Deciphering the Extremes: A Novel Approach for Pathological Long-tailed Recognition in Scientific Discovery
Authors: Zhe Zhao
HaiBin Wen
Xianfu Liu
Rui Mao
Pengkun Wang
Liheng Yu
Linjiang Chen
Bo An
Qingfu Zhang
Yang Wang
Venue: NeurIPS 2025 spotlight ()
Keywords: Long-tailed learning
Imbalanced datasets
Primary area: general_machine_learning

Abstract: Scientific discovery across diverse fields increasingly grapples with datasets exhibiting pathological long-tailed distributions: a few common phenomena overshadow a multitude of rare yet scientifically critical instances. Unlike standard benchmarks, these scientific datasets often feature extreme imbalance coupled with a modest number of classes and limited overall sample volume, rendering existing long-tailed recognition (LTR) techniques ineffective. Such methods, biased by majority classes or prone to overfitting on scarce tail data, frequently fail to identify the very instances—novel materials, rare disease biomarkers, faint astronomical signals—that drive scientific breakthroughs. This paper introduces a novel, end-to-end framework explicitly designed to address pathological long-tailed recognition in scientific contexts. Our approach synergizes a Balanced Supervised Contrastive Learning (B-SCL) mechanism, which enhances the representation of tail classes by dynamically re-weighting their contributions, with a Smooth Objective Regularization (SOR) strategy that manages the inherent tension between tail-class focus and overall classification performance. We introduce and analyze the real-world ZincFluor chemical dataset ($\mathcal{T}=137.54$) and synthetic benchmarks with controllable extreme imbalances (CIFAR-LT variants). Extensive evaluations demonstrate our method's superior ability to decipher these extremes. Notably, on ZincFluor, our approach achieves a Tail Top-2 accuracy of $66.84\%$, significantly outperforming existing techniques. On CIFAR-10-LT with an imbalance ratio of $1000$ ($\mathcal{T}=100$), our method achieves a tail-class accuracy of $38.99\%$, substantially leading the next best. These results underscore our framework's potential to unlock novel insights from complex, imbalanced scientific datasets, thereby accelerating discovery.

Deciphering the Extremes: A Novel Approach for Pathological Long-tailed Recognition in Scientific Discovery
Zhe Zhao
HaiBin Wen
Xianfu Liu
Rui Mao
Pengkun Wang" Yang Wang" if
Liheng Yu'
Linjiang Chen' Bo An
Qingfu Zhang²
University of Science and Technology of China
City University of Hong Kong
China University of Mining and Technology
*Nanyang Technological University
Abstract
Scientific discovery across diverse fields increasingly grapples with datasets exhibit- ing pathological long-tailed distributions: a few common phenomena overshadow multitude of rare yet scientifically critical instances. Unlike standard benchmarks, these scientific datasets often feature extreme imbalance coupled with a modest number of classes and limited overall sample volume, rendering existing long-tailed recognition (LTR) techniques ineffective. Such methods, biased by majority classes prone overfitting scarce tail data, frequently fail to identify the very in- stances-novel materials, rare disease biomarkers, faint astronomical signals-thal drive scientific breakthroughs. This paper introduces a novel, end-to-end frame- work explicitly designed to address pathological long-tailed recognition in scientific contexts. Our approach synergizes a Balanced Supervised Contrastive Learning (B- SCL) mechanism, which enhances the representation of tail classes by dynamically re-weighting their contributions, with Smooth Objective Regularization (SOR) strategy that manages the inherent tension between tail-class focus and overall classification performance. We introduce and analyze the real-world ZineFluor chemical dataset (T = 137.54) and synthetic benchmarks with controllable ex- treme imbalances (CIFAR-LT variants). Extensive evaluations demonstrate our method's superior ability to decipher these extremes. Notably, ZincFluor, our approach achieves Tail Top-2 accuracy of 66.84%, significantly outperforming existing techniques. On CIFAR-10-1E with imbalance ratio of 1000 (T 100), our method achieves tail-class accuracy of 38.99% substantially leading the next best. These results underscore our framework' potential to unlock novel insights from complex, imbalanced scientific datasets, thereby accelerating discovery. We provide the detailed code in https//githubb.com/ataaaaa httpa//aaaaaaaat//LTt-RR..
1 Introduction
Scientific discovery, spanning disciplines from materials science and drug development to astrophysics and genomics, increasingly relies harnessing vast datasets. However, pervasive and often underestimated challenge these domains is the pathological long-tailed distribution of data. Unlike common benchmark datasets (e.g., ImageNet-LT [19] Places365-LT (32)) scientific datasets often exhibit extreme imbalances: a few well-understood or easily observable phenomena constitute the majority classes, while multitude of rare, novel, or co-hrtcchaaacterizze instances form extensive tail. More critically, while many existing highly imbalanced benchmarks feature a large number
Equual contribution.
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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of classes and a relatively substantial total sample size, the pathological long-tailed distributions encountered in scientific exploration are frequently characterized by comparatively smaller number of classes coupled with limited overall sample volume. This scarcity of available information for each tail class imposes even more stringent demands on a model's learning capabilities. This is not an artifact but an intrinsic feature of scientific exploration: groundbreaking discoveries often reside in these sparse tail regions, representing new materials with unique properties, biomarkers for rare diseases, or faint astronomical signals indicative of new physical laws. The criticality of accurately identifying and understanding these tail-class instances in scientific domains cannot be overstated.
Standard deep learning models and existing Long-Tailed Recognition (LTR) techniques [3 29] often falter with such pathological imbalances. Current LTR methods, whether based on re-sampling 4 8) re- weighting 2 decoupled training [12 or specific loss designs [8] 3) primarily aim to mitigate head-class dominance. However, with extreme scarcity, re-weighting can overfit to noise, re-sampling may lose or redundantly add information, and decoupled training struggles if initial features for tail classes are poorly learned. These shortcomings are drastically amplified at pathological imbalance levels, leading to CATASTROPHIC FAILURES in identifying scientifically paramount tail instances. For example, in our ZincFluor dataset (T' = 137.54), rare, valuable fluorescent compounds are often missed, hindering discovery. This paper directly confronts pathological long-tailed recognition in
Number zomples
Pathological Long-tail Scientific Data T 137.54
Augesnnt
Head
Weigh Colculation
Balance
New ratensll
Feotuure Extractor
CPO
Clossifter
Keara
Labe
Class index
Resulting
Renaltun
(a) Pathological long-tail in scientific data. Critical findings often reside in rare tail classes.
Namber corples
(b) Our framework: B-SCL (Ca.c)) for tail classes, CPO (Ccro) for overall accuracy. balanced by SOR (Csok).
Figure I: Visualizing (a) the pathological long-tail challenge in scientific discovery (e.g., T' = 137.54 in the ZincFluor dataset), where critical findings are in sparse tails, and (b) our proposed framework leveraging Balanced Supervised Contrastive Learning (B-SCL), Classification Performance Objective (CPO), and Smooth Objective Regularization (SOR) to address it.
scientific data. We argue that extreme imbalance necessitates a paradigm shift from adapting existing LTR methods to designing bespoke solutions. To this end, we propose a novel, end-to-end trainable framework (overviewed in Figure lb with key contributions highlighted below:
We profoundly unveil and quantify the unique severity of the "pathological long-tail" prob- lem within scientific discovery contexts. By introducing and analyzing the real-world ZincFluor chemical dataset (T 137.54), and complementing with synthetic datasets we constructed featuring controllable extreme imbalance (variants of CIFAR-10-LT and CIFAR-100-LL [15), we systematically benchmark the performance bottlenecks of existing LTR methods in these extreme scenarios, thereby providing new benchmarks and challenges for research in this domain.
We introduce an innovative balanced supervised contrastive learning framework, inspired by [4), engineered to fundamentally enhance the model's capacity to perceive and represent rare yet critical scientific signals. Our approach dynamically adjusts the contribution weights of samples from different classes during contrastive learning and integrates multi-objective optimization strategies. This not only compels the model to focus on and learn fine-grained, discriminative features for tail classes but also, through artful loss function design, ensures stable learning of common head-class phenomena. Consequently, it achieves a balanced cognitive understanding across varying class frequencies, effectively preventing the neglect of scarce signals.
We demonstrate the remarkable efficacy our method through extensive evaluations. Critically, on the highly challenging real-world ZincFluor dataset, our approach achieves breakthrough in identifying rare fluorescent compounds, evidenced by, for instance, Tail Top-2 accuracy of 66.84%, significantly outperforming existing techniques. Furthermore, on synthetic

