Stochastic Self-Organization in Multi-Agent Systems
Authors: Nurbek Tastan
Samuel Horváth
Karthik Nandakumar
Venue: ICLR 2026 Poster (2026)
Keywords: multi-agent systems
contribution estimation
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: Large Language Models (LLMs) have enabled multi-agent systems (MAS) where agents collaborate to solve tasks beyond the reach of a single model. Yet most existing approaches rely on fixed topologies, pretrained graph generators, optimization over edges, or external LLM judges, thereby adding complexity. We introduce a response-conditioned framework that adapts communication on the fly. Agents independently generate answers and assess peer contributions using a Shapley~value-inspired approximation. A directed acyclic graph (DAG) is then constructed to route information from high-contribution agents to others, ensuring stable and efficient message passing without the need for additional supervision or training. We provide a theoretical analysis showing that multiple agents increase the chance of correctness and that the correct answers naturally dominate information flow. Experiments with both strong and weak LLM backends demonstrate robust performance, with significant gains in the weak regime where prior methods collapse.

Published as a conference paper at ICLR 2026
STOCHASTIC SELF-ORGANIZATION IN MULTI-AGENT SYSTEMS
Nurbek Tastan' Samuel Horváth
Karthik Nandakumar 1.2
"Mohamed bin Zayed University of Artificial Intelligence (MBZUAD, UAE
2 MMichigan State University (MSU), USA (nurbek. tastan, samuel.. horvath www.ttthiemm.iiiaaaaaamaaa ae, nandakumšmsu.er edu
ABSTRACT
Multi-agent systems (MAS) based on Large Language Models (LLMs) have the potential to solve tasks that are beyond the reach of any single LLM. However, this potential can only be realized when the collaboration mechanism between agents is optimized. Specifically, optimizing the communication structure be- tween agents is critical for fruitful collaboration. Most existing approaches rely on fixed topologies, pretrained graph generators, optimization over edges, or employ external LLM judges, thereby adding to the complexity. In this work, we introduce a response-conee conditioned framework that adapts communication on-the-fi Agents independently generate responses to the user query and assess peer contributions using an approximation of the Shapley value. directed acyclic graph (DAG) is then constructed to regulate the propagation of the responses among agents, which ensures stable and efficient message transmission from nigh-contributing agents others. This graph is dynamically updated based on the agent responses from the previous collaboration round. Since the proposed framework enables the self- organization of agents without additional supervision as SELFORG. The SELFORG framework goes beyond task- and query-level opti- mization and takes into account the stochastic nature of agent responses. Experi- ments with both strong and weak LLM backends demonstrate robust performance, with significant gains in the weak regime where prior methods collapse. We also theoretically show that multiple agents increase the chance of correctness and that the correct responses naturally dominate the information flow.
training, refer to it
INTRODUCTION
Qwen al., 2025) have rapidly advanced capabilities across planning, analysis, coding, and dialog, yet a single LLM still faces notable limitations: stochastic unreliable generations, hallucinations, and diffi- culty with long- hong-horizon, multi-step tasks. A natural response has been to move from a solitary model multi-agent system (MAS) of LLMs, where agents interact, critique, and refine another's outputs (Li et al. 20233 Chen et af., 2024) iZhuge et al.) [024] QQin et al.) (022bb] Ye et al.] (2025a). In principle, this collective can surpass an individual model by pooling complementary reasoning paths; in practice, however, the gains depend critically on how the agents are orchestrated: who communicates with whom, when, and how final outputs are aggregated.
Large Language Models (LLMs) (OpenAl 2023 Dubey et al., 2024 Anthropic, 2025
Prior work has explored a spectrum of communication topologies. Fixed structures include chains, trees, complete graphs, and random graphs; scalable studies compare these patterns across task families such as mathematical reasoning, knowledge reasoning, and coding (Qian et al.) 2025) Beyond static designs, some approaches treat the topology as optimizable: edges are sampled and trained with policy gradients or masks (e.g., GPTSwarm (Zhuge et al. 2024). AgentPrune (Zhang ct al., (025a))). A complementary line delegates topology design to a separate model that outputs lask/query-specific communication graph (e.g.. G-Designer (Zhang et al.) 2025b), MAS-GPT (Ye jet al. 2025b). Others rely on an external LLM "judge" to rank, filter, or make final decisions (Ebrahimi et al. 2025). While effective in certain settings, these strategies introduce substantial overhead: pretraining a graph generator; reinforcement learning over edges; repeated calls to a judge I.I.M.

