REVIEW 2 major objections 5 minor 73 references
Enterprise LLM agents become auditable when product rules leave prompts and live in a code-owned harness of manifests, claims, contracts, and validators.
Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →
T0 review · grok-4.5
2026-07-10 13:25 UTC pith:QMQ7Y7TX
load-bearing objection Solid systems paper with real measurements: code-owned contracts hold under model swap and beat prompt-only and bolt-on guardrails on a pinned public slice—useful, not oversold. the 2 major comments →
From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper's central claim is that code-owned harness enforcement is load-bearing for enterprise answer contracts and is not reproducible by prompt instruction alone. Across fixed scenarios and fault injection the harness preserves source-grounding, entity-routing, trace, output-hygiene, and recommendation-language contracts; under model substitution the code-owned checks passed on all 270 composition-boundary runs with failures confined to the model-composed side; and in an enforcement-layer ablation holding the model fixed, prompt-only admitted every adversarial recommendation and leak violation while the harness blocked all of them and preserved 120/120 utility where a bolt-on external gua
What carries the argument
The harness: a code-owned control layer (source gates, entity routing, claim eligibility, answer contracts, trace generation, and validation) wrapped around a replaceable composition boundary, with source-backed claims—not the model or the wiki—as the authority for what may be asserted at runtime, and deterministic fallback when live composition fails the contract.
Load-bearing premise
That a fixed set of hand-authored scenarios, expected-claim pins, and a small suite of adversarial bait prompts is a good enough stand-in for the product failures that matter in real enterprise deployment, while correctness of the promoted claims themselves is treated as out of scope.
What would settle it
Rerun the enforcement-layer ablation with the same model and paired scenarios: if prompt-only (or another non-code-owned layer) blocks every recommendation-language and internal-trace-leakage violation on the adversarial set while still answering all benign scenarios at full utility—matching the harness on both safety and 120/120 utility—then the claim that code-owned enforcement is load-bearing fails.
If this is right
- Productization of prompt-dominant LLM demos should relocate source policy, routing, answer structure, and leakage rules into versioned code and schemas rather than longer prompts.
- Swapping the composition model need not move reader-facing safety or audit contracts if those checks live in the harness rather than in model behavior.
- Bolt-on refuse/redact guardrails can stop the same violations but may sacrifice utility; a validation-plus-deterministic-fallback path can keep both.
- Each produced answer becomes an auditable artifact with routing, claim selection, source states, and validation results recorded outside the visible text.
- The same manifest-and-claim pattern can register non-document operational signals once they are treated as governed sources rather than unbounded context.
Where Pith is reading between the lines
- Teams that treat RAG faithfulness scores as the main reliability metric may still ship unauthorized recommendations or leaked internal fields unless a per-answer contract gate is enforced in code.
- Orchestration frameworks that compose agents but leave claim admission and output contracts to the application will inherit the same prompt-vs-harness gap shown in the ablation.
- A practical next measurement would stress the promotion gates themselves—incorrect or incomplete claim admission—since the paper intentionally scopes those out of the reported pass rates.
- In regulated briefing domains, the adoption ladder implies that moving from retrieval-supported assistants to claim-governed agents is the step where audit and compliance become feasible rather than aspirational.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes harness engineering: relocating enterprise LLM-agent behavior from prompt-dominant prototypes into code-owned contracts (source manifests, source-backed claims, entity routing, answer structure, traces, and validators) around a replaceable composition boundary. Instantiated on a public slice of five Korean corporate groups (25 listed companies, 113 runtime claims), it evaluates three RQs: (1) fixed-scenario contract preservation plus one-to-one fault-injection negative control; (2) model substitution at the composition boundary (3 hosted models × 30 scenarios × 3 repeats = 270 runs), separating code-owned from model-composed failures; (3) an enforcement-layer ablation (harness vs prompt-only vs bolt-on external guardrail) holding the model fixed, showing prompt-only admits recommendation-language and internal-trace-leakage violations while the harness blocks them and preserves full utility (120/120) versus the guardrail’s 88/120. Claims are scoped to contract preservation, not investment-answer quality.
