Title: NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neurosymbolic

Large Language Model

Embodied Agent
Abstract: We address the challenge of adopting language models (LMs) for embodied tasks in dynamic environments, where online access to large-scale inference engines or symbolic planners is constrained due to latency, connectivity, and resource limitations. To this end, we present NeSyPr, a novel embodied reasoning framework that compiles knowledge via neurosymbolic proceduralization, thereby equipping LM-based agents with structured, adaptive, and timely reasoning capabilities. In NeSyPr, task-specific plans are first explicitly generated by a symbolic tool leveraging its declarative knowledge. These plans are then transformed into composable procedural representations  that encode the plans' implicit production rules, enabling the resulting composed procedures to be seamlessly integrated into the LM's inference process. This neurosymbolic proceduralization abstracts and generalizes multi-step symbolic structured path-finding and reasoning into single-step LM inference, akin to human knowledge compilation. It supports efficient test-time inference without relying on external symbolic guidance, making it well suited for deployment in latency-sensitive and resource-constrained physical systems. We evaluate NeSyPr on the embodied benchmarks PDDLGym, VirtualHome, and ALFWorld, demonstrating its efficient reasoning capabilities over large-scale reasoning models and a symbolic planner, while using more compact LMs.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces NESYPR, a framework that helps language models plan actions in embodied environments without relying on external symbolic planners. It learns from symbolic plans during training and compiles them into reusable procedures stored as vector memories. At test time, the model uses these memories—and feedback from the environment—to adapt and generate plans efficiently. The key idea is to turn multi-step symbolic reasoning into fast, single-step neural inference.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nA major strength of the paper is that it avoids online symbolic planners during inference, yet still achieves strong performance. What's impressive is that NESYPR runs significantly faster than both symbolic planners and large-scale reasoning baselines like BoT or GPT-4, while using much smaller models (e.g., 3B vs. 70B), as demonstrated through latency and token comparisons.\n\nThe idea of combining contrastive reconstruction with symbolic plan generation is novel and well-motivated. It offers a promising direction for making planning more adaptive without relying on traditional solvers.\n\nThe authors run NESYPR across several popular embodied benchmarks with both seen and unseen tasks. The results are solid and consistent within each environment, suggesting the approach generalizes well to new tasks in familiar domains.\n\nWeaknesses\n\nWhile the model is evaluated on unseen tasks within the same environment, it is unclear how well it generalizes across environments with different object vocabularies, action schemas, or symbolic representations. A stronger demonstration of generalization would involve training on one environment and testing zero-shot on another.\n\nSince the unseen tasks come from the same environment as the training tasks, it would be natural to construct few-shot prompts using training examples and evaluate models like GPT-4 in an in-context setting. This kind of prompt-based reasoning is a competitive alternative to finetuning or memory-based methods, especially with today's large models. Without such a comparison, it's hard to tell how much of NESYPR's performance comes from its memory mechanism versus simply reusing examples in a smarter prompt.\n\nThe contrastive planning mechanism is a core contribution, but the ablation study on it is fairly limited. Table 2 shows results with and without contrastive decoding, but doesn't explore variants (e.g., what happens if only M+ is used, or if memory is retrieved differently). A deeper analysis would help isolate where the performance gains are truly coming from.\n\nQUESTIONS:\n1. Have you tried training NESYPR on one environment (e.g., PDDLGym) and testing on a structurally different one (e.g., ALFWorld)? This would provide stronger evidence for generalization beyond task-level variation.\n\n2. Given that the unseen tasks come from the same environment as the training tasks, have you considered using few-shot prompting baselines, such as GPT-4 with training examples as prompts? \n\n3. Could you provide more detailed ablations of the contrastive planning component? For instance, what happens when using only successful memory (M+) or changing how memories are retrieved?\n\n4. Do you have any insights into what the procedural memory vectors encode? Are they interpretable or reusable in a symbolic form?\n\nLIMITATIONS:\nWhile NESYPR shows strong performance within individual environments, its ability to generalize across environments with different symbolic structures remains unclear. The evaluation also lacks comparisons to prompt-based baselines using few-shot examples from training tasks, which could offer competitive alternatives. Additionally, the contrastive planning component, though promising, would benefit from a more thorough ablation to clarify its exact contribution.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a neuro-symbolic framework that distills compositional procedure knowledge from symbolic solvers to a language model. During inference, the system can avoid using symbolic solvers and leverage contrastive planning to generate solutions for unseen tasks. The authors also conduct exhaustive experiments and analysis to demonstrate the effectiveness of their method.