Reading list: LLMs in Business Process Management

Curated reading list assembled 2026-04-21 from the wiki's ingested and referenced corpus. Scope: LLMs applied to Business Process Management — agentic BPM, LLM-agent execution in processes, LLM-assisted discovery/redesign/monitoring, LLM observability and benchmarking in process-heavy domains. Excludes pure-PPM work without an LLM angle (classical LSTM-PPM, DECLARE, rule-based mining) and pure-LLM work without a BPM angle.

Organised into five layers:

A. Core LLM+BPM — ingested sources on the agentic-BPM paradigm itself.
B. Referenced-not-ingested — LLM+BPM citations living inside our ingested pages but not yet processed.
C. Adjacent: LLM-assisted research methodology — LLM+SLR cluster (flagged separately since it is about reviewing BPM literature, not applying LLMs to processes).
D. Context: AI adoption & workforce evidence — empirical baselines that frame the APM opportunity but are not themselves BPM.
E. Entry points & gaps — wiki hubs to start from, and gaps the corpus acknowledges.

A. Core LLM+BPM (ingested)

A.1 Agentic BPM paradigm (APM / ABPMS)

sources/2026-calvanese-agentic-bpm-manifesto — APM Manifesto (18 authors, arXiv 2026). Defines agentic BPM; four ordered capabilities (framed autonomy → explainability → conversational actionability → self-modification); agents as first-class.
sources/2023-dumas-ai-augmented-bpms — ABPMS Manifesto (ACM TMIS 2023). Direct predecessor; four system-level characteristics (adaptable, proactive, explainable, context-sensitive). Read alongside APM for paradigm evolution — see syntheses/abpms-to-apm-evolution.
sources/2025-calvanese-autonomy-business-process-execution — Position paper promoting goals + normative frames to first-class BPM abstractions for governing autonomous agents. Operationalises framed autonomy.
sources/2025-elyasaf-self-modifying-abps — Self-modifying ABPS via MAPE-K; 5-level SAE-inspired autonomy roadmap; adaptation vs. evolution taxonomy.

A.2 Agent observability & evaluation

sources/2025-fournier-agentic-ai-process-observability — IBM Research. Treats LLM-agent execution (CrewAI, LangGraph, AutoGen) trajectories as event logs; process-mining + causal-discovery for behavioral-variability detection.
sources/2024-xu-the-agent-company-benchmark — CMU. 175 professional tasks in a simulated company (SDE, PM, HR, Finance, Admin). Best LLM-agent: 30.3% full success / 39.3% with partial credit. Closest public analogue to BPM-style task execution.

A.3 LLM in process-relevant domains

sources/2026-padella-llm-features-ppm — Flagship LLM-PPM source (Padella, de Leoni & Dumas 2026, arXiv 2601.11468). Gemini 2.5 Flash Thinking trained on 100 traces matches/surpasses CatBoost + PGTNet trained on full event logs across BPI12 / Bac / Hospital × Total Time + Activity Occurrence. Introduces: ρ_seq trace-to-string encoding · semantic-hashing probe for embodied-knowledge isolation (Hospital MAE +1702 % under hashing) · β-learner distillation for reasoning interpretability (LLM beats every β-learner by 6–80 %). Future work: prescriptive extension. Anchor for concepts/llm-based-ppm.
sources/2025-riess-jorgensen-brage-benchmark-norwegian-llm — Telenor / NoDaLiDa. Zero-shot LLM classification over 300 Norwegian customer-service transcripts; instruction-tuned Gemma2 reaches ~60-62% accuracy. Motivation: LLM classification as drift-robust alternative to retraining classifiers — direct bridge to concepts/concept-drift and PrPM.
sources/2024-riess-synbps-simulation-framework — Parametric event-log simulator (NOT an LLM paper itself) — included here because it underpins the syntheses/study-sketch-synbps-apm programme for controlled agentic-BPM evaluation.