Significance. If the results hold under the paper’s stated scope, this is a useful systems contribution for enterprise LLM productization: it supplies a concrete architecture (source-to-claim pipeline, replaceable composition boundary, deterministic fallback), a measurement design that separates harness guarantees from model phrasing, and evidence that code-owned enforcement can preserve both safety and utility where prompt instructions alone and a bolt-on guardrail do not. Strengths that should count in the assessment include the public repository and Zenodo-pinned baseline, committed evaluation artifacts, fault-injection negative control, Wilson intervals and χ² on model pass rates, McNemar tests on the paired ablation, and explicit separation of code-owned vs model-composed checks. The work is engineering evidence, not a domain-value claim about investment briefings; within that frame it is a reusable pattern with inspectable artifacts.
major comments (2)
- [Section 5.5 / Appendix A5] Section 5.5 and Appendix A5: The RQ3 load-bearing claim rests on ten author-written adversarial prompts (two templates × five groups) that explicitly solicit buy/sell/target-price language or internal claim IDs, JSON traces, and raw extractions—the same patterns the language and leakage validators already check. Internally, the contrast (prompt-only 30/30 violations admitted vs harness 0/0, utility 120/120 vs external-guardrail 88/120) is well measured and statistically reported. Externally, the baits are co-designed with the contract suite, so the ablation shows the gate blocks the violations it was written to detect rather than that code-owned enforcement is necessary against broader product-level failures (subtle recommendation framing, mixed-entity provenance, partial leakage, stale sources). Either add at least one further adversarial family not isomorphic to the existing validators
- [Section 5.2 / Table 6] Section 5.2, Table 6, and Section 7: “Source grounding” is operationalized as resolution of author-pinned expected claim references (109/109) and related trace fields, while correctness of the 113 promoted claims is explicitly out of scope. That is a coherent engineering check, but the contract-area label and abstract phrasing (“source-grounding … contracts”) can be read as factual provenance correctness. Rename or gloss the metric as expected-claim reference integrity / contract-envelope preservation, and state in the RQ1 discussion that pass rates do not certify promotion quality or mixed-entity source errors. Without that clarification, the strongest reader-facing claim overreaches the measured quantity.
minor comments (5)
- [Figure 1 / Figure 5] Figure 1 and Figure 5 are clear architecturally; ensure the camera-ready versions keep the ablation-only branches of Figure 5 visually distinct from the production path (caption already helps).
- [Table 7] Table 7 final pass rates (e.g., Claude 62/90) sit below the 270/270 code-owned pass; the text explains this well, but a one-line callout in the table caption that final failure is model-composed only would reduce misreading.
- [Section 2] Related Work is thorough; a short explicit contrast table (schema/rail tools vs source-to-claim authority layer vs this paper’s product-level measurement) would help readers place the contribution faster.
- [Table 10 / Section 6.4] Table 10’s adoption ladder is labeled a planning heuristic; keep that disclaimer adjacent to any abstract/conclusion mention so it is not taken as an empirical maturity model.
- [References] Minor consistency: arXiv id and some reference years in the provided text look future-dated relative to conventional timelines; verify all access dates, DOIs, and citation years before camera-ready.
Circularity Check
Deterministic fixed-scenario passes are largely by construction; ablation and model-substitution results are empirical, not circular derivations.
specific steps
-
self definitional
[§3.5 Reference Implementation; §5.2 Scenario Contract Outcomes / Table 6]
"The baseline reported in the paper uses a deterministic, schema-checked composer for fixed validation scenarios and runtime-interface tests. The composer is a rule-based template engine that fills validated answer sections from selected source-backed claims using no generative model call. This baseline fixes the system contract... Within the bounded public-data slice, all configured source, trace, answer, and output-hygiene contracts passed. ... Total 30/30 109/109 30/30 30/30 60/60 0"
For the deterministic fixed-scenario path, the system under test is a rule-based template engine written to emit the contracted sections from pinned expected claims. Reporting that those scenarios pass source-claim, trace, answer, and hygiene checks is therefore largely equivalent to verifying that the code implements the contracts it was written to implement—not an independent prediction. Fault injection and the live-LLM/ablation arms are not subject to this reduction.