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long taigttiled benchmarks with tunable pathological imbalance, our model consistently surpasses state-of-the-- LTR methods, especially when the imbalance is more extreme. For instance, with an imbalance ratio of 1000 on CIFAR-10-HE 10-IT (T = 100), our method achieves a tail-class accuracy of 38.99%, substantially leading the next best method at 28.55%. These results underscore the immense potential of our approach to unlock novel insights from complex, imbalanced scientific datasets, offering a potent tool to accelerate scientific discovery.
By developing a robust solution tailored to the pathological long-tailed distributions inherent in scientific research, this work aims to bridge the gap between advanced machine learning capabilities and the pressing need to extract knowledge from the most challenging, yet often most valuable, segments of scientific data.
2 Related Work
2.1 Long-Tailed Phenomena in Scientific Tasks
Long-tailed distributions, where a few common observations dominate numerous ones, intrinsic to many scientific domains. For instance, in materials science, novel materials with exceptional functionalities
far rarer than common stable compounds 20 Similarly, drug discovery and genomics face challenges identifying rare genetic variants or novel drug targets from vast datasets [5] [26] Astrophysics also encounters this, with rare celestial events objects
being crucial yet sparsely observed compared to common ones [13 9 Distinct from typical large- scale LTR benchmarks like ImageNet-LT [19] Places365-LT 32
scientific datasets often exhibit a pathological long-tail: extreme imbalance ratios coupled with a modest number of total classes and often limited overall sample sizes. This unique setting challenges generic LTR methods and motivates our tailored approach.
2.2 Long-Tailes Recognition (LTR)
LTR techniques aim to mitigate semantic and structural biases toward majority classes. We categorize prevalent strategies as follows:
Data and Loss Manipulation: Early methods rely on re-sampling (e.g., SMOTE 4 under-
sampling (8] to balance the training distribution. Re-weighting strategies further refine this by assigning class-specifi costs, such (lass-Balanced Loss (7]. Focal Loss [18). and LDAM (2]. Notably, recent studies provide a unified theoretical framework for these loss-oriented approaches
via localization (28)
Decoupled and Manifold Learning: Decoupled training 12 separates feature learning from classifier adjustment. To enhance the robustness of the learned features, recent works delve into semantic scale imbalance [22 and survature-balancee feature manifolds (24), aiming for fairer
DNNs by optimizing the geometry of perceptual manifolds (23].
Logit Adjustment and Distillation: Post-hoc adjustments, such label over-smoothing 25
and logit retargeting (21), calibrate the model's confidence. Knowledge distillation [10
and hicrarchical label distribution strategies 330 have also proven effective for test-agnostic
scenarios.
Contrastive learning for LTR has emerged potent direction. Building upon Supervised Contrastive Learning (SupCon) [14], methods like Parametric Contrastive Learning (BCL) (6] and Targeted SupCon [17] leverage sample-t-ssampl relationships foster discriminative embeddings. Our B-SCL extends this paradigm by integrating class-frequency aware weights specifically tailored for pathological distributions, where standard benchmarks like iNaturalist (27) fail to capture the extreme scarcity and low total volume typical of scientific discovery datasets.
Methodology: Balanced Contrastive Representation Learning under Dynamic Multi-Objective Constraints for Pathological Long-Tails
Our methodology addresses the critical challenge of pathological long-tailed recognition, prevalent in scientific discovery, by architecting synergistic learning framework. This framework prioritizes
3

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the discriminative representation of tail classes while ensuring overall classification efficacy and robustness. We formalize this as a multi-objectivv optimization problem and derive a tractable loss function that dynamically balances these, often conflicting, objectives.
3.1 Formalizing Pathological Long-Taile Recognition as a Multi-Objective Optimization Problem
We consider dataset {(z,, y:) } characterized by a pathological long-tailed distribution across C' classes, where z; E X and y/ E {O...... C 1}. The per-class sample count Nº exhibits extreme imbalance, quantified by T (max, N: (min, C). Our goal is to learn model parameters 8 for a feature extractor ftuckbume: projection head Trojj and classifier Jels
In this setting, we identify three primary, potentially conflicting, learning objectives:
1. Robust Classification Performance (0,(0)); The model must achieve high classification accuracy across all classes, for both original and augmented data views. This is quantified by the Classification Performance Objective (CPO):
(f) E (( (eCreg9ssffaakttte (z; 0)), y) + (9ets (z): v)]
(1)
where (c = - log(softmax(()), is the standard cross-entropy loss. Let LCF. (0) [ (allgaa((haacae (huckhone (2:; 0)), v)] and LCF.au( (0) E f.C..ooig (0) + LCE.aug())
(r'; (z); v)). Thus, LcPo
2. Tail-Centric Discriminative Representation (O2(0)): The model must learn highly discrimina- tive features, particularly for information-stae starved tail classes, to enable their identification. This is addressed by the Balanced Supervised Contrastive Learning (B-SCL) objective:
LB-Sc (0) Ag-SC
1
Σ EyES
Wy, (sc(zy;0)
(2)
2B
where (sc(z,;0) 0) the standard per-anchor SupCon loss for anchor 2, with label yy, computed using embeddings z Trug (fauj(fbacttooee (:; (): The weights = exp(s Σ. exp(s)) with s (Nc-1-*) up- up-weight tail-class contributions.
The challenge is that minimizing LcpO (often dominated by head classes) can conflict with minimizing LB.sC (emphasizing tail classes). We seek solution 0" that is Pareto-optimal with respect to (EcE,ng fcE.sgg LB.Sc)-
Optimization Target 1 (Constrained Multi-Objective Formulation) We aim to find parameters 8" that minimize a primary combined objective while ensuring no individual sub-objective becomes excessively large. This can be conceptualized
min
(CPO + LB-sc(0)
subject to
LCRong S 61
LCF.oug (0) < 62
Lc.sc(() S 63
where 61, f2, f3 are dynamically adjusted upper bounds.
Solving Optimization Target I directly is intractable. Instead, we formulate penalty- approach.
3.2 Derivation the Training Objective from Multi-Objective Constraints
To find solution approximating the Pareto front of (Lct,,rigg LCE.aug LB.sc), employ scalariza- tion technique that incorporates a penalty for deviations from a balanced state.
Proposition 1 (LogSumExp as a Smooth Maximum) The LogSumExp (LSE) function, LSE(V) log Σ, exp(v:), is differentiable, convex approximation of the maximum function, ie., max; 0ç S LSE(v) I max; 1ú + log M for vector of M components.

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We introduce a Smooth Objective Regularization (SOR) term designed to penalize solutions where any of the fundamental objectives (org, Ler.auy or LB.cc) becomes disproportionately large. This aligns with the Tchebycheff (min-max) approach for multi-objective optimization. Let Cconstitunt (0) [LcEEnng(0) (0); Ler.ang (0), LB sc() The SOR term is defined as:
SOR (0) ASOR LSE (Constiun (8)/TS)R)
(4)
where ASor is a regularization strength and TSOR is a temperature parameter. For simplicity and alignment with the paper's s practical implementation, we set TSOR Thus,
LSOR (0) ASOR log (exp(Ccr.orig (0)) + exp(Cct..u (0)) + cxp(CB:sc(O))
(5)
The final training objective (f) combines the primary objectives with this dynamic regularization:
L LCE.orig()) (0) + LCE.sug ( sc(( (0)
(6)
(C)o(
Substituting Eq. innto Eq. 6
Livial (0) = Li)o( + (B)
ASOR log (exp(Lcr.oni(()) + exp(Ccr,ang + exp(Ca.sc()))
Theoretical Justification. Minimizing Luaat (0) aims to achieve state where: 1. The sum of the primary objectives (Ccpo + Lp.sc) is low. 2. The SOR term, leveraging Proposition || ensures that the maximum of the constituent objectives (Cer.orig LeRaug; LB.sc) is also kept low.
This formulation implicitly seeks solution where no single objective can be significantly improved without degrading another, which is characteristic of Pareto-optimal solutions. The SOR term dynamically adjusts the pressure on each constituent objective. If, for instance, LB.sc becomes very large (e.g., due to difficulty in representing extremely rare tail classes overfitting), the gradient contribution from the SOR term with respect to LBsse will increase, effectively pushing the optimizer to reduce it. Similarly, if LeE.ng is high (poor classification original data), SOR will penalize this.
This dynamic balancing is crucial for pathological long-tails.
B-SCL (O2) provides the necessary focus on tail classes by up-weighting their contribution to representation learning, fostering discriminative features despite data scarcity.
CPO (O₂) ensures general classification utility.
SOR acts as the arbiter, preventing either the tail-class specific learning or the general clas- sification learning from excessively dominating and destabilizing the other, thus guiding the optimization towards robust equilibrium suitable for the extreme imbalances encountered in scientific discovery. The Appendix C provides more theory.
4 Experiments
In this section, we conduct extensive experiments to evaluate the efficacy of our proposed method, referred to Ours, in addressing pathological long-tailed recognition. We first detail the datasets and evaluation metrics (Section 4.T.. We then outline the experimental setup, including baselines and
implementation details (Section 4.2 Subsequently, we present quantitative results on both real-world
scientific datasets and synthetic long-tailed benchmarks (Section 4.3), followed by ablation studies (Section 4.4) and qualitative analyses (Section 4.5)
4.1 Datasets, Metrics, and Pathological Imbalance
The variable T is used to quantify the degree of pathological imbalance the dataset. A higher value of T corresponds to more pronounced imbalance. It is defined as:
N Nmajority
(8)
N minority Nelasses
where Nmujority represents the number of samples in the majority class, N Nminority represents the number of samples in the minority class, and N denotes the total number of classes.
5

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X10
Ci with diffrent
X10
ZINC FLUOR DATASET
x10
X10
3.0
Number of T'aiin Samples
2.5
Number Samples
2.0
2-
0.5
1.5
0
Orange
Red
Infrare
0.0
hone
ship
Irick
0.5
0.0
automobile sirplane
bird
Car
deer
dog
frog
horse
ship
truck
Ultravolet
Vinler
Blue
Green
Yellow
Orange
Red
Inftared
Meta sel
Train sel
(a) Training sample distribution per class in CIFAR- 10-LT under different settings.
Comparison of meta-samples per class with train- ing samples ZineFluor.
Figure 2: Dataset characteristics: (a) CIFAR-10-LT class distributions. (b) ZincFluor sample counts.
Table 1: The anonymized ZincFluor dataset examples.
Index
SMILES
Pred Fluor Colour
Intensity
Fluor Value
ZINC1
Ultraviolet
Weak
ZINC2
Ultraviolet
Weak
ZINC3
Ultraviolet
Weak
ZINC4
Violet
Weak
Violet
Weak
ZINC6
Blue
Weak
Real Dataset: ZincFluor. This classification dataset from a chemical laboratory. Its general content is exemplified in Table As shown in Figure 2b the dataset exhibits an extremely patho- logical class imbalance with an imbalance degree T 137.54 after an 8:2 train-test split. This severe imbalance poses a significant challenge to existing long-tailed learning methods. The dataset comprises 8 distinct fluorescence levels used as classes.
Synthetic Datasets: CIFAR-LT. To comprehensively evaluate robustness, we use long-tailed variants of CIFAR-10 and CIFAR-100 [L]] (i.e., CIFAR-IO-LT and CIFAR-100-LT). We control the imbalance ratio (IR = Nmajority / /imiorrtt) to construct datasets with varying degrees of pathological imbalance T. Figure 2a visualizes the training sample distribution across classes in CIFAR-10-LT under different T settings.
Evaluation Metrics. We report Top-1 accuracy the primary metric. For ZinoFluor, show per- class Top-1 accuracy and aggregated tail-class accuracies (Tail Top-6, Top-4, Top-2). For CIFAR-LT, we report overall Top-1 accuracy ("All"), and accuracies on "Head",, "Medium", and "Tail" class splits based on training sample counts.
4.2 Experimental Setup
Baselines.
To rigorously evaluate our framework, we compare Ours against a diverse set of represen- tative LTR baselines: (1) CE: Standard Cross Cross-EEtroppy training. (2) BS [19]: Balanced Softmax for
logit adjustment. (3) BCL [6]: Balanced Contrastive Learning. (4) CE-DRW and LDAM-DRW [2]: Re-weighting and margin-based based loss combined with Deferred Re- Reightig,,, (5) KPS 16: Key Point Sensitive loss focusing on tail-class anchors. (6) LORT 21: Logits Retargeting approach. For the ab- lation study ZineFluor, the "base" refers to the bosee method (Logits Over (ver-Smoothing) 25 which serves as our primary classification backbone.
Implementation Details. All models were implemented using PyTorch and PyTorch Geometric. The experiments were conducted on a single NVIDIA Tesla A100 GPU, with results reported accordingly. Specifically, for the ZincFluor dataset, RDKit was utilized to convert SMILES strings into graph data,

