Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration
Authors: Xinjie Yao
Yu Wang
Pengfei Zhu
Wanyu Lin
Ruipu Zhao
Zhoupeng Guo
Weihao Li
Qinghua Hu
Venue: ICML 2025 poster (2025)
Keywords: socialized coevolution
cross-task collaboration
dynamic weighting
knowledge interaction
Primary area: general_machine_learning->transfer_multitask_and_metalearning
TL;DR: We introduce a practical socialized coevolution paradigm with rigid mathematical motivation and explanation rooted in information theory.

Abstract: Traditional machine societies rely on data-driven learning, overlooking interactions and limiting knowledge acquisition from model interplay. To address these issues, we revisit the development of machine societies by drawing inspiration from the evolutionary processes of human societies. Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Coevolution (SC). Compared to most existing methods focused on knowledge distillation and multi-task learning, our work addresses a more challenging problem: not only enhancing the capacity to solve new downstream tasks but also improving the performance of existing tasks through inter-model interactions. Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks. Specifically, we introduce the dynamic hierarchical collaboration and dynamic selective collaboration modules to enable dynamic and effective interactions among models, allowing them to acquire knowledge from these interactions. Finally, we explore potential future applications of combining SL and SC, discuss open questions, and propose directions for future research, aiming to spark interest in this emerging and exciting interdisciplinary field. Our code will be publicly available at https://github.com/yxjdarren/SC.

Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration
Xinjie Yao 123 Yu Wang" 123
Pengfei Zhu 123 Wanyu Lin Ruipu Zhao Zhoupeng Guo
Weihao Li
Qinghua Hu 123
Abstract
1. Introduction
Traditional machine societies rely on data-driven learning, overlooking interactions and limiting knowledge acquisition from model interplay. To address these issues, revisit the development of machine societies by drawing inspiration from the evolutionary processes of human societies. Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Co- evolution (SC). Compared to most existing meth- ods focused on knowledge distillation and multi- task learning, our work addresses a more chal- lenging problem: not only enhancing the capac- ity to solve new downstream tasks but also im- proving the performance of existing tasks through inter-model interactions. Inspired by cognitive science, we propose Dynamic Information So- cialized Collaboration (DISC), which achieves SC through interactions between models special- ized in different downstream tasks. Specifically, we introduce the dynamic hierarchical collabo- ration and dynamic selective collaboration mod- ules to enable dynamic and effective interactions among models, allowing them to acquire knowl- edge from these interactions. Finally, we ex- plore potential future applications of combining SI. and SC, discuss open questions, and propose directions for future research, aiming to spark interest in this emerging and exciting interdisci- plinary field. Our code will be publicly available at
Learning
Sockety
Cocvolution
Society
Before Commosiccttio
Before Evolation
Skilled
Skilled in cudling A
Skilled classficcation
Skälled in detection p
mothematiics
Amalggons
After Communication
Evolation
Mathematics advanced codiig
Coding advanced mathematics
Classification via detection
Detection advanced classification
MATH
Figure I: Social learning in human society versus socialized cocvolution in machine socicty.
Similar to human society (Humphrey, 1976; Dunbar, 1998). each model in machine society evolves through knowledge acquisition, as illustrated in Figure 1. Traditional learn- ing paradigms largely depend on data-driven, single-mode learning. While some paradigms (Caruana, 1997: Hinton, 2015; Zhu et al., 2024) allow knowledge transfer from other models, they are limited by blind imitation and difficulty in retaining specialized expertise. In human history, cultural evolution has been driven by continuous capability enhance- ment, surpassing existing critical skills for survival (Henrich, 2016; Laland, 2017). This enhancement is achieved through ongoing interaction with the environment, rather than ex- tensive trial and data collection. This effective learning paradigm, known socialized coevolution (SC), has been studied in cognitive science (Thompson et al., 2022).
'College of Intelligence and Computing, Tianjin University. Tianjin, China 'Engincering Research Center of City Intelligence and Digital Govemance, Ministry of Education of the Pcople's Republic of China, Tianjin, China HHaihe Lab of ITAI, Tianjin, China "Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China 'School of Automation, Southeast University, Nanjing, China "chooo of New Media and Commu- nication, Tianjin University, Tianjin, China. Correspondence to: Pengfei Zhu cahupengfci@rtu.cdu.ccm
In machine learning, Knowledge Distillation (KD) (Hinton, 2015) is closely related to SC, as overcomes the limitation of relying solely on data for knowledge acquisition. How- ever, KD often blindly mimics the teacher model's predic- tions, which may not always represent valuable knowledge. Unlike humans, KD lacks mutual teaching and learning. An alternative is Multi-Task Learning (MTL) (Caruana, 1997), shared learning paradigm. While MTL improves perfor- mance by leveraging task correlations, often sacrifices task-specific expertise for generalization, making it hard to maintain performance on individual tasks, particularly when
Proceedings of the nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
tasks are highly heterogeneous. Thus, current paradigms limited in achieving effective coevolution.
These contributions are detailed as follows:
We introduce a practical learning paradigm, Socialized Coevolution (SC), in which models achieve coevolu- tion through mutual interaction.
Existing paradigms struggle to learn new knowledge and im-
prove capabilities through interaction, as humans do. Due to task and data heterogeneity, models find it difficult to interact effectively. For instance, KD causes blind imitation, hindering the learning of complementary task information and degrading original performance. Similarly, MTL sac- rifices task-specific expertise for generalization. In intelli- gent autonomous systems, the challenge is to acquire more knowledge while maintaining or enhancing original task expertise. By revisiting existing paradigms, we find that two problems are still open: (1) dynamic interactive learning in heterogeneous task scenarios, and (2) expanding general capabilities without compromising task specialization.
We discuss SC using an information-theowe frame- work, where sociability enables models with comple- mentary strengths to mutually and dynamically learn.
We propose novel insight into SC methodology, where organizational structures, interaction modes, and communication mechanisms ensure a trade-off between specialization and generalization.
Knowledge Distillation
Learn from Models
To address these problems, analyze two paradigms: KD and MTL. As shown in Figure 2, both KI) and MTI fail to balance the enhancement of original task capabilities and the expansion of general task capabilities due to blind imitation and an overemphasis on generalization. Moreover, KD and MTL rely heavily on large datasets for knowledge transfer, whereas SC enables dynamic cross-task interaction low cost, facilitating coevolution. Therefore, two significant issues should be explored to realize SC:
HET
Performance
Clesification
Detection
Logits
Features
Agent-Student
Agant-Tencher
Before Evulution
After Evolution
(a) Knowledge distillation paradigm
Multi-Tane Learning
Learn from Sharing
DacCLTS HEREDET
Performance
Classffiation
Detection
Layers
1: How to achieve dynamic interactive learning?
Parameters
2: How to balance specialization and generalization?
O00
Agent- Agrnt-CL--DE CLS-DET
Before Evolution
After Evolution
(b) Multi-task learning paradigm
In line with cognitive science (Tomasello et al., 1993: Mesoudi, 2021; Yao et al., 2024), new knowledge should be learned from experts different tasks and integrated with individual needs for coevolution. To achieve this, adopt dynamic collaboration, allowing models selectively inter- act and acquire valuable knowledge while avoiding blind imitation. Most cultural evolution, driven by population genetics (Mesoudi, ) and cognitive science (Tomasello, 2016), inspires our approach, which combines population- genetic-style modeling with directional bias transformation. By organizing experts into society, maintaining their inde- pendence, and enabling interactions, we expand capabilities while enhancing original performance, achieving SC.
Socialized Coevolution
Learn from Dy Dynamism
Performance
Interactivity
Hicrarchy
Agent-CLS
Agent-DET
Before Evolution After Evolution
(c) Socialized coevolution paradigm
Figure 2: Comparison of different learning paradigms.
2. Related Work
To validate this premise, we design an SC-based frame- work. We first establish that sociability is fundamental to SC, enabling models with complementary strengths to learn mutually. Based on this, the framework is designed around organizational structures, interaction modes, and commu- nication mechanisms. SC is implemented using hierarchi- cal organizational structures, progressive interaction modes, and strong- gooogg communication mechanisms. Specifi- cally, we create a society of independent classification and detection models, enabling selective collaboration for logits- based mutual learning and hierarchical feature interaction.
2.1. Knowledge Distillation
Knowledge Distillation (KD), as paradigm for knowledge transfer, focuses on enabling student models to efficiently inherit and apply the knowledge and capabilities of teacher models. Based the type of information emphasized dur- ing the distillation process and the levels at which distillation occurs, existing KD approaches (Gou et al., 2021) can be classified into three categories: (1) Response- lation emphasizes the outputs or logits of the teacher model, aiming to align the logits of the student model as closely
distil-
2