full rationale
This is a systems-engineering evaluation paper, not a first-principles derivation. It does not fit parameters and rename them as predictions, import uniqueness theorems from the authors, or smuggle an ansatz via self-citation. The only mild self-consistency is that RQ1's all-pass rates on the fixed scenarios use a rule-based deterministic composer built to satisfy the same contracts it is scored against—so those positive passes are largely by design. The paper is transparent about that baseline choice and supplies independent empirical content: fault injection (validators fire on deliberately broken dimensions), model substitution (code-owned checks held 270/270 while model-composed checks varied), and the enforcement-layer ablation (prompt-only admitted 30 adversarial rec/leak violations that the harness blocked; external guardrail over-refused). The skeptic concern that adversarial baits match the validators is a construct-validity / external-validity issue about whether the scenario suite is a sufficient proxy for enterprise failure modes—not a reduction of a claimed prediction to its fitted inputs. Claim correctness and investment usefulness are explicitly out of scope. Score 2 for the minor by-construction element in the deterministic baseline; central load-bearing and substitution claims remain independently measured.
Axiom & Free-Parameter Ledger
free parameters (4)
- live-LLM temperature
- latency budget (1500 ms)
- claims-per-answer bound and expected-claim pins
- 25 priority claims reporting layer / 113-claim runtime layer
axioms (5)
- domain assumption Enterprise reliability for this product class is adequately measured by per-answer contracts (source grounding, entity routing, trace completeness, output hygiene, recommendation-language absence) rather than by answer quality or investment correctness.
- domain assumption Source manifests and promoted source-backed claims—not retrieved raw text or the compiled wiki—are the runtime authority for factual statements.
- ad hoc to paper A deterministic schema-checked composer is an acceptable utility-preserving fallback when live model output fails the output contract.
- ad hoc to paper The 30 fixed scenarios plus 10 adversarial bait prompts sufficiently stress the contracts under test for the reference slice.
- standard math Standard software-engineering notions of test harnesses, schemas, and validation gates apply to LLM agent control layers.
invented entities (2)
-
Enterprise LLM agent harness (code-owned control layer with replaceable composition boundary)
independent evidence
-
Source-backed runtime claim (promoted atomic statement with provenance, entity scope, and use policy)
no independent evidence
read the original abstract
Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context. Productization adds requirements for source boundaries, entity routing, answer contracts, and reproducible traces. We present a harness-engineering approach that reconstructs this pattern into a traceable, auditable LLM-agent architecture: deterministic behavior moves into code, manifests, schemas, and validation artifacts around a replaceable composition boundary, while source-backed claims remain the authority for runtime answers. We instantiate it on a public-data slice of five Korean corporate groups (25 listed companies) and evaluate three research questions. (1) The harness preserves its source-grounding, entity-routing, trace, output-hygiene, and recommendation-language contracts across the fixed validation scenarios; a fault-injection control confirms the validators flag deliberately broken contracts. (2) The checks the harness enforces held under model substitution: across three hosted models, they passed on all 270 composition-boundary runs; failures were confined to the model-composed side and were caught and recorded. (3) The code-owned guarantees are load-bearing, not reproducible by prompting alone: holding the model fixed and varying only the enforcement layer, prompt instructions alone let recommendation-language and internal-trace-leakage violations reach the reader, which the harness blocks entirely. A bolt-on external guardrail prevents such violations too but over-refuses, dropping utility to 88/120 where the harness preserves full utility (120/120); in this ablation, only code-owned enforcement preserves both safety and utility. The result is a reusable engineering pattern for turning exploratory prototypes into auditable applications with versioned source, control, and validation artifacts.
