Pith. sign in

REVIEW 4 major objections 5 minor 64 references

ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A retrieval-augmented chatbot built on a small coding model beats larger proprietary models on niche coding and lifts domain knowledge accuracy.

desk verdict A useful, transparently reported RAG/GraphRAG application for a niche library, with a checkable benchmark-contamination question that should be answered before the Big version's gains are taken at face value. read the letter →

arxiv 2507.05279 v1 pith:WQEMBYAU submitted 2025-07-04 cs.SE cs.AIcs.CLcs.NE

Virgile Boraud (Mnemosyne) , Yannis Bendi-Ouis (Mnemosyne) , Paul Bernard (Mnemosyne) , Xavier Hinaut (Mnemosyne) This is my paper · ORCID
classification cs.SEcs.AIcs.CLcs.NE
keywords Retrieval-AugmentedGenerationknowledgegraphRAGReservoirComputingPycodedebuggingassistanceLLMhallucinationinteractivedocumentation
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 sets out to show that a small language model, given the right external memory, can answer and code in a narrow technical field about as well as far larger general-purpose models. Its vehicle is ReservoirChat, a ChatGPT-style assistant wrapped around the coding model Codestral-22B and fed with the ReservoirPy library's documentation, resolved issues, code samples, and research papers through two retrieval channels: chunked text matched by embedding similarity, and a knowledge graph whose communities are summarized into local answers. On the authors' 20-question test of reservoir-computing knowledge, the largest ReservoirChat version scores 18.66 out of 20, up from 11 for its base model, while on 14 code and debugging questions it scores 8.66, above ChatGPT-4o's 7 and far above Codestral's 5. The authors read this as evidence that retrieval-augmented generation with a knowledge graph reduces hallucination in an underrepresented domain without retraining, making interactive documentation a practical alternative to static docs for specialized libraries.

What carries the argument

The carrying mechanism is the combination of retrieval-augmented generation and a knowledge graph over the same corpus, a pipeline the paper calls GraphRAG. Documents are cut into chunks, embedded with the nomic-embed-text-v1.5 model, and matched to each query by cosine similarity above a 0.75 threshold, with the top matches injected into the model's context alongside conversation history. In parallel, an LLM extracts named entities and relations from the chunks into an undirected knowledge graph, the Leiden algorithm partitions it into thematic communities, and each community is summarized so the assistant can assemble focused local answers. The graph supplies the relational understanding that plain chunk retrieval lacks, and a document of code samples drawn from the library's GitHub repository is added to the retrieval database for coding queries.

What would settle it

Compare ReservoirChat Big against its base model on a fresh set of reservoir-computing questions written after the knowledge base was frozen, or check whether any of the 34 benchmark questions matches a question in the 245-pair Q&A database by embedding similarity. If the advantage over Codestral shrinks or vanishes on non-overlapping questions, the reported improvement is test-set memorization through retrieval, not better domain reasoning.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that augmenting a general coding model with a curated, graph-structured corpus turns it into a reliable specialist. The authors build ReservoirChat by connecting Codestral-22B to a corpus of ReservoirPy material, with retrieval-augmented generation supplying relevant excerpts and a GraphRAG-style knowledge graph supplying relational context: entities are extracted from the same documents, linked into an undirected graph, partitioned into communities, and summarized so answers can draw on connected concepts rather than isolated snippets. The benchmark evidence is the monotone improvement in knowledge answers as documents are added, from 15 up to 18.66 out of 20 against 11 for the bare base model, and code and debug scores of 9.66 and 8.66 that clear ChatGPT-4o's 7 and the base model's 5. The intended conclusion is that hallucination in underrepresented domains such as reservoir computing is best countered by giving the model trustworthy, well-organized references at answer time.

Load-bearing premise

The result only proves better retrieval-based answering if the benchmark questions were not already sitting in the assistant's own question-answer collection, and the paper does not state that they were kept apart.

Editorial extensions

If this is right

  • Because knowledge scores rise from 15 to 18.66 as documents are added, the authors' result implies that corpus curation, not model size, is what drives domain accuracy in this setting.
  • Because the Basic and Little versions reach 9.66 out of 14 on code and debugging while ChatGPT-4o scores 7, the result implies a small open-weight model with retrieval can beat a much larger proprietary model on niche coding.
  • Because the coding score drops for the Medium corpus (7 out of 14) before recovering for Big (8.66), the result implies document composition, not sheer volume, controls code-assistance quality.
  • Because the pipeline needs only embeddings and graph construction rather than training, the result implies interactive documentation can track library updates at low cost and stay current.