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
as possible with those of the teacher model to enhance per- formance (Jin et al., 2023); (Sun al., 2024); (Wei al., 2024). (2) Feature-based distillation focuses on knowl- edge transfer within the intermediate layers of the model, using the feature representations from the teacher model's s intermediate layers to guide the corresponding layers of the student model (Hao et al., 2024); (Lu et al., 2024b); (Huang distillation targets the rel- ative relationships between different samples in the feature space. By capturing the teacher model's 's rich representations of these relationships, the student model learns to capture the inherent structure of the data (Zhang et al., 2024); ( (Yang et al., 2022); (Xiao & Yamasaki, 2024).
3. Sociability in Coevolution
In this section, we address the issues outlined in Section I by providing precise definitions of the problem setup, followed by a concise theoretical analysis. The full proofs of the theorems are provided in the supplementary.
Problem setups: Let M = (M,, Mx) denote set of N models. Let X represent the input space, and Y denote the label space. The data associated with the n- th model is represented as DMM, = (Ii, Vi 1 where K KM., denotes the total number of samples for M,, Each sample xy E IRP corresponds to a label yi E YM.- To illustrate this setup more clearly, consider two models, M1 and M2, as examples. The expert task sets for these two models, MM and M2, are distinct, i.e., Ti and T2 represent the sets of tasks at which each model excels. Specificallyy the performance of the models these tasks is described as M > .M and M < _M2", indicating that M1 outperforms M2 on task Tı, while .M excels on task Tz. After cocvolution, the society S = M2), formed by the two models, can acquire superior expertise compared to each individual model, i.e., S MM and S > MA This collaboration enables broader task coverage and enhances performance on original tasks.
et al., 2024). (3)
Existing KD methods primarily rely on the knowledge ob- tained by the teacher model, often overlooking the reliability of the knowledge for each individual sample, as illustrated in Figure 2a. The key challenge in KD lies in how to effec- tively transfer knowledge by considering both the teacher's s capabilities and the student's needs. To address this, SC em- ploys dynamic interactive learning to avoid blind reliance, providing insights across different samples.
2.2. Multi-Tasss Learning
Multi-Task Learning (MTL) is a paradigm in machine learning that aims to learn multiple related tasks simulta- neously, enabling the knowledge from one task to benefit others. This approach seeks to enhance the generalization performance of all tasks involved. Existing methods (Van- denhende et al., 2022) can be broadly categorized into two main groups: (1) Encoder-focused architectures share information within the encoder, using either hard- or soft- parameter sharing, before decoding each task with an inde- pendent, sask-specific head (Agiza et al., 2024); (Xu al., 2024); (Yang et al., 2024b). (2) Decoder-focused archi- tectures first employ multi-task network to make initial task predictions, and then use features from these initial predictions to further share or exchange information during the decoding stage (Bhattacharjee et al., 2022); (Chen et al., 2024); (Lu et al., 2024a). In addition to the aforementioned approaches, other methods exist outside these categories. For instance, multilinear relationship networks (Long al.,, 2017) leverage tensor priors to capture task interactions, while soft layer ordering (Meyerson & Miikkulainen, 2017) enables flexible sharing of network layers across tasks.
Definition 3.1. (Specialization and Generalization) The society S learns more tasks while improving performance on original tasks, i.e., Ts = T U Tz, with STi > MM and S M T
To clearly elucidate the significance of coevolution, we define sociability information and subsequently conduct an analysis based on this premise:
Definition 3.2. (Sociability Information) Given the input variables XM,, XM2 and the target Y, the sociability in- formation provided by the models is defined as:
4 M. = 1(XM;;
(I)
+ M, = I[XXM;
The metrics 4,,, and 4 4, quantify the sociability informa- tion of XM, and XM, in SC. Higher values reflect greater sociability and coevolution potential, representing collab- oration effectiveness and information exchange between
X M, and X My. The following relation can be derived from information theory:
Existing MTL research primarily focuses on the sharing of general knowledge across different tasks, often neglecting the preservation of task-specific expertise, as illustrated in Figure 2b. The key challenge in MTL lies in the trade-off between task specialization and generalization. To address this, SC leverages social structures to categorize the model into main and auxiliary decision roles, offering insights into this dilemma.
T(XM, XMs V) = H,,, + +0 + V.. (3)
The Bayes error rate (Fukunaga & Hum- mels, 1987) represents the minimum error, with do and psin denoting the errors for multi-moded and single-model ee scenarios, respectively, for XM, and XMM-
Bayes error rate:
pmul Eery BM2 -Pe Me0,,,a NMy [1 max P(Y = y RM. sm.ll. (4)
VEY
3

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
Pein -BEM Px
[ max P(Y y/ yEY
(5)
4. Methodology
In this section, to address the two issues highlighted in Sec- tion 1, we introduce dynamic interactive learning as a means to balance specialization and generalization. This approach is realized in two ways. First, we facilitate dynamic interac- tions between teacher and student models, allowing them to adjust based on their respective strengths and needs. Second, we organize the models into a society where, depending on the task, some models take on the role of decision-made makers while others assist in the main process. The concepts of mutual growth through teaching and leveraging individual strengths considered forms of SC.
Theorem 3.3. (Key Factor Coevolution) Building on prior work (Cover, 1999; Feder & Merhav, 1994; Li al,, 2023a), we focus on preud and pain, as follows:
[XY XM,, log
Xx,) ) log?
pmuu $ 51-exp(-H(Y H(Y MM.ll (6)
HY 20Y1XM11 1- 2og2
log F < poin & 1 exp( II( XM.)).
Since
2M,, X(Y) (XM
(8)
We first analyze the driving factors of SC, introduce its core framework, and then detail the dynamic interactive learning mechanism along with our approach to balancing specialization and gencralization.
We can derive
H5( (XM)) log
oM log?_ 1 < 1 exp(- H[Y [xx,) ) m.)
(9)
Remark 3.4. The difference between prud and pesn reflects the effect of SC, with 4 M, being the key factor.
4.1. Key Factors of SC
In the face of the challenge to balance specialization and generalization within SC, a natural question arises: what are the key factors influencing SC?
Theorem 3.5. (Sociability Information Data) Building on prior work (Zuo et al., 2024). given the optimal parameter w" for the data DD and the normalizing constant pDD), have:
mi 15 20.69 70,
70.83 70: 75 70.81 70.75
(() = [DD du <. (D p() du p(D| w²) (10)
0.0 0. 0. 0.6 n5 0.4 03 0. D
10,00
nou72 1U.82
10.81
70072
10.10 70.12 70.96
70.75
71.00
Weights of Auncliary Logits
mi.53 70.6370775 70.81
70.83
70.75
70.73
70.76 70.78
70.73
70.75
70.51
10.50 70.77
70.78
70.85
70.81
70.52
70.81 30.95
70.82
70.83
70.78
Since
70.64 70.69 70.75
70.72
73.02 10.54 70.83
70.85
70, 70.92 70.52
Ds = DM, U UDass
(11)
-70,0M 70.75 70.000
70, 76 70.89
10. 20.39
70.92 70 10.57
We can derive
70.86
70.88 70.84
70.57
70.04
70.92
10.30
70.81
logp(w" (Ds) > logp(w" (.
(12)
70.68 70.77 70.96
70.91
70.89
m0.97 71. 71.05
70.85 70.83
70.51
-0.75 N0.22 $1.04
20
96
90.93
11 71 07 91 70.84 20.8)
70.78
70.60
Remark 3.6. Training on sociability data D Deng enhances w", proving the effect of SC in optimization.
m0.55 70.76
11.07
71 71.04 11.01 71 71 06 1.00 70.77 70.74
0.1
03
06 0.7
0.9
Weights of Auxillary Features
Theorem 3.7.
(Sociability Information in Model) Building on prior work (Zuo et al., 2024), given the optimal parame- ter w* consists of the backbone parameter wiy and the head parameter why we have:
(a) Comparison of different weights on CIFAR100
$3.21
$3.41 83.43
46 $3.42 83,
53.46
83.43 83
63.39
a2 0.1
63,22
83
46
63
83.50
Weights Auxiliary Logits
S3.26 63.43333
log((w" (es..)) logp (w7 Den..) logg ( (Do...)) (13)
08 0.7 06 0.5 64 03
48
K3.33 83, 00.00333.4
46
33.45
33.10
33.4633.
$3.47
33.51
33.51133.52
33.51
-83.36 83, 0334233.339
33,50
33.4333,,55
46
63.90
33
13.48
Since
-3.377 83. 393333333444
83.46
33.5 33.4
23 53.46
03
K3.49
-83.39 41.43331
41.4
33
63.41
81.43
49 31.42
31
63.44
log (w of (Dam,)) logp (wx (M)). (14)
83.31 83.39
46
19 83
50
83 83
81
83.38
We can derive
0.5
83.56
83.37
83.38
-3.25
$3.36
03.51
63.55
0
0.1
0.2
0.3
04
0.5
0.6
0.7
0.8 0.9
logp("" (Desy,) a logg (w) (Do..) logg [
[(Px)))
(15)
Weights of Auxiliary Fentures
(b) Comparison of different weights on VOC07+12
Remark 3.8. Training on sociability features ) M2 enhances w². proving the effect of SC in optimization.
Figure 3: Comparison of different weights on datasets.

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
To address the aforementioned question, consider two downstream tasks: classification and detection. Specifi- cally, features provide patch-wise information, while logits offer image-wise information. Consequently, we conduct exploratory experiments on the interaction between features and logits under varying weights. From Figure 3, can be inferred that neither extremely low nor high weights are suitable for effective interaction. When the weight is too low, the knowledge interaction provides limited assistance, failing to improve the performance. Conversely, when the weight is too high, the knowledge becomes overly com- plex for the model to learn effectively. Notably, the model exhibits superior performance under a variety of weight combinations. Based on this observation, we hypothesize that a dynamic weighting mechanism for interaction may effectively balance specialization and generalization.
aspects: organizational structures, interaction modes, and communication mechanisms, aiming design unified SC framework U--) and implement dynamic interactive learning to balance specialization and generalization.
M2M - MM()
(17)
where M2 and M2 are two models, sp()) represents the or- ganizational structures, 3 denotes the communication mech- anisms, and e-(-) represents the interaction modes.
4.3. Dynamic Information Socialized Collaboration
Driven by the above analysis and (Li et al., 2023b; Shao et al., 2021; Cao et al., 2024), our goal is to address SC from three aspects: hierarchical organizational structures, progressive interaction modes, and strong- communi- cation mechanisms. We introduce new approach based on SC, called Dynamic Information Socialized Collaboration (DISC), as shown in Figure 4. For clarity, we provide a detailed explanation of the three aspects included in DISC.
Theorem 4.1. (Generalization Bound of Collaboration) Building prior work (Zhang et al., 2023), we focus on the dynamic weighting mechanism. Let Duain = {24, We i== be training dataset of N samples, and cfr(f")) be the empirical error of the m-th model f on Dirain- For any hypothesis f E H (i.e., H: X + {-1,1}), with probability at least 5, the generalization error is bounded by:
Hierarchical organizational structures: In DISC, we de- sign the Dynamic Hierarchical Collaboration (DHC) module to effectively leverage hierarchical auxiliary information, aiding the main model in learning. Specifically, for different tasks, we treat the specialized model as the main entity and the other models as auxiliary information. For instance, in classification tasks, the model specialized in classifica- tion focuses on global-wise information, while the model specialized in detection emphasizes patch-wise information. The key to achieving SC lies in fully utilizing the patch- wise knowledge embedded in the detection model. The hierar- chical organizational structure facilitates the progressive abstraction and capture of both low-level and high-level im- age features, enabling effective handling of complex visual tasks. Therefore, we define patch-wise information in the form of a hierarchical structure, as follows:
M
GError(f) S Σ B(w"") B(wr")efr(("))
Σ
15 Elw"" Rm (fm
m=1
Term--I (ompirical loss)
Term-C (complexity)
In(1/8)
Σ Coo(wm m )
+M
2N
Term-Cov (covariance)
(16)
where B(w"") is the expected collaboration weights, Rm (fm)) is the Rademacher complexity of model fr and Cou(w"" eTm) is the covariance between the collaborative weight and the loss. In static collaboration, the weights White remain constant, so Cov For(wiiee {mm) = 0. By con- trast, in dynamic collaboration, the weight dynamic increases as the loss fr decreases, yiclding Cov( (uyymamie (T))= S O. This negative covariance lowers Term-Cov, thereby tightn-- ing the upper bound of generalization error of collaboration, i.e., (GGErroo([aaymmme/ I (GError( Remark 4.2. Dynamic collaboration yields a tighter gener- alization error bound compared to static collaboration.
impalimain oulput + DH-
(18)
where input denotes the input of the 1-th layer of the main model, oulput main denotes the output of the (1 - 1)- th layer of the main model, and DHC (1 - 1) denotes the hierarchical output of the auxiliary model. Next, we will pro- vide a detailed explanation of DHC (I - 1) in progressive interaction modes.
Progressive interaction modes: In DISC, DHC mitigates excessive reliance on auxiliary information by employing progressive interaction, effectively utilizing hierarchical aux- iliary data to support the main model's learning process. Initially, the model depends heavily on data, but as opti- mization advances, the information provided by the data becomes limited. At this stage, gradually incorporating aux- iliary information from other dimensions becomes essential for further learning. Overuse of auxiliary information early
4.2. Socialized Coevolution Framework
We take closer look at SC to identify the key factors forming its framework. Inspired by (Zuo et al., 2024; Li et al., 2023a), we perform an iiformation-theoretft analysis of SC in the previous section and explore the impact of sociability information. Based on this, we focus on three
5