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Published as a conference paper at ICLR 2026
Input
Decentralized Initialization
Contribution Estimation
Communication Graph
R1
R2
D bed
Embeddiing
Space
(
R
R4
BD
Shepley Value contribution
User Query 2
0.9
0.7
R
R
0.8
0.7
0.4
Agents
Figure 1: Overview of SELFORG. query Q distributed N agents, each producing response RR,- Responses are embedded, contributions estimated via Shapley-based valuation, and directed acyclic communication graph is formed where edges reflect contributions and high-contribution agents lead. The figure depicts single round; the process is iterated for ' rounds.
common hypothesis in this literature is that there exists "best" topology per task category (e.g.. math vs. coding). This idea has evolved toward finer granularity, that the query should determine the topology (one graph per problem). We argue that both views are ultimately brittle. Because LLM agents are inherently stochastic, the information that matters for coordination is not the static task label nor the problem identity, but the state the agents are actually in their concrete responses at given time step. Two agents may answer the same query differently across runs; a topology that was ideal yesterday may be suboptimal today. Thus, the communication pattern should be decided on the fly, conditioned the current pool of responses. Searching for universally superior topology per task or per query therefore potentially confounded and fragile: it risks overfitting to incidental response patterns to powerful base models whose single-shot accuracy already masks orchestration weaknesses.
This state-driven perspective is especially revealing in the weak-backend regime, where each agent has a modest chance of being correct. In such settings, the value of orchestration should be to amplify rare correct responses and suppress noise, not to lean Our approach embraces this principle: we propose a decentralized, response-conditioned framework in which agents (i) independently produce initial answers, (ii) locally assess peers via Shapley value-inspired contribution valuation, and (iii) construct a directed acyclic communication graph (DAG) that routes information from high contributtnnnn agents others. This yields a lightweight system with no external judge, no pretrained topology generator, and no edge-level reinforcement learning, yet adapts structure per instance.
already-compedd model.
We make the following contributions:
We construct a per-instance DAG directly from agents' current responses via semantic alignment, avoiding fixed topologies, pretrained graph generators, and edge-level RL.
2. We quantify influence with a shapley-inspired utility, together with efficient approximation and ranking-stability guarantees, enabling lightweight, model-agnostic credit assignment.
3. We analyze why multi-agent interaction amplifies correct signals and why correct respon- ders dominate contributions, and we validate SELFORG across various reasoning bench- marks and multiple backbones.
METHODOLOGY
We propose a multi-agent collaborative framework that adaptively constructs communication structure without relying external judges, pretrained graph generators, or reinforcement learning for edge optimization. The key principle to leverage agents' own responses to estimate their con- tributions, estimate these contributions using Shapley values, and enforce directed acyclic com- munication graph (DAG) for stable information propagation. In what follows, we describe each component in detail. The overall pipeline of SELFORG is illustrated in Figure]
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Published as a conference paper at ICLR 2026
2.1 SYSTEM OVERVIEW
We formalize the collaboration in multi-agent system as dynamic directed graph g where V = {ui..... ) represents the set of nodes (with V = .N and g(0) denotes the set of edges in collaboration round E (T]. Each node Uh represents an agent An. instantiated with backend LLM. Each agent A,, receives prompt (( and generates response (())
(V. ((),
R An = An (P(() n,ays! Piiusers ((() P..coll).
(1)
where Pn.sys represents the system prompt that describes the agent's role and current state, P,, denotes the user prompt, which includes the given tasks, and Pn,coll includes responses from other agents (if available) and externally retrieved knowledge.
directed edge em- E g()) indicates that agent A., incorporates information from agent Am.. in round f. The presence (or absence) of edge reflects the usefulness of Am's 's response for An- Thus, edges encode the information flow among agents. The graph can be equivalently expressed as an adjacency matrix A E
where A! 1 if e E g(1), otherwise O.
2.2 DECENTRALIZED INITIALIZATION
This first stage of SELFORG (referred as collaboration round t 0) aims generate a pool of diverse, but potentially noisy responses from N agents. Given the user query Q, each agent independently generates its own initial response R For this initial round, p(0)
n.coll
because
(0) agent Ar,. receives no input from other agents. We map each agent response R to an embed-
ding I' ((R!. with a lightweight model f (e.g., all- 1il1-MinLLM L6 (Reimers & Gurevych ). which need not be the same LLM used by the agents. These embeddings provide fixed- dimensional, semantically meaningful representation of the agent responses. Subsequent stages use these response embeddings infer contributions and construct the communication graph.
2.3 CONTRIBUTION ESTIMATION
Given responses (Ii...., ry] from the N agents, we wish to estimate the contribution individual agents towards generating the collective response. We frame the problem of contribution estimation as computing Shapley values (Shapley 1953). a well-known concept in cooperative game theory.
For cooperative game, the Shapley value of agent n is
|S|((N
|S| 1)! N!