Reading between the lines

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

  • The steady climb in knowledge scores with each added document batch hints at a corpus-size scaling curve for graph-RAG question answering; measuring score against document count at finer granularity would give a design rule for future assistants.
  • The coding answers cluster into two correlation blocks, NotebookLM versions with each other and ReservoirChat versions with each other, which suggests retrieval content shapes the pattern of right and wrong answers more than the base model does; swapping the base LLM while keeping the corpus would test that.
  • The paper fixes the retrieval similarity threshold at 0.75 and reports no sensitivity analysis, so a natural extension is to measure answer quality as the threshold and the number of injected chunks vary.
  • The same recipe should transfer to other libraries with small online footprints, where general models hallucinate because their training data contains few examples; the gains on ReservoirPy are a test case for that wider claim.
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

4 major / 5 minor

Summary. The paper presents ReservoirChat, a ChatGPT-style assistant for the ReservoirPy library that combines retrieval-augmented generation (RAG) with a GraphRAG-style knowledge graph built from ReservoirPy documentation, issue-tracker content, code samples, and research papers. Four configurations (Basic, Little, Medium, Big) are compared against Codestral-22B, ChatGPT-4o, Llama3, and NotebookLM on a custom 34-question multiple-choice benchmark: 20 knowledge questions and 14 code/debug questions, each asked three times. The authors report that all ReservoirChat variants outperform the base model Codestral, that ReservoirChat Big is competitive with NotebookLM and ChatGPT-4o on coding tasks, and that adding more documents generally improves knowledge-question accuracy.

Significance. If the results hold, the paper provides a useful demonstration that a modest open-weight model combined with domain-specific RAG and a knowledge graph can serve as a practical documentation assistant for a niche scientific library. The system is publicly deployed, and the code and benchmark are released on GitHub, which supports reproducibility and follow-up work. The comparison against several external models, including NotebookLM with and without domain documents, is a strength. The main limitation is that the evaluation is small (34 questions, three repetitions each), and the headline comparison for the deployed Big model is vulnerable to benchmark contamination unless the 34 benchmark questions are disjoint from the 245 Q&A entries added to the Big knowledge base; the paper does not state that they are.

major comments (4)
  1. [II.C, II.E] The paper never states that the 34 benchmark questions are disjoint from the 245 prepared Q&A entries added to ReservoirChat Big's retrieval database. Section II.C says Big includes "a compilation of 245 prepared questions and answers," and Section II.E describes a benchmark of 20 knowledge and 14 code questions with no exclusion statement. Because answers are generated after retrieving chunks from this database, any overlap would allow Big to answer by near-exact retrieval rather than by reasoning or graph traversal, directly affecting the abstract's claim of "significant improvement over Codestral" for the deployed model. The authors should either demonstrate disjointness from the public repository or remove overlapping questions and re-run the evaluation.
  2. [II.B, II.C] No ablation isolates the knowledge-graph contribution. All ReservoirChat variants add both RAG and GraphRAG relative to the base model, but the paper does not evaluate Codestral with RAG only on the same document corpus. Consequently, the observed gains (e.g., RC-Basic 15/20 versus Codestral 11/20 on knowledge questions) cannot be attributed specifically to the knowledge graph; they may come from ordinary retrieval alone. A RAG-only baseline is needed to make the GraphRAG-specific claim load-bearing.
  3. [III.C, Table II] The percentage improvement figures are internally inconsistent and should be recomputed or explained. For example, RC-Big scores 18.66/20 on knowledge questions and Codestral scores 11/20; the difference is 7.66 points, which is 69.6% of Codestral's score, yet Table II reports 43.54%. The coding comparison (73.20% for RC-Big versus Codestral, i.e., 3.66/5) uses a different basis. The abstract and Section III.C rely on these percentages for the "significant improvement" claim, so they need correction or a clear statement of the normalization formula.
  4. [II.E, III.B] The evaluation is too small to support fine-grained claims such as "Performance improves proportionally with the number of documents added" in Section III.B. With 34 questions and three repetitions, the differences among RC variants are within the range that chance could produce, and no error bars, confidence intervals, or significance tests are reported. Moreover, the code-question scores do not increase monotonically with database size (RC-Basic and RC-Little score 9.66, RC-Medium scores 7, RC-Big scores 8.66), which contradicts the proportional-improvement narrative. The authors should either substantially enlarge the benchmark, report per-question variance and significance tests, or soften the monotonic-scaling claim.
minor comments (5)
  1. [II.A, II.C] Both Section II.A and Section II.C refer to "245 prepared questions" or "245 prepared questions and answers"; please clarify whether these are the same set, since this bears directly on the contamination concern.
  2. [Figures 6 and 7] The results tables in Figures 6 and 7 are difficult to parse in the current rendering; standard tables with explicit column headers and numeric scores would improve readability.
  3. [III.C] The discussion speculates about Codestral's training data ("the model may not have been trained on a lot of data") without supporting evidence; please label this as speculation or remove it.
  4. [IV] The user-interaction examples in Section IV are presented without any correctness assessment; please clarify that they are illustrative only.
  5. [Throughout] There are numerous typos and wording issues, including "ouputs," "aberrant ouputs," "Heatmat," "PERSON correlation," "on can see," and "What is call the readout?"; a careful proofreading pass is needed.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline Big-vs-base comparison is vulnerable to benchmark contamination: the 245 prepared Q&A added to RC-Big are never shown to be disjoint from the 34-question custom benchmark.