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Socialized Cocvolution: Advancing Better World through Cross-Task Collaboration
Backbone
Head
-CLS
-CLS
Classification
Dynamic Hierarchical Collaboration
Dynamic Selective Collaburation
Layer-1
Layer-2
Layer-3
DHC
DHC
DSC
Layer-1
Layer-2
Layer-3
Backbone -DET
Head -DET
Detection
min(Ein Ep)
DHC
min (E Ki)
DSC
sin
sin
Ezopps
Erogge
log M P1 log 11 Pr
BN
BN
BN
BN
+
Conv s/2
Conv s/1
Conv s/2
Conv s/1
Figure 4: An overview of our proposed Dynamic Information Socialized Collaboration (DISC). We design the Dynamic Hi- erarchical Collaboration (DHC) and Dynamic Selective Collaboration (DSC) modules, integrating organizational structures, interaction modes, and communication mechanisms as the core elements of SC.
the process can impede model's ability to learn from data. Thus, the key to SC lies the gradual integration of auxiliary information, enabling the model to overcome the limitations of data-driven learning. This progressive interaction mode is defined through DHC as follows:
based on how well each model handles different samples, enabling effective learning for every sample. This dynamic weighting defined through strong-guided communication mechanisms as follows:
log [1|MI Ps
DHC sin
mini
outputtion (19)
DSC (m) = (pm +
log II M
output Logits.
(20)
Elosyo
where En represents the current training epoch, E, denotes the threshold epoch, Eroops indicates the total number of
where Pm is the prediction of m-th model.
training epochs, and output denotes the output of the i-th layer of the auxiliary model.
The DHC and DSC modules, featuring hierarchical orga- nizational structures, progressive interaction modes, and strong- communication mechanisms, are integrated into the overall loss function as follows:
Strong-guided communication mechanisms: In DISC, we designed the Dynamic Selective Collaboration (DSC) module to leverage strong- strong-guided communication mecha- nisms to enhance the learning process of the main model. Specifically, the weights assigned to the main and auxiliary models vary dynamically depending on the sample, mean- ing the weight of the main model is not necessarily greater than that of the auxiliary model. Fixing the weights con- stants prevents the models from adapting flexibly to their performance. The key to achieving SC is to assign appropri- ate weights dynamically to each model. A model's weight should negatively correlate with its own loss and positively correlate with the losses of other models. Strong-guidee communication mechanisms dynamically assign weights
Loverall =Loss (y. ( (PHC(x))) + Lreg (y. DSC (DDHC())))
(21)
where is the input, y the label, Cets represents the clas- sification loss, Lreg represents the regression loss, (DHC(.) denotes the collaboration with DHC, and DSC denotes the collaboration with DSC. Lats is employed for classi- fication tasks, while both Lets and Creg are utilized for detection tasks. For a clearer understanding of training and inference, we have described the detailed algorithms in Al Algorithms 1 and 2.
6

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
Algorithm 1 Training for DISC.
nism from MTL, based KD methods such LSKD (Sun et al., 2024), CrossKD (Wang et al., 2024), and PPAL (Yang et al., 2024a). In this approach, the hidden layers trained under the guidance of the teacher model are shared across all tasks, while separate output layers for specific tasks are retained. Additionally, we compare several classic methods, including SwAV (Caron et al., 2020), DeepClus- ter'2 (Caron al.. 2018). MoCo v2 (Chen et al., 2020), and CLIP (Radford et al., 2021).
Input: Datasets DM.,, model Mr. main task i
Output: Embedding with DHC JMM,, (.). embedding with
DSC fM (.), downstream task head (M, (-);
1: if Tmain classification then
2: while not converged do
3: Fix detection model Maai
4: Get a mini-batch of training data from D i
5: Calculate the overall loss Loverall using Eq. (21);
6: Update the classification model, i.e. PDHC(.). (.).
[Maa..) (.), and MMa (-);
5.2. Specialization and Generalization are All You Need
7: end while
We analyze the Fine-Tune (FT), KD, and MTL knowledge transfer methods based on expert models for classification and detection, shown in Table 1. Then, we compare DISC with offte-of-the-ar methods, as shown in Table 2.
8: end if
9: if
Tiaann detection then
10: while not converged do
11:
Fix classification model Mass
12:
Get mini-batch of training data from D M Calculate the overall loss Loverall using Eq. (21);
Before evolution (
After evolution (%)*
Method
13:
CLS
DET
AVG
CLS
DET
AVG
14:
Update the detection model, i.e., fMaa(()
Expeni(CLS++FTT
70.68 0.00
35.34
0.68
65.00
32.84
Expeni(CLS)-KD
70.68
0.00
35.34
0.61
66.35
33.48
fM (-), and flond (-);
ExpentCLS)+-MLL
70.68
0.00
35.34
70.68
59.92
65.30
15:
end while
Expen(DET))FFT
0.47
83,89
42.18
64.08
0.00
32.04
16: end if
Expen(DET)>KD
0.47
83.89
42.18
66.82
0.00
33.41
Expen((ETT++TTL
0.47
83.89
42, 12
43.05
83.89
63.47
DISC
70.68
0.00 35.34
72,28
(+000)
84.92
(1100)
78.60
10,30)
Algorithm 2 Inference for DISC.
Table 1: Comparison of performance across different tasks before and after evolution CIFAR 100 and VOC07+12 datasets. The 1dt12nd best results are indicated in red/blue.
Given components: Backbone f&a, (.), downstream task
head [M)..
Input: Test sample x;
Output: Final prediction y";
1: Calculate image feature fiu., (x) and Maa (z);
Method
CLS (S) 1
DET (5)
AVG (%)
2: Calculate weights of the feature WDHC and WDDHC. det 3: Calculate weights of the logits WLlsc and WDSC. det
SWAV (Caron al., 2020)
65.90
69.20
67.55
DeepCluster22 (Caron et al., 2018)
66.
70.00
68.05
MoCo v2 (Chen al., 2020)
66.00
70.20
68.10
4: Return final prediction y"
CLIP (Radfont al., 2021)
63.65
LSKDICLS)MMTL (Sun et al., 2024)
71.05
63.37
67.21
LSKD(DET)+MTL (Sun et al., 2024)
50.13
83.91
67.02
CressD S)+MTI (Wang et al., 2024)
71.13
63.85
67.49
5. Experiments
CreskD(DET) (Wang et al., 2024)
52.96
68.49
PPALICLS)+MTLL (Yang al., 2024a)
70.95
62.31
66.63
PPALIDETI-MTL (Yang et al., 2024a)
50.68
83.95
67.32
In this section, we compare DISC with state-of-the-art meth- ods on the CIFARI00 and VOC07+12 datasets. We discuss task-driven knowledge transfer methods, and the ablation studies demonstrate the effectiveness of the DHC and DSC modules. Our model is implemented in PyTorch (Paszke al., 2019) and deployed on an NVIDIA RTX 3090 GPU.
DISC
72.28
-1.15)
84.92
+0.91)
(8.60
Table 2: Comparison of performance across different tasks on CIFAR100 and VOCO7+12 datasets. The 12t/2nd best results are indicated in red/blue.
FT and KD lose task generalization: As shown in Table 1, both FT and KD obtain new downstream task capabilities at the cost of sacrificing performance on existing tasks. This raises the question of whether acquiring new task abilities alone constitutes evolution. Our goal is for the model to retain or even improve performance on old tasks while learn- ing new ones. Additionally, even focusing new tasks, FT and KD underperform compared to DISC, as they fail to utilize cross-task knowledge. Both FT and KD can cause models to blindly follow the evolutionary process, degrading original task capabilities and losing generalization.
5.1. Implementation Details
We evaluate the proposed method on CI- FARI00 (Krizhevsky et al., 2009) and VOCO7+12 (Ever- ingham al., 2010; 2015) datasets using data-efficient setting. Specifically, we employ 10% of the training set and the complete test set, with CIFAR 100 used for evaluating classification and VOCO7+12 for assessing detection.
Datasets:
Compared methods: To enable comparison with KD and MTL. in the SC setting, we adopt the hard-sharing mecha-
7