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n- Encoding procedure rules into implicit LM knowledge is an interesting idea, and contrastive planning during inference is also intriguing to me.\n- The experimental supports are quite strong, which cover various benchmarks and baselines.\n- The problem addressed in this work is important to me, i.e., compositional generalization of neural networks.\n\n**Weaknesses**\nThe major weakness of this work is the writing issue. \n- The experimental section is 4 pages long, making the related works + methodology only 2.5 pages long (with figures). This makes the information in the paper largely biased, and readers can hardly understand the technical details of your algorithm. I'd recommend put some experimental content in the appendix and making more room for the method writing (~3 pages). Here are something I feel confused after trying to carefully read the method:\n- What is the input to the LM during training? Is it the plan from the symbolic engine/problem description? What is the specific state space? There should be a running example here.\n- How is the procedural knowledge/Rule computed, and what is its format? Is it based on LLM's summarization/ symbolic induction? Please add an example for understanding.\n- There are heavy notations across the paper; the authors are expected to provide a notation table.\n- Are the unseen tasks from the same domain but with different entity composition? Or are the unseen tasks from different domains but same types?\n\nQUESTIONS:\n- I see one potential weakness is the reliance on existing symbolic solvers during training time, which means the system can only solve problems with defined symbols/structure -> It is just making planning more efficient by learning. Have the authors considered distilling LM's knowledge back to the symbolic solver / guide the solver so it could potentially solve more tasks that previously had no existing domain knowledge? Doing this in a cycle to continually evolve the LM seems to be very interesting.\n- For PDDL representations, there exist domains where procedural knowledge (efficient imperative rules/feedforward programs if I understand correctly) does not exist, e.g., Sokoban. What is the procedural knowledge learned/computed there?\n\nLIMITATIONS:\nSee questions.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents NESYPR, a neurosymbolic proceduralization based reasoning framework inspired by ACT, which compiles multi-step symbolic reasoning into single-step LM inference. The paper claims this framework eliminates the need for online symbolic planners in embodied tasks. Additionally, the paper develops compositional NeSy procedure learning, which encodes production rules into a vector quantized procedural memory whose vectors can be compositionally combined to generate task specific plans. The paper also implements NeSy procedure contrastive planning, which adaptively generates plans by contrastively reconstructing task-specific procedures from stored procedures labeled with environmental feedback. The paper shows effectiveness and efficiency of NESYPR through extensive\n evaluations on 3 embodied benchmarks and 9 experimental scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is well written and easy to follow\n2. The proposed method outperforms strong baselines on all the benchmarks and tasks that authors consider for task planning in the paper which shows effectiveness of the method.\n3. Additionally, the method is also efficient at inference time and performs effectively under a time constraint compared to symbolic planners.\n4. The analysis in figure 4 also shows neat results where procedure codebooks across tasks show shared features\n5. The ablations provided in the paper are also thorough and valuable to show effectiveness of the method.\n6. Additionally, the analysis in figure 5 is also insightful. The finding that increasing codebook size leads to drop in composition performance is a interesting finding.\n\nWeaknesses:\n\n1. In Table 3 why does Deepseek 8B model (row 4) have a higher latency at almost 1/2 number of tokens compared to llama 3.1 8b (row 1)? This seems counterintuitive. Can authors explain why this is the case? Instead of latency isn’t it better to compare efficiency by FLOPs? Can author show similar analysis using FLOPs?\n2. One thing that is a bit unclear is the effectiveness of pretrained representations which come from using a pretrained language model when learning the procedural memory for neurosymbolic planning. I understand the motivation to equip LLMs with neurosymbolic planning ability with procedural memory but the current experiments do not ablate the effectiveness of using a pretrained LLM as initial starting weights vs using a LLM from scratch to learn such neurosymbolic planning. Currently, it is unclear whether the gains in performance are coming from pretrained representation + architecture change vs just the changes in architecture and training recipe. I would appreciate it if authors can run this experiment as I believe it will make the paper stronger.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nThe proposed method is making a non-standard change to the LLM architecture which likely leads to catastrophic forgetting of pretrained knowledge of the LLM which limits applicability of this method to more general problems and domains.