  1. fitted input called prediction [Sections II.B (retrieval mechanism), II.C (Big corpus), II.E (benchmark)]
    "Finally, Big includes all previous resources with 10 additional scientific papers, and a compilation of 245 prepared questions and answers, including 55 code-related questions, and 195 about various concepts related to Reservoir Computing and ReservoirPy. The benchmark consisted of 20 knowledge-based questions and 14 coding-related questions, including 8 debugging tasks."

    RC-Big’s retrieval database is seeded with 245 prepared Q&A, while the evaluation is a custom Q&A benchmark written by the same team, and the paper never states that the 34 benchmark questions or their answers were withheld from the 245-item set. Because RAG inserts retrieved chunks into the LLM prompt, any benchmark prompt whose answer text appears in the 245 set is answered by stored-content retrieval, not by the knowledge graph or by reasoning. The knowledge score rises from 15 (Basic, no 245 set) to 18.66 (Big, with 245 set), so part of the claimed improvement over Codestral is potentially forced by corpus construction.

full rationale

The only substantial circularity candidate is the unstated relation between the 245 prepared Q&A in RC-Big’s knowledge base and the 34-question custom benchmark. The paper’s own pipeline description makes the reduction mechanical if overlap exists: retrieved chunks are placed into the prompt, so a stored benchmark answer is reproduced verbatim. No withholding statement is provided, and Section IV even says user data were analyzed for inspiration on benchmark questions, increasing overlap risk. This does not fully collapse the contribution: RC-Basic and RC-Little, which lack the 245 Q&A, also outperform Codestral on knowledge (15/20 vs 11/20) and coding (9.66/14 vs 5/14), so a broader 'retrieval helps' finding is independently supported. Self-citations to ReservoirPy and to the authors’ LLM deployment work are ordinary references and are not load-bearing; no uniqueness theorem, ansatz-by-citation, or renaming pattern is present. Score 6 reflects partial circularity in the central deployed-model comparison rather than a fully self-referential derivation.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper is an empirical system evaluation rather than a derivation, so the ledger lists design choices and assumptions that the performance claim depends on. The most consequential item is the fourth axiom: the benchmark may be inside the retrieval database.