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
MTL overlooks specialization for new tasks: As shown in Table 1, we observe that MTL. preserves performance existing tasks by freezing the model for those tasks. While MTL trains new task-specific parameters for learning new downstream tasks, the model cannot fully optimize for the new tasks due to the need to maintain performance on exist- ing ones. MTL achieves basic evolution but still falls short of our goal. MTL struggles to fully learn the capabilities for new downstream tasks while failing to enhance performance on existing tasks. In the evolutionary process, MTL priori- tizes maintaining generalization for existing tasks, overlook- ing specialization for new tasks.
bility information of cross-task models. In contrast, direct interaction between different downstream task models fol- lows static interaction mechanism that aggregates features from the same layer with uniform weights, which restricts improvements in both specialization and generalization.
We observe significant performance im- provement. This indicates that hierarchical cross-task fea- tures interacting dynamically and progressively are highly effective in enhancing representational capability. DHC provides insight into cross-task knowledge transfer.
Using only DHC:
We observe that the model's performance improves, but not as significantly as with DHC. While DHC focuses on enhancing representational capability, DSC fo- cuses on improving decision preision-makinn capability. This sug- gests that both representational and decision-making capa- bilities are crucial for model performance. DSC provides insight into dynamic decision-making.
Using only DSC:
DISC balances specialization and generalization: Based on the SC paradigm, DISC trains task- parameters while dynamically leveraging auxiliary information from other models for collaborative learning. DISC's hierarchical organizational structures utilize both task-specific data and guidance from other tasks, acquiring intra-task and cross- task knowledge. Its progressive interaction modes enable curriculum-like learning. while strong strong-guided communica- tion mechanisms allow dynamic weighting of learning and inference. DISC not only enhances new task capabilities but also improves existing task performance through model interactions, shown in Tables I and 2.
Combining DHC and DSC: We find that the model achieves the expected results, not only adding new down- stream task capabilities but also improving the performance of existing tasks through model interactions. This shows that representational ability forms the foundation for achiev- ing good performance, while decision-making ability helps push the performance beyond its limits.
Analysis of specialization and generalization: Special- ization and generalization are critical in SC. Specialization means the model can effectively learn new tasks or improve performance on existing ones, while generalization refers to the model's s ability to perform well across multiple tasks. As shown in Table 2, classical models struggle to maintain their original performance in data-efficient settings and on ResNet50, indicating an over-reliance on large datasets and model parameters. Although the combination of KD) and MTL.comes close to achieving the SC objective, there is still a noticeable gap compared to the DISC model we propose.
5.4. Further Analysis
The DISC, designed with SC, incorporates task-specific characteristics through the DHC and DSC modules. Classi- fication focuses on image-wise logits interaction, while de- tection emphasizes patch-wise feature interaction. Despite their different focuses, the complementary information from both tasks enhances representational and decision-makiin capabilities, illustrated in Figure 5.
Adversarial competition: As shown in the red dashed box in Figure 5a, DHC and DSC do not consistently improve performance across all classes and may even experience performance degradation due to adversarial competition. DHC focuses on low-level intra-class information, while DSC emphasizes high-level inter-class information. Relying only one of these modules is affected by large intra- class variation and high inter-class similarity, leading to adversarial competition.
5.3. Ablation Study
To further verify the significance of each module, i.e., DHC and DSC, in DISC based on SC, we conduct the ablation study as shown in Table 3. The results reveal that the model with DHC and DSC attains the best performance.
Method
DHC
DSC
CLS t
DET 1
AVG (
70.74
83.28
77.01
Dynamic coevolution: As illustrated in Figure 5b, the syn- ergistic integration of DHC and DSC significantly improves class-wise performance, culminating in enhanced overall model efficacy. This underscores the critical role of both low-level and high-level information in augmenting the model's perceptual capabilities. Mastering the effective collaboration of this complementary information is funda- mental to realizing SC, with DISC offering valuable insights into this complex interplay.
71.26
84.25
77.76
DISC
71.15
84.04
77.60
(..5)
84.92 (4.92(+1.64) (41.64)
78.60
Table 3: Ablation study on CIFARI00 and VO In the table. "V" denotes DISC with the module.
Without DHC and DSC: We observe that DHC and DSC are essential to DISC, as they effectively utilize the socia-
8

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
iii) Dynamism: Model interaction should be weighted by each model's proficiency with each sample.
AAP-w: DET vs. wi Nome AAPow USC None
Performaace (%)
iv) Hierarchy: Model interaction should be diverse and comprehensive to maximize knowledge utilization.
1:25
1.36
Existing KD and MTL paradigms face challenges in balanc- ing specialization and generalization dynamic environ- ments. SC overeomes this by enabling knowledge sharing and coevolution via DHC and DSC modules across different downstream tasks.
4047
Acsopline
bicyyls
Bind
Boat
Botds
Bus
Cat
Cluir
Cow
Diningabll
Dog
Horse
Mosochike
Pooso
Portcdplant
Sheep
Solaa
Tymunice
6. Conclusion
Name of Classes
(a) Comparison of various modules on VOCO7+12 dataset
In this paper, we introduce a practical SC paradigm with a rigorous mathematical foundation and an information- theoretic explanation. Additionally, we carefully design DISC based SC to not only incorporate new downstream task capabilities but also enhance the performance of exist- ing tasks through model interactions. Essentially, we view specialization and generalization as two sides of the same coin, not independent issues. The dynamic interaction be- tween features and logits provides new insights into SC, highlighting the importance of tailored approaches maxi- mize strengths. In future work, we plan to further explore the potential of combining multiple modalities and tasks.
100
AP
AAP-ui (DDCCDDSC DSC lni None)
50-
0015
50
80:1
78.29
60.00
Performance (%)
60-
50
40
20-
0-
Acoolune
Bbovelo
Bira
Box
Donte
Bos
Crt
Chair
Cow
Dimingoble
Dop
Hooe
Motorblke
Pason
Pottodglast
Sheep
Sola
Tmin
Temoniter
Acknowledgements
Name of Classes
(b) Comparison DISC and vanilla on VOC07+12 dataset
This work was supported in part by the National Science and Technology Major Project under Grant 2022ZD01 16500, in part by the National Natural Science Foundation of China under Grants 62436002, 62476195, U23B2049, and 62222608, in part by Tianjin Natural Science Funds for Distinguished Young Scholar under Grant 23JCJQJC00270, in part by Zhejiang Provincial Natural Science Foundation of China under Grant LD24F020004, in part by Tianjin Young Scientific and Technological Talents Project under grant QN20230305, in part by Tianjin Science and Technol- ogy Plan Project under grant 24YDTPJC00150, in part by Natural Science Foundation of Tianjin under grant 24JCY- BJC00950, and in part by the Huawei Ascend Computing Platform.
Figure 5: The performance differences for each class.
5.5. Discussion
SC is cross-task collaborative paradigm that facilitates knowledge interaction, allowing models to co-evolve while improving both specialization and generalization. Along- side advancing learning paradigms, SC is applicable to defect detection, autonomous driving, and emergency res- cue. Table 4 presents a comparative analysis of learning paradigms in addressing diverse challenges.
Paradigm Specialization Generalization Dynamism Hicrarchy KD
Impact Statement
X
MTI.
x
X
X
This paper presents work aimed advancing the field of cocvolution in machine learning. Our goal is to propose socialized coevolution learning paradigm to enhance the performance of existing downstream tasks and facilitate the learning of new downstream tasks, making full use of the complementarities between different tasks. However, due to the presence of model heterogeneity in dynamic open envi- ronments, the application of our approach to real-world sce- narios may inevitably encounter heterogeneity challenges.
SC
Table 4: Comparison of different learning paradigms.
i) Specialization:
The model needs to preserve or improve the performance of original tasks while learning new tasks.
ii) Generalization: As new tasks arise, the model needs to adapt without compromising existing performance.

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
A. Supplementary Material
CLIP (Radford et al., 2021): state-of-the-art con- trastive learning method, which pre-trains on large dataset of pairs and enables zero-shot transfer across various tasks without task-specific training.
The supplementary material contains comprehensive details on the implementations and experimental results referenced in the main paper, along with supplementary theoretical analysis and in-depth discussions. It is organized as follows:
LSKD (Sun et al., 2024): state-of-the-art knowledge distillation method, which standardizes logits using Z-score before softmax.
In Section B, we offer thorough overview of the methods compared in the main paper, accompanied by a detailed description of the datasets utilized.
CrossKD (Wang et al., 2024): state-of-the-arr knowl- edge distillation method, which aligns student and teacher outputs through cross-head mimicking.
In Section C, we present the full set of experimen- tal results, accompanied by in-depth analysis that thoroughly evaluates the model's performance.
PPAL (Yang et al., 2024a): state-of-the-ar active learning method, which combines uncertainty-based and diversity-based sampling.
In Section D, we provide a comprehensive theoreti- cal analysis of sociability information, exploring its implications within the context of the study.
B.2. Datasets
In Section E, we explore SC's insights across real- world applications and learning paradigms, aiming to spark interest in this dynamic interdisciplinary field.
The VOCO7+12 training dataset contains 47,221 objects, while the testing dataset includes 14,976 objects. The CI- FAR100 training dataset consists of 50,000 images, with 10,000 images in the testing set. the data-efficient setting, we utilize 10% of the training data: for VOC07+12 (10%), the training set contains 4,696 objects, and the testing set re- mains 14,976 objects. For CIFAR100 (10%), the training set includes 5,000 images, with the testing set consisting of 10,000 images. We list the details of the datasets in Table 5,
B. Implementation Details
In this section, we offer a detailed description of the meth- ods compared in the main paper, as well as the datasets used. For classification as the primary task, the model is trained for 1500 epochs using SGD with a batch size of 64. The threshold is set to 300, with a learning rate of 0.01, momen- tum of 0.9, and weight decay of 0.001. For detection as the primary task, the model is trained for 600 epochs using SGD with batch size of 32. The threshold is also set to 300, with learning rate of 0.03, momentum of 0.9, and weight decay of 0.001. All reported results represent the mean values obtained from 3 independent trials.
C. Full Experimental Results
In this section, we present additional experimental results, including analyses of parameters and visualizations.
C.1. Analysis of Parameters
Dynamic progressive interaction: Inspired by curriculum learning (Bengio al., 2009), apply sine and cosine in Dynamic Information Socialized Collaboration (DISC) to achieve dynamic progressive interaction. DISC incorpo- rates two key hyper-paramettrs: training loops Essps and threshold E,, both of which influence the dynamic progres- sive interaction of the model. As shown in Figure 6, the sine function achieves superior performance on CIFAR 100 when Enoos 1500 and E, 300, while Encops 600 and E, 300 yield better results VOCO7+12.
B.1. Compared Methods
In this subsection, provide overview of the methods compared in the main paper, outlining their key characteris- tics. The methods considered are as follows:
SwAV (Caron et al., 2020): state-of-the-ar unsuper- vised learning method, which improves representation by using online clustering with swapped prediction.
DeepCluster V2 (Caron et al., 2018): state-of-the- art unsupervised learning method, which jointly trains convolutional networks and uses k-means clustering for end-to-end optimization.
C.2. Analysis of Visualizations
Competition and coevolution: In the main paper, we ana- lyze Figure 5 to demonstrate that using Dynamic Hierarchi- cal Collaboration (DHC) Dynamic Selective Collabora- tion (DSC) alone cannot fully realize cocvolution for certain categories, due to high intra-class variance and inter-class similarity. Only their combined application effectively over- comes these challenges. To further investigate, we perform
MoCov2 (Chen et al., 2020): state-of-the-art con- trastive learning method, which improves image rep- resentation by incorporating a projection head and stronger data augmentation.