Reference graph
Works this paper leans on
-
[1]
Andrej Karpathy. [Vibe coding], 2025. URL https : / / x . com / karpathy / status / 1886192184808149383. X Post, 2025-02-02; accessed 2026-07-09
work page 2025
-
[2]
Christian Meske, Tobias Hermanns, Esther von der Weiden, Kai-Uwe Loser, and Thorsten Berger. Vibe coding as a reconfiguration of intent mediation in software development: Definition, implications, and research agenda. IEEE Access, 13:213242–213259, 2025. doi: 10.1109/ACCESS.2025.3645466
-
[3]
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 2023. doi: 10.1145/3571730
-
[4]
Software engineering for AI-based systems: A survey
Silverio Martínez-Fernández, Justus Bogner, Xavier Franch, Marc Oriol, Julien Siebert, Adam Trendowicz, Anna Maria Vollmer, and Stefan Wagner. Software engineering for AI-based systems: A survey. ACM Transactions on Software Engineering and Methodology, 31(2):1–59, 2022. doi: 10.1145/3487043
-
[5]
Machine learning operations (MLOps): Overview, definition, and architecture
Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl. Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11:31866–31879, 2023. doi: 10.1109/ACCESS.2023.3262138
-
[6]
CEO AI. AI Leadership Program, 2026. URL https://ceoai.kr/ai-leadership. Accessed 2026-07-09
work page 2026
- [7]
-
[8]
NeMo guardrails: A toolkit for controllable and safe LLM applications with programmable rails
Traian Rebedea, Razvan Dinu, Makesh Narsimhan Sreedhar, Christopher Parisien, and Jonathan Cohen. NeMo guardrails: A toolkit for controllable and safe LLM applications with programmable rails. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 431–445, Singapore, 2023. Association for Co...
-
[9]
Building Guardrails for Large Language Models
Yi Dong, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, and Xiaowei Huang. Position: Building guardrails for large language models requires systematic design. In Proceedings of the 41st International Conference on Machine Learning, pages 11375–11394, 2024. doi: 10.48550/arXiv.2402.01822
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2402.01822 2024
-
[10]
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
Chenyu Zhou, Huacan Chai, Wenteng Chen, Zihan Guo, Rong Shan, Yuanyi Song, Tianyi Xu, Yingxuan Yang, Aofan Yu, Weiming Zhang, Congming Zheng, Jiachen Zhu, Zeyu Zheng, Zhuosheng Zhang, Xingyu Lou, Changwang Zhang, Zhihui Fu, Jun Wang, Weiwen Liu, Jianghao Lin, and Weinan Zhang. Externalization in LLM agents: A unified review of memory, skills, protocols an...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.08224 2026
-
[11]
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts. DSPy: Compiling declarative language model calls into self-improving pipelines. In The Twelfth International Conference on Learning Representati...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2310.03714 2024
-
[12]
Prompting is programming: A query language for large language models
Luca Beurer-Kellner, Marc Fischer, and Martin Vechev. Prompting is programming: A query language for large language models. Proceedings of the ACM on Programming Languages, 7 (PLDI):1946–1969, 2023. doi: 10.1145/3591300
-
[13]
Systems and software engineering – vocabulary
ISO/IEC/IEEE. Systems and software engineering – vocabulary. Technical Report ISO/IEC/IEEE 24765:2017, International Organization for Standardization, International Electrotechnical Commission, and Institute of Electrical and Electronics Engineers, 2017. URLhttps://www.iso.org/standard/71952.html. Accessed 2026-07-09
work page 2017
-
[14]
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, 27 and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems, volume 33, pages 9459–9474, 2020. doi: 10.48...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2005.11401 2020
-
[15]
Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2023. doi: 10.48550/arXiv.2312.10997
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2312.10997 2023
-
[16]
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-RAG: Learn- ing to retrieve, generate, and critique through self-reflection. In International Conference on Learning Representations, 2024. doi: 10.48550/arXiv.2310.11511
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2310.11511 2024
-
[17]
FActScore: Fine-grained atomic evalua- tion of factual precision in long form text generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. FActScore: Fine-grained atomic evalua- tion of factual precision in long form text generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12076–12100, Singapore, 2023. A...