free parameters (3)
  • cosine similarity retrieval threshold = 0.75
    Chosen in Section II.B to filter retrieved chunks; affects which documents enter the LLM context, but not fitted to the benchmark.
  • LLM sampling temperature = 0.1
    Set in Section II.E based on preliminary tests as a compromise near deterministic; influences answer variability and scores.
  • number of retrieved nearest embeddings in simple chatbot = top 5
    Fixed in Section II.A for the embedding-only baseline; not used in the final system, but part of the design evolution.
assumptions (5)
  • domain assumption GraphRAG's entity-relation extraction and Leiden clustering produce a knowledge graph that improves answer accuracy for this technical corpus.
    The paper adopts Microsoft's GraphRAG pipeline (Section II.C) without validating extraction quality on ReservoirPy documents.
  • domain assumption RAG reduces hallucinations when the retrieved documents are accurate and relevant.
    Stated in Section II.B as the motivation for the design; not directly measured in the evaluation.
  • domain assumption The 34-question multiple-choice benchmark is a valid measure of factual accuracy and coding ability for this domain.
    Section II.E describes the benchmark as the most objective option; no validation against human judgments or external benchmarks is provided.
  • ad hoc to paper The benchmark questions were not included in the 245-question knowledge base used by ReservoirChat Big.
    Nowhere stated; Section II.D lists 245 prepared Q&A in Big, while Section II.E introduces the benchmark separately. If the assumption fails, the performance comparison is invalid.
  • domain assumption MCQ scores aggregated over three repetitions can be compared across models despite different temperature controls.
    Section II.E uses temperature 0.1 for local models but notes ChatGPT-4o and NotebookLM do not expose temperature, so stochasticity differs across systems.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy." pith.science (2026). https://pith.science/paper/WQEMBYAU

@misc{pith2026250705279,
  author       = {Pith},
  title        = {Pith review of: ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WQEMBYAU}},
  note         = {Machine review of arXiv:2507.05279}
}
read the original abstract

We introduce a tool designed to improve the capabilities of Large Language Models (LLMs) in assisting with code development using the ReservoirPy library, as well as in answering complex questions in the field of Reservoir Computing. By incorporating external knowledge through Retrieval-Augmented Generation (RAG) and knowledge graphs, our approach aims to reduce hallucinations and increase the factual accuracy of generated responses. The system provides an interactive experience similar to ChatGPT, tailored specifically for ReservoirPy, enabling users to write, debug, and understand Python code while accessing reliable domain-specific insights. In our evaluation, while proprietary models such as ChatGPT-4o and NotebookLM performed slightly better on general knowledge questions, our model outperformed them on coding tasks and showed a significant improvement over its base model, Codestral-22B.

Figures

Figures reproduced from arXiv: 2507.05279 by the authors.

Figure 4
Figure 4. Comparison of training documents used for each ReservoirChat and [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 2
Figure 2. Zoom of the graph. We can see the links between reservoirpy and "SEQUENCETOSEQUENCE MOD "T "JAPANESE VOWELS" "REAL-VALUED CONTINUOUS DATA" "SEED" MODEL.FIT()" "HYPEROPT" "TIMESERIES PREDICTION" "RANDOM SEED" "OUTPUT FEEDBACK" CHOICE [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 6
Figure 6. Results of knowledge questions from the 20 questions benchmark [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figures from the paper (5 more)
Figure 7
Figure 7. Figure 7: Results of code and debug questions from the 14 questions benchmark [PITH_FULL_IMAGE:figures/full_fig_p004_7.png]
Figure 8
Figure 8. Figure 8: Pearson correlation matrix of every model for the knowledge related [PITH_FULL_IMAGE:figures/full_fig_p005_8.png]
Figure 10
Figure 10. Figure 10: Simple code produced by ReservoirChat Big for the prompt “Code me the initialization of a reservoir”. In [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]
Figure 11
Figure 11. Figure 11: Pearson correlation matrix of every model for the whole questions [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 12
Figure 12. Figure 12: Similarity Rate Table between the answers of ReservoirChat [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 46 canonical work pages

  1. [1]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

    2017

  2. [2]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

    arXiv 2023

  3. [3]

    Improving language understanding by generative pre- training,

    A. Radford, “Improving language understanding by generative pre- training,” 2018

    2018

  4. [4]

    Language models are unsupervised multitask learners,

    A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al., “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019

    2019

  5. [5]

    Language mod- els are few-shot learners,

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Language mod- els are few-shot learners,” Advances in neural information processing systems, vol. 33, pp. 1877–1901, 2020

    1901

  6. [6]

    The llama 3 herd of models,

    A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Yang, A. Fan et al. , “The llama 3 herd of models,” arXiv preprint arXiv:2407.21783 , 2024

    arXiv 2024

  7. [7]

    A survey of sustainability in large language models: Applications, economics, and challenges,

    A. Singh, N. P. Patel, A. Ehtesham, S. Kumar, and T. T. Khoei, “A survey of sustainability in large language models: Applications, economics, and challenges,” arXiv preprint arXiv:2412.04782 , 2024

    arXiv 2024

  8. [8]