A.4 LLM-driven process modelling

sources/2026-licardo-bpmn-assistant — BPMN Assistant (Applied Sciences 2026). LLM + hierarchical JSON intermediate representation + 5 atomic editing functions + schema-validation retry loop. Beats direct XML manipulation across all evaluated models (GPT-5.1, Claude 4.5 Sonnet, DeepSeek V3, …); 43% latency reduction, >75% output-token reduction. First ingested source on direct LLM→BPMN modelling. Partially fills gap §E.2.1.

A.5 Neuro-symbolic BPM vision and instantiations

sources/2024-kampik-large-process-models — Large Process Models: A Vision for BPM in the Age of Generative AI (Künstliche Intelligenz 2024). LPM = neuro-symbolic stack (process-fine-tuned LLM + knowledge graphs + process atoms + classical BPM tooling). APM Manifesto cites this as the LLM-centric BPM vision against which APM argues that symbolic frames remain essential. Now fully ingested with §5/§6/§7 detail (was previously a structural stub).
sources/2024-kampik-large-process-models-correction — Springer erratum to Fig. 1 (December 2024); restores missing labels in the LPM architecture diagram.
sources/2025-varsani-neuro-symbolic-ai-sap-erp — Neuro-Symbolic AI in SAP ERP. Concrete enterprise instantiation of LPM-style architecture: LLM (GPT-4 + LLaMA 2) + middleware (RAG + schema embeddings + RLHF) + SAP HANA + ABAP rule engine. Reports 89.6% query-translation accuracy vs 74.8% LLM-only. Year/venue unverified.

A.6 Referenced-not-ingested

sources/2023-chapela-campa-augmented-process-execution — From Process Mining to Augmented Process Execution. Stub; four-level analytics pyramid; complementary analytics-side lens on what APM now frames as agent-side.

A.7 Integrative reviews

sources/2026-theodorakopoulos-bi-bpm-genai-review — Business Intelligence and Business Process Management in the Era of Generative AI (MDPI Applied Sciences 2026, 55 pp). Narrative + conceptual review (NOT PRISMA). Proposes a 5-layer integrative framework (Data & Computation → Organisational Insight → Process Intelligence → Augmentation → Decision) with cross-layer feedback loops. Positions GenAI explicitly as the Augmentation layer — not the new core. Tempers techno-optimism: explainability ≠ effective oversight; trust must be calibrated. 5 limitation categories for GenAI in process-critical environments. Complementary to APM's agent-centric pyramid. Wiki-maintainer caveat: MDPI venue, management-science authors — treat as integrative/teaching reference, not theoretical contribution.

B. Referenced-not-ingested — priority LLM+BPM citations

Sources cited inside our ingested pages (APM Manifesto, autonomy paper, Fournier observability, ABPMS Manifesto) that would extend the corpus. Ordered by estimated relevance.

#	Citation	Cited from	Relevance
1	~~Kampik et al. 2024 — Large Process Models~~	APM Manifesto	Now ingested — see §A.5.
2	Acitelli, Alman, Maggi, Marrella 2025 — automated planning for framed-autonomy synthesis	sources/2025-calvanese-autonomy-business-process-execution	Operationalises normative frames via automated planning.
3	Kampik & Okulmus 2024 — SIGNAL	sources/2025-calvanese-autonomy-business-process-execution	Agent communication / signalling protocols; relevant to conversational actionability.
4	Dong et al. 2024 — AgentOps / trajectory analysis	sources/2025-fournier-agentic-ai-process-observability	Foundational to agent-process observability.
5	Chopra et al. 2018 — Handbook of Normative Multi-Agent Systems	sources/2025-calvanese-autonomy-business-process-execution	Normative-MAS theory underpinning frame design.
6	Tamkin et al. 2024 — Clio	sources/2025-handa-which-economic-tasks-ai	Privacy-preserving task classification; methodological primitive for agent-trajectory analysis at scale.
7	sources/2012-vanderaalst-process-mining-manifesto (stub)	APM Manifesto	Foundational manifesto APM positions itself as successor to; read for paradigm continuity.
8	sources/2021-dumas-process-mining-2-from-insights-to-action	APM Manifesto, PrPM corpus	Process Mining 2.0 keynote; foreshadows APM's Recommend role.