On
Σ SCINTIER
(m)) vS))
Here, v(S) is the utility of coalition S. Computing the true Shapley value using Eq. requires 2 evaluations, which is intractable for large N. Furthermore, an efficient mechanism is required to evaluate v(S). This challenge is well-known in collaborative learning scenarios, where quantifying each player's contribution is crucial for tasks such as incentive mechanisms, fairness, and robustness Wang et al. 2020 Xu et al. 202. Tastan et al. 2024 2025a b).
(Lyu al. 2020;
In this work, we adopt an approximation strategy inspired by Xu et al. (202)) Firstly, we define the utility of coalition S the cosine similarity between the average response embedding of the agents in and the average response embedding of all agents. Moreover, instead of enumerat- ing all coalitions, we compare each agent's embedding r., directly against the average embedding Favg (L/N) Σn Fn. In other words, the true Shapley value ó,, is approximated by the estimated contribution Vna of agent A., which is defined as
on R Un cOS(I Favg).
(3)
The above approximation reduces the complexity of Shapley value computation from exponential to linear in N. Intuitively, the contribution is estimated based on how well an agent's 's response aligns with the collective (average) response. We formalize the quality of this approximation.
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Published as a conference paper at ICLR 2026
Theorem 1 (Approximation Bound (Xu et al. 2021 Suppose ||rn||| (In, Farg) 2 for some > Then
I for all E [N] and
On L, ih S IT²
(4)
where Lu is multiplicatiive factor that can be normalized away (Xu et al. 2021).
Corollary (Ranking Stability). Let Ln be the multiplicative factor from Theorem ! and lei L = miny Lj. If
21T
Vn 1h
(5)
L
then the normalized Shapley scores on = On/Lnn satisfy on 2x.
All proofs are deferred to the appendix. Thus, the approximate Shapley value the not only pro- vides an efficient approximation but also preserves the relative ordering of contributions when the separation between agents is sufficiently large.
2.4 COMMUNICATION GRAPH FORMATION
Given the current responses (r1 from N agents, our goal is to form a directed acyclic communication graph ((++1) (V. ()E(+))) that governs how information flows among agents in the next round of collaboration (t + 1). To form this graph, first estimate the agent contributions as: Vn
Algorithm SELFORG
Require: Query Q. similarity threshold т, optional
neighbor budget k, total rounds T
Ensure: Final response R
I: Rn < An Vn E [N]
(g (0), (110)) < ALG. 2 (Rm T,,,k)
(!+1) = cos(rá) ( We also com-
for t = 1 do
pute pairwise similarities between the agent responses by computing the cosine similar- ity between their response embeddings, i.e., $!0 cos(r!)) (t) (())).
for n in ল (1-11) do
5:
Collect {RM (1-1)
Cm E(t-1))
Form prompt ((()) t- (2, peer outputs)
Update response R. An (P(( end for
To avoid fully connected graph, we retain only semantically meaningful links: for agent
(g(0,((1) )) 2 (R(1))
(t)
J,
An. an incoming edge 6m-+n E g(t+1) is
(f+1)
10:
Aggregate responses (Eq
activated if and only if Sn,m 2 т, where
11:
R" +- arg max,, cos(ra) centrold
(8)
is similarity threshold and (1+1))
12:
Set R* as round output (fed to the leading
in ((+1) Alternativlly, sparse connectivity may be achieved by limiting active edges to the l most similar neighbors each agent.
agent in next round)
13:
end for
14:
return R* from final round T
The communication graph formed based the above heuristics may still contain cycles. To avoid such cycles, we find the agent with the least estimated contribution within the detected cycle and remove the edge directed from the weaker agent (lower ()(++1) towards the stronger agent (higher (((+))). This approach guarantees that more contributive agents remain upstream in the information flow. After the removal of the cycle, a topological ordering of the graph is computed, with ties broken in favor of nodes (agents) with higher (1+1))
The resulting graph balances two principles:
(i) local alignment, since each agent selectively listens only to semantically aligned peers, and (ii) global reliability, since contribution scores govern the final order and ensure correctness amplification.
Since most decisions regarding graph formation (except cycle detection and removal) are made lo- cally, the resulting graph & is quite dynamic. Crucially, is not predetermined by human design, but emerges from the content of the agent responses, embodying a form of self-organizing team struc- ture. Each agent effectively votes on who should influence it, and the collective result a network that channels information from the most promising agents the that need help. For example,
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0.5
C1
1.00
Aiden and 12 his friends gaing cinema, and meet there. They each save a seat and then buy enough drinks and snacks to fill the seats, Each seat has enough room to hold person, two drinks, much money, group spent overall snacks and drinks?
