Kati8/ICLR_2023_2025 huggingface dataset:
authors: Beinan Xu
authors: Andy Song
authors: Jiti Gao
reviews: 
Comments: This paper presents a new prediction approach called Equilibrium State Evaluation for handling multi-system prediction problems where multiple interacting systems must be predicted simultaneously. Where the authors treat each system as independent time series and consider multiple-interacting systems as components of a "super-system". The method maintains an Equilibrium Index (EI) that measures the distance between the current state and the equilibrium state.
Strengths: There aren't any significant strengths in the paper
Weaknesses: - The paper is confusing to follow. The introduction is poorly written. The mathematical notation is very dense without sufficient explanation. 
- Complex method workflow is not fully explained
- It is not apparent to me why proportions must follow zero-sum rule. Doesn't explain how to handles growing-shrinking total system values.
- It is unclear why is specific normalization is chosen λᵢ,ⱼ = (αᵢ,ⱼ - mean(atⱼ)) / (max(atⱼ) - min(atⱼ)), why is this normalization beneficial as compared to other ways to normalize? 
- Algorithm 1 is vaguely described, and testing and training process aren't well defined. It doesn't map succinctly with the figure 2 of the paper.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: I believe this paper either has a high chance of being generated by a language model, or was rushed into submission. In both cases, I recommend a rejection.
Strengths: -
Weaknesses: **I stopped reviewing this paper, as there were multiple issues. The objective and the problem setting are not clear, assumptions are not clearly stated, related work are missing, and writing is very poor.**

The definition of a "system" in the first place is not clear. The authors casually drop "properties" of this said system/multi-system, which vaguely correspond to some concepts, such as Nash equilibria, but not clear at all if those are assumptions/setting the authors consider. 

The paper is almost impossible to read, with abuse of notation used absolutely for no reason. It is not really clear what problem the paper is dealing with and it is very poorly formulated throughout.

It is not clear how the COVID example fits into the Constraints 1 & 2. Why Eq. (4) needs to hold for the COVID case for instance?

Related work section mentions mostly datasets and equilibrium examples from different domains, but I could not spot any ML related work that consider a similar problem. 

Why do you define ${\cal M} {\cal S}$ to be the sum of the target variables in systems, whereas it was first defined as the set of systems? The notation should be improved across the board, but this is just one example.

Why does Eq. (1) hold *in general*? Is that a setting you consider?

You cannot refer to Constraint 1 when defining Constraint 1 itself?

Line 158 - Please refer to where did you describe your ESE method as claimed.

What is the takeaway from Figure 1 exactly?

The connection to the Nash equilibrium is never formally introduced or motivated. It is not clear where  Lemma 1 comes from. There are no proper citations as well.
Recommendation: 1
Reviewer confidence: 1
Soundness/correctness: 1
Clarity: 1