-
[18]
Enabling large language models to generate text with citations
Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. Enabling large language models to generate text with citations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6465–6488, Singapore, 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.emnlp-main.398
-
[19]
Liu, Tianyi Zhang, and Percy Liang
Nelson F. Liu, Tianyi Zhang, and Percy Liang. Evaluating verifiability in generative search engines. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7001–7025, Singapore, 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.findings-emnlp.467
-
[20]
PersonaRAG: Enhancing retrieval-augmented generation systems with user-centric agents
Saber Zerhoudi and Michael Granitzer. PersonaRAG: Enhancing retrieval-augmented generation systems with user-centric agents. arXiv preprint arXiv:2407.09394, 2024. doi: 10.48550/arXiv.2407.09394
-
[21]
Measuring attribution in natural language generation models
Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Lora Aroyo, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Reitter. Measuring attribution in natural language generation models. Computational Linguistics, 49(4):777–840, 2023. doi: 10.1162/coli_a_00486
-
[22]
Michael Wooldridge and Nicholas R. Jennings. Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2):115–152, 1995. doi: 10.1017/S0269888900008122
-
[23]
Jennings, Katia Sycara, and Michael Wooldridge
Nicholas R. Jennings, Katia Sycara, and Michael Wooldridge. A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 1(1):7–38, 1998. doi: 10.1023/A:1010090405266
-
[24]
ReAct: Synergizing Reasoning and Acting in Language Models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations, 2023. doi: 10.48550/arXiv.2210.03629
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2210.03629 2023
-
[25]
Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems, volume 36, 2023. doi: 10.48550/arXiv.2302.04761
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2302.04761 2023
-
[26]
A survey on large language model based autonomous agents
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Jirong Wen. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6): 186345, 2024. doi: 10.1007/s11704-024-40231-1
-
[27]
Deepak Bhaskar Acharya, Karthigeyan Kuppan, and B. Divya. Agentic AI: Autonomous intelligence for complex goals—a comprehensive survey. IEEE Access, 13:18912–18936, 2025. doi: 10.1109/ACCESS.2025.3532853
-
[28]
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie 28 Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. ToolLLM: Facilitating large language models to master 16000+ real-world APIs. In International Conference on Learning Represe...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2307.16789 2024
-
[29]
Autonomous AI agents for multi-platform social media marketing: A simultaneous deployment study
Joongho Ahn and Moonsoo Kim. Autonomous AI agents for multi-platform social media marketing: A simultaneous deployment study. Electronics, 14(21):4161, 2025. doi: 10.3390/ electronics14214161
work page 2025
-
[30]
Eliza: A Web3 friendly AI Agent Operating System
Shaw Walters, Sam Gao, Shakker Nerd, Feng Da, Warren Williams, Ting-Chien Meng, Amie Chow, Hunter Han, Frank He, Allen Zhang, Ming Wu, Timothy Shen, Maxwell Hu, and Jerry Yan. Eliza: A Web3 friendly AI agent operating system. arXiv preprint arXiv:2501.06781,
work page internal anchor Pith review Pith/arXiv arXiv
-
[31]
doi: 10.48550/arXiv.2501.06781
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2501.06781
-
[32]
Why Do Multi-Agent LLM Systems Fail?
Mert Cemri, Melissa Z. Pan, Shuyi Yang, Lakshya A. Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Dan Klein, Kannan Ramchandran, Matei Zaharia, Joseph E. Gonzalez, and Ion Stoica. Why do multi-agent LLM systems fail? In Advances in Neural Information Processing Systems 38 (NeurIPS 2025), Datasets and Benchmarks Track, 2025. doi...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2503.13657 2025
-
[33]
White, Doug Burger, and Chi Wang
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W. White, Doug Burger, and Chi Wang. AutoGen: Enabling next-gen LLM applications via multi-agent conversations. In First Conference on Language Modeling, 2024. doi: 10.48550/arXiv.2308. 08155
-
[34]
LangChain. Agents, 2026. URL https://docs.langchain.com/oss/python/langchain/ agents. Accessed 2026-07-09
work page 2026
-
[35]
LangChain. LangGraph, 2026. URL https://github.com/langchain-ai/langgraph . Accessed 2026-07-09
work page 2026
-
[36]
CrewAI Inc. CrewAI, 2026. URL https://github.com/crewAIInc/crewAI . Accessed 2026-07-09
work page 2026
-
[37]
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2005.14165 1901
-
[38]
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35, 2023. doi: 10.1145/3560815
-
[39]
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv preprint arXiv:2402.07927, 2024. doi: 10.48550/arXiv.2402.07927
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2402.07927 2024