\n\nI think the paper itself is interesting and has a bunch of interesting experiments. I am willing to increase my rating if authors can clarify the questions I asked in weaknesses section and add additional ablations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn my understanding, the paper proposes an approach for task planning by combining transformer-based models and symbolic solvers. First a transformer model is trained to generate actions that solve a given problem definition, supervised by the solution of the PDDL solver. The transformer is augmented by a procedure codebook, an explicit memory with latent codes that are used to generate the actions. Then, the pretrained LM samples a set of latent procedures and select the procedures that maximize the likelihood of the transformer model. The approach is tested in 3 different environments, modifying existing benchmarks to encode sequences of tasks. The proposed method improves over several LLM-based and symbolic planning based approaches, obtaining higher success rate and lower computation time.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Very thorough experiments and strong results. The method is compared in 3 different benchmarks in with a large number of baselines. The method obtains significant improvements across the 3 benchmarks compared to both LM-based baselines as well as baselines using symbolic planners. Furthermore, the latent procedures can be reused across tasks, leading to the agent being able to adapt as goals change in the environment, compare to other baselines.\n- Ablations of the effect of different LLMS to generate the symbolic predicates, as well as the effect of the different components of the NeSYPR approach.\n\nWeaknesses:\n\nClarity: In the current state the paper is too difficult to follow to be a valuable contribution for a conference. I would strongly advice to remove abstract statements that do not provide information about the method (e.g. grounding parts of the method on human knowledge) and try to ground the framework in a concrete problem earlier on. The method is stated too abstractly and it is really difficult to follow. Below more details. \n\n- The paper uses a lot of concepts without clear meaning. Why is \"multi-step symbolic structured path-finding and reasoning into single-step LM inference, akin to human knowledge compilation\"? What does \"triggered automatically as cognitive procedures\" (l42) mean? The second paragraph in the introduction doesn't provide any clarity on how declarative vs procedural memory are used in this framework, a concrete example to ground the framework would help improve clarity. The introduction is too abstract and it is really hard to understand what the method is solving or why it enables faster LM inference. \n- The methods in line 64 to 72 are referenced without further context which makes it hard to understand why the stated results are relevant. \n- Equation 1 is introduced without any explanation or reasoning. Why do we want to minimize the KL divergence between the LM policy and the tool policy? Are the outputs of those policies in the same domain? \n- Figure 1 includes a lot of details that are barely described in the main text. \n- Overuse of notation. In equation 2, is the M in the input the same as the M at the output of the decoder block? Is E_self the same as H_{l-1} ? What is M for the first layer?\n- What makes a procedure positive or negative in Equation 10? How are the procedures R+ and R- constructed?\n\nMethod:\n- The method seems overly complex, and doesnt justify some of the decisions. Why learning a codebook end to end with the planning? The general approach is to learn these in two stages. Does this lead to codebook collapse? Did authors follow the more common approach? Why isn't there a reconstruction loss in the VQ loss? The loss in equation 9 does not match the equation 1, what is the relationship between both equations?\n- Is the LM based agent represented in Figure 2? What makes that model a language model? Is it initialized from a pretrained LM? From the text and figure it looks like a transformer.\n- How does the proposed method enable planning under dynamic environments, particularly in changing dynanmics and goals over time? What part of the model is enabling that?\n- If I understand correctly, the input to the NeSyPR model is a PDDL like definition of the current problem and the domain. Where does that come from? How is it extracted from an environment? Is an LLM used to generate that?\n- Why does the process in section 3.2  supress failure patterns? (L169).\n\nResults:\n- Beyond the quantaitive results, it is very hard to evaluate why the propsoed method is doing better. Showing some qualitative examples or understanding how the propsoed approach is performing neurosymbolic reasoning would be helpful.\n- What do the evaluation phases represent in Table 4?\n\n\n- Related work: Some form of related work should be included in the main paper, it is really hard to understand where the presented work fits in the literature. It seems like the paper fits into the second family of approaches (neurosymbolic methods that integrate LMs) but it is unclear what the main differences are.\n\nQUESTIONS:\n- Provide one example to ground the method. State what are the inputs of the LM, and how is M formed to generate the procedural memory R.\n- Better interpretability of what the codebook R is doing. Would it be possible to decode the R codes back into task predicates?\n- How does Equation 1 and 9 relate to each other?\n- How are the positive and negative procedures obtained in the contrastive planning stage?\n- How does the approach compare to works like DualFormer? (https://arxiv.org/abs/2410.09918)\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}