Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems

Aniruddha Chattopadhyay; Kaushik Roy; Raj Dandekar

REVIEW 4 major objections 4 minor 18 references

Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems

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

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

Pith's one-line read The paper argues that instruction-tuned large language models are model-grounded symbolic systems, and that a critique-driven prompt-refinement loop called metatuning improves their mathematical reasoning accuracy over zero-shot prompting.

desk verdict The paper's own experiment does not implement its algorithm: the 'metatuning' condition is few-shot in-context learning from gold solutions, so the central claim is not empirically identified. read the letter →

arxiv 2507.09854 v1 pith:GXEWHMYO submitted 2025-07-14 cs.AI

Aniruddha Chattopadhyay , Raj Dandekar , Kaushik Roy This is my paper · ORCID

classification cs.AI

keywords neurosymbolicAIlargelanguagemodelssymbolgroundingmetatuningpromptrefinementin-contextlearningmathematicalreasoningsampleefficiency

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

This paper reinterprets instruction-tuned large language models as 'model-grounded symbolic AI systems': natural language is the symbolic layer, and symbols are grounded in the model's internal vector space rather than in external referents. Within that framing, the authors propose metatuning, a learning procedure in which an external judge critiques the model's errors on a training set and the corrections are accumulated into the prompt and chat context, mirroring gradient descent without weight updates. They test this procedure on a 100-problem subsample of the MATH500 dataset with GPT-4o and Gemini-1.5-Flash, comparing zero-shot accuracy against accuracy after metatuning. The reported gains are positive but uneven: up to +5.56 points for GPT-4o and +6.67 points for Gemini at a train-context size of 10, with flat or negative results at some larger context sizes. The paper's contribution is the conceptual reframing plus an initial empirical probe, not a definitive demonstration.

What carries the argument

The central mechanism is metatuning, defined as the iterative refinement of an LLM's prompt and context through critique-driven symbolic feedback. In Algorithm 1, the model generates an answer, a Judge.evaluate step scores it, a correction generator turns the critique into refined prompts, additional examples, or logical explanations, and the model's prompt context is updated accordingly. This accumulated 'solution-infused chat history' plays the role of a gradient or epoch in conventional training: it concentrates learning on the mistakes the model actually made, which the paper argues is a form of active or curriculum learning.

What would settle it

Run a few-shot control that receives the same solution-infused chat history as the metatuned condition—the same solved problems and correct answers—but without the iterative judge-critique loop, and compare accuracies on the same test split; if the few-shot control matches or exceeds the metatuned accuracy, the claim that the critique-driven refinement drives the improvement is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, an instruction-tuned LLM is already a model-grounded symbolic system: words are discrete symbols processed by a neural network, and their meaning lives as regions or directions in the model's learned representation space. 'Learning' in such a system need not mean updating weights; it can mean refining a task-functionality state consisting of the prompt plus a structured memory of critiques. The paper's metatuning loop works as follows: generate an output, have an external judge LLM evaluate it, convert that feedback into natural-language corrections, and fold the corrections into the context window for subsequent attempts. Empirically, the authors claim that this improves reasoning reliability and sample efficiency on axiomatic deductive reasoning tasks, and their experiments on a 100-problem subsample of MATH500 show that the largest gains appear at a train-context size of 10, with diminishing or negative returns at larger context sizes.

Load-bearing premise

The load-bearing premise is that the accuracy gains come from the critique-driven refinement process itself, and not merely from having extra worked examples in the prompt; the experiments do not compare against a control that receives the same examples without the judge's critiques.

Editorial extensions

If this is right

If metatuning works as claimed, in-context learning can be redescribed as a training procedure with train/test splits, epochs, gradient accumulation, and model saving and loading, giving prompt engineering a systematic vocabulary.
Because the corrections are natural-language and non-differentiable, the approach offers a route to inject symbolic rules or expert feedback into an LLM without backpropagation, which matters for black-box or non-differentiable systems.
The largest gains at small context sizes suggest that a few well-chosen corrected examples can deliver much of the benefit, supporting the paper's sample-efficiency claim for axiomatic deduction tasks.
The judge's fidelity is load-bearing: the paper states that if the judge's evaluations lack fidelity, the metatuning process becomes unreliable, so judge quality bounds the method's ceiling.

