Pith. sign in

REVIEW 5 major objections 5 minor 30 references

CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models

T0 review · 5 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A supervisor agent that mediates disagreements between two worker LLMs can transfer its expertise to both, improving their accuracy on e-commerce catalog enrichment by about 15% and 14%.

desk verdict Supervisor-mediated context engineering loop is a real and novel system design, but the 15.24% headline is measured on disagreement cases only and the general-catalog claim is unsupported. read the letter →

arxiv 2607.14396 v1 pith:4HQHCDQ4 submitted 2026-07-15 cs.AI cs.LG

Zhu Cheng , Zhenming Wang , Yu (Xuan) Tang , Dan Liu , Bryan Zhang , Athanasios N. Nikolakopoulos , Pranav Souri Itabada , Jing Zhang
show 8 more authors
Chih-Chi Chou Peng Gao Fatemeh Mansoori Bharat Bojja Sarath Chander Sameer Thombare Umit Batur Tarik Arici
This is my paper
classification cs.AIcs.LG
keywords catalogenrichmentstructuredattributepredictionLLM-as-a-judgeself-learningsystemcontextengineeringsupervisoragentmemorysummarizerdisagreementmining
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a system can continuously improve two lightweight LLMs—one that predicts missing product-attribute values and one that verifies those predictions—by having a stronger supervisor agent investigate cases where they disagree, store its reasoning, summarize patterns, and inject the resulting learnings back into the workers' prompts and metadata. The claim is that this closed loop, called CatalogAgent, improves the generator's accuracy by 15.24% and the evaluator's by 13.98% on catalog enrichment tasks, without human intervention. A sympathetic reader would care because it suggests a path for improving AI systems by engineering their context rather than retraining them, and it turns difficult disagreement cases into reusable improvements.

What carries the argument

The central mechanism is the Supervisor Agent's structured output schema, which captures three categories of insight per investigated case: prompt improvements (specific instructions for Generator or Evaluator), metadata refinement (missing or ambiguous attribute values), and image usefulness (which product images carry predictive signal for a given product-type/attribute pair). The Memory Summarizer aggregates these case-level records into consolidated learnings at the product-type/attribute level, and a regression-constrained optimization ensures any new configuration maintains near-equivalent performance on a golden dataset before deployment.

What would settle it

Run the system on a random sample of product-type/attribute pairs drawn from the full long-tailed distribution—not just the ones highlighted—and compare the accuracy of worker models that receive supervisor learnings against a control that receives none. If the average gain across the full distribution is near zero or negative, the central claim of generalizable self-learning fails.

Watch

Extended reading notes

Core claim

On the paper's own terms: when a Generator LLM predicts a missing attribute value and an Evaluator LLM rejects it, or when a seller disputes the result, a Supervisor Agent uses tool-augmented reasoning to decide the correct value and record why the workers erred. A Memory Base stores these per-case records; a Memory Summarizer aggregates them into generalizable instructions, metadata updates, and image-use rules. Injecting these learnings into the worker models' prompts and metadata—context engineering—transfers the Supervisor's capability to the workers, yielding the reported accuracy gains while regression testing on a golden dataset prevents degradation on well-understood cases.

Load-bearing premise

The learnings mined from 50,000 investigated disagreement cases transfer to the broader catalog and to a separate 50,000-case test set, and the handful of product-type/attribute pairs shown in the tables are representative of the aggregate gains.