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Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
Datasets
Training dataset
Testing dataset
Detailed classes
acroplane, bicycle, bird, boat, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, TV monitor
VOC07+12
47,221
14,976
aeroplane, bicycle, bird. boat, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, TV monitor
VOC07+12(10%)
4,696
14,976
apple, aquarium fish, baby, bear, beaver, bed, bee, beetle, bicycle, bottle, bowl, boy. bridge, bus, butterfly, camel, can, castle, caterpillar, cattle, chair, chimpanzee, clock, cloud, cockroach, couch, crab, crocodile, cup, dinosaur, dolphin, elephant, flatfish, forest, fox, girl, hamster, house, kangaroo, keyboard, lamp, lawn mower, leopard, lion, lizard, lobster, man, maple tree, motorcycle, mountain, mouse, mushroom, oak tree, orange, orchid, otter, palm tree, pear, pickup truck, pine tree, plain, plate, poppy. porcupine, possum, rabbit, raccoon, ray, road, rocket,rose, sea, seal, shark, shrew, skunk, skyscraper, snail, snake, spider, squirell, streetcar, sunflower, sweet pepper. table, tank, telephone, television, tiger, tractor, train, trout, tulip. turtle, wardrobe, whale, willow tree, wolf, woman, worm
CIFARI00
50,000
10,000
apple, aquarium fish, baby, bear, beaver, bed, bee, beetle, bicycle, bottle, bowl, boy, bridge, bus, butterfly, camel, can, castle, caterpillar, cattle, chair, chimpanzee, clock, cloud, cockroach, couch, crab, crocodile, cup, dinosaur, dolphin, elephant, flatfish, forest, fox, girl, hamster, house, kangaroo, keyboard, lamp, lawn mower, leopard. lion, lizard, lobster, man, maple tree, motorcycle, mountain, mouse, mushroom, oak tree, orange, orchid, otter, palm tree, pear, pickup truck, pine tree, plain, plate, poppy. porcupine, possum, rabbit, raccoon, ray, road, rocket,rose, sea, seal, shark, shrew, skunk, skyscraper, snail, snake, spider, squirrel, streetcar, sunflower, sweet pepper, table, tank, telephone, television, tiger, tractor, train, trout, tulip, turtle, wardrobe, whale, willow tree, wolf, woman, worm
RIFAR100(109)
5,000
10,000
Table 5: Detailed datasets.
ilar textures, while DSC focuses on high-level inter-class features, which may confuse categories with high inter-class similarity. To address these issues, we integrate DHC and DSC into the DISC model, eliminating adversarial competi- tion and promoting coevolution.
Values of Thresbolls
500 400 300 2001 100
77.61 71.611..4774 71.66 71.65
71.63
72.20
Values of Threshookd
500 400 200 200 100
71.59
71.54
71.51
71.45
71.70
71.6371.89
71.63
71.99
71.67
71.67
7L.61
21.58
11.55
F1.53
71.49
71.79 72.28
22.2371.88
72.00
71.65
71.62
01.59
71.55
71.51
71.60
71,62
11.95
71.76
71.80
71.5
71.50
71.58 71.5871.71 77.81 71.72
71 11.72 71.69 71.65 71.69
71.39.1.55
71.60
500 1000 1500 2000 2500
500 1000 2000
2500 Values Loops
D. Theoretical Analysis
Values of Loops
(a) Sine on CIFAR100
(b) Cosine on CIFAR100
Below, we provide the omitted proofs from Sections 3 and 4 (refer to the main paper).
Values Thresholds
500 400 300 2001 100
83.90
84.08
84
84.22
$4.14
$4.80
Values Thrcsholl
500 400 300 200 100
$3.68
83.65 83.56
83:41
83.96
84.05
24
84.03
$4.06
$4.60
$3.82
83.72
83.61
D.1. Proof of Theorem 3.3
84.15 34.33
84.91
84.83
$4.40
76
33.65883.62
Proof. We leverage previous results (Cover, 1999; Feder & Merhav, 1994; Li et al., 2023a):
84.59
$4,20
$4.01
83.50 83,77
83.71
83.62
94.0684. 84.39
84.85
64
K3.88 83.81 03..9883.77 78
K3.65
200
HIKI 1000
$4.00
400
800
1000
- -log (1 - ( < H(Y XM,, XM2),
(22)
Values of Loops
Values of Loops
(c) Sine on VOC07+12
(d) Cosine on VOC07+12
H(Y XM, XM,) I log 2 + pinul log YY).
(23)
Combine the two inequalities and put pmuu in the middle:
Figure 6: Comparison of different weights.
HOVIN H Xaad-loe2 < pamn < 1 exp(-H(Y XM.XXM.)) (24)
kig YI
which is the first result in the theorem. Then we apply the results to pain,
Class Activation Maps (CAMs) analysis (Zhou et al., 2016), as shown in Figure 7. DHC emphasizes low-level intra-class features, which can cause confusion for categories with sim-
< pain 51 exp(-H(( (M.))- (25)
log 1
2

---

Socialized Cocvolution: Advancing a Better World through Cross-Taie Task Collaboration
(a) Aeroplane-ve w/ None
(b) Aeroplane-w w/ DHC
(c) Aeroplane-we w/ DSC
(d) Aeroplane w/ (DHC+DSC)
(e) Car-w/ None
Car-w/ DHC
g Car-w/ DSC
(h) Car-w/ (DHC+DSC)
ii) Potted pl None
(i) Potted plant-w/ DHC
(k) Potted plant-w/ DSC
()) Potted plant-w/ (DHC+DSC)
(m) Sheep-w/ w/ None
(n) Sheep-w/ DHC
(0) Sheep-w/ DSC
(p) Sheep-w/ (DHC+DSCC
Figure 7: The visualization with different modules.
Since
D.2. Proof of Theorem 3.5
4x I(XMgiY | XMM))
Proof. We leverage previous results (Zuo et al., 2024; Li et al., 2023a) and assume that w" is the optimal parameter for the given dataset DM Hence, we have:
X(, XM,)
=[H(Y)- - H(Y XM.))- H(Y [Xm))
-H(Y|Xm)) H(Y Xm.)
log (w+ (DM;) -logp (DMi (=)) + logp (w*) logr(D,,)) (28)
(26)
We derive
log2 pmul & - HY XMM.3 + ) (27)
For ),D,,, we have
p(Dm.) = / p(DMs w)p(w) dw.
(29)
3

---

Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
Since
have
J (DM, ())((w) dw < ppp(D, w²) p(w) dw. (30)
logp (wh (Dima))
logp (f (D4m2) wh) +lopp(ws) logp (f (Duma))
We can derive
< logp (f(M) jom wh/ + logg (wf))
(DMM. <D(MM, w=)))
(31)
logg (DMM)
After integrating DM, and Do.u,> the combined dataset is Ds = DM1 U "Duee yielding
logp (w (((M)) MM2)
(36)
We can derive
logp (w" (Dema)
logp(w" " (Ds) P(Ds w²) )- + logp(w") (lgg((ss)
= -logp (wi Den,) + logp (w] (D
log Dim= w²) + logp(w")
< logp (wi Donu)) + logp (wh ((DM,) )032)
(37)
-logp(DMM, Dzms)
log | w²) + logpllDoooooooo p(Dom w*) + log p(w")
- (lggp(DM,) - log p(D mzm
D.4. Proof of Theorem 4.1
logg | w)) log p(w") - ((M
Proof. Let Duain = {x,, 3 be a training dataset of samples, and c²r(fm) be the empirical error of the m- th model fm on Dirain For any hypothesis f (i.e.. H.S. X - {-1,1]). with probability least 8, the generalization error is bounded by:
+ loggp(DD w²) log (D
(32)
Assuming the supplementary augmented dataset D closely resembles the data distribution of DM,, we have
(D#m2 1 wn)) p (D
(33)
M B(w""))er(( (f
M
We can derive
GError (f) S
+
E (w H (f
m==1
m=1
log p(w" (Dz)
Term-L (empirical
Term-C (complexity)
log (Dg "²") + logg(w"") - log (34) logp(w" (DM.).
In(1/8)
Coolwm (mm) +
2N
m=
Temn-Cov (covariance)
(38)
D.3. Proof of Theorem 3.7
Proof. We decompose the DISC into two components: the backbone with parameters wi and the head with parameters wr. Using the supplementary augmented dataset Dam,, we obtain
where E(w")) the expected collaboration weights, Rm (fm) is the Rademacher complexity of model fT and Cov(w"" pm) is the covariance between the weight and the loss.
log (w") m2)
In static collaboration, the weights with are constant, hence
logp (wT. wh D)m,))
Cov (attiee cm) 0.
(39)
logp (w77 (oo,,)) + logg (wh. Domus)
(35)
logp (w7 D + logp (wh (Derm)))
In dynamic collaboration, the collaboration weight w dynamic increases as the model loss fm decreases. Thus,
log (w) Dom,) + logp (f (Dom,) wT))
+ log. (wf)) log (f (DoM)))
Cov(w Synamiie (Tr) < O,
(40)
Since Dam, is the supplementary augmentation of the dataset D a has similar distribution with DM, we
which effectively reduces the Term-Cov and thereby lowers the generalization bound.