Reading between the lines

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

We infer that the reported gains may be an in-context learning effect rather than a critique-specific effect, because no few-shot control presents the same solved examples without the judge loop; a testable prediction is that such a control matches the metatuned accuracy.
We infer that the prompt-gradient analogy could be formalized into a 'symbolic gradient descent' whose convergence conditions depend on judge error rates, an analysis the paper leaves implicit.
We infer the metatuning loop transfers to any task with an external verifier, such as code debugging or plan validation, though the paper demonstrates only math word problems.
We infer from the non-monotonic accuracy pattern that context-length interference, not just correction quality, shapes the outcome; this could be isolated by ablating the order and length of injected context.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The headline claim of this paper is not actually tested by its own experiment. Section 5 describes feeding the model a 'solution-infused chat history' built from gold answers and solutions for the training split, then testing on the rest. That is standard few-shot in-context learning from correct exemplars; the judge is used only to grade outputs, not to produce corrective feedback, and there is no iterative loop as in Algorithm 1. So the reported gains—when they appear—are fully explained by a known in-context learning effect. The stress-test note holds up on reading.

What is genuinely here is a cleanly written conceptual framing: treating instruction-tuned LLMs as symbolic systems grounded in internal vector spaces, with prompt/context updates as a kind of training. The authors are also honest about the lack of convergence guarantees and the dependence on judge quality (Section 4). That discussion is reasonable and might be useful to someone thinking about LLM prompting through a neurosymbolic lens.

The empirical part is the soft spot, and it is load-bearing. Only 100 problems from MATH500, no repeated splits, no error bars, and deltas like +5.56% are a handful of items. There is no few-shot control, so the zero-shot baseline comparison cannot separate the claimed metatuning effect from simply adding exemplars. Table 2 mislabels five different context sizes as '5' and shows different totals, which suggests careless data handling. No code or data is provided, so reproducibility is limited.

My take: the conceptual discussion might justify a position paper or workshop note, but as a research contribution the central claim is not empirically identified. I would not cite this in my own work, and I don't think it deserves a full referee process in its current form. If the authors reran the experiment with a true judge-driven loop and compared against a matched few-shot baseline on a larger sample with error bars, the question would be interesting.

Referee Report

4 major / 4 minor

Summary. The paper proposes reinterpreting instruction-tuned LLMs as 'model-grounded symbolic AI systems' in which natural-language prompts act as symbolic representations grounded in the model's vector space. It introduces 'metatuning,' an iterative procedure (Algorithm 1) in which an external judge critiques model outputs and the resulting natural-language feedback is used to revise the prompt/context, analogized to gradient-based learning. The authors report experiments on a 100-problem subsample of the MATH500 dataset, comparing zero-shot performance with performance after 'metatuning' for GPT-4o and Gemini-1.5-Flash. The empirical results show small positive, zero, or negative accuracy deltas depending on model and train context size. The paper also includes illustrative pre/post reasoning traces in Appendix A.

Significance. The conceptual framing is potentially interesting for the neurosymbolic AI literature: treating natural-language prompts as a differentiable-free optimization channel with a black-box judge is a coherent and reasonably novel perspective, and the authors are candid about its lack of convergence guarantees (Section 4). The paper's strengths include a clear algorithm sketch, explicit acknowledgement that judge quality is critical, and transparent presentation of example reasoning traces. However, the claimed empirical validation ('Demonstrate improved reasoning reliability, adaptability, and sample efficiency,' Contributions) is not supported by the reported experiments, because the protocol does not implement the proposed iterative judge-driven loop and lacks even a few-shot control. As it stands, the paper is a conceptual position piece with preliminary, confounded observations rather than a validated learning method.

major comments (4)

