CSPaper: sidekick of peer review

Source code:
tgn-adpt.zip

A data point:

GNN work, got

Novelty: 3, 2, 2, 3, 2
Technical Quality: 2, 2, 2, 2, 2
Confidence: 3, 4, 3, 4, 4

Need to rebuttal? anyone knows more? 2 weeks challenge ahead!

Continued sample scores from zhihu

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🟩 Borderline to Promising Scores

• 4 / 3 / 3 (Confidence: 3 / 4 / 4)

  Hoping for Main Conference acceptance.

• 4 / 3 / 2.5 (Confidence: 3 / 4 / 4)

  Reviewer hinted score could increase after rebuttal.

• 3.5 / 3.5 / 3 (Meta: 3)

  For December submission. Ask: is that enough for Findings?

• 3.5 / 3.5 / 2.5 (Confidence: 3 / 3 / 4)

  Author in the middle of a tough rebuttal. Main may be ambitious.

• 3.5 / 3 / 2.5

  Open question: what's the chance for Findings?

• 3 / 3 / 2.5 (Confidence: 3 / 3 / 3)

  Undergraduate author. Aims for Findings. Rebuttal will clarify reproducibility.

• 3.5 / 2.5 / 2.5 (Confidence: 3 / 2 / 4)

  Community sees this as borderline.

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🟨 Mediocre / Mixed Outcomes

• 2 / 3 / 4

  One reviewer bumped the score after 6 minutes (!), but still borderline overall.

• 2 / 2.5 / 4

  Rebuttal effort was made, but one reviewer already dropped. Probably withdrawn.

• 2 / 3.5 / 4

  Surprisingly higher for a paper the author didn’t expect to succeed.

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🟥 Weak or Rejected Outcomes

• 4 / 1.5 / 2 (Confidence: 5 / 3 / 4)

  Likely no chance. Community reaction: “Is it time to give up?”

• 3 / 2.5 / 2.5 (Confidence: 3 / 3 / 5)

  Rebuttal might help, but outlook is dim.

• 1 / 2.5, confidence 5

  Probably a confused or low-effort review.

• OA 1 / 1 / 1

  A review like this existed (likely invalid). Community flagged it.

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Additional comment from community

• Some reviewers are still clearly junior-level, or appear to use AI tools for review generation.
• Findings threshold widely believed to be OA ≥ 2.5–3.0, assuming some confidence in reviews.
• Review score inflation is low this round: average OA above 3.0 is rare, even among decent papers.
• Several December and February round submissions are said to be evaluated independently due to evolving meta-review policies.

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️ Summary

• Score distributions reported in the Chinese community largely align with Reddit’s (see my previous post), which is 3.0 is the magic number for Findings, 3.5+ needed for Main.
• Rebuttal might swing things, but expectations are tempered.
• Many junior researchers are actively sharing scores to gauge chances and strategize next steps (rebut, withdraw, or resubmit elsewhere).

Some review scores I have seen

Stronger or Mid-range Submissions

• OA: 4/4/4, C : 2/2/2
  Concern: Low confidence may hurt chances.

• OA: 4, 4, 2.5, C : 4, 4, 4
  Community says likely for Findings.

• OA: 3, 3, 3, C : 5, 4, 4
  Possibly borderline for Findings.

• OA Average: 3.38, Excitement: 3.625
  Decent shot, though one reviewer gave 2.5.

• OA average: 3.33
  Reported as the highest OA seen by one reviewer – suggests bar is low this cycle.

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Weaker Submissions

• OA: 2.5, 2.5, 1.5, 4, 3, 3
  Unlikely to be accepted.

• OA: 2, 1.5, 2.5, 4, 4, 4
  Most agree no chance for Findings.

• OA: 3, 3, 2.5, 4, 3, 4
  Marginal; some optimism for Findings.

• Only two reviews, one with meaningless 1s and vague reasoning
  ACs often unresponsive in such cases.

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Some guessing from community

Findings Track:

• Informal threshold: OA ≥ 3.0
• Strong confidence and soundness can help borderline cases

Main Conference:

• Informal threshold: OA ≥ 3.5 to 4.0
• Very few reports of OA > 3.5 this cycle

Score Changes During Rebuttal:

• Rare but possible (e.g., 2 → 3)
• No transparency or reasoning shared

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Info on review & rebuttal process

• Reviews were released gradually, not all at once
• Emergency reviews still being requested even after deadline
• Author response period extended by 2 days
  • Confirmed via ACL 2025 website and ACL Rolling Review
• Meta-reviews and decisions expected April 15

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To summarize

• This cycle’s review scores seem low overall
• OA 3.0 is a realistic bar for Findings track
• OA 3.5+ likely needed for Main conference
• First-time submitters often confused by lack of clear guidelines and inconsistent reviewer behavior

334 222 234 124 335
223 344 445 233 122

Table 1. Performance Comparison on Benchmark Datasets

Method	CIFAR-10 (Acc %)	CIFAR-100 (Acc %)	TinyImageNet (Acc %)	Params (M)	FLOPs (G)
ResNet-18	94.5	76.3	64.1	11.2	1.8
ViT-Small	95.2	77.9	65.7	21.7	4.6
Ours (GraphFormer)	96.1	79.5	67.3	19.8	3.9

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Table 2. Ablation Study on Temporal Encoding

Method Variant	CIFAR-10 (Acc %)	TinyImageNet (Acc %)
Ours (No Time Encoding)	95.4	66.1
Ours (Sinusoidal Only)	95.8	66.8
Ours (Learnable Time)	96.1	67.3

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Table 3. Robustness to Input Noise on CIFAR-10 (% Accuracy)

Method	No Noise	Gaussian (σ=0.1)	Gaussian (σ=0.3)	FGSM (ε=0.1)
ResNet-18	94.5	91.2	83.7	78.9
ViT-Small	95.2	92.4	85.1	80.3
Ours	96.1	93.5	87.0	83.7

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Table 4. Generalization to Out-of-Distribution (OOD) Data

Method	In-Domain (CIFAR-10)	OOD (SVHN)	OOD (CIFAR-10-C)
ResNet-18	94.5	76.3	71.2
ViT-Small	95.2	78.1	73.0
Ours	96.1	80.5	75.3