    The impact of ai on developer productivity: Evidence from github copilot,

    S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer, “The impact of ai on developer productivity: Evidence from github copilot,” arXiv preprint arXiv:2302.06590, 2023

    arXiv 2023

Show all 64 references
  1. [9]

    Evaluating large language models trained on code,

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y . Burda, N. Joseph, G. Brockman et al., “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021

    2021 arXiv

  2. [10]

    A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

    L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin et al. , “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” arXiv preprint arXiv:2311.05232 , 2023

    2023 arXiv

  3. [11]

    The “echo state

    H. Jaeger, “The “echo state” approach to analysing and training recur- rent neural networks-with an erratum note,” Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, vol. 148, no. 34, p. 13, 2001

    2001

  4. [12]

    Gpt-4o system card,

    A. Hurst, A. Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford et al., “Gpt-4o system card,” arXiv preprint arXiv:2410.21276 , 2024

    2024 arXiv

  5. [13]

    Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast,

    T. Fert ´e and et al., “Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast,” in ICML’24: Proceedings of the 41st International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 235...

    2024

  6. [14]

    Reservoirpy: an efficient and user-friendly library to design echo state networks,

    N. Trouvain, L. Pedrelli, T. T. Dinh, and X. Hinaut, “Reservoirpy: an efficient and user-friendly library to design echo state networks,” in International Conference on Artificial Neural Networks. Springer, 2020, pp. 494–505

    2020

  7. [15]

    Create efficient and complex reservoir computing architectures with reservoirpy,

    N. Trouvain, N. Rougier, and X. Hinaut, “Create efficient and complex reservoir computing architectures with reservoirpy,” in International Conference on Simulation of Adaptive Behavior . Springer, 2022, pp. 91–102

    2022

  8. [16]

    reservoirpy: A Simple and Flexible Reservoir Computing Tool in Python,

    N. Trouvain and X. Hinaut, “reservoirpy: A Simple and Flexible Reservoir Computing Tool in Python,” Jun. 2022, working paper or preprint

    2022

  9. [17]

    Which hype for my new task? hints and random search for echo state networks hyperparameters,

    X. Hinaut and N. Trouvain, “Which hype for my new task? hints and random search for echo state networks hyperparameters,” in Interna- tional Conference on Artificial Neural Networks . Springer, 2021, pp. 83–97

    2021

  10. [18]

    Nomic embed: Training a reproducible long context text embedder,

    Z. Nussbaum, J. X. Morris, B. Duderstadt, and A. Mulyar, “Nomic embed: Training a reproducible long context text embedder,” arXiv preprint arXiv:2402.01613, 2024

    2024 arXiv

  11. [19]

    Retrieval-augmented generation for knowledge- intensive nlp tasks,

    P. Lewis and et al., “Retrieval-augmented generation for knowledge- intensive nlp tasks,” in Advances in Neural Information Processing Systems, H. Larochelle and et al., Eds., vol. 33. Curran Associates, Inc., 2020, pp. 9459–9474

    2020

  12. [20]

    Retrieval-augmented generation for large language models: A survey,

    Y . Gao, Y . Xiong, X. Gao, K. Jia, J. Pan, Y . Bi, Y . Dai, J. Sun, and H. Wang, “Retrieval-augmented generation for large language models: A survey,” arXiv preprint arXiv:2312.10997 , 2023

    2023 arXiv

  13. [21]

    Codestral: Hello, world,

    A. Mistral, “Codestral: Hello, world,” Section: news, 2024

    2024

  14. [22]

    Knowl- edge graph completion: A review,

    Z. Chen, Y . Wang, B. Zhao, J. Cheng, X. Zhao, and Z. Duan, “Knowl- edge graph completion: A review,” Ieee Access , vol. 8, pp. 192 435– 192 456, 2020

    2020

  15. [23]

    From local to global: A graph rag approach to query-focused summarization,

    D. Edge and et al., “From local to global: A graph rag approach to query-focused summarization,” 2024

    2024

  16. [24]

    From louvain to leiden: guaranteeing well-connected communities,

    V . A. Traag, L. Waltman, and N. J. van Eck, “From louvain to leiden: guaranteeing well-connected communities,” Scientific Reports , vol. 9, no. 1, Mar. 2019

    2019

  17. [25]

    Introducing notebooklm,

    Google, “Introducing notebooklm,” Section: news, 2024

    2024

  18. [26]

    Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,

    G. Team, P. Georgiev, V . I. Lei, R. Burnell, L. Bai, A. Gulati, G. Tanzer, D. Vincent, Z. Pan, S. Wang et al., “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,” arXiv preprint arXiv:2403.05530, 2024

    2024 arXiv

  19. [27]

    The effect of sampling temperature on prob- lem solving in large language models,

    M. Renze and E. Guven, “The effect of sampling temperature on prob- lem solving in large language models,”arXiv preprint arXiv:2402.05201, 2024

    2024 arXiv

  20. [28]

    Mistral 7b,

    A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier et al., “Mistral 7b,” arXiv preprint arXiv:2310.06825 , 2023

    2023 arXiv

  21. [29]

    Mixtral of experts,

    A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bam- ford, D. S. Chaplot, D. d. l. Casas, E. B. Hanna, F. Bressand et al. , “Mixtral of experts,” arXiv preprint arXiv:2401.04088 , 2024

    2024 arXiv

  22. [30]

    Deploying open-source large language models: A performance analysis,

    Y . Bendi-Ouis, D. Dutarte, and X. Hinaut, “Deploying open-source large language models: A performance analysis,” arXiv preprint arXiv:2409.14887, 2024. ACKNOWLEDGMENTS We thank the following grants: the Inria AEx BrainGPT project (https://www.inria.fr/en/braingpt) and the ANR...

    2024 arXiv

  23. [31]

    A place where anything is kept in store

    What is an echo state network? • A. A place where anything is kept in store. • B. A recurrent neural network in which usually only the output neurons are trained. • C. A black box component that receives an input signal to be read out and mapped by another process. • D. A larg...

  24. [32]

    A machine learning paradigm for timeseries

    What is Reservoir Computing? • A. A machine learning paradigm for timeseries. • B. The construction of a computer model of a petroleum reservoir. • C. A branch of fluid dynamics that studies the design of tank trucks to reduce rollover accidents. • D. It is only a computationa...

  25. [33]

    A linear regression that models the Markovian process of a support vector machine

    What is call the readout? (in the context of Reservoir Computing) • A. A linear regression that models the Markovian process of a support vector machine. • B. An efficient way for input processing in order to read only part of the inputs. • C. A black box component that receiv...

  26. [34]

    Video segmentation

    On which task is Reservoir Computing known to compete with the state-of-the-art? • A. Video segmentation. • B. Natural language processing. • C. Image recognition. • D. Chaotic timeseries prediction. Correct: D

  27. [35]

    A task with small univariate dataset

    On which task is it challenging to apply Reservoir Com- puting compared to other state-of-the-art methods? • A. A task with small univariate dataset. • B. Video segmentation. • C. Chaotic timeseries prediction. • D. Classification of timeseries with few classes. Correct: B

  28. [36]

    Around 1 thousand neurons

    Approximately how many neurons are used inside an echo state network? • A. Around 1 thousand neurons. • B. Around 100 thousand neurons. • C. Around 10 neurons. • D. Around 1 million neurons. Correct: A

  29. [37]

    Reduce the computational cost

    What is the purpose of using ridge regression instead of linear regression for the readout? • A. Reduce the computational cost. • B. Improve numerical stability. • C. Improve explainability of the model. • D. Avoid the exploding/vanishing gradient problem. Correct: B

  30. [38]

    They can be randomly initialized and then scaled to have a specific spectral radius

    How are the weights most often initialized in an echo state network? • A. They can be randomly initialized and then scaled to have a specific spectral radius. • B. They are tuned according to an autocorrelation Heb- bian learning rule. • C. Trained using a linear regression. •...

  31. [39]

    Reservoir Computing is a type of recurrent neural network model based on the philosophy of the echo state network

    What is the difference between ‘echo state network’ and ‘Reservoir Computing’? • A. Reservoir Computing is a type of recurrent neural network model based on the philosophy of the echo state network. • B. There is no difference, we can use the terms Reservoir Computing and echo...

  32. [40]

    No, an echo state network is not even a form of Reservoir Computing

    Are there other forms of Reservoir Computing than echo state networks? • A. No, an echo state network is not even a form of Reservoir Computing. • B. Yes, any random kernel method, even those who don’t apply to timeseries, are considered to be a form of Reservoir Computing. • ...