[Section 5] The experimental protocol does not implement Algorithm 1. The text states: 'For the training set, we identify instances where the LLM's initial responses were incorrect. For these incorrect cases, we construct a solution-infused chat history by incorporating the correct answers and their corresponding solutions. This enriched context is then provided to the model during inference on the test set.' There is no iterative refinement loop, no judge-generated critique is used as a corrective signal, and no prompt revision is triggered by evaluation. The condition labeled 'With Metatuning' therefore differs from the zero-shot baseline only by the presence of correct problem-solution pairs in the context, which is the textbook setting for few-shot in-context learning. The central claim that critique-driven metatuning improves reasoning is not empirically identified.
[Section 5.1, Tables 1 and 2] There is no few-shot control. The baseline is zero-shot prompting, while the metatuned condition receives solution-infused chat history. To attribute any gain to the metatuning loop (as opposed to standard in-context learning from additional exemplars), the authors would need to compare against few-shot prompting with the same number of exemplars, ideally matched by difficulty or selection method. Reported deltas (e.g., +5.56% on 90 test items at context size 10 in Table 1) are equivalent to about five additional correct answers and are within plausible sampling noise.
[Table 2] The table is internally inconsistent: all five rows are labeled 'Train Context Size 5' yet contain different totals (95, 90, 80, 70, 60 correct+incorrect), while the surrounding text (Section 5.2) discusses results at context sizes 5, 10, 20, 30, 40. Similarly, Table 1's totals shrink as train context size grows, so percentages are computed on different denominators and deltas are not comparable across rows. The experimental results as reported are not reproducible and need to be corrected before any substantive claim can be assessed.
[Sections 4 and 5.2] The external judge is load-bearing in Algorithm 1, and the authors state that 'if the Judge LLM's evaluations lack high fidelity, the entire metatuning process becomes unreliable.' However, no validation of the judge against ground truth is provided, no agreement statistics are reported, and the judge is also used to grade the dependent variable (accuracy). Given that the measured deltas are small, judge measurement error alone could account for the observed differences. Furthermore, Section 5.2's claim that 'metatuning improves the accuracy of both models in most cases' is contradicted by Table 1, where GPT-4o shows positive deltas only at context sizes 5 and 10, zero at 20 and 40, and negative at 30.

minor comments (4)

[Section 5] The phrase 'using the Maths 500 Dataset' should be 'MATH500 dataset,' and the word 'difficulity' is misspelled.
[Figures] Section 5 references 'Figure 5' for example problems, but the examples are shown in Figure 2; Section 5 also says 'as illustrated in Figure 3' for the workflow, and Appendix A references Figure 4 for the improvement distribution. The figure numbering needs to be reconciled throughout.
[Appendix A, Level 2] The zero-shot solution is labeled '256,' but the zero-shot reasoning concludes 'the magnitude is 16,' and the final line of the reasoning admits 'There was an error... The magnitude is 16.' This internally inconsistent example undermines the appendix's illustrative purpose.
[General] There are numerous grammatical errors (e.g., 'intrinstic', 'sufficent', 'inferenced', 'it's prompt is revised'); a careful copyedit is needed. Also, the MATH500 dataset and the term 'metatuning' are not formally cited or defined in the references.

Circularity Check

0 steps flagged · score 0.0 of 10

No construction-level circularity found: the framework is an interpretive reframing, and the measured accuracies are not forced by construction to equal their inputs.

full rationale

The paper does not contain a derivation chain in which an output quantity is defined in terms of the quantity it is supposed to predict, nor does it fit a parameter and then relabel that fit as a prediction. The central conceptual move—treating instruction-tuned LLMs as model-grounded symbolic systems—is explicitly presented as a reinterpretation rather than as a theorem or first-principles result. The only quantitative results are the MATH500 accuracy comparisons in Tables 1 and 2; those accuracies are measured on held-out test items, and the metatuned condition is constructed from train-set correct answers and solutions rather than test-set answers, so the reported numbers are not forced by construction to equal the inputs. The more serious issue is the mismatch between Algorithm 1's judge-driven iterative correction loop and the Section 5 protocol, which replaces judge-generated corrections with gold-solution exemplars; this undermines the attribution of the gains to metatuning and leaves few-shot in-context learning as a viable alternative explanation, but that is an experimental identification confound rather than circularity. The one self-citation (Sheth, Pallagani, and Roy 2024) appears in the introduction as background and is not load-bearing. No uniqueness theorem or ansatz is imported from the authors' prior work. Therefore no circular step can be exhibited with a quote and a specific reduction, and the circularity score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 2 invented entities