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and backbone network consisting of six stacked GCN layers was employed. During training, the number of epochs for the ZincFluor dataset was set to 100. For all other experiments, configurations followed those of LOS, Models were trained for 200 epochs using the SGD optimizer (learning rate 1r=0001, momentum=0.9, weight decay=5e-3) in conjunction with the CosineAnnealing..k learning rate scheduler.
4.3 Quantitative Results
Table 2: Top-1 accuracy on ZincFluor T = 137.54. The grayed-out section indicates the primary
observation indicator. Blod indicates the best performance while underline indicates the second best.
Fluor Leval
Tail Top acc
Method
2
3
4
7
8
Top-6
Top-4
Top-2
CE
85.19
70.49
19.71
25.62
0.00
0.00
73.40
0.00
19.78
18.35
36.70
BS
82.73
30.66
43.21
28.51
0.00
25.00
72.34
0.00
28.17
24.33
36.17
BCL
86.45
51.17
51.82
22.31
17.43
40.38
69.15
50.00
41.84
44.24
59.57
CE-DRW
94.52
45.62
27.59
26.86
12.84
42.31
67.02
33.33
34,99
38.87
50.17
LDAM-DRW
91.93
47.27
28.91
20.66
22.94
28.85
69.15
33.33
33.97
38.56
51.24
KPS
91.10
45.70
51.09
23.97
1.83
19.23
71.28
0.00
27.90
23.08
35.64
LORT
72.23
25.81
1.75
33.88
0.00
26.92
75.53
0.00
23,01
25.61
37.76
Ours
90.97
42.21
58.10
21.49
11.01
34.62
67.02
66.67
43.15
44.83
66.84
Performance on ZincFluor. Table details the Top-1 accuracy on ZincFluor (T 137.54). Our method demonstrates highly competitive performance individual "Fluor Levels" and substantially outperforms all baselines in tail-class focused metrics. Notably, Ours achieves a Tail Top-2 accuracy of 66. 84%, significant improvement over the second- best, BCL (59.57%). This underscores method's capability in handling real-world, pathologically imbalanced scientific data.
Table 3: Top-1 accuracy on CIFARIO-LT with different Imbalance ratio. The grayed-out section indicates the primary observation indicator. Blod indicates the best performance while underline indicates the second best.
IK-10000
1R-500
IR-00
IK-100
Method
7
100
50
T
20
T
10
Heal
Mediam
Tail
All
Head
Mediom
Tail
All
Heal
Mediumm
Thil
Al1
Heal
Medium
Thill
All
45.0
71.00
15.31
76.68
79.82
57.3
28.55
65.06
41.25
70.79
82.47
71.30
79.18
83.25
81.2
82.84
CF-DRW
77.97
55.15
4.15
58.64
$1.58
56.15
31.2
66-43
79.34
65.17
75.09
81.94
68.9
70.33
LDAM DRW
75.57
$2.0
15.25
61.19
78.27
59.75
40.7
67.05
78.79
63.7
74.29
$1.95
68.55
79.29
KPS
78.9
56.85
6.65
60.04
78.95
45.2
42.75
61.96
82.27
57.23
74.76
82.73
61.0
73.38
LORT
80.75
65
0.05
61.52
0.05
60.61
83.36
53.50
75.9
83.76
$4.00
Table 4: Top-1 accuracy CIFAR100-LT with different Imbalance ratio. The grayed-out section indicates the primary observation indicator. Blod indicates the best performance while underline indicates the second best.
IR=500
IR=200
IR=100
Method
T
T
Head
Medium
Tail
All
Head
Medium
Tail
All
Head
Medium
Tail
All
CE
80.96
46.15
7.37
36.59
79.07
51.55
6.87
42.38
78.09
48.51
10.97
47.6
78.81
50.35
40.57
55.06
46.87
75.46
$2.06
27.23
52.8
BCL
78.31
51.31
14.96
40.88
76.73
53.48
20.44
47.57
74.57
52.66
26.23
52.4
CE- DE-DRW
77.58
47.08
13.58
38.93
74.87
52.55
18.71
46.05
75.89
51.69
22.07
51.27
LDAM-DRW
74.73
49.58
15.83
39.92
73.97
52.29
18:21
45.5
72.74
51.09
21.80
49.88
KPS
78.96
48.35
12.94
39.31
77.27
52.84
16.97
46.18
76.54
45.6
22.6
50.93
LORT
67.69
39.46
7.44
31.43
71.63
56.9
20.21
47.01
70.11
55.37
33.33
53.92
Ours
68.57
56.65
22.52
43.37
68.26
60.38
30.30
51.02
71.57
62.02
32.03
56.37
Performance on CIFAR-LT Benchmarks. Across CIFAR-LT benchmarks (Tables 3 41 our method consistently achieves superior overall accuracy and, more critically, demonstrates substantial gains in tail class accuracy across all tested imbalance ratios. For instance, on CIFAR- 10-LT with extreme imbalance (IR=1000), our tail accuracy reaches 38. 88.99% significantly outperforming BCL (28.55%), alongside leading overall accuracy (69. 20% vs. 65.06%). This superior tail performance extends to CIFAR-100-LT,, where at IR=100, our 32.03% tail accuracy notably exceeds competitors (e-g.. BS 27.23 27.23%), and IR=500, achieve 22.52% against BCL's 14.96% while consistently maintaining

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base
1.0
0.8
ONTTS w.d.
OurTS W.O.
ours
0.6
Accurey
Similarity
0.4
0.6
0.5
0.2
0.4
Ours
Base
0.0
0.3
Ultraviolet
Violet
Blue
Greem
Yetlow
Orange
Red Infurrd
aipliane
autumobile
bird
doar
day
frog
horse
ship
truck
(a) Ablation study ZincFluor. "sc." denotes B- SCL, "st." denotes SOR. Our full method outperforms ablated versions and the base.
(b) Cosine similarity between original and augmented sample features on CIFAR- R0-LT (IR=10, trained on IR=1000), Our method shows higher robustness.
Figure 3: Ablation study and representation robustness: (a) Component analysis of method. (b)
Feature similarity across augmentations.
the highest overall accuracies. These comprehensive results validate our approach' robustness and effectiveness in enhancing recognition of underrepresented tail classes, particularly under severe imbalance conditions.
4.4 Ablation Studies
To dissect the contributions of the components of method, conduct ablation studies on the ZincFluor dataset, with results shown in Figure Sa Removing the Balanced Supervised Contrastive learning loss ("se.") from our full model ("ours") leads significant drop in per-class performance, particularly for the tail classes, highlighting the importance of B-SCL for learning discriminative representations under severe imbalance. Similarly, removing the Smooth Objective Regularization term ("st.") also results in degraded performance compared to the full model, indicating that SOR plays vital role in balancing the different learning objectives and stabilizing training. The performance of our ablated models still generally surpasses the "base" LOS- based baseline. These studies confirm that both B-SCL and SOR crucial for achieving the superior performance of our proposed framework.
4.5 Qualitative Analysis
Representation Robustness to Augmentation. FigureB3) 3hossows the cosine similarity between the model outputs (features) of original samples and their augmented counterparts on CIFAR-10-LT (IR=10, models trained on IR=1000). Ours generally maintains higher similarity across classes compared to a Base method, suggesting that our approach learns representations that are more invariant and robust to data augmentations.
Class-Level Feature Discriminability. The quality of learned feature representations is further assessed visualizing class-level cosine similarity matrices on CIFAR-10-LT (IR=1000), as shown in Figure | Panel (a) (standard CE loss) exhibits a diffuse similarity matrix with poor separation between classes. In contrast, panel (b) (Ours) displays much clearer block-diagonal structure. indicating strong intra-class compactness and high inter-class separability. This demonstrates the superior ability of our method to learn discriminative features, which is fundamental for effective long-tailed recognition.
4.6 Discussion of Experimental Findings
The comprehensive experimental results consistently validate the efficacy of our proposed method. The substantial gains observed on the pathologically imbalanced ZincFluo dataset, especially in

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Our Method
Figure 4: Class-level feature representation cosine similarities on CIFAR-1O-LT (IR=1000). (a)
Standard cross-entropy loss. (b) Our proposed method, showing improved class separability.
recognizing rare tail classes, highlight its practical utility for scientific discovery tasks. Furthermore, its robust and superior performance across a wide spectrum of imbalance ratios on synthetic CIFAR- LT benchmarks underscores its generalizability and strength in handling varying degrees of data imbalance. The ablation studies confirm the synergistic contributions of the B-SCL and SOR components, and qualitative analyses provide visual evidence of the improved representation quality and feature discriminability achieved by our approach. These findings strongly support our central claim that tailored framework integrating balanced contrastive representation learning with dynamic multi-objective optimization is pivotal for effectively addressing pathological long-tailed recognition.
Conclusion
This paper tackled the critical issue of pathological long-tailed recognition scientific discovery, where rare instances crucial for breakthroughs are often missed by standard methods. We introduced a novel framework combining Balanced Supervised Contrastive Learning (B-SCL) to enhance tail- class representation and Smooth Objective Regularization (SOR) to dynamically balance competing learning objectives. Our approach ensures focused learning on sparse tail data without compromising overall performance. Extensive experiments the real- world ZincFluor dataset and synthetic CIFAR- LT benchmarks with extreme imbalances demonstrated significant improvements over state-of-the-an LTR techniques, particularly in identifying critical tail classes. This work provides robust tool for extracting valuable insights from severely imbalanced scientific datasets, paving the way for accelerated discovery. Future directions include incorporating domain knowledge and extending to other scientific data modalities.
Acknowledgements
The authors gratefulll acknowledge the support from the National Natural Science Foundation of China (NSFC) under Grant Nos. 62402472, and 12227901. This work was also supported by the Natural Science Foundation of Jiangsu Province (No. BK20240461), the Key Basic Research Foundation of Shenzhen (No. JCYJ202208 8 100005501),, 1), the Research Grants Council of the Hong Kong Special Administrative Region (GRF Project No. CityU 11215723), the Project of Stable Support for Youth Team in Basic Research Field, CAS (No. YSBR-005), and the Academic Leaders Cultivation Program USTC. The Al-driven experiments, simulations and model training were performed on the robotic Al-Scientist platform of Chinese Academy of Sciences.