-
[40]
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, volume 35, pages 24824–24837, 2022. doi: 10.48550/arXiv.2201.11903
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2201.11903 2022
-
[41]
Nikolaos Malamas, Emmanouil Tsardoulias, Konstantinos Panayiotou, and Andreas L. Symeonidis. Toward efficient vibe coding: An LLM-based agent for low-code software development. Journal of Computer Languages, 85:101367, 2025. doi: 10.1016/j.cola.2025. 101367
-
[42]
567 Labs. Instructor, 2026. URL https://github.com/567-labs/instructor. Accessed 2026-07-09. 29
work page 2026
-
[43]
Guidance AI. Guidance, 2026. URL https : / / github . com / guidance-ai / guidance. Accessed 2026-07-09
work page 2026
-
[44]
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive NLP. arXiv preprint arXiv:2212.14024, 2022. doi: 10.48550/ arXiv.2212.14024
work page internal anchor Pith review Pith/arXiv arXiv 2022
-
[45]
Stanford NLP. DSPy, 2026. URL https://github.com/stanfordnlp/dspy . Accessed 2026-07-09
work page 2026
-
[46]
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa. Llama Guard: LLM-based input-output safeguard for human-AI conversations. arXiv preprint arXiv:2312.06674, 2023. doi: 10.48550/arXiv.2312.06674
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2312.06674 2023
-
[47]
Constitutional AI: Harmlessness from AI Feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, K...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2212.08073 2022
-
[48]
Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Zhiheng Xi, Xuanjing Huang, Hang Yan, Zhenhua Han, Tao Gui, and Yu-Gang Jiang. Agentic harness engineer- ing: Observability-driven automatic evolution of coding-agent harnesses. arXiv preprint arXiv:2604.25850, 2026. doi: 10.48550/arXiv.2604.25850
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.25850 2026
-
[49]
Meta-Harness: End-to-End Optimization of Model Harnesses
Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, and Chelsea Finn. Meta-Harness: End-to-end optimization of model harnesses. arXiv preprint arXiv:2603.28052,
work page internal anchor Pith review Pith/arXiv arXiv
-
[50]
doi: 10.48550/arXiv.2603.28052
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2603.28052
-
[51]
AI Harness Engineering: A Runtime Substrate for Foundation-Model Software Agents
Hailin Zhong and Shengxin Zhu. AI harness engineering: A runtime substrate for foundation- model software agents. arXiv preprint arXiv:2605.13357, 2026. doi: 10.48550/arXiv.2605. 13357
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605 2026
-
[52]
Xuying Ning, Katherine Tieu, Dongqi Fu, Tianxin Wei, Zihao Li, Yuanchen Bei, Jiaru Zou, Mengting Ai, Zhining Liu, Ting-Wei Li, Lingjie Chen, Yanjun Zhao, Ke Yang, Bingxuan Li, Cheng Qian, Gaotang Li, Xiao Lin, Zhichen Zeng, Ruizhong Qiu, Sirui Chen, Yifan Sun, Xiyuan Yang, Ruida Wang, Rui Pan, Chenyuan Yang, Dylan Zhang, Liri Fang, Zikun Cui, Yang Cao, Pa...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.18747 2026
-
[53]
Harness engineering for language agents: The harness layer as control, agency, and runtime
Chaoyue He, Xin Zhou, Di Wang, Hong Xu, Wei Liu, and Chunyan Miao. Harness engineering for language agents: The harness layer as control, agency, and runtime. Preprints.org preprint,
-
[54]
Version 2; accessed 2026-07-09
doi: 10.20944/preprints202603.1756.v2. Version 2; accessed 2026-07-09
-
[55]
Andrej Karpathy. LLM Wiki, 2026. URL https : / / gist . github . com / karpathy / 442a6bf555914893e9891c11519de94f. Accessed 2026-07-09
work page 2026
-
[56]
Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
Haoliang Ming, Feifei Li, Xiaoqing Wu, and Wenhui Que. Retrieval as reasoning: Self- evolving agent-native retrieval via LLM-wiki. arXiv preprint arXiv:2605.25480, 2026. doi: 10.48550/arXiv.2605.25480. 30
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.25480 2026
-
[57]
Juan M. Huerta. WiCER: Wiki-memory compile, evaluate, refine: Iterative knowledge compilation for LLM wiki systems. arXiv preprint arXiv:2605.07068, 2026. doi: 10.48550/ arXiv.2605.07068
work page internal anchor Pith review Pith/arXiv arXiv 2026
-
[58]
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
Rob Ashmore, Radu Calinescu, and Colin Paterson. Assuring the machine learning lifecycle: Desiderata, methods, and challenges. ACM Computing Surveys, 54(5):1–39, 2021. doi: 10.1145/3453444
-
[59]
Auditing large language models: A three-layered approach
Jakob Mökander, Jonas Schuett, Hannah Rose Kirk, and Luciano Floridi. Auditing large language models: A three-layered approach. AI and Ethics, 4(4):1085–1115, 2024. doi: 10.1007/s43681-023-00289-2
-
[60]
RAGAs: Automated evaluation of retrieval augmented generation
Shahul Es, Jithin James, Luis Espinosa-Anke, and Steven Schockaert. RAGAs: Automated evaluation of retrieval augmented generation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 150–158, St. Julians, Malta, 2024. Association for Computational Linguistics. doi: 10...
