-based exploration methods remain unexplored despite favorable mathematical structure. These opportunities range from discovering novel solutions to the Einstein field equations in exotic spacetime configurations to systematically exploring parameter spaces in stellar astrophysics and planetary dynamics. We present the mathematical foundations for implementing AD in each domain and propose GRASP (Gradient-based Reconstruction of Astrophysical Systems & Phenomena), a unified framework for differentiable astrophysical computations that transforms traditional optimization problems into systematic exploration of solution spaces. To our knowledge, this is the first work to systematically delineate unexplored domains in astrophysics suitable for automatic differentiation and to provide a unified, mathematically grounded framework (GRASP) to guide their implementation.

astro-ph.IM↗

arXiv2025

Dataless Neural Networks for Resource-Constrained Project Scheduling

Dataless neural networks represent a paradigm shift in applying neural architectures to combinatorial optimization problems, eliminating the need for training datasets by encoding problem instances directly into network parameters. Despite the pioneering work of Alkhouri et al. (2022) demonstrating the viability of dataless approaches for the Maximum Independent Set problem, our comprehensive literature review reveals that no published work has extended these methods to the Resource-Constrained Project Scheduling Problem (RCPSP). This paper addresses this gap by presenting the first dataless neural network approach for RCPSP, providing a complete mathematical framework that transforms discrete scheduling constraints into differentiable objectives suitable for gradient-based optimization. Our approach leverages smooth relaxations and automatic differentiation to unlock GPU parallelization for project scheduling, traditionally a domain of sequential algorithms. We detail the mathematical formulation for both precedence and renewable resource constraints, including a memory-efficient dense time-grid representation. Implementation and comprehensive experiments on PSPLIB benchmark instances (J30, J60, and J120) are currently underway, with empirical results to be reported in an updated version of this paper.

cs.LG↗

arXiv2025

Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing

We present the first theoretical framework for applying spiking neural networks (SNNs) to synthetic aperture radar (SAR) interferometric phase unwrapping. Despite extensive research in both domains, our comprehensive literature review confirms that SNNs have never been applied to phase unwrapping, representing a significant gap in current methodologies. As Earth observation data volumes continue to grow exponentially (with missions like NISAR expected to generate 100PB in two years) energy-efficient processing becomes critical for sustainable data center operations. SNNs, with their event-driven computation model, offer potential energy savings of 30-100x compared to conventional approaches while maintaining comparable accuracy. We develop spike encoding schemes specifically designed for wrapped phase data, propose SNN architectures that leverage the spatial propagation nature of phase unwrapping, and provide theoretical analysis of computational complexity and convergence properties. Our framework demonstrates how the temporal dynamics inherent in SNNs can naturally model the spatial continuity constraints fundamental to phase unwrapping. This work opens a new research direction at the intersection of neuromorphic computing and SAR interferometry, offering a complementary approach to existing algorithms that could enable more sustainable large-scale InSAR processing.

cs.NE↗

arXiv2025

Accessibility Barriers in Multi-Terabyte Public Datasets: The Gap Between Promise and Practice

The promise of "free and open" multi-terabyte datasets often collides with harsh realities. While these datasets may be technically accessible, practical barriers -- from processing complexity to hidden costs -- create a system that primarily serves well-funded institutions. This study examines accessibility challenges across web crawls, satellite imagery, scientific data, and collaborative projects, revealing a consistent two-tier system where theoretical openness masks practical exclusivity. Our analysis demonstrates that datasets marketed as "publicly accessible" typically require minimum investments of \$1,000+ for meaningful analysis, with complex processing pipelines demanding \$10,000-100,000+ in infrastructure costs. The infrastructure requirements -- distributed computing knowledge, domain expertise, and substantial budgets -- effectively gatekeep these datasets despite their "open" status, limiting practical accessibility to those with institutional support or substantial resources.

cs.CY↗