The framework rests on three domain assumptions (natural language as symbolic layer, vector-space grounding as sufficient, reliable judge) and introduces two conceptual entities (the model-grounded symbolic system and the task functionality state). The only free parameter is the train context size, which is scanned rather than fit. No external falsifiable handles are provided for the invented entities.

free parameters (1)

Train context size (number of gold-solution exemplars) = 5, 10, 20, 30, 40
Hyperparameter scanned in experiments; accuracy changes non-monotonically with it (GPT-4o improves at 10, regresses at 30), so results depend on this hand-chosen value.

assumptions (3)

domain assumption Natural language can serve as a symbolic layer equivalent to formal symbolic systems for the purpose of learning and reasoning.
Section 2.1 asserts natural language is already a symbolic representation; this premise underpins the entire framework.
domain assumption Grounding symbols in the model's vector space is sufficient to give them meaning for task performance.
Section 2.2 argues symbol grounding reduces to aligning symbols with vector-space directions; this is a contested philosophical claim used to justify the framework.
domain assumption The judge LLM (Gemini 2.0 Flash) provides accurate and unbiased error feedback.
Section 5 states a high-fidelity judge is essential; the paper does not measure judge accuracy on the MATH500 subsample.

invented entities (2)

Model-grounded symbolic AI system
purpose: A conceptual label for instruction-tuned LLMs that treats prompts as symbols grounded in internal vector spaces.
The paper introduces this as a new perspective but provides no falsifiable handle outside its own experiments.
Task functionality state
purpose: A prompt plus structured memory of critiques, claimed to be the analog of learned model parameters.
Defined in Section 1 as the state refined by metatuning; it is a bookkeeping construct, not independently observable.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems." pith.science (2026). https://pith.science/paper/GXEWHMYO

@misc{pith2026250709854,
  author       = {Pith},
  title        = {Pith review of: Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GXEWHMYO}},
  note         = {Machine review of arXiv:2507.09854}
}

read the original abstract

Neurosymbolic artificial intelligence (AI) systems combine neural network and classical symbolic AI mechanisms to exploit the complementary strengths of large scale, generalizable learning and robust, verifiable reasoning. Numerous classifications of neurosymbolic AI illustrate how these two components can be integrated in distinctly different ways. In this work, we propose reinterpreting instruction tuned large language models as model grounded symbolic AI systems where natural language serves as the symbolic layer and grounding is achieved through the models internal representation space. Within this framework, we investigate and develop novel learning and reasoning approaches that preserve structural similarities to traditional learning and reasoning paradigms. Preliminary evaluations across axiomatic deductive reasoning procedures of varying complexity provide insights into the effectiveness of our approach in improving learning efficiency and reasoning reliability.

Figures

Figures reproduced from arXiv: 2507.09854 by the authors.

**Figure 2.** Dataset Examples 8 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Workflow for Evaluating Metatuning on MATH500 [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Metatuning Improvement at 10 problems context training [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 10 canonical work pages

[2]

Blank and Steven T

Idan A. Blank and Steven T. Piantadosi. Symbols and grounding in large language models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381 0 (2237): 0 20220041, 2023. doi:10.1098/rsta.2022.0041. URL https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0041

arXiv 2023
[3]

A framework for neurosymbolic robot action planning using large language models

Alessio Capitanelli and Fulvio Mastrogiovanni. A framework for neurosymbolic robot action planning using large language models. Frontiers in Neurorobotics, 18: 0 1342786, 2024. doi:10.3389/fnbot.2024.1342786. URL https://www.frontiersin.org/articles/10.3389/fnbot.2024.1342786/full

arXiv 2024
[4]