C2
0.4
7804773 more
C3
0.95
C4
0.3
three snacks. drinks and
C5
0.90
W1
0.2
W2
0.85
0.1
W3
W4
0.80
0.0
W2
W3
W4
W5
W6
W7
Wy
W101
W11
W1
W344
W15
W17
W18
W19
W20
W21
W22
W22
W22
W23
W26
W5
C1
C2
C3
C4
C5
WIL
W2 W3 W4
ws
(a) Countplot generated answers: the correct answer appears re-
(b) Heatmap plot of cosine similarities
peatedly, while wrong answers scatter across many alternatives with
embeddings from correct and
little agreement. Y-axis denotes no, of times the answer occurred.
wrong responses.
Figure Analysis of Qwen-1.5B over 100 runs on the same math problem (GSM-Hard).
one agent produces particularly strong response and others recognize its value, many edges will point from the stronger agent to others, making it a hub of influence akin to spontaneously elected leader. Thus, the topology adapts on-the-fly to the query at hand and the stochastic responses of the agents, rather than being fixed in advance. The full procedure summarized in Algorithm
2.5 RESPONSE PROPAGATION AND AGGREGATION
Once the communication graph G(+++) is formed, the next round of collaboration (t + 1) is ini- tiated. There could be cases when the leader (root node) receives a message from the previous round (Algorithm line 12) or it could coincide with its own response; in the latter case it is al- lowed self-reflect on its previous response, i.e., pft+1) 2 R/ This ensures that the round begins with the most reliable response far, while still leaving room for refinement. For the subse- quent nodes in the graph, the response from the previous node is included in their collective prompt (++1) 1. This response propagation procedure continues until all nodes n.cooll 2 11++1) if cm-n
in the current communication graph processed. At the end of the response propagation, the agent contributions are re-estimated and the communication graph for the next collaboration round formed. This process is repeated for fixed number of collaboration rounds T until some early stopping criterion is met.
Thus, multi-round procedure naturally emerges: (i) the first round establishes contributions and the influence structure, (ii) the highest-contributor's response initializes the next round, and (iii) subsequent agents refine align their responses through the updated communication graph. In practice, two rounds are typically sufficient: the first for exploration, the second for consolidation.
After response propagation over multiple collaboration rounds, the final aggregate response of the multi-agent system is obtained as follows. First, the contribution-wwiihtee centroid of the response embeddings after round T is computed as:
(T) Fn
(6)
centroid
>
where ro is the response embedding agent A, the last round and (T) its contribution score. The final aggregate response is not generated anew, but chosen among the existing responses
( J Specifically, we select the response whose embedding aligns closest to the centroid:
Rinal Rn. where n, arg max COS
(r((T)
Feentroid
(T)
[E(N]
2.6 PROBABILISTIC MODELING OF MULTI-AGENT SYSTEM
We now provide probabilistic perspective to explain why our framework amplifies correct re- sponses, particularly when the underlying L.LMs weak. The following analysis highlights two
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Published as a conference paper at ICLR 2026
complementary mechanisms: (i) with multiple agents, the probability that at least two agents correct grows rapidly with N: and (ii) whenever multiple agents agree on the same response, that response is overwhelmingly likely to be correct. Together, these principles explain why correctness not only appears more often in multi-agent settings but also dominates the contribution scores.
We begin with the experiments in Figure 2 Figure 2a shows that while the correct answer sistently appears across 100 runs of Qwen-1.5B, wrong answers are scattered with little overlap. Panel (bnel2b)shure shows a cosine similarity of embeddings from correct and 5 incorrect responses: correct answers form a tight cluster, whereas incorrect ones are scattered. Finally, an intervention study shows that when an agent receives input from the top-contributor, its probability of solving the task rises from 49% to 69%. These findings motivate the need for contribution estimation and leader selection in SELFORG.
If each agent independently answers correctly with probability 2. E (0, 1), then the probability that least two of N agents correct is (1 p) Np(1 p) 2) This an increasing function with N that quickly approaches 1. Therefore, even weak agents collectively increase the chance that agreement on correctness present in the system. The role of SELFORG is then to identify these consensuses and amplify them. In the following straightforward lemma, argue that consensus about correct answer (Xx)) is more likely than consensus about an incorrect answer (X,) using observations from Figure [
Lemma 1 (Agreement Concentration). Let one agent be correct with probability E (0,1) and
otherwise choose one of K incorrect answers with probabilities . PK: E Pk- = 1 For two independent agents,
Pr[X.] - = >
Pk = Pr[Xi]
whenever the errors are sufficiently dispersed (as in Fig. 2al. e.g., max; Ps S F.p
We now connect the above probabilistic intuition to the contribution estimation of SELFORG. Fig- ure 2b empirically supports the following assumption: embeddings of correct answers cluster gether, while embeddings of wrong answers remain scattered.