  33. [41]

    An architecture in which the reservoir part is a pool of spiking neurons

    What is a liquid state machine? • A. An architecture in which the reservoir part is a pool of spiking neurons. • B. A physical reservoir in which the reservoir is a reservoir of liquid, usually water. • C. Liquid state machine and Reservoir Computing desig- nate the same conce...

  34. [42]

    Because it is common to add noise (and thus chaos) to the reservoir in order to stabilize its activity

    Why is it called ‘computing at the edge of chaos’? • A. Because it is common to add noise (and thus chaos) to the reservoir in order to stabilize its activity. • B. Because Reservoir Computing works best for chaotic timeseries forecasting. • C. Because Reservoir Computing ofte...

  35. [43]

    The echo state property allows the reservoir to operate with chaotic behavior to enhance computational power

    What is the ‘echo state property’? • A. The echo state property allows the reservoir to operate with chaotic behavior to enhance computational power. • B. The ability to memorize past activity and cycle over it (the echo). • C. The ability to perfectly reconstruct any input si...

  36. [44]

    The spectral radius of the recurrent weight matrix, the leak rate, the input scaling

    What are some of the most important hyper-parameters? • A. The spectral radius of the recurrent weight matrix, the leak rate, the input scaling. • B. The spectral radius of the recurrent weight matrix, the reservoir connectivity, the weight distribution. • C. The spectral radi...

  37. [45]

    How explainable are Reservoir Computing models? • A. Reservoir Computing models are generally less ex- plainable due to their reliance on complex, nonlinear dynamics within the reservoir, making it difficult to trace the exact path of information processing. • B. Reservoir Com...

  38. [46]

    The results between two differently initialized reser- voirs are completely unpredictable and random, regard- less of the input data

    To what extent do the results vary between two differently initialized reservoirs? • A. The results between two differently initialized reser- voirs are completely unpredictable and random, regard- less of the input data. • B. The results between two differently initialized re...

  39. [47]

    The real spectral radius of the matrix W, that is always a bit different from the specified spectral radius

    What is the effective spectral radius? • A. The real spectral radius of the matrix W, that is always a bit different from the specified spectral radius. • B. A weighted sum of all eigenvalues norms, that takes into account the distribution of the spectrum. • C. A value that ha...

  40. [48]

    What is a deep reservoir? • A. A deep reservoir is a Reservoir Computing architecture that consists of multiple layers of interconnected reser- voirs, allowing for hierarchical processing and the capture of more complex temporal dynamics. • B. An underground gas or petroleum r...

  41. [49]

    An orthogonal matrix can be represented in a con- densed form, improving matrix multiplication computa- tion time

    What is the use of an orthogonal matrix in the reservoir equation? • A. An orthogonal matrix can be represented in a con- densed form, improving matrix multiplication computa- tion time. • B. An orthogonal matrix in the reservoir equation is used to prevent any interaction bet...

  42. [50]

    A conceptor is a mathematical function used to compress the data within a reservoir, reducing its dimen- sionality for faster processing

    What is a Conceptor? • A. A conceptor is a mathematical function used to compress the data within a reservoir, reducing its dimen- sionality for faster processing. • B. A conceptor is a hardware component that accelerates the computation of reservoir dynamics by offloading cal...

  43. [51]

    I want to train my echo state network on multiple timeseries that have different lengths. I have seen in the documentation that you can put a 3D numpy array with shape (timeseries, timesteps, dimensions), but it wouldn’t work in my case as the timeseries have different lengths...

  44. [52]

    uniform

    Make me a reservoir, with 1000 neurons, and with a uniform distribution of weights, and a sparsity of 95%. • A. from reservoirpy as rpy reservoir = rpy.nodes.Reservoir(neurons=1 _000, connectivity=0.05, weights=" uniform") • B. from reservoirpy as rpy reservoir = rpy.nodes.Res...

  45. [53]

    1"), Reservoir(100, name=

    Create a model in which there are several reservoirs connected in a chain, and a readout at the end. • A. from reservoirpy.nodes import Reservoir, Ridge model = [Reservoir(100, name="1"), Reservoir(100, name="2"), Reservoir(100, name="3"), Reservoir(100, name="4"), Reservoir(1...

