reviews: 
Comments: This paper enhances previous open-world semi-supervised learning (OWSSL) methods such as ORCA and NACH by adding various contrastive learning losses, including supervised (SupCon), nearest neighbor (SupNN), and self-supervised (SelfCon) ones.
Strengths: - Open-world semi-supervised learning is a practical problem.
- The proposed method improves previous approaches, such as ORCA and NACH.
Weaknesses: **Unclear presentation and unjustified claims**

Before delving into the other concerns, the presentation lacks clarity and is missing essential information.

- In Sec. 1 - paragraph 3, the paper categorizes OWSSL methods into "BCE-based" and "Other" methods. Why is the use of BCE loss a meaningful categorization?
- In Sec. 1 - paragraph 4, the paper claims that using MSE instead of BCE lacks "flexibility." What is the definition of flexibility in this context, and why is BCE considered better than MSE for this purpose?
- In Sec. 1 - paragraph 5, the paper suddenly discusses "seen-unseen pairs." Why is this concept important, and how is it related to the problem? This relationship is not explained.
- In Sec. 2 - paragraph 2, the paper claims that previous works do not consider optimization at the feature level. However, they also jointly optimize the feature and classifier.
- In Sec. 2 - paragraph 3, the paper claims that OpenCon is incapable of sustaining "continuous optimization" of representations. What is continuous optimization, and why can't OpenCon achieve this?
- In Sec. 3.2, why should one consider BCE-based methods? BCE-based methods are just one specific approach by Cao et al. and some follow-ups. Eq. (1) does not represent all OWSSL methods.
- ...but not limited to.

Aside from the unclear overall logic, the sentences are significantly unpolished. Many grammatical errors make it difficult to grasp the meaning.
There are also typos and inconsistent use of terminologies. For example, "L" in the abstract, uncapitalized "all" in Table 1, unformatted "seen/unseen" in Sec. 3.1, and "OWSSL" is not defined in Sec. 3.2.


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**Proposed method is merely a combination of existing techniques**


The motivation if the method is (1) enhance the separability of known class features and (2) prevent the collapse of unknown class features.
The paper employs SupCon (and a NN variant) for (1) and SelfCon for (2).

There are two issues here:
1. It is obvious that contrastive learning would enhance performance. In fact, this benefit for OWSSL has already been demonstrated in prior works, such as GCD [1] and OpenCon [2].
2. The addition of Sup/SelfCon generally enhances the models, not only for the OWSSL problem. To verify this, one can apply the proposed method to (1) the standard SSL setup and (2) other SSL methods. I believe it will also result in improvements in those cases, suggesting that the method does not exclusively target the OWSSL issue.

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**Missing important baselines**

The paper omits a comparison with a highly related work, OpenCon [2]. The reasons provided in the paper for avoiding this comparison are not convincing. Additionally, GCD [1] is highly relevant to the paper but is missing from the comparison.
Furthermore, the paper does not include a comparison with papers published in 2023, such as RoPAWS [3], which also employs contrastive learning for SSL. Not only them, but there is also a long list of related works.
The OpenCon paper has 6 pages of references, in contrast to this paper with only 2 pages. Checking them would enable a more comprehensive context in the related work section, thereby making the paper more self-contained.

[1] Generalized Category Discovery, CVPR'22.\
[2] OpenCon: Open-world Contrastive Learning. TMLR'22.\
[3] RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data, ICLR'23.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 1 poor