Assumption 1. Suppose there exist constants a 3 such that:
(i) For all n, m S (correct cluster), cos(In. Im) 2 a;
(ii) For all S S, cos(r,, (In: rk) S B,
(iii) For all k, l & S, cos re) S 3,
Lemma 2 (Contribution Dominance), Under Assumption for every E S and k & S we have dn Vhs where vin- = (os(In, Fall) is the contribution score.
Lemmas 1 and together yield the following guarantee:
Corollary 2 (Correctness Amplification). If at least two agents output the correct response, then this response strictly more likely to receive high contribution scores than any incorrect alternative. The communication graph, therefore, routes information preferentially from correct agents, amplifying their signals while suppressing noise.
Together, these results formalize why SELFORG remains effective under the weak-backend regime.
3 EXPERIMENTS
Our empirical evaluation largely follows the MASLab benchmark protocol Yee et al.) 2025a). We test SELFORG across various LLM backbones: Qwen (Qwen-2.5-(1..5, 3, 7, 14, 32, B) (Qwen 3. -no (Dubey al. 2024), Fal- TII 2024, Almazrouei et al. 2023), and Mistral (Mistral- ---nstral-B-nsstuuctt v0.3) (Jiag et al. 2023a) mathematics (MATH (Hendrycks et al.) (021). GSM8K (Cobbe ct al. 2021), GSM- Hard (Gao et al. 2023). AQUA-RAT (Ling et al. 2017), AIME-2024), science
et al. 2025), LLaMA (LLaMA-33 CLMMM----B-Instrree
con (Falcon3-77 I-Intrucct)
(GPQA (Rein al.
2024)). and knowledge (MMLU (Hendrycks al. 202T), MMLU-Pro (Wang

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Published as a conference paper at ICLR 2026
Table I: Main results on Qwen-225-1.5B-Insttteee Comparison of SELFORG with single-agent prompting and multi-agent baselines across seven reasoning benchmarks. AVG reports mean accu- racy, while AVG-R reports average rank across methods (lower better).
Method
MATH
GSMSK
AQUA
GSM-H
MMLU
MMLU-P
AIME
AVG
AVG-R
Qwen 2. 5-1. 5B Instruct
Single
49.20
70.40
51.18
36.20
49.60
28,80
3.33
41.24
2.57
CoT
46.80
69.20
53.54
36.20
50.60
28.60
3.33
41.18
2.71
DyLAN 49.80
67.80
51.18
27.20
50.00
15.40
3.33
37.82
4.00
MacNet
45.40
64.20
49.21
29.40
42.00
26.00
0.00
36.60
4.57
G- G-Designer
42.20
61.40
44.48
24.20
40.00
22.00
0.00
33.47
5.86
AgentVerse
45.20
69.00
50.39
27.80
38,20
24.00
0.00
36.37
4.86
AutoGen 11.60
69.40
28.74
5.40
12.20
5.20
0.00
18.93
6.06
SELFORG
52.40
74.60
58.27
38.00
53.80
31.60
6.67
45.05
1.00
Table 2: Main results on large models (LLaMA-33--0BB-nmm- & Dwwen22.5-22B-nnsttee
Comparison of SELFORG with baselines across reasoning benchmarks. AVG reports mean accuracy and AVG-R reports average rank across methods (lower is better).
Method
MATH
GSMBK
AQUA
GSM-H MMLU
MMLU-P
GPQA
AIME
AVG
AVG-R
LLaMA-3.C 3-7 Instruct
Single
74.80
96.20
77.56
54.00
84.40
68.40
55.36
66.76
3.88
CoT
75.00
95.80
79.92
57.40
85 15.20
71.00
56.70
26.67
68.46
2.50
DyLAN
77.60
95.20
76.38
53.00
83.60
31.60
58.04
26.67
62.76
4.25
MacNet
74.80
96.00
79.13
55.20
83.00
65.40
58. 26
26.67
67.31
3.63
Agent Verse
76.80
94.60
76.38
51.20
83.60
69.20
55.36
26.67
66.73
4.50
AutoGen
79.50
51.40
82.60
64.60
52.68 68
30.00
65.57
SELFORG
79.80
96.60
81.10
85.00
72.40
59.82
30.00
70.19
1.25
Qwen 2, 72-72--111 188--Insttee ruct
Single
83.00
95.00
81.10
63.80
82.40
70.60
16.65
20.00
67.82
2.88
C6T
82.80
95.20
80.71
62.00
$2.80
71.40
44.20
16.67
06.97
3.50
DyLAN
80.60
95.40
77.95
63.20
84.20
69.20
46.43
13.33
66.29
3.75
MacNet
81.40
95.40
79.13
62.80
83.20
65.60
40.40
16.67
65.58
4.13
Agent Verse
82.80
95.20
77.17
57.80
81.40
71.20
45.98
23.33
66.86
4.13
AutoGen
81.20
95.80
78.35
64.20
82.60
69.40
45.54
13.33
66.30
3.75
SELFORG
84.40
96.20
80.71
64.20
83.80
71.20
47.77
23.33
68.95
1.38
ct al.