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A ZincFluor Dataset: Molecular Fluorescence and Pathological Imbalance
This section provides more detailed introduction the ZincFluor dataset used our experiments (Section 4.2) We describe its origin, the underlying scientific task of molecular fluorescence prediction, and elaborate on why its inherent data distribution constitutes "pathological long-tail" challenge, distinct from standard long-tailed benchmarks.
A.1 Molecular Fluorescence and Its Scientific Signiticance
Molecular fluorescence is photophysical process where a molecule absorbs light specific wavelength and then re-emits light at longer wavelength. This phenomenon is of immense scientific and technological importance, underpinning applications in diverse fields such as:
Materials Science:
Development of organic light-emitiing diodes (OLEDs), fluorescent probes for material characterization, and luminescent sensors.
Biology and Medicine: Bioimaging (using fluorescent tags probes), drug discovery (screening for compounds with desired fluorescence properties or using fluorescent markers), and diagnostic assays.
Chemistry: Understanding molecular electronic structure, reaction monitoring, and analyti- cal techniques.
Discovering and synthesizing novel molecules with tailor-made fluorescence properties (e.g., specific emission wavelengths, high quantum yield, photostability, environmental sensitivity) is a core pursuit in chemistry and materials science. Predicting these properties computationally from molecular structure a valuable tool to accelerate this discovery process, avoiding costly and time-consuming experimental synthesis and screening.
A.2 The ZincFluor Dataset: Structure and Task
The ZincFluor dataset originates from experimental measurements conducted in a chemical laboratory. It comprises collection of molecular compounds, each represented by its SMILES (Simplified Molecular Input Line Entry System) string, which provides a concise textual description of the molecule's 's structure. For each compound, associated fluorescence property has been measured and categorized into one of distinct levels. These levels, serving as the classification labels in our task, represent different aspects of the molecule's fluorescent behavior, such intensity or emission characteristics (exemplified by the 'Pred Fluor Colour', 'Intensity', and 'Fluor Value' columns in Table I in the main paper). The task is predict the fluorescence level of molecule given its SMILES string (or its graph representation derived from it, as described in Section ??).
A.3 Pathological Imbalance in ZincFluor
As highlighted in the main paper (Figure pp), the ZincFluor dataset exhibits an exceptionally severe class imbalance across its fluorescence levels. Following an 8:2 training-test split, the imbalance factor T (defined as NNmajorit/ /aaee//((nnnann minorrtty NNelusee)) is calculated to be 137.54. This metric quantifies the extreme disparity between the most frequent and least frequent fluorescence levels relative to the total number of classes.
The distribution is not merely skewed; it pathologically imbalanced in the context of scientific discovery for the following key reasons:
Scientific Value Concentrated the Tail: The rare fluorescence levels (minority classes) often correspond to molecules exhibiting unusual, extreme, or highly specific fluorescent properties. These are precisely the properties that are most sought after for cutting-edge applications (e.g.. molecule with exceptionally high brightness for bioimaging, compound emitting light at a very specific wavelength for sensing). In essence, the "rare" instances in this dataset represent the potential scientific breakthroughs or novel materials, not jot.just less common variations of typical compounds.
2. Limited Sample Volume: Unlike large-scale public benchmarks like ImageNet-I.T or Places365-LT which have millions of images and hundreds/thousands of classes, scientific
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datasets like ZincFluor often arise from costly and labor-intensive experimental processes. The combination of extreme imbalance and limited total data volume means that the tail classes have critically few samples, sometimes only handful, making robust learning for these classes incredibly difficult.
3. Fundamental Task Objective:
The implicit goal in analyzing such scientific data is often to discover or identify these rare, high-value instances for further investigation. A model that performs well on the frequent (head) classes but fails to identify the rare fluorescent compounds in the tail effectively misses the primary scientific objective.
The ZincFluor dataset represents a "pathological long-tail" problem because the tail instances are not just rare data points, but are scientifically critical signals buried within sea of common observations. Standard LTR methods, primarily designed to mitigate majority bias in general classification tasks, struggle significantly with this unique combination of extreme imbalance, limited data, and the paramount importance of correctly identifying the scarce tail instances that drive scientific advancement. Our work is specifically motivated to address this particular challenge.
B Evaluation on Custom Places-LT Datasets with Reduced Class Counts
To further demonstrate the robustness and generalizability our method across different domains and varying degrees of pathological imbalance complexity, we conducted additional experiments on modified versions of the standard Places-LT dataset [2]. While the original Places-LT features
large number of classes (365), our definition of pathological long-tail in scientific discovery contexts highlights scenarios with extreme imbalance coupled with a modest number of classes and limited overall sample volume(Section I. To better align with this, we constructed synthetic long long-tailed datasets derived from Places-LL that maintain a high imbalance ratio but reduce the total number of classes.
B.1 Custom Places-LT Dataset Construction
We generated new synthetic datasets based on the original Places-LT dataset (with an imbalance ratio of 996) by performing class-level sampling. Specifically, we followed procedure to select subset of classes:
We identified the class with the maximum number of samples (most frequent) and the class with the minimum number of samples (least frequent) in the original Places-LT dataset. These two classes were always included in our custom datasets.
From the remaining classes in the original Places-LT, we uniformly sampled additional classes until the desired total number of categories was reached.
Using this procedure, we constructed three new datasets containing a total of 10, 50, and 100 classes, respectively. These datasets referred as "Places-LT (IR 996, 10 Categories)", "Places-LT (IR 996, 50 Categories)", and "Places-LT (IR 996, 100 Categories)".
Importantly, these newly constructed datasets maintain the same imbalance ratio (IR Nmajority /Mminonyy 996) as the original Places-LT. However, by significantly reducing the to- tal number of classes while keeping a very high IR, these datasets exhibit an even more pronounced "pathological" nature terms of the *relative sparsity tail classes within a smaller overall class space*, aligning more closely with the characteristics observed in datasets like ZincFluor compared the original 365-class Places-ITT, 3 shows the sample distribution characteristics for these three custom datasets, illustrating the persistent long-tat tail across varying numbers of categories.
B.2 Experimental Setup
B.3 Results and Analysis
Table presents the quantitative results the three custom Places-LT datasets.
The results show that our method consistently achieves the best overall Top- accuracy across all three variants of the custom Places-LT datascts, ranging from 10 to 100 categories, while maintaining
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Class Distribution in the Places-LT Dataset (10 Categories)
Number of Samples (log scale)
10
10²
10
Class Distribution in the Places-LT Dataset (50 Categories)
Number of Samples (log scale)
10a
102
10
Class Distribution in the Places-LT Dataset (100 Categories)
Number of Samples (log scale)
10a
102
10
Figure 5: The Sample Distribution Characteristics of Various Datasets Generated via Places-LT (IR 996) with 10, 50, and 100 Categories.
a very high imbalance ratio (996). More critically, our method demonstrates superior performance on the challenging Tail classes in all scenarios. For the 10-category dataset, Ours achieves Tail accuracy of 62.0 %, significantly outperforming the next best method, L.DA--DRV (60.33%). of classes increases to 50 and 100, while maintaining the extreme IR, the Tail task becomes even more challenging, and many baselines exhibit severely degraded performance (e.g., CE, CE-DRW. KPS, LORT showing zero or very low tail accuracy). In these more complex pathological settings
the number
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Table 5: Top-1 accuracy on Custom Places-LT Datasets (IR 996) with 10, 50, and 100 Categories. The grayed-out column indicates the overall accuracy. Bold indicates the best performance while underline indicates the second best.
IR 996 / 10 Categories
IR 996 50 Categories
IR 996 / 100 Categuries
Method
Head
Medium
Tail
All
Head
Medium
Tail
All
Head
Medium
Tail
All
CF
61.50
0.00
0.00
24.60
40.28
0.00
0.00
14.50
28.14
0.00
0.00
10.13
BS
93.25
56.00
39.00
65.80
66.17
51.00
35,60
53.38
53.39
43.27
32.95
44.85
BCL
89.00
82.00
56.00
77.00
77.22
62.23
39.10
63.00
63.78
58.52
50.70
58.85
CE- DE-DRW
94.50
62.67
37.67
67.90
66.28
44.36
15.10
46.44
53,97
39.77
16,90
40.31
LDAM-DRW
91.00
77.00
60.33
77.60
76.83
62.73
43.10
63.88
62.75
55.75
44.30
55.97
KPS
91.00
85.67
15.67
66.80
77.28
48.77
3,90
50.06
63.56
45,77
8,90
44,80
LORT
95.75
42.50
18.36
36.11
14.64
Ours
85.00
77.50
62.0
83,60
60.22
76.71
58.50
67.13
47.92
59.81
53.55
54.27
(50 and 100 categories), Ours continues to achieve the highest Tail accuracy (58.50% and 53.55 % respectively). substantially leading the second best methods (LDAM-DRW at 43. 13% and 44.30%).
Furthermore, Ours also shows strong performance on Medium classes (best on 50 and 100 categories) and competitive, albeit not always leading, performance on Head classes (best on 100 categories), demonstrating its ability to balance learning across the entire frequency spectrum. The strong overall performance (83.60% %, 67.13%. %, 54.27% %) underscores this capability.
These experiments on custom Places-LT datasets with varied, but modest, class counts further validate the effectiveness of our proposed framework in handling pathological long-tailes distributions beyond the specific domain of ZincFluor. They highlight our method's unique ability to maintain high accuracy on scarce tail classes while ensuring robust overall performance, a critical requirement for scientific discovery applications.
C Detailed Theoretical Analysis
This section provides detailed theoretical analysis of our proposed framework, emphasizing its formulation as an implicitly constrained multi-objective optimization problem and the role of Smooth Objective Regularization (SOR) in achieving a dynamically balanced solution for pathological tailed recognition. Our approach deviates from methods solely focused on manipulating classification logits or sample/loss weights based on class frequencies. by instead directly influencing the optimization trajectory to balance competing objectives via a principled penalty mechanism.
We define the model parameters as 0 TR.. The learning process is driven by three constituent loss functions defined on the dataset DD:
(0) LCE.orig (f):: Cross-Entren loss original data.
(0) = Leaang (0): Cross-Entropy loss on augmented data.
(0) = (n)) Balanced Supervised Contrastive Learning loss, which incorporates frequency-aware weighting Wy for tail classes (details in Appendix Section ??).
Let L(0) =
E 3 be the vector of constituent losses.
In the context of pathological long-tailed data, minimizing the simple sum Σh (0) can lead to suboptimal solutions where one loss is significantly higher than others. Our framework implicitly aims to solve a constrained multi-objective optimization problem: finding 0* that minimizes a primary objective while keeping all constituent losses below certain thresholds eg. This is conceptually similar to:
min
(O) s.t. (x( S Ek: k 1,2,3
Directly solving this is challenging. We approximate this via penalty method, augmenting the sum of losses with term that penalizes the maximum of the constituent losses.
Proposition 2 (LogSumExp as a Smooth Maximum) The LumExxp function, I.S(V)) = log Ad exp(vx), is differentiable, convex approximation of the maximum function, satisfying max; 2 < LSE(v) < max; v, + log M for any vector [Vi...., UM] R
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Let Umax ()..,, Ug. For the lower bound:
exp(vi) 2 exp(Umax).
(9)
Taking the logarithm (monotonic):
log Σ exp(1:) 2 (cxp((mmxx) Umax-
(10)
Thus, max; 2s S LSE(V).
For the upper bound: Since v/ S Umax for all i, exp(U;) S exp(Umaax) Summing:
M
M
Σ 2=1
exp(e:) -
exp(Umaxx = M exp(Umaxx).
(11)
Taking the logarithm:
M
log Σ exp(v:)
< log( M
= log M + log(exp("max)) log M + Umax
(12)
Thus, LSE(v) I max, Uj + log M. Differentiabillity and convexity are standard properties of LSE.
We define the Smooth Objective Regularization (SOR) term using the LSE function applied to vector of constituett losses L(0):
CSOR ASoR LSE(((())/s) (TOR))
(13)
where ÀSOR and TSOR 0. In our implementation, set TSoR and M 3. yielding:
LSOR (0) ASOR log (cxp(41 exp((2()) + exp(C3
(14)
Minimizing LSOR directly penalizes the largest constituent loss, pushing it down relative to the others.
Our total training objective is defined as the sum of the constituent losses augmented by the SOR term:
Ciotal (0) (x) + CSOR(()
(15)
Substituting the expression for LSOR:
3
Liotal (0)
Σ Lx(0) + ASOR log
Σ exp((c((0)
(16)
k=1
Minimizing Liotal serves as a penalty method approximation to the constrained multi-objective problem. It drives down the sum of losses while simultaneously using LSOR to keep the maximum loss value in check, acting as soft "tail penalty" (when L3 high) and a "smooth constraint" promoting balance across all objectives.
The dynamic balancing property is evident from the gradient of (0);
V Crotall (
Σ VoLu(O) VoLSOR (0).
(17)
The gradient of the SOR term derived using the chain rule. Let La = (x):):
Exxp(Ly)
VoLsOR (0) = ASOOR
V log
i log Σ3 exp(I,,) [.7))
>SOOR
VOL4
Th
exp(Le) LT) VoLi(O).
= ASOR
3 1 exp(L,)
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Let (0) ش ( These Pa values form probability distribution over the constituent
(x1)
losses, where Pa is high when Ly is large. The total gradient becomes:
V aaonlll (0) VoLu(0) + ASOR Σ Pr(O)Voox(O)
(18)
Rearranging terms:
+ XSORD:
(19)
Equation 19 reveals the dynamic balancing. The gradient of each constituent loss VoLx contributes to the total gradient with weight (1 + \SORPk) When specifie loss La becomes significantly larger than others, Ps: - 1, and the weight (1 ASORPE) + 1 + ASOR, effectively amplifying the gradient VoLa. Conversely, for small loss Lj. Pj O, and its gradient weighted by approximately 1. This mechanism ensures that the optimization actively targets the largest loss component, pulling down.
This adaptive weighting, governed by the relative magnitudes of L1, L2, La, imposes the "smooth constraint" by discouraging any single loss from dominating. For pathological long-tails, this is crucial: it prevents the model from solely optimizing the easily satisfied CPO on head classes while neglecting the critical Ly for tail classes, and vice-versa. Instead, promotes balanced decrease across all objectives, leading to a more robust model capable of deciphering the challenging extremes. This principled approach, rooted in approximating constrained multi-objective optimization via SOR's s gradient dynamics, provides a theoretical basis for our method's superior performance on pathological long-tailed data.
NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
We list our contributions in detail the end of the Introduction.
Guidelines:
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NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer:
[Yes]
Justification:
We discuss the limitations in Appendix.
Guidelines:
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Reviews:
[{"review_id": "dEW30ETPOd", "reviewer": "Reviewer_a3v3", "summary": "This paper presents a very practical solution to the extremely imbalanced classification problem in science. The proposed scheme significantly improves the performance on the real-world ZincFluor dataset and CIFAR-10/100-LT, especially in identifying rare classes under extreme long-tailed distributions.", "questions": "Please refer to the weaknesses proposed in strength and weaknesses section.\n\nAnd I have some minor questions:\n\n- The model shows substantial gains on tail classes even under IR=1000, which is rare in related work. Do the authors believe the method can extend to cross-modal or higher-dimensional data such as protein structure prediction or astronomical spectra?", "limitations": "Limitations are discussed in the Appendix.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths：\n\n1. I think this paper has good practical value for real-world science tasks. The authors are encouraged to explore deployment strategies, parameter sensitivity, and dynamic tail identification in future work to improve community adoption.\n2. The proposed approach fits the characteristics of real-world scientific data: the limited number of classes, the small total sample size, and the critical nature of tail classes that are difficult to learn.\n3. The ZincFluor dataset is compelling and the experiments on CIFAR are rigorous.\n4. I think the objective design of this paper is balanced and well structured.\n\nWeaknesses：\n\n1. Some related works need to be discussed in more depth. This will help to more fully explain why their proposed method is particularly suitable for this problem scenario, especially when compared to other methods. For example, [1, 2, 3, 4].\n2. Furthermore, given that the concept of multi-objective optimization appears to be an inspiration for this paper, it would be beneficial to provide a more thorough comparison with relevant works in this area, such as, [5, 6].\n\n[1] Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach\n\n[2] Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition\n\n[3] Key Point Sensitive Loss for Long-Tailed Visual Recognition\n\n[4] AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation\n\n[5] Pareto Deep Long-Tailed Recognition: A Conflict-Averse Solution\n\n[6] Two Fists, One Heart: Multi-objective Optimization based Strategy Fusion for Long-tailed Learning", "was_revised": true, "final_justification": "After reading the authors’ response, my concerns have been resolved. I believe it is a strong paper that deserves to be published at NeurIPS. Therefore, I have maintained my positive evaluation.", "rebuttal": "### **Response to Reviewer a3v3**\n\nWe are very grateful to Reviewer a3v3 for the high praise of our work and the valuable suggestions provided. We are pleased that you recognize the practical value of our method in solving real-world scientific tasks, as well as the rigor of our experimental design. Below are our detailed responses to the weaknesses you have raised.\n\n#### **Response to Weaknesses**\n\n*   **W1: On the deeper discussion with related works (Refs [1-4])**\n\n    Thank you very much for providing these high-quality related works. We agree that a deeper comparison with these works can better highlight the uniqueness of our method. These works [1-4] have indeed addressed important long-tail challenges in their respective domains, such as classifier retraining [1], test-time distribution shifts [2], specific loss designs [3], and semantic segmentation [4].\n\n    However, a key difference between our method and theirs lies in the **problem scenario** we focus on. Our work attempts to capture a specific aspect of the \"pathological long-tail\" we define: that extreme class imbalance is **concentrated within a few classes**. This scenario is common in scientific exploration, for example, when building surrogate models for molecular design, where the data is severely imbalanced, and the classes with the potential for new discoveries are extremely rare. This implies a bias in importance, where the value of identifying these extremely rare classes far outweighs that of others. Our proposed framework is designed to address this dilemma where \"a few mainstream classes overwhelmingly dominate the entire dataset, while extremely valuable samples are exceptionally rare.\" At this level, the efforts of these other methods are not applicable, or rather, they aim to solve broad and general long-tailed distributions.\n\n    Despite the different focus, some of the excellent design ideas from these works, such as the logit adjustment strategy in [1] or the focus on key points in [3], could potentially be combined with our framework in the future to further enhance performance. **We will properly cite and discuss these works in the final version to more clearly define the scope and contribution of our method.**\n\n*   **W2: On the comparison with multi-objective optimization (MOO) related works (Refs [5,6])**\n\n    Thank you again for supplementing these key preceding works. Placing our method in the broader context of MOO research is very beneficial. These works do share similarities with ours, but the core objectives and levels are different:\n    *   **[5] Pareto-LTR** aims to use multi-objective optimization to resolve the intrinsic conflicts **between different classes**, especially the competitive relationship between head and tail classes during the learning process.\n    *   **[6] Two-in-One-Box** attempts, at a more abstract level, to pursue a Pareto-optimal performance upper bound by fusing different long-tailed learning strategies.\n\n    In contrast, the similarity with them lies in the introduction of multi-objective and trade-off ideas. However, our **multi-objective level and specific goals are not the same**. We do not directly address inter-class conflicts but rather treat **classification performance** and **tail representation learning** as two macroscopic objectives. Our core contribution is to use multi-objective ideas to **construct a novel optimization framework and a unique trade-off strategy** for the special scenario of \"pathological long-tail\" that we define.\n\n#### **Response to Questions**\n\n*   **Q1: Can the method be extended to cross-modal or higher-dimensional data?**\n\n    **We believe this is entirely possible.** The significant gains in tail-class performance at IR=1000 that you observed are a testament to the core strengths of our framework. The foundation of our framework (balanced contrastive learning for representation, multi-objective optimization for balance) is data-modality-agnostic, which gives it a broad scope of applicability and a unique perspective.\n\n    *   For **protein structure prediction**, we can encode the 3D structure or sequence information of proteins into graph or sequence data, and then use our framework to identify those structural motifs with rare but critical functions.\n    *   For **astronomical spectra**, we can treat them as one-dimensional signals and use our framework to find those extremely faint \"tail\" spectral features produced by unknown celestial objects or physical phenomena.\n\n    Admittedly, as Reviewer FP2j pointed out, when tackling these more challenging scientific problems, our core framework may need to be combined with **domain-specific components**, such as more powerful feature extractors designed for specific data types (e.g., GNNs or Transformers). We are very excited about this direction of expansion and believe our work provides a solid foundation for it."}, {"review_id": "mKfk25PfvR", "reviewer": "Reviewer_FP2j", "summary": "This work presents a meticulously designed and empirically robust framework, explicitly tailored to address the critical domain of pathological long-tailed recognition within scientific datasets.", "questions": "**Questions**\n\n1. Could the authors elaborate on the practical considerations or potential challenges in applying this framework to scientific datasets where the 'tail' is not just rare but also inherently noisy or ambiguously labeled?\n2. Given the multi-objective nature, are there specific criteria or metrics beyond accuracy that could be used to quantify the 'Pareto-optimality' achieved by the 'L\\_total' objective, especially in a scientific discovery context where different types of errors might have varying costs?\n3. How might the class-frequency aware re-weighting in B-SCL be adapted if the class frequencies are not precisely known a priori, which might be the case in some emerging scientific discovery scenarios?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths:**\n\n1. The paper's strength lies in its elegant formalization of pathological LTR as a multi-objective optimization problem. This principled approach, and the subsequent derivation of the SOR term, provides a strong theoretical underpinning for balancing competing objectives.\n2. The synergistic design of B-SCL and SOR is highly effective. B-SCL, with its class-frequency aware re-weighting, directly tackles the representation bottleneck for rare classes, while SOR acts as a dynamic arbiter, preventing overfitting to scarce data or dominance by head classes. The ablation studies convincingly validate the contribution of each component.\n3. The introduction of ZincFluor, a real-world dataset exhibiting the 'pathological' characteristics, provides invaluable validation. The use of CIFAR-LT variants with tunable imbalance ratios allows for systematic benchmarking across varying degrees of severity, thoroughly demonstrating the method's robustness.\n\n**Weaknesses:**\n\n1. While the 'TSOR = 1' simplification is mentioned, a more detailed analytical or empirical sensitivity analysis of ASOR and TSOR to different dataset characteristics (e.g., number of classes, total sample size, degree of imbalance) would provide deeper insights into the framework's generalizability and practical tuning.\n2. The paper utilizes a standard SupCon foundation. A brief discussion or comparison with more advanced contrastive learning techniques, or how they might be integrated into the B-SCL framework, could further highlight its extensibility and potential for future enhancements.", "was_revised": true, "final_justification": "This paper is clear to me and all concerns are addressed in the rebuttal.", "rebuttal": "### **Response to Reviewer FP2j**\n\nWe are very grateful to Reviewer FP2j for the deep understanding and high praise of our work. The questions you've raised are profound and directly address the core considerations of our method in practical applications. Below are our detailed responses to your points.