An overview of using large language models for the symbol grounding problem

Hiu Fung Chan, Zhiwei Lin, Wenlong Li, Zhiyuan Chen, Zhiyuan Liu, and Juanzi Li. An overview of using large language models for the symbol grounding problem. In Proceedings of the 1st International Joint Conference on Learning and Reasoning (IJCLR 2023), volume 3644, pages 41--52, 2023. URL https://ceur-ws.org/Vol-3644/IJCLR2023_paper_41_New.pdf

work page 2023
[5]

Colelough and William Regli

Brandon C. Colelough and William Regli. Neuro-symbolic ai in 2024: A systematic review. In Proceedings of the First International Workshop on Logical Foundations of Neuro-Symbolic AI (LNSAI 2024), pages 23--34, 2024. URL https://ceur-ws.org/Vol-3819/paper3.pdf

work page 2024
[6]

Large language models are neurosymbolic reasoners

Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, and Jun Wang. Large language models are neurosymbolic reasoners. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/aaai.v38i16.29754. URL https://doi.org/10.1609/aaai.v38i16.29754

doi:10.1609/aaai.v38i16.29754 2024
[7]

Automated curriculum learning for neural networks

Alex Graves, Marc G Bellemare, Jacob Menick, R \'e mi Munos, and Koray Kavukcuoglu. Automated curriculum learning for neural networks. In Proceedings of the 34th International Conference on Machine Learning, pages 1311--1320. PMLR, 2017

work page 2017
[8]

Pragmatic norms are all you need -- why the symbol grounding problem does not apply to LLM s

Reto Gubelmann. Pragmatic norms are all you need -- why the symbol grounding problem does not apply to LLM s. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11663--11678, Miami, Florida, USA, November 2024. Association for Computational Linguistics. doi:10.18653/v1/2024.emnlp-main.651. URL https://aclantho...

doi:10.18653/v1/2024.emnlp-main.651 2024
[9]

The symbol grounding problem

Stevan Harnad. The symbol grounding problem. Physica D: Nonlinear Phenomena, 42 0 (1-3): 0 335--346, 1990. doi:10.1016/0167-2789(90)90087-6. URL https://doi.org/10.1016/0167-2789(90)90087-6

doi:10.1016/0167-2789(90)90087-6 1990

Show all 18 references

[10]

Training neural networks using the property of negative feedback to inverse a function

Md Munir Hasan and Jeremy Holleman. Training neural networks using the property of negative feedback to inverse a function. arXiv preprint arXiv:2103.14115, 2021

2021 arXiv
[11]

Rabe, Sarah M

Dennis Lee, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, and Kshitij Bansal. Mathematical reasoning in latent space. arXiv preprint arXiv:1909.11851, 2019. URL https://arxiv.org/abs/1909.11851

1909 arXiv
[12]

Krakauer

Melanie Mitchell and David C. Krakauer. The debate over understanding in ai's large language models. Proceedings of the National Academy of Sciences, 120 0 (13): 0 e2215907120, 2023. doi:10.1073/pnas.2215907120. URL https://www.pnas.org/doi/10.1073/pnas.2215907120

2023 doi
[13]

ART : Automatic multi-step reasoning and tool-use for large language models

Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. ART : Automatic multi-step reasoning and tool-use for large language models. In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023. ...

2023 arXiv
[14]

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 Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. ToolLLM : Facilitating large language models to ...

2024
[15]

Neurosymbolic ai for enhancing instructability in generative ai

Amit Sheth, Vishal Pallagani, and Kaushik Roy. Neurosymbolic ai for enhancing instructability in generative ai. IEEE Intelligent Systems, 39 0 (5): 0 5--11, 2024

2024
[16]

Curriculum learning: A survey

Petru Soviany, Paolo Rota, Dimitrios Tzionas, and Nicu Sebe. Curriculum learning: A survey. International Journal of Computer Vision, 129 0 (5): 0 1--21, 2021

2021
[17]