2024)) benchmarks. We set the default max token limit as 2048 and a temperature 0.5. Our baselines include single call, chain-of-thought (CoTT) (Wei et al.) 2022), AutoGen (Wu et al. (024). AgentVerse (Chen al. 2024), G-Designer (Zhang al. 2025b), DyLAN (Liu al. (0224), and MacNet (Qian et al. 2025). SELFORG defaults to use N agents, top-2 neighbors and at most rounds. Additional configurations, baseline methods, and other details provided in Appendix
3.1 MAIN EXPERIMENTAL RESULTS
Table D highlights the key advantage of SELFORG in scenarios where orchestration most chal- lenging. With Qwen-1.5B. all multi-agent baselines cluster around average accuracies of roughly 33 37%, showing limited ability to harness collaboration when the underlying agents are weak. In contrast, SELFORG achieves average accuracy of 45.05%, a clear margin above all baselines, while also attaining the best average rank (AVG-R). This represents gain of nearly +4 points over the strongest non-collaborative baseline (single agent or CoT). These results confirm our central hypothesis: when responses are noisy and correctness sparse, response-conditioned, adaptive graph provides the necessary amplification mechanism elevate correct signals and suppress noise. We include G-Designer at small scale; see Appendix B for discussion.
We also test SELFORG on stronger backbone models (Table 2b. For LLaMA-70B, SELFORG achieves the highest average accuracy (70.19%) and best AVG-R (1.25), outperforming all base- lines. The same holds for the Qwen-72B model, where SELFORG attains the best average rank (1.38) with clear gains over prior methods. These results demonstrate that SELFORG remains effec- tive even with frontier-scale models, providing complementary improvements.

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Published as a conference paper at ICLR 2026
AQUA-RAT
MMLU-Pro
Dataset
AQUA-RAT
MMLU-Pro
70-
Model
Single
SELFORG
Single
SELFORG
60
1.5B
51.18
58.27
28.80
31.60
Accurecy
70
3B
65.35
73.62
42.60
46.20
7B
73.62
78.35
53.20
56.40
40
14B
75.79
81.50
61.80
65.40
Single
Single
32B
79.53
83.07
67.40
70.20
SelfOrg
30
SelfOrg
72B
81.10
80.71
70.60
71.20
38
78
149
1.58
728
Figure 3: Scaling laws of Qwen-2.5-XIInssee X-nstrutt models across two reasoning benchmarks (AQUA- RAT and MMLU-Pro). The table shows exact accuracy values for different model sizes under the Single and SELFORG settings, while plot visualizes performance trends.
Together, these results demonstrate that SELFORG consistently outperforms prior orchestration frameworks, Gains are most pronounced the low-capacity regime, where amplification of cor- rect signals is crucial, but remain competitive even for frontier-scale models.
3.2 SCALING LAWS
We analyze how SELFORG scales with model size by evaluating Qwen-2.5-X- Instruct models rang- ing from 1.5B 72B parameters on AQUA-RAT and MMLU-Pro (Table B) Across most sizes, SELFORG consistently improves over the single-agent baseline. For example, gains are most pro- nounced in the weak-to- weak-to-medium medium regime, with the 3B model improving from 65.35 to 73.62 on AQUA-RAT and from 42.60 to 46.20 MMLU-Pro. At larger scales, improvements persist but become smaller, reflecting that strong single agents already achieve high reliability.
Interestingly. at the extreme high end (72B), the benefit nearly vanishes AQUA-RAT, where ac- curacy slightly decreases from 81.10 to 80.71. This suggests diminishing returns when base models are sufficiently strong that agreement across agents offers limited additional signal. Nevertheless, SELFORG never underperforms substantially, and its advantages are clearest when individual models weak or moderately strong, confirming the theoretical expectation that multi-agen collaboration amplifies correctness most in the low-resource regime.
3.3 HETEROGENEOUS AGENTS
We evaluate SELFORG in set-
tings where agents are instanti- ated with heterogeneous back-
Model
AQUA-RAT
MMLU-Pro
40
Qwen
76.38
51.60
Falcon
61.42
47.00
bones: Qwen2.5-7B, Falcon3- 7B, Llama-3-8B, and Mistral- 7B. Although similar in param- eter count, these models differ substantially in ability (Table top), with Qwen strongest, Mis- tral weakest, and Falcon serving as the second-best. Since multi- agent success depends on agree- ment among strong contributors, the system's s performance is ef- fectively bounded by Falcon's reliability while aiming to approach Qwen's level.