---

Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
Based on the principle of convexity, it can be concluded that:
weather conditions, visibility, and road situations. Ensuring that SC between different models can effectively enhance each other's performance in such dynamically changing en- viromments is significant challenge. By applying SC to autonomous driving, the detection model can assist the seg- mentation model in accurately identifying different traffic signs, pedestrians, vehicles, and other objects, while the segmentation model can provide more precise background information and spatial distribution for object detection. Fu- ture research should focus on designing adaptive feedback mechanisms that allow models to mutually promote each other during joint training and flexibly adjust according to the demands of different scenarios and tasks.
Σ E B(Wllymamic) cfr(f S Σ Waltiic cfr(s"))
(41)
Σ 1B(w (B(Wiyynmic)) Rim (fm))
S
Σ
Wamne Rm (fm))
(42)
Since the confidence term My the collaboration strategy, it remains the same. Therefore, suppose the hypothesis space is H X - [-1,1}. Then for any faynamics fsaticc E H. and for 1 8 0, it holds that
In(1/5)
2N is independent of
Emergency rescue: In emergency rescue scenarios, Aug- mented Reality (AR) technology can play a crucial role, In complex rescue environments, AR can help rescue per- sonnel quickly find the best route by real-time planning and displaying safe paths and obstacle avoidance informa- tion. For lost item classification, AR can overlay real-time classification data and item features onto the real-world scene, assisting rescue personnel in quickly identifying and categorizing items. The application of SC in AR for emer- gency rescue scenarios presents the following challenges. Emergency rescue scenes are typically highly dynamic and uncertain, requiring models to not only have high accuracy in detection and classification but also to possess strong ro- bustness. Additionally, real-time detection and classification tasks have different requirements: object detection focuses on quickly and accurately locating and identifying key tar- gets at the rescue site, while item classification focuses on the rapid classification and archiving of items. In this con- text, research should focus on designing SC mechanisms that enable the two models to mutually enhance each other, creating a synergistic effect.
(foymii(( S O(GErro((fsam:))
(43)
E. Future Applications and Future Research
In this section, we provide an in-depth exploration of SC's insights across diverse real-world applications and learning paradigms, highlighting its potential to bridge disciplinary boundaries. By shedding light on its foundational principles and practical implications, we aim to spark broader interest in this rapidly evolving and interdisciplinary domain.
E.1. Future Applications of SC
This section delves into the future applications of SC in emerging fields. We have studied various potential appli- cation scenarios, analyzed their unique requirements, chal- lenges, and opportunities, explored the different uses of SC across different domains, and provided practical guidance on how to implement it.
Defect detection and segmentation tasks for industrial products typically involve processing high- dimensional, noisy image data. Moreover, the manifes- tations of faults are diverse, involving different materials, structures, and processes, and may appear as small cracks, corrosion, wear, etc. This requires models to handle com- plex and heterogeneous data in diversified manner, which poses a significant challenge for the application of SC. Fu- ture research should focus on ensuring that the coevolution between different models effectively enhances their individ- ual performance without causing decline performance. Additionally, the interaction between detection and segmen- tation models requires precise design, enabling them to mutually promote each other's 's learning, rather than simply optimizing their own objectives independently.
Defect detection:
E.2. Future Research and Expansion of SC
In addition to the future applications of SC mentioned above, in future research, SC can also be integrated with various ex- isting learning paradigms to be further extended, achieving better performance across a range of tasks.
Federated Learning (FL.): FL is a form of distributed learn- ing that allows models on different devices to be trained locally and only share updates with a central server. This approach ensures data privacy, making it particularly suit- able for collaborative learning in multi-device environments. Future research should focus on ensuring collaborative learn- ing between models, optimizing communication efficiency, and accelerating the evolution process in multi-device envi- ronments, while adhering to data privacy protection require- ments.
Autonomous driving: In autonomous driving, road envi- ronment segmentation and object detection tasks need to handle complex and dynamic scenarios, including varying
In the context of FL, models on different devices can au- tonomously evolve locally and share the results of their
5

---

Socialized Cocvolution: Advancing a Better World through Cross-Task Collaboration
local updates through socialization mechanism enhance collaborative effects. For example, in industrial defect de- tection tasks, due to the involvement of proprictary research and development secrets in industrial products, full open- source sharing is not feasible. However, companies can contribute their defect detection models, which can then be shared via the socialization mechanism on a central server, driving advancements in industrial defect detection and seg- mentation. The integration of SC with FL enables models distributed across different locations collaborate in evolu- tion, safeguarding data privacy while enhancing the overall intelligence level of the system.
often contains missing values, noise, and biases, leading to potential shifts in sample distribution. This presents major challenge for OL. However, within the SC framework, different models can collaborate with each other, where the detection model can help the segmentation model identify missing or partially damaged objects, and the segmentation model can provide more precise contextual information to improve the detection model's adaptation the environ- ment. In this way, SC enhance the model's adaptability, ensuring that it maintains high performance and robustness even when faced with distribution shifts and drift in the data.
Continual Learning (CL): CL aims to enable models to gradually expand their knowledge while continuously re- ceiving new tasks and data, without forgetting previously learned information. This learning paradigm is particularly suitable for intelligent systems that operate over long peri- ods. Future research should focus on combining continual learning with SC, ensuring that models can share data and knowledge while avoiding catastrophic forgetting.
In the framework of CL, models can autonomously evolve when faced with constantly changing environments, and share local updates through socialization mechanism to enhance collaborative effects and reduce forgetting. For ex- ample, in autonomous driving tasks, as road environments and traffic conditions continuously change, new objects or scenarios may emerge, such as suddenly appearing wild animals, different forms of accident vehicles, or even un- seen obstacles. Although these new targets or situations might represent entirely new categories, through CL., the autonomous driving system can gradually adapt and expand its knowledge base, while SC helps prevent catastrophic forgetting, ensuring that the model does not lose the ability to handle previous tasks while adapting to new ones. The in- tegration of SC and CL enables autonomous driving models in different environments evolve collaboratively, not only enhancing model adaptability but also ensuring the system as whole maintains stronger robustness and performance over the long term.
Online Learning (OL): OL is a process in which models continuously learn from streaming data, typically updating as data arrives gradually, thus avoiding the computational burden associated with batch training. SC can leverage the real-time feedback mechanism of OL, allowing different models to update collaboratively based on the latest data, rather than waiting for batch data accumulation.
In a constantly changing environment, the coevolution be- tween models can accelcrate their learning process and en- sure that collaborative optimization does not cause perfor- mance fluctuations due the dynamic changes in the data stream. For example, in emergency rescue scenarios, due to the complexity of on-site conditions, the collected data
6