\n\n*   **W1: Sensitivity analysis of $\\lambda_{SOR}$ and $\\tau_{SOR}$**\n\n    This is a very pertinent suggestion. Although we set $\\tau_{SOR}=1$ for simplification in the paper, a more in-depth analysis of these two hyperparameters would indeed provide valuable insights.\n\n    **Theoretical Analysis:**\n    *   **$\\tau_{SOR}$ (Temperature Coefficient)**: In the LogSumExp function, $\\tau$ acts as a smoothness regulator. As $\\tau \\to 0$, the LSE function becomes \"sharper,\" approaching the non-differentiable `max` function. As $\\tau \\to \\infty$, the LSE becomes \"flatter,\" approaching the average of all loss terms. Thus, $\\tau$ controls the penalty strength of our SOR term on the \"worst-performing\" objective. A smaller $\\tau$ implies a stricter \"min-max\" constraint, while a larger $\\tau$ is closer to simple loss aggregation. Our choice of $\\tau_{SOR}=1$ is a balanced one in both theory and practice, as it maintains focus on the worst objective while ensuring sufficient smoothness for optimization.\n    *   **$\\lambda_{SOR}$ (Regularization Strength)**: This parameter controls the weight of the entire SOR penalty term. A larger $\\lambda_{SOR}$ means the model will more aggressively balance the objectives to prevent any single one from becoming too large, while a smaller $\\lambda_{SOR}$ implies less intervention, closer to the sum of the base objectives.\n\n    We will provide the relevant analysis for your review as soon as possible during the rebuttal period.\n\n*   **W2: Integration with more advanced contrastive learning techniques**\n\n    You've pointed out an excellent future direction. Our B-SCL framework is designed to be **modular and extensible**. It is entirely possible to integrate more advanced contrastive learning strategies to achieve stronger feature extraction capabilities, thereby better serving scientific problems. For example, when extending our method to fields like **genomics**, we could incorporate more complex Graph Neural Network (We have done this in ZincFluor to better adapt to the molecular field) architectures or self-attention mechanisms (like Transformers) as feature extractors to better capture key patterns from complex gene regulatory networks. We will discuss these potential extensions in the conclusion to highlight the future potential of our framework.\n\n*   **Q1: Practical considerations for applying the framework to noisy or ambiguously labeled scientific datasets**\n\n    This is a very practical challenge. In real-world scientific exploration, tail data is not only scarce but often accompanied by high noise or label uncertainty (e.g., the fluorescence intensity of a molecule might be mislabeled due to experimental fluctuations). Our framework has certain intrinsic advantages in this regard:\n    1.  **Regularization Effect of SOR**: Our **SOR mechanism, through dynamic balancing across different batches, prevents any single loss term (including $L_{B-SC}$, which is dominated by tail samples) from becoming excessively large**. This means that even if $L_{B-SC}$ attempts to surge due to noisy samples, the SOR term will impose a strong penalty, pulling it back and thus suppressing overfitting to noise.\n    2.  **Relative Learning of B-SCL**: Contrastive learning inherently learns the **relative relationships** between samples rather than fitting an absolute label. It focuses on \"sample A is more similar to sample B than to sample C.\" This learning paradigm is more robust to isolated label noise compared to cross-entropy loss, which directly fits one-hot labels.\n\n*   **Q2: Specific metrics to quantify \"Pareto optimality\"**\n\n    In the multi-objective optimization problem of long-tail recognition, the \"Pareto optimality\" of a solution is manifested in finding the best balance between the performance on head classes and tail classes. If we wish to construct a metric different from accuracy, we might assign weights to different classes based on the specific task to better quantify the Pareto front.\n\n    Beyond accuracy, we can use more task-oriented metrics. For example, in **new material discovery**, misclassifying an unstable compound (typically a head/medium class) as \"high-potential stable\" (a rare tail class, a false positive) could lead to significant investment of R&D resources in the wrong direction, with a cost far greater than missing a truly potential material (a false negative). We could define a **cost-sensitive loss** that heavily penalizes false positive predictions for tail classes. In this case, quantifying \"Pareto optimality\" becomes a matter of finding the best balance between overall prediction accuracy and the **total expected R&D cost**.\n\n*   **Q3: The case where class frequencies are not precisely known a priori**\n\n    This scenario partially highlights the advantages of our framework. In situations with unknown or dynamically changing class frequencies, relying on a fixed re-weighting or re-sampling strategy would lead to very unstable performance. However, our **SOR dynamic balancing mechanism does not directly depend on precise class frequencies**. Its role is to monitor the magnitude of various learning objectives (like classification loss and contrastive loss). If an objective (e.g., the tail contrastive loss) suddenly increases due to unknown changes in the class distribution, SOR will **automatically and dynamically** apply a penalty to prevent the model from spiraling out of control. This adaptive balancing capability allows our framework to provide an effective exploration when facing unknown or dynamic class distributions."}, {"review_id": "u8aYsaxRvi", "reviewer": "Reviewer_GXBa", "summary": "This paper focus on the challenge of pathological long-tailed recognitions, where rare but scientifically important instances are often underrepresented.\nThe authors propose a framework that combines Balanced Supervised Contrastive Learning (B-SCL) with Smooth Objective Regularization (SOR) to improve tail-class representation.\nExperiments on the real-world ZincFluor dataset and synthetic CIFAR-LT variants show improved performance, especially in tail-class accuracy.", "questions": "1. Line 150, is Target 1 referring to Eq. 3?\n\n2. The quantitative results show notable drops in performance on head classes. Could the authors provide a deeper analysis of this trade-off? \n\n3. The composition of Places-LT does not follow the full 365-class structure of the original Places365. Does reducing the number of categories lower the difficulty of the dataset?\n\n4. In Eq. 3, are $\\epsilon_1$ $\\epsilon_2$ and $\\epsilon_3$  pre-defined hyperparameters? Or trainable parameters?", "limitations": "See Question 2. Additionally, the proposed method involves several hyperparameters, which may affect its stability and generalizability.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "strengths:\n1. The paper introduces a new real-world benchmark, the ZincFluor dataset, which captures the severe imbalance characteristics common in scientific discovery scenarios. This contribution can be valuable to the community.\n\n2. The formal description for the identification of pathological long tails appears to be principled and well-structured.\n\n3. The combination of different strategies is targeted and has a clear design motivation.\n\n\nweakness: \n\n1. The definition of $\\mathcal{T}$ (E1.8) is ambiguous and possibly misleading. \nFor a fixed imbalance ratio, increasing the number of classes reduces $\\mathcal{T}$. \nDoes a lower $\\mathcal{T}$ imply a less pathological distribution, or a harder dataset due to more classes with fewer samples each? If T cannot be reliably interpreted as a measure of dataset difficulty, its utility is unclear.\n\n2. Although the author(s) use LogSumExp, they do not specify whether they employ a numerically stable implementation. \nProviding more discussion, such as on implementation details or numerical stability, will be helpful.\n\n\n3. The real-world evaluation is limited to a single dataset (ZincFluor), and the synthetic benchmarks are relatively small-scale (CIFAR-10/100-LT).", "was_revised": true, "final_justification": "After reading the rebuttal from the authors and the comments from other reviewers, I believe the authors have addressed the concerns well. I acknowledge their efforts and will raise my score to Accept. \n\nI recommend the authors incorporate the following revisions in the final version:​​\n\n- ​Clarify ambiguous descriptions, particularly regarding W1 and Q1;\n- ​Including Q4 response.", "rebuttal": "### **Response to Reviewer GXBa**\n\nWe sincerely thank Reviewer GXBa for the meticulous review and the valuable questions raised. Your insights have helped us identify and clarify key points in our paper. Below are our responses to the weaknesses and questions you have raised.\n\n*   **W1: On the ambiguity of the definition of $\\mathcal{T}$ (Eq. 8)**\n\n    We completely agree with your observation. The **intention** behind our custom metric $\\mathcal{T}$ was to capture a specific aspect of the \"pathological long-tail\" we define: that extreme class imbalance (reflected by $N_{majority}/N_{minority}$) is **concentrated within a few classes**. The $N_{classes}$ term in the denominator was meant to \"penalize\" datasets with many classes, thereby highlighting the \"few classes, extreme imbalance\" scenario we focus on, making the T-value higher in this specific context. This stems from an implicit bias we identified in our practical experience with special requirements in the field of science, where, due to the small total number of classes, the sample size disparity among the few classes is more pronounced, a challenge that existing strategies struggle to address.\n\n    We acknowledge that in more general long-tail scenarios, this metric may not fully or reliably explain the dataset's difficulty. **Thank you very much for your suggestion. In the final version, we will more rigorously explain the specific scope and limitations of this metric, as well as its distinction from the imbalance ratio, to avoid confusion.**\n\n*   **W2: On the numerical stability of LogSumExp**\n\n    You've raised a very important implementation detail. Yes, we did consider using a scaling factor to prevent numerical explosion. However, in our experiments, given the framework and the range of loss values, we found that numerical overflow situations hardly ever occurred. Therefore, for the sake of simplicity, we did not include this extra hyperparameter in the final version. **We will discuss this point further in the appendix of the subsequent manuscript to ensure the full reproducibility of our work.**\n\n*   **W3: Evaluation is limited to a single real-world dataset and smaller synthetic benchmarks**\n\n    We understand this limitation. To demonstrate the generalization capability of our method and its competitiveness on larger-scale benchmarks, we have **made our best effort to supplement experiments on the widely-adopted iNaturalist 2018 dataset**.\n\n##### **Performance Comparison on iNaturalist 2018**\n\n| Method | Top-1 Accuracy (%) |\n| :--- | :---: |\n| *MiSLAS [ICCV'21] | 70.7 |\n| *RIDE [ICLR'21] | 71.8 |\n| *SADE [CVPR'22] | 72.9 |\n| **Ours** | **74.9** |\n\\* Performance data marked with an asterisk is cited from \"Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition\" [CVPR'23].The results show that our method also achieves state-of-the-art performance on this large-scale benchmark, demonstrating its strong generalization ability.\n\n### **Response to Questions**\n\n*   **Q1: In line 150, does objective 1 refer to Equation 3?**\n\n    **Yes, objective 1 conceptually corresponds to the main optimization objective in Equation 3.** We apologize for not making this clearer in the original text. We will revise the wording in the final version to explicitly state this correspondence.\n\n*   **Q2: A deeper analysis of the trade-off regarding the performance drop in head classes**\n\n    This is a very insightful observation. Essentially, our method can be viewed as a **constrained optimization problem that leans more towards learning hard samples (driven by the B-SCL term) while ensuring overall performance is maintained (constrained by the CPO term)**. This is fundamentally different from many existing strategies, which typically still prioritize maximizing overall performance and only add some balancing strategies as an auxiliary. Therefore, our method may appear to place less emphasis on head-class performance in some scenarios. This is a **principled trade-off** made to achieve significant gains in tail-class performance, and the dynamic constraint ensures the degree of this sacrifice is managed. This also aligns with the real-world requirements of the \"pathological long-tail\" we define (e.g., in scientific discovery), where the value of identifying rare but critical instances far outweighs gaining a few more points of accuracy on common instances.\n\n*   **Q3: The composition and difficulty of Places-LT**\n\n    Regarding Places-LT, reducing the number of classes does indeed change the dataset's characteristics. For the **imbalance ratio (IR)**, since the denominator (number of samples in the minority class) and the numerator (number of samples in the majority class) remain unchanged, the IR value itself is not affected by the reduction in the number of classes. However, for our custom metric $\\mathcal{T}$, since the denominator includes $N_{classes}$, reducing the number of classes would increase the $\\mathcal{T}$ value, which, from the \"intent\" of our metric, represents a more \"pathological\" problem. Of course, the overall difficulty of a dataset is a complex concept that depends not only on the degree of imbalance but also on the number of classes, inter-class similarity, and other factors. For a large dataset, the change in difficulty may not be solely attributable to this.