Editorial extensions

If this is right

  • If the system works as described, catalog enrichment can be improved by mining disagreement cases rather than by manual feature engineering or model retraining.
  • The same supervisor-mediated loop could generalize to other domains where a generator and verifier disagree and a stronger model can arbitrate the conflict.
  • The regression constraint formulation offers a template for making self-improvement safe in production, accepting only changes that do not degrade previously solved cases.
  • Systematic analysis of disagreements can surface metadata gaps—such as missing valid attribute values—that directly improve classification accuracy when fixed.
  • Image usefulness learnings enable cost savings by routing each product-type/attribute pair to the optimal model type (text-only or multimodal) and the minimal set of images needed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reported aggregate gains (15.24% and 13.98%) are demonstrated on a selected set of product-type/attribute pairs; the paper's own tables show wide variation across pairs, so the headline number likely depends on how those pairs are weighted relative to the long-tailed catalog distribution.
  • Because the learnings are injected as prompt text and metadata, the approach may plateau once prompts become too long or start conflicting; the paper's own future-direction of fine-tuning workers on supervisor labels is a natural extension to overcome that ceiling.
  • The system's economics depend on the supervisor model being strong enough to arbitrate reliably and cheaply enough to process the disagreement traffic; if disagreement rates are high, the cost of supervision could outweigh the accuracy gains.
  • A testable extension would be applying the same disagreement-mining loop to other structured-prediction tasks (e.g., named-entity extraction or document classification) to see whether the transfer of supervisor knowledge via context engineering holds beyond product catalogs.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper introduces CatalogAgent, a supervisor-mediated self-learning system for e-commerce catalog enrichment. A lightweight Generator and Evaluator LLM process Product-Attribute tasks; when the two models disagree, or when seller feedback conflicts with the model-approved value, a Supervisor Agent with tool access investigates and arbitrates. The Supervisor's case-level activities are stored in a Memory Base, aggregated by a Memory Summarizer into PT-A-level learnings, and fed back to the worker LLMs through prompt updates, metadata refinement, and image-usefulness-based model routing, subject to a regression constraint on a golden dataset. The abstract claims that this transfers the Supervisor's capabilities to the Generator and Evaluator, improving their performance by 15.24% and 13.98%, respectively. Experiments use 4.89M disagreement cases, with 50k used for Supervisor investigation and a separate 50k for held-out testing. Human evaluation of the Supervisor reports 97.26% generation accuracy and 94.56% evaluation accuracy on Generator-Evaluator disagreements, and 85.65% precision / 94.28% recall on Model-Seller disagreements. An appendix provides a formal analysis of the Supervisor's effect on catalog precision and accuracy.

Significance. If the central empirical claim is robust, the work is practically significant: it demonstrates a closed-loop system that mines disagreement cases, converts them into context updates, and improves lightweight worker LLMs without human intervention—an approach that could generalize beyond catalog enrichment. The manuscript has real strengths: it uses a held-out test set (though conditioned on disagreements), includes independent human auditing of the Supervisor, proposes a concrete structured schema for learnings, and ships a formal appendix that is not circular. However, the current evidence does not substantiate the headline improvements on the general catalog. The evaluation slice, the absence of aggregate numbers, and the lack of ablations leave the main claim conditional and under-supported. The paper is best viewed as an industrial case study whose empirical core needs to be respecified or strengthened.