Wagner and Artur d'Avila Garcez

Benedikt J. Wagner and Artur d'Avila Garcez. A neuro-symbolic approach to ai alignment. Neurosymbolic Artificial Intelligence, 1 0 (1): 0 1--12, 2024. doi:10.3233/NAI-240729. URL https://neurosymbolic-ai-journal.com/system/files/nai-paper-729.pdf

2024 doi
[18]

Large language models and logical reasoning

Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, and Jun Zhao. Large language models and logical reasoning. AI, 3 0 (2): 0 49, 2022. doi:10.3390/ai3020049. URL https://www.mdpi.com/2673-8392/3/2/49

2022 doi
[19]

Neurosymbolic reinforcement learning and planning: A survey

Huanle Xu, Hankz Hankui Zhuo, and Subbarao Kambhampati. Neurosymbolic reinforcement learning and planning: A survey. IEEE Transactions on Cognitive and Developmental Systems, 14 0 (2): 0 422--439, 2022. doi:10.1109/TCDS.2022.3142281. URL https://par.nsf.gov/servlets/purl/10481273

2022

Pith tools

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

[1] [2]

Blank and Steven T

Idan A. Blank and Steven T. Piantadosi. Symbols and grounding in large language models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381 0 (2237): 0 20220041, 2023. doi:10.1098/rsta.2022.0041. URL https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0041

arXiv 2023

[2] [3]

A framework for neurosymbolic robot action planning using large language models

Alessio Capitanelli and Fulvio Mastrogiovanni. A framework for neurosymbolic robot action planning using large language models. Frontiers in Neurorobotics, 18: 0 1342786, 2024. doi:10.3389/fnbot.2024.1342786. URL https://www.frontiersin.org/articles/10.3389/fnbot.2024.1342786/full

arXiv 2024

[3] [4]

An overview of using large language models for the symbol grounding problem

Hiu Fung Chan, Zhiwei Lin, Wenlong Li, Zhiyuan Chen, Zhiyuan Liu, and Juanzi Li. An overview of using large language models for the symbol grounding problem. In Proceedings of the 1st International Joint Conference on Learning and Reasoning (IJCLR 2023), volume 3644, pages 41--52, 2023. URL https://ceur-ws.org/Vol-3644/IJCLR2023_paper_41_New.pdf

work page 2023

[4] [5]

Colelough and William Regli

Brandon C. Colelough and William Regli. Neuro-symbolic ai in 2024: A systematic review. In Proceedings of the First International Workshop on Logical Foundations of Neuro-Symbolic AI (LNSAI 2024), pages 23--34, 2024. URL https://ceur-ws.org/Vol-3819/paper3.pdf

work page 2024

[5] [6]

Large language models are neurosymbolic reasoners

Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, and Jun Wang. Large language models are neurosymbolic reasoners. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/aaai.v38i16.29754. URL https://doi.org/10.1609/aaai.v38i16.29754

doi:10.1609/aaai.v38i16.29754 2024

[6] [7]

Automated curriculum learning for neural networks

Alex Graves, Marc G Bellemare, Jacob Menick, R \'e mi Munos, and Koray Kavukcuoglu. Automated curriculum learning for neural networks. In Proceedings of the 34th International Conference on Machine Learning, pages 1311--1320. PMLR, 2017

work page 2017

[7] [8]

Pragmatic norms are all you need -- why the symbol grounding problem does not apply to LLM s

Reto Gubelmann. Pragmatic norms are all you need -- why the symbol grounding problem does not apply to LLM s. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11663--11678, Miami, Florida, USA, November 2024. Association for Computational Linguistics. doi:10.18653/v1/2024.emnlp-main.651. URL https://aclantho...

doi:10.18653/v1/2024.emnlp-main.651 2024

[8] [9]

The symbol grounding problem

Stevan Harnad. The symbol grounding problem. Physica D: Nonlinear Phenomena, 42 0 (1-3): 0 335--346, 1990. doi:10.1016/0167-2789(90)90087-6. URL https://doi.org/10.1016/0167-2789(90)90087-6

doi:10.1016/0167-2789(90)90087-6 1990