\n\n*   **Q4: The nature of $\\epsilon_1$, $\\epsilon_2$, and $\\epsilon_3$ in Equation 3**\n\n    **They are neither pre-defined hyperparameters nor trainable parameters.** Equation 3 is our way of **conceptually formalizing** the problem, representing our approach of converting the original problem into a constrained optimization problem. Here, $\\epsilon_i$ represents an **implicit, ideal constraint boundary**. In the actual optimization process, because we use **LogSumExp as a smooth approximation to the Min-Max strategy**, we bypass the need to directly handle or set these $\\epsilon_i$. These constraint boundaries are **automatically and implicitly managed** by the penalty mechanism of the SOR term, without us needing to know their specific values."}, {"review_id": "jKXpzYt1KL", "reviewer": "Reviewer_7mHX", "summary": "This paper addresses a long-tail distribution problem by proposing a novel framework that combines Balanced Supervised Contrastive Learning and Smooth Objective Regularization, formulated as a multi-objective optimization problem. The key point of the proposed method is the use of the log sum exponential loss function to handle a multi-objective optimization problem. Experiments on both real-world and synthetic datasets show significant improvements compared to prior methods.", "questions": "Q1. What would be the performance difference if LogSumExp in SOR were replaced by a simple weighted sum? \n\nQ2. Why were widely adopted long-tailed benchmarks like ImageNet-LT and iNaturalist not used? Can the proposed method generalize to more standard LTR scenarios?\n\nQ3. Why were strong recent baselines like RIDE, MiSLAS, and SADE omitted from the comparison? \n\nQ4. How were the key hyperparameters tuned, and how sensitive is the model performance to them? Without sensitivity analysis, it is difficult to assess whether the proposed method (with numerous hyperparameters) can be applied to new datasets in real-world scenarios.\n\nQ5. Given the focus on tail-class recognition, why does the paper not include detailed error analysis such as tail-class confusion matrices or Top-K misclassifications?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strenghts:\n\n(+): The use of LogSumExp in SOR provides a reasonable method to yield balanced states across multiple objectives, beyond simple loss aggregation.\n\n(+): The proposed method achieves strong results on a real-world chemical dataset, with appropriate ablation experiments to validate the framework’s components.\n\nWeakness:\n\n(-): The study lacks evaluations on standard long-tailed benchmarks such as ImageNet-LT and iNaturalist, which are critical for demonstrating generalization and scalability.\n\n(-): Key recent baselines in long-tail distributions like RIDE, MiSLAS, and SADE are missing from the comparisons, despite their strong performance and structural similarity to the proposed method.\n\n(-): Although the paper emphasizes the role of LogSumExp, it does not include an ablation study comparing LogSumExp to a simpler weighted sum formulation of loss components, leaving the benefits of LogSumExp unverified.\n\n(-): The paper introduces many additional important hyperparameters, but offers no details or sensitivity analysis, raising questions about reproducibility and robustness.", "was_revised": true, "final_justification": "The concerns regarding generalization, comparisons with additional baselines, and the ablation study on LogSumExp have been satisfactorily addressed.", "rebuttal": "### **Response to Weaknesses and Questions**\n\n#### **Q1. Difference in performance if LogSumExp and a simple weighted sum.**\n\nThis is a very critical question. The difference is not only reflected at the theoretical level but also manifests in the optimization process and final performance.\n\n1. Theoretical \n\n**Simple Weighted Sum**: A method like $L_{total} = w_1 L_{CE} + w_2 L_{B-SC}$ represents a **static, pre-defined trade-off**. The weights $w_1$ and $w_2$ must be determined before training. However, in the pathological long-tail scenarios of unknown scientific tasks, a simple weighted sum can easily get stuck in a suboptimal solution due to improper weight settings.\n\n**Our SOR Method (LogSumExp)**: In contrast, our SOR term stems from the **Min-Max idea of multi-objective optimization**, aiming to minimize the worst-performing objective. The core advantage of LogSumExp, as a smooth approximation of the max function, lies in its **dynamic balancing**. If any loss term (e.g., $L_{B-SC}$ surging due to overfitting) becomes excessively large, the gradient of the SOR term will predominantly act on this \"out-of-control\" term, automatically and dynamically pulling it back to a reasonable level.\n\n2. Optimization \n\nA simple weighted sum faces a major practical challenge: the **complexity of hyperparameter search**. For N loss terms, N-1 weight parameters need to be tuned. These weights interact with each other, making it nearly impossible to find a \"universal\" weight combination that performs well across all datasets and imbalance ratios. This greatly reduces the method's **practicality and reproducibility**. The entire SOR term introduces only **one key hyperparameter, $\\lambda_{SOR}$**. This single parameter controls the overall strength of the \"dynamic balancing system\" without having to deal with the complex relative weights between individual loss terms. \n\n3. Experimental Analysis\n\nWe conducted a series of comparative experiments on CIFAR-10-LT, comparing our full method with a \"simple weighted sum\" version (where we set the weight of $L_{B-SC}$ to 1, a common baseline setting) and other SOTA methods.\n\n#### **Performance Comparison on CIFAR-10-LT (All: Overall Accuracy, Tail: Tail Class Accuracy)**\n\n| **Method** | **IR=100** | | | | **IR=200** | | | | **IR=500** | | | | **IR=1000** | | | |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | Head | Medium | Tail | All | Head | Medium | Tail | All | Head | Medium | Tail | All | Head | Medium | Tail | All |\n| **Simple Weighted Sum** | 86.32 | 82.25 | - | 85.51 | 82.07 | 60.10 | - | 75.48 | 83.62 | 72.75 | 28.35 | 70.39 | 82.26 | 68.55 | 2.50 | 63.57 |\n| **Ours** | 87.59 | 89.90 | - | **88.04** | 84.05 | 84.33 | - | **84.14** | 81.68 | 79.64 | **59.39** | **77.94** | 76.80 | 76.60 | **38.99** | **69.20** |\n| *SOTA (SADE)* | 87.14 | 85.05 | - | 86.72 | 84.74 | 68.23 | - | 79.79 | 84.33 | 72.20 | 28.60 | 70.76 | 83.80 | 61.90 | 5.60 | 63.78 |\n\nThe table clearly shows that as the imbalance ratio (IR) increases, the advantage of our method over the \"simple weighted sum\" becomes **increasingly huge**:\n*   At **moderate imbalance (IR=500)**, our method's tail accuracy (**59.39%**) is **more than double** that of the simple weighted sum (28.35%).\n*   At **extreme imbalance (IR=1000)**, the difference is even more staggering. The simple weighted sum almost completely fails on tail classes, with an accuracy of only 2.50%, and may require re-tuning to adapt. This robustness in extreme scenarios is one proof of our dynamic balancing strategy's effectiveness.\n\n---\n\n#### **Q2. Why not use widely adopted long-tail benchmarks like ImageNet-LT and iNaturalist?**\n\nThank you for this important question.\n\nFirst, we want to reiterate the **core motivation and specific focus** of our work. As emphasized in the paper, our research is **explicitly and intentionally** targeted at a specific, under-studied problem prevalent in scientific discovery: **\"pathological long-tailed recognition.\"** We define this as scenarios characterized by: (1) **extreme class imbalance** (e.g., IR > 500); (2) a **limited number of classes** (e.g., < 20); and (3) a **limited overall sample size**. This setup is designed to simulate the real-world challenge of finding \"needles in a haystack\" in fields like materials science and rare disease diagnostics.\n\nTherefore, our initial experimental design (focusing on ZincFluor and extremely imbalanced CIFAR-LT variants) was intended to deeply investigate and solve this specific yet crucial \"pathological\" problem. \"General\" long-tail benchmarks like ImageNet-LT and iNaturalist, with their large scale, many classes, and large data volumes, do not fully represent this \"data-scarce and extremely imbalanced\" pathological condition we initially aimed to address.\n\nNevertheless, we recognize the importance of your question regarding whether our method can generalize to more standard LTR scenarios, as this helps test its competitiveness in a broader context. During the rebuttal period, we have made our best effort to **supplement our experiments on the iNaturalist 2018 dataset**. The results are as follows:\n\n##### **Performance Comparison on iNaturalist 2018**\n\n| Method | Top-1 Accuracy (%) |\n| :--- | :---: |\n| *MiSLAS [ICCV'21] | 70.7 |\n| *RIDE [ICLR'21] | 71.8 |\n| *SADE [CVPR'22] | 72.9 |\n| **Ours** | **74.9** |\n\\* Performance data marked with an asterisk is cited from \"Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition\" [CVPR'23].\n\nThe core mechanism of our method—dynamically balancing competing learning objectives via **Smooth Objective Regularization (SOR)**—is also applicable in more general long-tail scenarios. We greatly appreciate this suggestion. We will include this new experimental result and its analysis in the appendix of the final version to fully demonstrate the robustness and broad applicability of our proposed framework.\n\n---\n\n#### **Q3. Why were powerful recent baselines like RIDE, MiSLAS, and SADE omitted from the comparison?**\n\nRegarding the choice of baselines, in our initial experimental design, because our work **explicitly focuses on the specific scope of \"pathological long-tailed recognition\"** (i.e., extreme imbalance, few classes, limited sample size), we primarily chose representative LTR methods with significant conceptual differences as baselines to study the effectiveness of traditional strategies. Nevertheless, we completely understand the necessity of comparing our method with these strong recent baselines to more comprehensively demonstrate its competitiveness.\n\nWe have made our **best effort to supplement direct comparisons with RIDE, MiSLAS, and SADE on the more challenging CIFAR-100-LT benchmark**. All experiments were run under our unified, standardized settings to ensure a fair comparison.\n\n##### **Performance Comparison on CIFAR-100-LT (All: Overall Accuracy)**\n\n| **Method** | **IR=100** | **IR=200** | **IR=500** |\n| :--- | :---: | :---: | :---: |\n| **Simple Weighted Sum** | 43.25 | 44.45 | 39.14 |\n| **Ride** | 42.71 | 40.74 | 36.72 |\n| **MiSLAS** | 45.39 | 42.53 | 40.69 |\n| **SADE** | 44.70 | 42.86 | 37.21 |\n| **Ours** | **56.37** | **51.02** | **43.37** |\n\nWe will include this important set of comparative experiments in the final manuscript. Thank you again for your valuable suggestion.\n\n---\n\n#### **Q4. How are the key hyperparameters tuned, and how sensitive is the model's performance to them?**\n\nOur method, through its theoretical design, **simplifies the complexity of hyperparameter tuning**. We do not use the traditional \"simple weighted sum\" strategy, which requires manually tuning a large number of relative weights (like $w_1, w_2, ...$) for multiple loss terms. Instead, we **formalize the problem as a constrained multi-objective optimization problem** (as in Eq. 3 of the paper) and then transform it into an easy-to-solve unconstrained problem by introducing a **Smooth Objective Regularization (SOR) term based on LogSumExp**. Ultimately, our total loss function contains **only one key, tunable hyperparameter, $\\lambda_{SOR}$**, which controls the strength of the entire \"dynamic balancing system.\" This simplifies a complex multi-dimensional weight search problem into a single-dimensional strength adjustment problem.\n\nIn our response to Q1, we have already demonstrated through experiments the effectiveness of our design. Due to time constraints, we were unable to provide an exhaustive sensitivity analysis during the rebuttal period. We are committed to supplementing the final manuscript with a **sensitivity analysis** of the key hyperparameter $\\lambda_{SOR}$ to fully demonstrate the robustness and ease of application of our method in real-world scenarios.\n\n---\n\n#### **Q5. Given the focus on tail-class recognition, why does the paper not include a detailed error analysis?**\n\nThank you for this valuable suggestion. We have **preliminarily supplemented a confusion matrix analysis for the two rarest tail classes in the CIFAR-10-LT (IR=1000) setting**. The analysis shows that our method can successfully identify a considerable number of tail samples, with the main confusion occurring between the two equally rare classes.\n\n##### **Tail Class Confusion Matrix**\nWhen CIFAR-10 IR=1000, we have 2 tail classes, and we achieve an accuracy of 38.99% on these classes. Below is our confusion matrix for the tail classes.\n$$\n\\begin{bmatrix}\n478 & 748 \\\\\\\\\n473 & 301 \\\\\\\\\n\\end{bmatrix}\n$$\n\nThis preliminary analysis has already provided us with valuable insights. We will include this in the final manuscript and are committed to conducting more detailed error analyses (e.g., Top-K misclassifications) to more comprehensively demonstrate the behavior and potential areas for improvement of our method."}]