major comments (5)
  1. [§3.1] The 50k held-out test set is sampled from the 4.89M Product-Attribute pairs 'where the Evaluator rejected the Generator's predictions.' This is a disagreement-only slice, not a random sample of catalog enrichment tasks. Any improvement measured on this set is conditional on hard cases and does not support the abstract's unqualified claim of improving Generator/Evaluator performance by 15.24% and 13.98%. The authors should either report aggregate micro/macro results on a representative sample of all Product-Attribute tasks, or explicitly rescope the central claim to disagreement cases. At minimum, they should report the aggregate before/after metrics on the full 50k disagreement test set, with confidence intervals.
  2. [Abstract and §3.4, Tables 5–6] The headline numbers 15.24% and 13.98% do not appear in any table or equation. Table 6 reports 'up to 15.01%' on selected PT-As for the Generator, and no table reports an Evaluator-specific improvement. The paper should specify exactly which metric, which aggregation, and which test set produce the abstract's percentages. Without this mapping, the central quantitative claim is not verifiable from the manuscript.
  3. [§3.4, Tables 5–6] The empirical support consists of a handful of selected PT-As with no confidence intervals, no full per-PT-A distribution, and no macro/micro averages over the 50k held-out set. Given the acknowledged long-tailed PT-A distribution, selected rows cannot establish the claimed transfer. Furthermore, there is no ablation separating the contribution of the self-learning loop from the contribution of the context updates themselves. The paper's core claim of 'self-improvement without human intervention' requires comparing the autonomous Memory Summarizer loop against, for example, one-time manual prompt engineering or a static context-update baseline.
  4. [§2.5, Eq. (1)] The regression constraint only checks non-degradation on a golden set G with |G| << |A|. It cannot establish positive transfer to the broader catalog or to long-tail PT-As. The text asserts that disagreement cases are by definition not in G, but no evidence is given for this. In addition, the disagreement rate D(θ), the actual objective being minimized, is never reported as an outcome metric in Section 3; the experiments measure accuracy on selected PT-As, not disagreement rates. The formal optimization framework is therefore not operationalized in the experimental section.
  5. [Appendix A] Theorems 1 and 2 are formal identities or conditional statements, not empirical evidence for the magnitude of improvement. In particular, Theorem 2's non-regression result follows from the definition of A_t: incorrect Supervisor publications on slices that previously had no published value do not change the count of correct values. The appendix does not estimate the slice traffic masses or p_ok rates from data, so it cannot substitute for the missing aggregate experimental results. It is a useful formal scaffold, but it does not support the quantitative headline.
minor comments (5)
  1. [Abstract / §3.4] The abstract states 15.24% and 13.98%, while Table 6 reports 'up to 15.01%'. Reconcile these numbers and report the exact aggregate metric.
  2. [§2.5 / Table 7] Equation (1) defines P(θ;G) as accuracy, but Table 7 reports precision and recall. Clarify which metric is used for the regression constraint and provide the tolerance δ values.
  3. [§3.4, Table 8] The image-usefulness results are purely qualitative; include quantitative accuracy or cost comparisons for the image-routing decisions.
  4. [Table 4] Typo: 'in arbitrating' should be 'in arbitrating' or simply 'arbitrating'.
  5. [§4] Reference [30] (Agentic Context Engineering) appears very close in spirit. The related-work paragraph should state more concretely how disagrement mining and regression constraints differentiate this work from [30].

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: held-out test split and independent human validation support the improvement claim.

full rationale

The paper's central claim is an empirical system result, not a derivation from first principles. The 50k test set is explicitly disjoint from the 50k used to generate learnings (§3.1), so the reported improvements are not fitted on the test set. The Supervisor's correctness is independently benchmarked against human auditors on 148 and 567 cases (§3.3), providing an external anchor for the labels used to generate learnings. The Appendix's Theorems 1 and 2 are algebraic restatements of the definitions of precision/accuracy (e.g., Theorem 1's condition is exactly the sign of Δprec by construction); they are not used to predict the empirical improvement and do not constitute a circular derivation. Self-citations in Related Work are background context and not load-bearing: no uniqueness theorem, ansatz, or fitted parameter is imported from the authors' prior work. The representativeness of the golden set G is an empirical limitation, not a circularity. Hence no circular step can be exhibited with a specific reduction.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No new physical entities or fitted numerical constants are introduced. The system relies on standard probability assumptions, the representativeness of the golden dataset, and the accuracy of the Supervisor as a learning signal. The only explicit hand-chosen parameter is the regression tolerance δ, whose value is undisclosed.

free parameters (1)
  • Tolerance threshold δ (and δ_G, δ_E)
    Hand-chosen tolerance in the regression constraint (Eq. 1 and 2). The value is not reported, but it controls which prompt updates are accepted and directly affects the reported improvements.
assumptions (3)
  • domain assumption Conditional independence (D,RE)⊥⊥RG | (X_E, V) in Appendix
    Assumes the Evaluator's decision is independent of the Generator's rationale given the evaluator's inputs and the generated value. Structurally plausible since the Evaluator does not see RG, but unverified empirically.
  • domain assumption Golden dataset G is a representative sample of the catalog
    Used to validate regression constraints; if G is not representative, regression testing may miss real degradations on the long tail.
  • domain assumption Supervisor decisions are accurate enough to serve as learning signal
    Human evaluation shows high but not perfect accuracy; errors in Supervisor decisions could propagate into learnings and degrade worker models.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models." pith.science (2026). https://pith.science/paper/4HQHCDQ4