LLaMA
41.09
40.00
TI1
24.0
28.4
24.8
22.8
-3
Mistral
Single (7)
AQUA RAT
0
18.9
22.4
31.9
26.8
Model
Single
SELFORG
Mix
53.91
66.14
11.8
20.5
27.6
40.2
Mix
41.60
50.40
Rank
Figure 4: Heterogeneous Agents. Left: accuracies on AQUA- RAT and MMLU-Pro for each backbone and for the mixed-pool baseline (Single) vs. SELFOR. Right: percentage of times each agent attains contribution rank r (rank- highest).
The lower part of Table 4 compares the Single baseline (where one model is randomly sampled per query) and SELFORG. The Single setting yields 53.94 accuracy on AQUA-RAT and 41.60 on MMLU-Pro, whereas SELFORG improves to 66.14 and 50.40. Thus, SELFORG leverages agree- ment between strong models while still extracting useful signals from weaker ones, outperforming the stochastic baseline and approaching the best single-agent.
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Contribution rank distributions (Figure 4 further illustrate this effect: Qwen and Falcon dominate higher ranks, while LLaMA and Mistral are usually relegated lower, though occasionally contribut- ing mid-rank when aligned with stronger peers.
We further evaluate configurations that mix strong and weak agents, with detailed results presented in Appendix [D. Beyond accuracy, pendix D.2p. Additional ablation studies examine the impact of the number of agents, the effect of reform across rounds, and the role of the embedding model in contribution estimation (Appendix F
also analyze efficiency in terms of token usage (Ap-
4 RELATED WORK
Multi-Agent Systems. Early multi-agent systems such as CAMEL (Li et al.
2023 and AutoGen
(Wu et al. 2024) introduced role-based LLM agents that collaborate through dialogue. Debate-style
systems encourage adversarial or diverse reasoning to refine answers (Du et al.
2023
Liang et al.
2024
Subramaniam et al. 2025). while dynamic orchestration (Agent Verse (Chen al. 2024).
DyLAN (Liu et al. 2024)) adapts team composition or roles during execution.
More recent efforts
aim for automatic workflow generation (Hu et al., 2025 Zhang et al., 2025c b
Ye et al. 2025b),
though these rely on strong meta-agents or pretraiedd generators, adding overhead and limiting autonomy. Multi-agent collaboration has also been applied to diverse domains including software
(Hong et al. 2024] Qian et al. 2024a), recommendation (Zhang et al. 2024). medicine (Tang et al.)
2024). finance (Li ct al. 2024), education (Zhang et al. (025e)), and science (Zeng ct al. 2024).
Communication Graphs. Prior work has explored a spectrum of communication topologies. Fixed structures include chains, trees, complete graphs, and random graphs, with recent studies system- atically comparing these patterns across task families such as mathematical reasoning. knowledge reasoning, and coding (Qian et al. 2025). Beyond static designs, some approaches treat the topol- ogy optimizable: edges are sampled and trained with policy gradients or masks (Zhuge et al. 2024 Zhang et al. 2025a Qian et al.) 2025). A complementary line delegates topology design to seseaarate model that outputs task- query-specific communication graph (Zhang al.. 2025b Ye et al., 2025b). Other frameworks rely on an external LLM "judge" to rank, filter, or finalize
outputs (Liu al. 2024 Zhang et al. 2025c Zhuge et al., 2025 Ebrahimi et al. 2025 extend
to decentralized settings Yangg et al., 2025 Lu et al. [024]) While effective in constrained settings,
these strategies incur substantial overhead: pretraining graph generators, optimization over edges,
or repeated calls to judge LLM.
These approaches assume that an optimal or near-optimal graph exists either per task category or even per query. However, such assumptions can be misleading: because LLM agents are stochastic, the same agent may succeed on one query and fail on another. Our method instead constructs the graph on-the-fly, adapting dynamically to the actual responses produced.
Contribution Assessment in Collaborative Systems. Numerous systems LLM-based MAS as- sess agent quality with additional LLMs. For instance, LLM-Blender (Fiang et al., (023b) uses an additional LLM for pairwise comparisons, incurring ((N²) operations for N agents, while DyLAN (Liu et al.) 2024) introduces dedicated LLM agent to score responses; other MAS frameworks similarly rely on judge models to value and select contributions (Ebrahimi et al. 2025). Outside multi-agent systems, the broader literature on contribution valuation offers principled tools originat- ing from cooperative game theory (Shapley 1953), with concrete instantiations in federated learning
Jia et al., 2019). FL works measure participant contributions via Shapley 202T Liu et al., 2022; Tastan et al.) 2024), influence functions (Rokvic et al.) 2024), self-reported information (Kang et al. 2019), and utility- -game formulations
[McMahan et
2017
values (Jia et al.