-
[61]
ARES: An automated evaluation framework for retrieval-augmented generation systems
Jon Saad-Falcon, Omar Khattab, Christopher Potts, and Matei Zaharia. ARES: An automated evaluation framework for retrieval-augmented generation systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 338–354, Mexico City, Mexico, 2024. Association for...
-
[62]
Holistic Evaluation of Language Models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2211.09110 2023
-
[63]
Evaluation and benchmarking of LLM agents: A survey
Mahmoud Mohammadi, Yipeng Li, Jane Lo, and Wendy Yip. Evaluation and benchmarking of LLM agents: A survey. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’25), pages 6129–6139, 2025. doi: 10.1145/3711896. 3736570
-
[64]
promptfoo. promptfoo, 2026. URL https://github.com/promptfoo/promptfoo. Accessed 2026-07-09
work page 2026
-
[65]
enterprise-llm-agent-harness, 2026
Joongho Ahn. enterprise-llm-agent-harness, 2026. URL https://github.com/hammerbaki/ enterprise-llm-agent-harness. Release tag public-baseline-v0.5.16.4, commit e8e60fb; accessed 2026-07-09
work page 2026
-
[66]
2026 Korean corporate-group designation results, 2026
Korea Fair Trade Commission. 2026 Korean corporate-group designation results, 2026. URL https://www.ftc.go.kr/www/selectBbsNttView.do?bordCd=3&key=12&nttSn=47410& searchCtgry=01%2C02. Press release with attached PDF; accessed 2026-07-09
work page 2026
-
[67]
OpenDART: Introduction to open API, 2026
Financial Supervisory Service. OpenDART: Introduction to open API, 2026. URL https: //opendart.fss.or.kr/intro/main.do. Accessed 2026-07-09
work page 2026
-
[68]
KRX OPEN API: Service usage guide, 2026
Korea Exchange. KRX OPEN API: Service usage guide, 2026. URL https://openapi. krx.co.kr/contents/OPP/INFO/OPPINFO003.jsp. Accessed 2026-07-09
work page 2026
-
[69]
NAVER Developers. Search API: News search, 2026. URL https://developers.naver. com/docs/serviceapi/search/news/news.md. Accessed 2026-07-09. 31
work page 2026
-
[70]
Edwin B. Wilson. Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158):209–212, 1927. doi: 10.1080/01621459.1927. 10502953
-
[71]
Karl Pearson. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302):157–175, 1900. doi: 10.1080/14786440009463897
-
[72]
Note on the sampling error of the difference between correlated proportions or percentages
Quinn McNemar. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153–157, 1947. doi: 10.1007/BF02295996
-
[73]
Paulk, Bill Curtis, Mary Beth Chrissis, and Charles V
Mark C. Paulk, Bill Curtis, Mary Beth Chrissis, and Charles V. Weber. Capability maturity model, version 1.1. IEEE Software, 10(4):18–27, 1993. doi: 10.1109/52.219617. 32
work page internal anchor Pith review doi:10.1109/52.219617 1993
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.