@misc{pith2026260714396,
  author       = {Pith},
  title        = {Pith review of: CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4HQHCDQ4}},
  note         = {Machine review of arXiv:2607.14396}
}
read the original abstract

Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) -- such as material, color, and shape -- often have missing values. Typically, SA values are extracted from product information, including titles and descriptions. While LLM-based generator-evaluator frameworks have demonstrated effectiveness for SA prediction -- where an LLM generates SA values and another evaluates them -- they face challenges when the Generator and Evaluator produce conflicting outputs, as either component can make mistakes. We introduce \texttt{CatalogAgent}, a novel agentic system that continuously improves Generator and Evaluator models for e-commerce catalog enrichment. When disagreements arise from (1) internal conflicts between the LLM-based Generator and Evaluator, or (2) external feedback from sellers on LLM outputs, a Supervisor Agent intervenes to mediate these conflicts and make final decisions. The system also incorporates a Memory Base and a Memory Summarizer that stores Supervisor Agent activities from individual cases and aggregates patterns into learnings. These learnings are fed back to the worker Generator and Evaluator LLMs, enabling self-improvement without human intervention. Through context engineering -- injecting learnings and insights into worker LLMs' contexts -- the system successfully transfers the Supervisor's capabilities to the Generator and Evaluator, improving their performance by 15.24\% and 13.98\%, respectively. Our experiments demonstrate a new paradigm of Supervisor Agent-mediated self-learning systems for improving generative AI model accuracy.

Figures

Figures reproduced from arXiv: 2607.14396 by the authors.

Figure 1
Figure 1. Overview of CatalogAgent system. The Supervisor Agent intervenes when dis￾agreements arise — either between the Generator and Evaluator, or between models and Sellers. Memory Base stores individual case learnings from Supervisor investiga￾tions, and the Memory Summarizer aggregates them into generalizable insights that are fed back to the Generator and Evaluator through context engineering, forming a closed-loop sel… view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

30 extracted references · 7 linked inside Pith

  1. [1]

    Nikolakopoulos, A.N., Kaul, S., Gade, S.K., Dubrov, B., Batur, U., Khan, S.A.: Sage:Structuredattributevaluegenerationforbillion-scaleproductcatalogs.arXiv preprint arXiv:2309.05920 (2023)

    arXiv 2023

  2. [2]

    In: Proceedings of EMNLP 2025, Industry Track (2025)

    Huang, Y., Ramo, K., Iovine, A., Monteiro, M., Gokalp, S., Bakshi, A., Turalic, H., Kumar, A., Neumeier, J., Yates, R., Monir, R., Hartmann, S., Manglik, T., Yakout, M.: AttributeForge: An agentic LLM framework for automated product schema modeling. In: Proceedings of EMNLP 2025, Industry Track (2025)

    2025

  3. [3]

    In: Proceedings of EMNLP 2025, Industry Track (2025)

    Satyadharma, S., Sheikholeslami, F., Kaul, S., Batur, A.U., Khan, S.A.: Auto prompting without training labels: An LLM cascade for product quality assess- ment in e-commerce catalogs. In: Proceedings of EMNLP 2025, Industry Track (2025)

    2025

  4. [4]

    arXiv preprint (2026)

    Gao, P., Nikolakopoulos, A., Cheng, Z., Scarinci, A., Batur, A.U., Khan, S.A.: Agentic Prompt Optimization for E-Commerce Catalog Enrichment at Scale. arXiv preprint (2026)

    2026

  5. [5]

    arXiv preprint arXiv:2411.16594 (2025)

