Longitudinal Report Generation with LLM-Based Agents: Architecture, Consistency Mechanisms, and Empirical Evidence

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DOI: 10.5281/zenodo.18928461  64stabilfr·wdophcgmx
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Large language model (LLM) based agents are increasingly deployed as autonomous report-generation systems — producing research summaries, analytical outputs, and monitoring digests across extended time horizons without continuous human supervision. This paper examines the fundamental challenges of longitudinal consistency in such systems: context window exhaustion, semantic drift, hallucination...

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DOI: 10.5281/zenodo.18928461 64stabilfr·wdophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
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[a]DOI92%✓≥80% have a Digital Object Identifier
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[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
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AI Architecture Comparison Observatory: AADA vs LLM-First Agents

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18928461  45stabilfr·wdophcgmx
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[t]Trusted62%○≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
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[l]Academic62%○≥80% from journals/conferences/preprints
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[w]Words [REQ]749✗Minimum 2,000 words for a full research article. Current: 749
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
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[x]Cited by0○Referenced by 0 other hub article(s)
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Interactive comparison of AI-Augmented Agentic Deterministic Architecture (AADA) vs LLM-First Agent paradigms — with real systems, real data, and real citations.

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18928461 45stabilfr·wdophcgmx
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[a]DOI62%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]749✗Minimum 2,000 words for a full research article. Current: 749
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
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Beyond the Benchmark: What AI Looks Like When It Actually Works

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DOI: 10.5281/zenodo.18926904  61stabilfr·wdophcgmx
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[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI61%○≥80% have a Digital Object Identifier
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[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The most consequential question in applied artificial intelligence is not whether a model achieves state-of-the-art on a leaderboard. It is whether the model does something useful when connected to reality — to messy data, constrained infrastructure, and users who need answers rather than probabilities. This article examines what AI actually looks like when it crosses that boundary. Drawing on ...

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DOI: 10.5281/zenodo.18926904 61stabilfr·wdophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI61%○≥80% have a Digital Object Identifier
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[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
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[w]Words [REQ]2,336✓Minimum 2,000 words for a full research article. Current: 2,336
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
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Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Stabilarity Research Platform Is Now Open — Free API Access for All Researchers

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DOI: 10.5281/zenodo.18928330  56stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
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[l]Academic41%○≥80% from journals/conferences/preprints
[f]Free Access65%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
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[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016), providing composable, versioned endpoints for: (1) medical imaging classi...

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DOI: 10.5281/zenodo.18928330 56stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
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[l]Academic41%○≥80% from journals/conferences/preprints
[f]Free Access65%○≥80% are freely accessible
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Agent Auditor — Part 2: Skills, Tools & Frameworks

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18923680  35stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Part 1 of this series established the structural case for the Agent Auditor as a distinct professional role — a response to the accountability gaps, hallucination drift, and regulatory pressures that accompany enterprise-scale agentic AI deployment. Part 2 examines what that role actually requires: the specific skill taxonomy an Agent Auditor must hold, the tooling landscape that supports their...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18923680 35stabilfr·wdophcgmx
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[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
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[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
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[w]Words [REQ]2,453✓Minimum 2,000 words for a full research article. Current: 2,453
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18923680
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]7%✗≥60% of references from 2025–2026. Current: 7%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Agent Cost Optimization as First-Class Architecture: Why Inference Economics Must Be Designed In, Not Bolted On

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18916800  39stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
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[l]Academic33%○≥80% from journals/conferences/preprints
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[w]Words [REQ]3,178✓Minimum 2,000 words for a full research article. Current: 3,178
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In 2026, inference costs account for 85% of enterprise AI budgets, yet most agentic system architectures treat cost optimization as an operational afterthought rather than a foundational design constraint. This paper argues that agent cost optimization must be elevated to a first-class architectural concern — embedded in system design decisions from the ground up alongside correctness, reliabil...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18916800 39stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,178✓Minimum 2,000 words for a full research article. Current: 3,178
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18916800
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Coverage Gap: What AI Can Do vs. What We Actually Use It For

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911661  44stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted64%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef27%○≥80% indexed in CrossRef
[i]Indexed27%○≥80% have metadata indexed
[l]Academic45%○≥80% from journals/conferences/preprints
[f]Free Access73%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,645✗Minimum 2,000 words for a full research article. Current: 1,645
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18911661
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% of references from 2025–2026. Current: 10%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (49 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Anthropic published something rare this week: a paper that uses actual usage data instead of speculation. Most labor displacement research asks "what tasks could AI theoretically do?" and then declares a crisis. Massenkoff and McCrory asked a different question: "what tasks are people actually using it for?" The gap between those two answers is the most important number in AI economics right no...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911661 44stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted64%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef27%○≥80% indexed in CrossRef
[i]Indexed27%○≥80% have metadata indexed
[l]Academic45%○≥80% from journals/conferences/preprints
[f]Free Access73%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,645✗Minimum 2,000 words for a full research article. Current: 1,645
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18911661
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% of references from 2025–2026. Current: 10%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (49 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911437  54stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed40%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,833✗Minimum 2,000 words for a full research article. Current: 1,833
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18911437
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]44%✗≥60% of references from 2025–2026. Current: 44%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Agentic platforms are racing on capability. The decisive variable will be economics — and none of the flagship papers (Anthropic guide, Wang et al., Magentic-One) model it. Token cost curves, context handoff overhead, Jevons effects at scale: all missing.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911437 54stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed40%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,833✗Minimum 2,000 words for a full research article. Current: 1,833
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18911437
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]44%✗≥60% of references from 2025–2026. Current: 44%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910811  48stabilfr·wdophcgmx
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[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI56%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
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This article reflects my thinking from early 2025, based on papers available at that time (Anthropic engineering guide, Wang et al. 2024, Magentic-One). I am keeping it here because the reasoning was honest and the core economic argument was right — but the field moved, new January 2026 surveys added important context, and my framing evolved.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910811 48stabilfr·wdophcgmx
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Feedback Loop Economics: The Cost Architecture of Self-Improving AI Systems

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910135  41stabilfr·wdophcgmx
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Feedback loops are the metabolic engine of enterprise AI — the mechanism by which deployed models ingest operational signals, update their representations, and compound value over time. Yet the economics of this metabolic process remain poorly understood in enterprise planning. This article presents a systematic economic analysis of AI feedback loop architectures, decomposing their cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910135 41stabilfr·wdophcgmx
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