  46. [54]

    backend",

    Write me an echo state network that can efficiently use the many CPU cores my machine has. • A. import reservoirpy as rpy rpy.set_param("backend", "parallel") from reservoirpy.nodes import ESN model = ESN(units=100) model.fit(train_data, train_data) • B. from reservoirpy.utils...

  47. [55]

    R1") >> Ridge (name=

    I have a model with several trainable readouts inside as such: from reservoirpy.nodes import Reservoir, Ridge model = Reservoir(100, name="R1") >> Ridge (name="readout1") model >>= Reservoir(100, name="R2") >> Ridge(name="readout2") model >>= Reservoir(100, name="R3") >> Ridge...

  48. [56]

    How can I do that in ReservoirPy? • A

    I have a NumPy array X of shape (timeseries, timesteps, dimensions) and I want to classify them according to my Y array of shape (timeseries, ) which contains numbers from 0 to 9 according to the class the timeseries belongs to. How can I do that in ReservoirPy? • A. from rese...

  49. [57]

    Calling .fit on a model erases the previous trained results

    Here is my code: from reservoirpy.nodes import Reservoir, Ridge model = Reservoir(units=200, lr=0.2, sr =1.0) >> Ridge(ridge=1e-4) for x_series, y_series in zip (X_train, Y_train): model.fit(x_series, y_series, warmup =10) y_pred = model.run(X_test[0]) Is that correct? • A. Ca...

  50. [58]

    What’s wrong? • A

    Here is my code: from reservoirpy.nodes import Reservoir, Ridge model = Reservoir(units=200, lr=0.2, sr =1.0, iss=0.2) >> Ridge(ridge=1e-4) model.fit(X_train, Y_train, warmup=200) Y_pred = model.run(X_test) I have an error. What’s wrong? • A. iss is not a parameter. For scalin...

  51. [59]

    What’s wrong? • A

    Here is my code: from reservoirpy.nodes import Reservoir, RLS model = Reservoir(units=200, lr=0.2, sr =1.0) >> RLS(alpha=1e-4) for x_series, y_series in zip (X_train, Y_train): model.fit(x_series, y_series, warmup =10) y_pred = model.run(X_test[0]) I have an error. What’s wron...

  52. [60]

    readout" ) path1, path2 = R1 >> R6, R2 >> R5 path3 = Input(name=

    Here’s my code: from reservoirpy.nodes import Input, Output, Reservoir, Ridge R1 = Reservoir(100, lr=0.01, sr=1.) R2 = Reservoir(100, lr=0.03, sr=1.) R3 = Reservoir(100, lr=0.09, sr=1.) R4 = Reservoir(100, lr=0.3, sr=1.) R5 = Reservoir(100, lr=0.9, sr=1.) R6 = Reservoir(100, l...

  53. [61]

    By calling the method .fit, the readout forgets its previous training

    Is this the correct usage of the method partial_fit? reservoir, readout = Reservoir(100, sr=1), Ridge(ridge=1e-8) for x, y in zip (X, Y): states = reservoir.run(x) readout.partial_fit(states, y) readout.fit() model = reservoir >> readout • A. By calling the method .fit, the re...

  54. [62]

    No, you can connect the readout to the reservoir in order to loop the results back as an input after training: readout >> reservoir

    Here is my code: reservoir, readout = Reservoir(100, sr=1), Ridge(ridge=1e-8) model = reservoir >> readout model.fit(X[:800], Y[:800], warmup=10) steps = 1000 results = np.zeros((steps, 1)) last_output = X[800] for i in range (steps): last_output = model(last_output) results[i...

  55. [63]

    Here is my code: weights = np.random.choice([1, -1], p =[0.6, 1 - 0.6], replace=True, size =(200, 200)) reservoir = Reservoir(W=weights, sr=0.9, lr=0.6) I created my reservoir this way, but it seems the reservoir has a very chaotic behavior, even though the spectral radius is ...

  56. [64]

    readout" ) path1, path2 = R1 >> R6, R2 >> R5 path3 = Input(name=

    Here’s my code: from reservoirpy.nodes import Input, Output, Reservoir, Ridge R1 = Reservoir(100, lr=0.01, sr=1.) R2 = Reservoir(100, lr=0.03, sr=1.) R3 = Reservoir(100, lr=0.09, sr=1.) R4 = Reservoir(100, lr=0.3, sr=1.) R5 = Reservoir(100, lr=0.9, sr=1.) R6 = Reservoir(100, l...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.