    Li, D., Jiang, B., Huang, L., Beigi, A., Zhao, C., Tan, Z., Bhattacharjee, A., Jiang, Y., Chen, C., Wu, T., et al.: From generation to judgment: Opportunities and challenges of llm-as-a-judge. arXiv preprint arXiv:2411.16594 (2025)

    arXiv 2025

  6. [6]

    arXiv preprint arXiv:2411.15594 (2024)

    Gu, J., Jiang, X., Shi, Z., Tan, H., Zhai, X., Xu, C., Li, W., Shen, Y., Ma, S., Liu, H., et al.: A survey on llm-as-a-judge. arXiv preprint arXiv:2411.15594 (2024)

    arXiv 2024

  7. [7]

    In: The Eleventh Interna- tional Conference on Learning Representations (2022)

    Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K.R., Cao, Y.: React: Synergizing reasoning and acting in language models. In: The Eleventh Interna- tional Conference on Learning Representations (2022)

    2022

  8. [8]

    In: Pro- ceedings of EMNLP, pp

    Chiticariu, L., Krishnamurthy, R., Li, Y., Reiss, F., Vaithyanathan, S.: Domain adaptation of rule-based annotators for named-entity recognition tasks. In: Pro- ceedings of EMNLP, pp. 1002–1012 (2010)

    2010

Show all 30 references
  1. [9]

    arXiv preprint (2021)

    Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., Qiu, X.: A unified generative frame- work for various NER subtasks. arXiv preprint (2021)

    2021

  2. [10]

    In: Proceedings of WSDM ’22, pp

    Yang, L., Wang, Q., Yu, Z., Kulkarni, A., Sanghai, S., Shu, B., Elsas, J., Kana- gal, B.: Mave: A product dataset for multi-source attribute value extraction. In: Proceedings of WSDM ’22, pp. 1256–1265 (2022)

    2022

  3. [11]

    In: Proceedings of EMNLP 2025, Industry Track (2025)

    Zhang, B., Khan, S.A., Walter, S.: Leveraging product catalog patterns for multi- lingual e-commerce product attribute prediction. In: Proceedings of EMNLP 2025, Industry Track (2025)

    2025

  4. [12]

    In: KDD 2025 Workshop on LLM4ECommerce (2025)

    Zhang, B., Khan, S.A., Walter, S.: CatalogRAG: Retrieval-guided LLM predic- tion for multilingual e-commerce product attributes. In: KDD 2025 Workshop on LLM4ECommerce (2025)

    2025

  5. [13]

    Zhang, B., Walter, S.: Don’t just translate, summarize too: Cross-lingual product titlegenerationine-commerce.In:LREC-COLING2024Workshopone-Commerce and NLP (2024)

    2024

  6. [14]

    In: Proceedings of the 1st Workshop on Customizable NLP (Cus- tomNLP4U), pp

    Cheng, Z., Zhang, W., Chou, C.-C., Jau, Y.-Y., Pathak, A., Gao, P., Batur, U.: E-commerce product categorization with LLM-based dual-expert classifica- tion paradigm. In: Proceedings of the 1st Workshop on Customizable NLP (Cus- tomNLP4U), pp. 294–304 (2024) CatalogAgent: Self...

    2024

  7. [15]

    Trabelsi, A., Zontak, M., Qian, Y., Jackson, B., Khan, S., Batur, U.: What matters when building vision language models for product image analysis? In: IEEE Winter Conference on Applications of Computer Vision (WACV) Workshops (2025)

    2025

  8. [16]

    Advances in Neural Information Processing Systems36, 68539–68551 (2023)

    Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettle- moyer, L., Cancedda, N., Scialom, T.: Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems36, 68539–68551 (2023)

    2023

  9. [17]

    arXiv preprint arXiv:2205.12255 (2022)

    Parisi, A., Zhao, Y., Fiedel, N.: Talm: Tool augmented language models. arXiv preprint arXiv:2205.12255 (2022)

    2022 arXiv

  10. [18]

    arXiv preprint arXiv:2504.12330 (2025)

    Liu, P., Liu, X., Yao, R., Liu, J., Meng, S., Wang, D., Ma, J.: Hm-rag: Hier- archical multi-agent multimodal retrieval augmented generation. arXiv preprint arXiv:2504.12330 (2025)