Recurring author programmes to sweep: Kampik, Rebmann, Warmuth, Polyvyanyy, Rinderle-Ma, Lesperance, Marrella. These names appear repeatedly in APM citations without individual works ingested.

LLM-driven BPMN modelling — referenced via sources/2026-licardo-bpmn-assistant

The BPMN Assistant paper provides a comprehensive review of concurrent LLM-BPMN tools. Priorities for ingest:

#	Citation	Relevance
L1	Kourani et al. 2024 — ProMoAI: Process Modeling with Generative AI (IJCAI)	Constrained Python POWL code as intermediate representation; Claude 3.5 Sonnet achieves 0.93 conformance.
L2	Kourani et al. 2024 — Evaluating LLMs on Business Process Modeling: Framework, Benchmark, and Self-Improvement (arXiv:2412.00023)	Benchmarking framework that powers ProMoAI.
L3	Hörner, Möller, Reichert 2026 — BPMNGen (BISE)	Comprehensive human-centred evaluation: cognitive load, acceptability, comprehension. Complements BPMN Assistant's structural focus.
L4	Köpke & Safan 2024 — BPMN-Chatbot (Business Process Management)	Voice/text interface, 95% correctness, 94% token reduction.
L5	Klievtsova, Benzin, Kampik, Mangler, Rinderle-Ma 2023 — Conversational Process Modeling (BPM Forum)	State-of-the-art review of conversational modelling.
L6	Grohs, Abb, Elsayed, Rehse 2024 — LLMs Can Accomplish Business Process Management Tasks (BPM Workshops)	Survey of LLM capabilities across BPM tasks.
L7	Bellan, Dragoni, Ghidini 2023 — Process Extraction from Text: Benchmarking (arXiv:2110.03754)	The BPMN-extraction benchmark study Licardo et al. cite as evidence the field needs standardised evaluation.
L8	Rebmann, Schmidt, Glavaš, van der Aa 2024 — Evaluating LLMs to Solve Semantics-Aware Process Mining Tasks (ICPM)	LLM evaluation on process-mining-specific tasks; complements modelling-focused work.

C. Adjacent: LLM-assisted research methodology

Flag: these are about using LLMs to review (BPM) literature — not about applying LLMs within business processes. Useful operationally if you want to run an LLM-assisted systematic review of the LLM+BPM frontier itself.

sources/2023-qureshi-chatgpt-sr-automation — Early critical commentary. "Uncanny valley": fluent output, unreliable citations.
sources/2024-agarwal-litllms-are-we-there-yet — LitLLMs: retrieval (keyword + dual search + re-rank) + plan-then-generate. -18-26% hallucinated citations.
sources/2024-dennstaedt-llm-title-abstract-screening — Four-model screening benchmark; Mixtral best-balanced (81.9% sens / 75.2% spec).
sources/2025-scherbakov-llms-as-tools-literature-reviews — Meta-SLR of 172 LLM-SR studies; self-demonstrating Covidence + GPT-4o pipeline (83% prec / 86% rec on extraction).
Hub page: concepts/llm-assisted-literature-review — stage-by-stage capability map.

D. Context: AI adoption & workforce

Not BPM-direct, but establish the empirical backdrop for any APM claims about organisational deployment, skill impact, and task distribution.

sources/2025-handa-which-economic-tasks-ai — 4M+ Claude conversations mapped to O*NET; 57% augmentation / 43% automation.
sources/2025-tomlinson-working-with-ai — Bing Copilot across occupations; user-goal vs. AI-action split.
sources/2025-korst-wharton-gen-ai-enterprise-adoption — Wharton-GBK Year-3 survey; 82% weekly use, 72% measure ROI, 43% warn of skill atrophy.
sources/2025-becker-metr-ai-developer-productivity — RCT: AI slowed experienced OSS devs 19% despite perceived speed-up. Perception-reality gap.
sources/2026-shen-ai-skill-formation — RCT: AI-assisted learners score ~17% lower on post-task competency.
sources/2026-shen-anthropic-coding-skills-post — Anthropic companion post.