Author rebuttal:

Meta-review: This paper tackles the challenge of pathological long-tailed distributions in scientific datasets. The authors propose a novel framework combining Balanced Supervised Contrastive Learning (B-SCL) and Smooth Objective Regularization (SOR) to enhance tail-class representation while balancing overall classification performance. Extensive experiments on both real and synthetic long-tailed benchmarks show significant improvements over existing methods, especially in identifying rare but important classes.

The paper has several strengths:

•	The paper introduces a novel and well-motivated framework specifically tailored to pathological long-tailed distributions in scientific data, going beyond generic LTR methods.

•	The experimental results are strong and convincing, with consistent improvements across real-world and synthetic benchmarks, especially on rare tail classes.

•	The methodological contributions are clear, with both B-SCL and SOR shown to be effective through ablation studies.

•	The paper is well-written and well-organized, and easy to follow. 

After the rebuttal, most of the reviewers’ concerns have been satisfactorily addressed. The authors clarified theoretical aspects of the multi-objective formulation and provided more justification for the choice of contrastive learning components. They also conducted extensive experiments in various datasets and achieved SOTA performance during the rebuttal period. Reviewers generally agreed that these responses strengthened the paper and resolved their major reservations.

I recommend accept. The paper makes a significant and well-validated contribution to the problem of pathological long-tailed recognition in scientific discovery. That said, I strongly encourage the authors to incorporate the reviewers’ suggested revisions into the final version. Addressing these points will further strengthen the paper’s clarity, reproducibility, and long-term impact.
Decision: Accept (spotlight)