    2025 arXiv

  11. [19]

    arXiv preprint arXiv:2410 (2024)

    Cai, S., Zhang, J., Bao, K., Gao, C., Feng, F.: Flow: A feedback loop framework for simultaneously enhancing recommendation and user agents. arXiv preprint arXiv:2410 (2024)

    2024

  12. [20]

    In: Findings of the Association for Computational Linguistics: NAACL 2025, pp

    ShengbinYue, S., Huang, T., Jia, Z., Wang, S., Liu, S., Song, Y., Huang, X.-J., Wei, Z.: Multi-agent simulator drives language models for legal intensive interaction. In: Findings of the Association for Computational Linguistics: NAACL 2025, pp. 6537–6570 (2025)

    2025

  13. [21]

    arXiv preprint arXiv:2501.17167 (2025)

    Hu, Y., Zhou, Q., Chen, Q., Li, X., Liu, L., Zhang, D., Kachroo, A., Oz, T., Tripp, O.: Qualityflow: An agentic workflow for program synthesis controlled by llm quality checks. arXiv preprint arXiv:2501.17167 (2025)

    2025 arXiv

  14. [22]

    In: Forty-first International Conference on Machine Learning (2023)

    Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factual- ity and reasoning in language models through multiagent debate. In: Forty-first International Conference on Machine Learning (2023)

    2023

  15. [23]

    arXiv preprint arXiv:2411.00492 (2024)

    Long, D.X., Yen, D.N., Luu, A.T., Kawaguchi, K., Kan, M.-Y., Chen, N.F.: Multi- expert prompting improves reliability, safety, and usefulness of large language mod- els. arXiv preprint arXiv:2411.00492 (2024)

    2024 arXiv

  16. [24]

    In: NeurIPS (2023)

    Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al.: Self-Refine: Iterative refinement with self-feedback. In: NeurIPS (2023)

    2023

  17. [25]

    In: NeurIPS (2023)

    Shinn,N.,Cassano,F.,Gopinath,A.,Narasimhan,K.,Yao,S.:Reflexion:Language agents with verbal reinforcement learning. In: NeurIPS (2023)

    2023

  18. [26]

    arXiv preprint arXiv:2412.01928 (2024)

    Motwani, S.R., Smith, C., Das, R.J., Rafailov, R., Laptev, I., Torr, P.H.S., Piz- zati, F., Clark, R., de Witt, C.S.: Malt: Improving reasoning with multi-agent llm training. arXiv preprint arXiv:2412.01928 (2024)

    2024

  19. [27]

    In: ICLR (2024)

    Huang, J., Chen, X., Mishra, S., Zheng, H.S., Yu, A.W., Song, X., Zhou, D.: Large language models cannot self-correct reasoning yet. In: ICLR (2024)

    2024

  20. [28]

    In: Proceedings of the AAAI Conference on Artificial Intelli- gence, vol

    He, H., Liu, Q., Xu, L., Shivade, C., Zhang, Y., Srinivasan, S., Kirchhoff, K.: CriSPO: Multi-aspect critique-suggestion-guided automatic prompt optimization for text generation. In: Proceedings of the AAAI Conference on Artificial Intelli- gence, vol. 39, no. 22, pp. 24014–24...

    2025

  21. [29]

    In: ICLR (2024)

    Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., et al.: DSPy: Compiling declarative language model calls into self-improving pipelines. In: ICLR (2024)

    2024

  22. [30]

    arXiv preprint arXiv:2510.04618 (2025) 16 Zhu Cheng et al

    Zhang, Q., Hu, C., Upasani, S., Ma, B., Hong, F., Kamanuru, V., Rainton, J., Wu, C., Ji, M., Li, H., Thakker, U., Zou, J., Olukotun, K.: Agentic context en- gineering: Evolving contexts for self-improving language models. arXiv preprint arXiv:2510.04618 (2025) 16 Zhu Cheng et ...

    2025 arXiv

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.