E. Entry points & gaps

E.1 Wiki pages to read first (synthesis hubs)

concepts/agentic-bpm — paradigm hub.
syntheses/abpms-to-apm-evolution — what shifted 2023 → 2026.
syntheses/apm-business-themes — APM research agenda mapped to 12 business themes.
concepts/framed-autonomy, concepts/agent-process-observability, concepts/behavioral-variability, concepts/self-modification, concepts/conversational-actionability, concepts/explainability-apm.
concepts/ai-agent-benchmarks, concepts/ai-adoption, concepts/ai-skill-formation.
Study sketches as research-programme exemplars: syntheses/study-sketch-synbps-apm, syntheses/study-sketch-temporal-consistency-agents, syntheses/study-sketch-agent-trajectory-drift.

E.2 Gaps the corpus acknowledges

The following are flagged in ingested sources as open — candidates for targeted literature search beyond this wiki:

LLM-based process discovery & redesign — flagged by APM Manifesto (agent-oriented process mining, C1). Partially filled by sources/2026-licardo-bpmn-assistant for the editing/refinement side; LLM-assisted extraction from event logs still uncovered.
LLM-based conformance checking & prescriptive monitoring — PARTIALLY FILLED 2026-05-11 by Padella et al. 2026 for predictive monitoring with LLMs in data-scarce settings. The paper explicitly flags prescriptive extension as future work. LLM-based conformance checking remains uncovered.
Multi-agent coordination / cross-organisational choreography — APM M3 open question.
Frame elicitation & specification languages — autonomy paper flags hybrid symbolic/sub-symbolic frame elicitation as a bottleneck; no ingested work operationalises it.
Prompt injection & LLM-agent security in BPM — APM C2 raises zero-trust architecture for agents as critical; Microsoft NLWeb incident cited but not ingested.
Benchmark contamination risk (C3) — PARTIALLY ADDRESSED 2026-05-11: Padella et al. 2026's semantic-hashing probe offers a concrete protocol to detect embodied-knowledge leakage from public event logs (BPI12 et al.) into LLM predictions. Systematic application across BPM benchmarks remains future work.
APM "killer applications" — APM Outlook §5 admits these are underexplored; no real-world agentic-BPM deployment cases are ingested.
Process-worker skill formation — analogue of sources/2026-shen-ai-skill-formation for business analysts, RPA ops, case managers is missing.
LLM hallucination-mitigation for process state — no ingested work on preventing agents from fabricating case history or compliance status.
Domain-specific fine-tuning for BPM — sources/2025-riess-jorgensen-brage-benchmark-norwegian-llm finds domain SFT can hurt; no ingested follow-up.

Reading order (suggested)

Paradigm orientation — syntheses/abpms-to-apm-evolution, then sources/2026-calvanese-agentic-bpm-manifesto, then sources/2023-dumas-ai-augmented-bpms.
Neuro-symbolic alternative paradigm — sources/2024-kampik-large-process-models (LPM vision) + concepts/neuro-symbolic-bpm hub; read sources/2025-varsani-neuro-symbolic-ai-sap-erp as concrete instantiation.
Capability deep-dives — sources/2025-calvanese-autonomy-business-process-execution (frames), sources/2025-fournier-agentic-ai-process-observability (observability), sources/2025-elyasaf-self-modifying-abps (self-modification).
LLM→BPMN modelling — sources/2026-licardo-bpmn-assistant (BPMN Assistant) + concepts/llm-assisted-process-modelling hub.
Evaluation reality check — sources/2024-xu-the-agent-company-benchmark (what LLM-agents can actually do) + sources/2025-becker-metr-ai-developer-productivity (perception-reality gap).
Process-relevant LLM work — sources/2025-riess-jorgensen-brage-benchmark-norwegian-llm.
Context layer (optional) — AI-adoption cluster; read last or skim.
Gap-driven search — pick from §E.2 based on research focus; or sweep the §B LLM-driven BPMN modelling list (L1-L8).