Reviews:
[{"review_id": "OgrkfekeGQ", "reviewer": "Reviewer_aRWf", "summary": "The authors introduce a learning paradigm called Socialized Coevolution (SC), designed to enhance the performance of existing tasks with the support of an auxiliary model. Specifically, the main model progressively integrates hierarchical auxiliary information through a dynamically weighted communication mechanism. Experimental results on the CIFAR-100 and VOC07+12 datasets demonstrate the effectiveness of the proposed method compared to baseline approaches.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful feedback. We provide detailed responses to the issues. Please refer to the link (https://anonymous.4open.science/r/Supplementary-materials-for-SC/) for the Additional tables and proofs.\n\n>Q1. MTL setting\n\nR1. The MTL setting is introduced to validate Sociability Information (SI) as the premise of Socialized Coevolution (SC). We include MAD [1] and Pix2SeqV2 [2] as MTL baselines (Table 6), emphasizing the specialization-generalization trade-off in SC.\n\n- **SI as the premise of SC:** Freezing the backbone reveals common capacity. Classification (CLS) and detection (DET) focus on image- and patch-wise, respectively, and exhibit the potential of SC (Eq. 16) due to their SI (Definition 3.2).\n\n- **Characteristics of MTL:** MAD and Pix2SeqV2 show that enhancing generalization sacrifices specialization.\n\n- **Trade-off in MTL:** MAD and Pix2SeqV2 enhance generalization at the cost of specialization, while freezing the backbone preserves specialization but limits generalization.\n\n[1] Masked AutoDecoder is effective multi-task vision generalist. CVPR, 2024.\n\n[2] A unified sequence interface for vision tasks. NeurIPS, 2022.\n\n>Q2. Relation to cultural evolution\n\nR2. Human and machine societies share a common principle: individuals improve through interaction and collectives through collaboration [1-2]. Cultural evolution includes Social Hierarchy (**SH**), Progressive Learning (**PL**), and Cultural Learning (**CL**), which motivates the SC (Eq. 16) and DISC (Table 7).\n\n- **SH:** Rooted in human societies, we use layer-wise hierarchies to enable structured collaboration (Eq. 17).\n\n- **PL:** Inspired by PL in human education, we use progressive interaction modes to bridge capacity gaps across models (Eq. 18).\n\n- **CL:** CL mirrors guided learning in human culture, informing our strong-guided communication mechanisms with capability awareness (Eq. 19).\n\n[1] A social path to human-like artificial intelligence. NMI, 2023.\n\n[2] The secret of our success: How culture is driving human evolution, domesticating our species, and making us smarter. Princeton University Press, 2016.\n\n>Q3. Differences and similarities from model merging\n\nR3. Model merging and SC share similar knowledge sources, but they differ in premises and goals.\n\n- **Differences:** 1) *Premises*. Model merging assumes redundancy in parameters and orthogonality of task vectors, whereas SC is grounded in sociability. 2) *Goals*. Model merging aims to get a better-merged model, overlooking individual evolution. In contrast, SC improves individual models via interaction and the collective via collaboration, achieving coevolution at both levels.\n\n- **Similarities:** Both acquire knowledge from other models.\n\n>Q4. Connection between theory and methodology & theoretical foundation\n\nR4. We theoretically prove stronger SI leads to better SC (Theorem 3.3). In dynamic machine societies, SI manifests through data and models, enhancing their capacity to facilitate SC (Theorems 3.5 and 3.7). Thus, we propose the dynamically SI-driven DISC and further provide a theory.\n\n- **Theory-guided method:** 1) Definition 3.2 formalizes SI, and Theorem 3.3 proves SI is essential to SC. 2) Theorems 3.5 and 3.7 show SI exists in both data and models. 3) We propose DISC based on SC (Theorem 3.3), and design DHC and DSC from different tasks (Theorems 3.5 and 3.7), offering insights into SI utilization (Eq. 20).\n\n- **Theory based on dynamic collaboration:** Dynamic outperforms static [1], with dynamism manifested through progressive interactions and dynamically weighted collaboration. Weights of DSC (Eq. 19) negatively correlate with individual loss and positively correlate with collaborator loss.\n\n$$\n\\begin{align}\n&GE(f)\\\\leq |\\\\mathcal{M}| \\\\left( \\\\mathcal{R}_N(\\\\mathcal{H}) + \\\\sqrt{\\\\frac{\\\\ln(1/\\\\Delta)}{2N}} \\\\right)+\\\\sum\\_{m=1}^{|\\\\mathcal{M}|} \\\\hat{err}(f^m) \\\\ + \\\\sum\\_{m=1}^{|\\\\mathcal{M}|} \\\\left[ \\\\frac{1}{|\\\\mathcal{M}|} \\\\underbrace{Cov(\\\\omega^m, \\\\ell^m)}\\_{\\\\textit{Negative self-correlation in DSC}} - \\\\frac{|\\\\mathcal{M}|-1}{|\\\\mathcal{M}|} \\\\sum\\_{j \\\\neq m} \\\\underbrace{Cov(\\\\omega^m, \\\\ell^j)}\\_{\\\\textit{Positive collaborative correlation in DSC}} \\\\right]\n\\end{align}\n$$\n\n[1] Provable dynamic fusion for low-quality multimodal data. ICML, 2023.\n\n>Q5. Training and inference details\n\nR5. For CLS and DET, we set 79 and 147 iterations per epoch, respectively. DSC is active in training and inference. For more details, please see Section B in Appendix.\n\n>Q6. Robustness and generalization\n\nR6. We assess robustness and generalization across the backbone, training strategy, and task (Table 8), finding sociability benefits from expert-based models.\n\n- **Backbone:** ResNet152 outperforms ResNet50 due to its design and better capacity.\n\n- **Training strategy:** ImageNet pre-training yields the best result, benefiting from the scale of the data.\n\n- **Task:** CLS, DET, and SEG focus on image-, patch-, and pixel-wise, respectively, exhibiting sociability.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "The proposed method is evaluated on CIFAR-100 for the classification task and VOC07+12 for the detection task, using only 10% of the training set. The comparisons are conducted against classical methods under the settings of Knowledge Distillation (KD) and Multi-Task Learning (MTL).", "theoretical_claims": "The authors present theoretical analysis about the sociability Information.", "experimental_designs_or_analyses": "The authors have conducted corresponding tests on multiple tasks (classification and detection) and provided ablation experiments to demonstrate the effectiveness of the proposed method.\n\nThe comparison between the proposed method and KD/MTL in Table 1 raises some concerns. For example, it is unclear why the MTL setting involves freezing the model rather than training it with a specific method. Additionally, further analysis is provided.", "relation_to_broader_scientific_literature": "The proposed problem is closely related with prior research in relevant areas, such as knowledge distillation (KD), multi-task learning (MTL), continual learning (CL) and federated learning (FL).", "essential_references_not_discussed": "The authors mention that the proposed method is inspired by cultural evolution; however, a more detailed discussion on related previous works and their connection to this approach would be beneficial. Additionally, further clarification on the differences and similarities between the proposed setting and model merging would help provide a clearer understanding.", "other_strengths_and_weaknesses": "Strengths:\nThe paper shows the possibilities that the hierarchical information can complementary for each other, as the combination of them shows improvements.\n\nWeaknesses:\n\n1.Although the authors claim to analyze the proposed method from an information-theoretic perspective, the connection between the theoretical framework and the proposed method appears to be weak.\n\n2.The proposed method lacks a solid theoretical foundation. For example, the specific motivations for employing the sine function in the progressive interaction module and the rationale behind the dynamic weighting mechanism are not adequately discussed.\n\n3.The training procedure is not clearly described. In the supplementary material, the pseudo-algorithm presents the joint training of different tasks. However, critical details regarding the training process, such as the number of iterations and other relevant training parameters, are not provided. Furthermore, it remains unclear whether the proposed DSC module is employed during inference. \n\n4.The robustness and the generalization of the proposed method has not been thoroughly analyzed. Specifically, it is unclear whether the method performs consistently across different backbone architectures, including both pre-trained backbones and those trained from scratch, as well as different backbones across different tasks. Additionally, the generalizability of the proposed method to a broader range of tasks has not been sufficiently examined.", "other_comments_or_suggestions": "All suggestions are included in the \"Strengths and Weaknesses\" section.", "questions_for_authors": "All questions are included in the \"Strengths and Weaknesses\" section.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "N/A"}}, {"review_id": "btdI5qEnBf", "reviewer": "Reviewer_7Pyh", "summary": "This paper proposes a practical learning framework, DISC, to improve model performance across two task domains. The proposed method allows two expert models to exchange intermediate states and weights in a structured manner to achieve superior performance in both task domains. The authors evaluated DISC on image classification and detection tasks, comparing it to SOTA methods in fine-tuning, knowledge distillation, and multi-task learning. Results show that DISC outperforms baselines in both original and transfer tasks.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback. We provide detailed responses to the issues. For the supplementary tables and proofs, please refer to the anonymous link (https://anonymous.4open.science/r/Supplementary-materials-for-SC/).\n\n>Q1. Connection with social learning\n\nR1. Human and machine societies share a common principle: individuals improve through interaction and collectives through collaboration [1-2]. Both DISC and Social Learning (SL) [3] emphasize acquiring knowledge directly and indirectly, which is empirically validated (Table 9). DISC is inspired by social learning (Eq. 16): its layer-wise hierarchy (Eq. 17), DHC (Eq. 18), and DSC (Eq. 19) correspond to social hierarchy, progressive learning, and cultural learning (Table 7), respectively.\n\n- **SL inspires DISC in machine societies.** SL involves learning through observation (direct experience) and interaction (indirect experience), while DISC enables Socialized Coevolution (SC) through both data (direct experience)  and models (indirect experience), reflecting SL's dual nature.\n\n- **Empirical evidence:** Experiments (Table 9) show that direct learning from data builds basic ability, while indirect learning from collaborators depends on their ability. DISC combines both to realize SC through SL.\n\n[1] A social path to human-like artificial intelligence. NMI, 2023.\n\n[2] The secret of our success: How culture is driving human evolution, domesticating our species, and making us smarter. Princeton University Press, 2016.\n\n[3] Social learning theory. Prentice Hall, 1977.\n\n>Q2. Scalability and generalization across models and tasks\n\nR2.   We analyze scalability and generalization from the backbone, training strategy, and task (Table 8), and find that sociability benefits from expert-based models. \n\n- **Backbone:** ResNet152 outperforms ResNet50 due to its design and stronger representational capacity.\n\n- **Training strategy:** Among ResNet50 variants, ImageNet pre-training yields the best performance, benefiting from the scale and diversity of the ImageNet dataset.\n\n- **Task:** Each task corresponds to a different model. Classification (CLS), detection (DET), and segmentation (SEG) focus on image-, patch-, and pixel-wise, respectively, exhibiting sociability.\n\n>Q3. Influence of tasks and datasets characteristics on SC\n\nR3. Tasks and data with higher sociability information (SI) better support SC (Theorem 3.3). As in multi-view learning [1], consistency and complementarity among views are essential for effective representation, SC similarly relies on SI (Definition 3.2) among models. Theorem 3.3 proves that SI is a prerequisite for SC and positively correlated with it.\n\n- **SI-SC positive correlation:** In Definition 3.2, we formalize SI as $$\\\\Phi\\_{\\\\mathcal{M}\\_2} = I(X\\_{\\\\mathcal{M}\\_2}; Y \\\\mid X\\_{\\\\mathcal{M}\\_1})$$ Bayes error rate shows a positive correlation between SI (e.g., tasks and datasets) and SC.\n\n$$\n\\begin{align}\n    \\\\frac{{H}({Y}|{X}\\_{\\\\mathcal{M}\\_{1}})-\\\\Phi\\_{\\\\mathcal{M}\\_{2}}-\\log 2}{\\log |{Y}|} \n            \\\\leq {P}^{mul}\\_{e\\_c} \\\\leq 1-\\\\exp (-{H}({Y}\\\\mid {X}\\_{\\\\mathcal{M}\\_{1}})+\\\\Phi\\_{\\\\mathcal{M}\\_{2}})\n\\end{align}\n$$\n\n- **Data-level SI arises from diverse samples (Theorem 3.5):** Structurally diverse datasets, like CIFAR-100 (more classes but low resolution) and VOC07+12 (fewer classes with high resolution and rich scene complexity), offer complementary information, enhancing SI and improving SC.\n\n- **Task-level SI derives from multi-wise information (Theorem 3.7):** While all tasks aim to learn better representations, they focus on different levels, CLS (image-wise), DET (patch-wise), and SEG (pixel-wise). This complementarity yields SI, which benefits SC.\n\n[1] A survey of multi-view representation learning. IEEE TKDE, 2018.\n\n>Q4. Interpretation of Fig.3\n\nR4. Fig. 3 shows performance with fixed weight combinations for features and logits. If an optimal combination existed, only one red patch would appear. The multiple red patches motivate dynamic weighting to enhance inter-model interactions. We provide a theoretical analysis demonstrating that dynamic weighting outperforms static weighting [1]. Specifically, the dynamic weight is negatively correlated with individual loss and positively correlated with collaborator loss (Eq. 19).\n\n$$\n\\begin{align}\n&GE(f)\\\\leq |\\\\mathcal{M}| \\\\left( \\\\mathcal{R}_N(\\\\mathcal{H}) + \\\\sqrt{\\\\frac{\\\\ln(1/\\\\Delta)}{2N}} \\\\right)+\\\\sum\\_{m=1}^{|\\\\mathcal{M}|} \\\\hat{err}(f^m) \\\\ + \\\\sum\\_{m=1}^{|\\\\mathcal{M}|} \\\\left[ \\\\frac{1}{|\\\\mathcal{M}|} \\\\underbrace{Cov(\\\\omega^m, \\\\ell^m)}\\_{\\\\textit{Negative self-correlation in DSC}} - \\\\frac{|\\\\mathcal{M}|-1}{|\\\\mathcal{M}|} \\\\sum\\_{j \\\\neq m} \\\\underbrace{Cov(\\\\omega^m, \\\\ell^j)}\\_{\\\\textit{Positive collaborative correlation in DSC}} \\\\right]\n\\end{align}\n$$\n\n[1] Provable dynamic fusion for low-quality multimodal data. ICML, 2023.\n\n>Q5. Formatting issue\n\nR5. Thank you for your suggestions, we will fix it in the final version.", "claims_and_evidence": "I am not totally convinced by the definition of socialized coevolution where the authors metaphorize two models exchanging intermediate outputs as \"collaboration\" and exchanging weights as \"communication.\" To me, the proposed method is more of a practical learning framework than a social learning process where autonomous agents interact and learn with each other. More empirical evidence is needed to justify the connection between the proposed methods and social learning. For example, does DISC have any similar characteristics to social learning?\n\nAs claimed to be a unified learning framework, social coevolution needs to be evaluated in diverse domains and model structures. Given the social motivation, it would be interesting to extend the proposed method from two models to multiple models.\n\nThe hierarchical and progressive parts of DISC are based on human prior knowledge (i.e., only exchanging adjacent layers and gradually introducing auxiliary information) instead of learning from data. Does this impact the generalization of the proposed method to different model structures and task domains?", "methods_and_evaluation_criteria": "The experiment design and evaluation process looks convincing to me.", "theoretical_claims": "In the proof of Theorems 3.5 and 3.7, the assumption that the supplementary augmented dataset DM2 closely resembles the data distribution of DM1 might not hold in situations where the two tasks/domains are loosely connected. It is less clear how the nature of multiple objective tasks and the distribution of datasets influence the effectiveness of social co-evolution.", "experimental_designs_or_analyses": "It is unclear to me how Figure 3 is generated and how this visualization led to the dynamic weighting mechanism.", "relation_to_broader_scientific_literature": "No", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "No", "other_comments_or_suggestions": "There is a minor format issue that the paper has a header of Submission and Formatting Instructions for ICML 2025.", "questions_for_authors": "No.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "29HZPVXF4m", "reviewer": "Reviewer_LUex", "summary": "This paper introduces a practical learning paradigm of Socialized Coevolution (SC), to overcome the limitations of methods that rely heavily on data-driven, single-model approaches and often fall short in leveraging inter-model knowledge transfer. The paper argues that existing methods like Knowledge Distillation (KD) and Multi-Task Learning (MTL) suffer from issues such as blind imitation, loss of task-specific expertise, and a lack of dynamic interaction. The learning paradigm is the Dynamic Information Socialized Collaboration (DISC) framework. DISC incorporates two key modules: Dynamic Hierarchical Collaboration (DHC) for feature-level interaction and Dynamic Selective Collaboration (DSC) for logit-level interaction.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful feedback. We provide detailed responses to the issues. Please refer to the anonymous link (https://anonymous.4open.science/r/Supplementary-materials-for-SC/) for the supplementary tables and proofs.\n\n>Q1. Specialization-generalization trade-off\n\nR1. DISC balances specialization and generalization by combining interactive learning with a dynamic master–assistant architecture, enhancing individual capabilities and task-level collaboration.\n\n- **Dynamic master–assistant architecture enables balance:** For each task, DISC adaptively assigns the model most suited to the task as the master, with others serving as assistants. This role assignment ensures specialization without sacrificing generalization.\n\n- **Interactive learning enhances specialization:** Classification (CLS) emphasizes image-wise, while detection (DET) attends to patch-wise. Specialization is enhanced through interaction.\n \n>Q2. Connection between theory and empirical results\n\nR2. We further clarify the connection between our theory and DISC through additional experiments (Table 9) and analysis. Sociability Information (SI) exists in both data and models (Definition 3.2), and Theorems 3.5 and 3.7 suggest that enhanced data and model representations enable SC. Therefore, we observed performance under different data and model enhancements, finding that DISC can better leverage SI (Theorem 3.3).\n\n- **Theory-guided method:** 1) SI (Definition 3.2) is the premise of SC (Theorem 3.3). 2) Data augmentation improves SC (Theorem 3.5). 3) Representation enhancement improves SC (Theorem 3.7). 4) Driven by SI, we enhance the dataset through task-sharing and design DISC with DHC and DSC modules, improving representational capacity and achieving SC.\n\n- **Theory-empirical relationship:** In machine society, data represents direct experience, and model interaction reflects indirect experience. Theorem 3.5 shows that diverse datasets strengthen the direct experience, while Theorem 3.7 highlights that expert model interaction improves the indirect experience. DISC leverages both through shared datasets and enhances representations via DHC and DSC, enabling SC (Table 9).\n\n>Q3. Paper title\n\nR3. If allowed by the conference, we will revise the title to “Socialized Coevolution: Cross-task Collaboration Driven by Dynamic Interactions”, which better reflects the technical contribution.\n \n>Q4. Ablation on progressive interaction and adaptive weighting\n\nR4. Progressive interaction and adaptive weighting allow models to dynamically collaborate per sample without overwhelming the main model. In contrast, direct or static strategies often lead to excessive interaction and degrade learning.\n\n- **Progressive interaction:** We explored sine- and cosine-based progressive modes under varying thresholds (Fig. 8 in Appendix). Sudden or deep interactions early in training can disrupt the main model, while gradual interaction maintains learning stability.\n\n- **Direct integration:** We tested non-progressive integration by setting auxiliary weights to 1 (Fig. 3 in paper and Fig. 7 in Appendix). Results show that abrupt inclusion of auxiliary information fails to improve representation and may even hinder learning.\n\n- **Static weighting:** If an optimal combination existed, only one red patch would appear (Fig. 3 in paper). The multiple red patches motivate dynamic weighting to enhance inter-model interactions. We provide a theoretical analysis showing that dynamic weighting outperforms static weighting [1], with dynamic weight negatively correlated with individual loss and positively correlated with collaborator loss (Eq. 19).\n\n$$\n\\begin{align}\n&GE(f)\\\\leq |\\\\mathcal{M}| \\\\left( \\\\mathcal{R}_N(\\\\mathcal{H}) + \\\\sqrt{\\\\frac{\\\\ln(1/\\\\Delta)}{2N}} \\\\right)+\\\\sum\\_{m=1}^{|\\\\mathcal{M}|} \\\\hat{err}(f^m) \\\\ + \\\\sum\\_{m=1}^{|\\\\mathcal{M}|} \\\\left[ \\\\frac{1}{|\\\\mathcal{M}|} \\\\underbrace{Cov(\\\\omega^m, \\\\ell^m)}\\_{\\\\textit{Negative self-correlation in DSC}} - \\\\frac{|\\\\mathcal{M}|-1}{|\\\\mathcal{M}|} \\\\sum\\_{j \\\\neq m} \\\\underbrace{Cov(\\\\omega^m, \\\\ell^j)}\\_{\\\\textit{Positive collaborative correlation in DSC}} \\\\right]\n\\end{align}\n$$\n\n[1] Provable dynamic fusion for low-quality multimodal data. ICML, 2023.\n\n>Q5. Evaluation on diverse and large-scale datasets\n\nR5. We additionally conducted experiments (including standard deviations) on CIFAR10, Food-101, and WIDER FACE (Table 10). Consistent results across multiple datasets show that DISC achieves better performance through dynamic interaction and collaboration.\n\n>Q6. Scalability and complexity\n\nR6. With sufficient computational power, DISC can scale to any number of ensembles. For pairwise interactions, the total cost for N models is N(N−1)K, where K represents the per-interaction cost. KD methods are efficient but sacrifice generalization for specialization (Table 11). Compared to MTL, DISC incurs lower costs while better balancing specialization and generalization, facilitating other paradigms and applications.", "claims_and_evidence": "While the paper presents experimental results that show performance improvements using DISC, I would suggest that some claims could use some further support. For instance, the claim that DISC 'balances specialization and generalization' is somewhat weakly substantiated. The experiments do demonstrate improved average performance and ablation studies show that DHC and DSC are beneficial individually, and even more so together, but they don't deeply analyze how these modules specifically achieve this balance. Is it truly about specialization and generalization, or is it, for example, primarily driven by improved representation learning? \n\nFurthermore, the theoretical analysis, while interesting, does feel somewhat disconnected from the empirical results. Theorems 3.5-3.8 are presented, but the paper lacks a clear explanation of how these theoretical findings/properties directly predict or explain the observed performance gains of DISC. More explicit connections between the theory and what these imply for the empirical results could be useful for improving the claims. Finally, the titles's broad claim of \"Advancing a Better World\" is hyperbolic and not necessarily supported by the paper's scope. This statement should be replaced with a more grounded description of the paper's technical contributions and their potential impact within the machine learning community.", "methods_and_evaluation_criteria": "The proposed DISC method appears to be a reasonable approach to SC. The design of DHC and DSC modules, focusing on hierarchical feature interaction and dynamic selective logit collaboration, is logically motivated by both the identified limitations of existing KD and MTL methods, and Sec 4.1. Regarding baselines, the selection is relatively broad, including KD and MTL variants, as well as self-supervised methods.", "theoretical_claims": "I have not checked the correctness of the proofs, and would defer to a reviewer with a stronger expertise in information theory to do so.", "experimental_designs_or_analyses": "The experiment design is generally sound. \n\nThe ablation study (Table 3) is a good starting point, but it could be more comprehensive. While it ablates DHC and DSC individually, it would also be informative to see ablations of specific components within DHC and DSC to understand the contribution of each sub-module. For instance, where any other forms tested for the progressive interaction mode in DHC? What if the auxiliary information is not integrated gradually? In DSC, what if we don't use the adaptive weighting?\n\nThe paper reports mean performance values, but should also report standard deviations or confidence intervals, especially for the results that are only slight improvements.", "relation_to_broader_scientific_literature": "Overall, the paper makes a valuable contribution and extends existing research in Knowledge Distillation (KD) and Multi-Task Learning (MTL). The paper's unique aspect is drawing inspiration from Social Learning (SL) in human societies and cognitive science and it is well positioned at the intersection of these fields.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "**Strengths**\n- Novelty - The paper introduces a genuinely novel learning paradigm. Drawing inspiration from human social learning and translating it into a practical algorithmic framework is a fresh perspective on knowledge transfer and collaborative learning in AI. The specific design of DISC, with its DHC and DSC modules for dynamic hierarchical and selective interaction, further contributes to the originality of the approach.\n- **Clarity:** The paper is generally well-written and structured, with clear explanations of the motivation, methodology, and experimental setup. The figures and tables are mostly informative and contribute to understanding the approach.\n\n**Weaknesses**\n- **Scope of Evaluation:** The evaluation, while using standard benchmarks, might be limited in fully showcasing the potential of SC in more complex and real-world scenarios. Demonstrating SC's effectiveness on more diverse, challenging, and larger-scale datasets would significantly strengthen the empirical validation.\n- **Scalability and Complexity:** The paper does not explicitly address the scalability of SC. It's unclear if the dynamic interaction mechanisms in DISC will remain efficient and effective as the \"society\" of models grows. Is the society of models limited to two models? Furthermore, the computational complexity of DISC compared to simpler KD or MTL methods should be discussed.\n- **Limitations**: No obvious discussion of the limitations of the framework.", "other_comments_or_suggestions": "NA", "questions_for_authors": "1. The authors propose some interesting future applications of the work in the appendix, however I do not know what the difficulties are for scaling to this problems? How well does this framework scale to any of the problems discussed as future applications in the appendix?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The reviewers broadly agree that this paper should be accepted, noting its novel and well-motivated concept of socialized coevolution, the practical framework it introduces for investigating socialized coevolution, that its contributions draw from multiple areas (e.g., distillation, MTL, and cognitive-inspired AI), and promising results on standard benchmarks. There were some concerns with the theoretical grounding of socialized coevolution (which is somewhat disconnected from the empirical results), scalability, and some of the methodological choices — these issues were mostly clarified during the rebuttal. Consequently, the authors are strongly urged to address these concerns and incorporate aspects of the discussion into the final version of this paper. Despite these few, relatively minor concerns, the paper seems like it overall is a solid contribution.
Decision: Accept (poster)