2019 Xu et al.
Wang et al. 2019). We draw a direct parallel to MAS and instantiate Shapley-style contribution es-
timates over agent responses (Section 2.3). eliminating external judges and additional training while
maintaining principled contribution estimation.
CONCLUSION
We presented SELFORG, framework for orchestrating LLM-based multi-agent systems without ex- ternal pretrained topology generators or reinforcement learning. By leveraging response-condition contribution estimation and adaptive graph formation, SELFÖRG amplifies correct signals and sup- presses noise. Our theoretical analysis and empirical results across diverse reasoning benchmarks confirm that it consistently outperforms prior orchestration baselines.

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This material is partly based on work supported by the Office of Naval Research 400014--4-1-2116.
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Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Xu Yu, and Tianlong Chen. Cut the crap: An economical communication pipeline for LLM-based multi-agent systems. The Thirteenth International Conference on Learning Rep- resentations, 2025a. URL ptsttp:://openneview..ro net/ htte://oommmmmmmdda.
Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. G-designer: Architecting multi-agent communication topolo- gies via graph neural networks. Forty International Conference on Machine Learning, 2025b. URL
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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianx- iao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, and Juanzi Li. Simulating classroom education with LLM-empowered agents. In Luis Chiruzzo, Alan Ritter, and Lu Wang (eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Associ- ation for Computational Linguistics: Human Language Technologies (Volume I: Long Pa- pers), pp. 10364-10379, Albuquerque, New Mexico, April 2025e. Association for Computa- doi: 10.18653/v1/2000 ht055///2222.mmaaooonn...... maaccaaoaanoa.2200 URL https: http://aaaaaattol....co. http:///222..aaaaaaaaaaaea... hwacc-lonnee 520/
tional Linguistics. ISBN 979-8-89176-18888
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Mingehen Zhuge. Changsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yun- yang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, and Jürgen Schmidhuber. agent-as-a-judge: Evaluate agents with agents. In Porty-second International Conference on Machine Learning, 2025. URL https:// openreview net/ forum? -d-NNi
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Contents
Introduction
2
Methodology
2.1
System Overview
2.2
Decentralized Initialization
2.3
Contribution Estimation
2.4
Communication Graph Formation
2.5
Response Propagation and Aggregation
2.6
Probabilistic Modeling of Multi-Agent System
3
Experiments
3.1
Main Experimental Results
3.2
Scaling Laws]
3.3
Heterogeneous Agents
4
Related Work
5
Conclusion
Mathematical Proofs
17
A.J
Proof of Theorem
17
A.2 Proof of Corollary
18
A.3 Proof of Lemmal
18
Proof of Lemmal
18
implementation Details
19
Graph Formation Function
21
D Additional Experiments
21
Weak Agent Pool
D.2
Token Consumption
22
D.3
Probabilistic Modeling. Additional Correlation Results
D.4
Embedding Model
24
D.5
Heterogeneous Agents with Dynamic Roles
25
Efficient SELFORG
25
Termination Breakdown.
27
Ablation Study
27
F.1
Number of Agents
27
F2
To Reform or Not To Reform
28
F3
Aggregation Strategies
29
F F.4
SELFORG with Memory and Tools
29
Additional Coding Experiments
29
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A MATHEMATICAL PROOFS PROOFS
A. PROOF OF THEOREM 1
Proof. We adapt the argument of et 2021) our setting.
By definition,
S|!(N |S|
1)!
On E (S), An(S) v [n)) v(S),
us
(8)
N!
SC(N
Let Ejes I' and recall Favg N Ej=1 Fm-
Exact decomposition. Expanding the marginal contribution (difference in the utilities) (S)
and regrouping gives
An(S) = v(S [n]) v(5)
(9)
(xr Fn: Favg)
(xr, Favg)
(10)
x rn
F Favgll
| Tavgl |I
1
(2, Favg)
(r,, ravg.
(rn, TavRl
(11)
rravll
0 + rn
x
x + rn
1
x + Fra
(x), Favgl
nn Favg?
+
(12)
Tavgll
20 + ra
|00|
ax + rn
[
| rnlll
(xe, Favg)
(nn Favg)
(13)
[ + rn
1:00 + rn
Fawgll
||0c + Fall
||Tmm]]
-0(S)+ +
(14)
+ Fn
| + Fm
As
Bg
||0||
00 Fn
||
where v(8) cos(ar, l'agg) and v/n cos(rns Fawg). As
and Bs
|
rn
Plugging this back into the original equation of Shapley value gives the exact split
On = Σ As v(S)+
Σ ws Bs v/n = Ln th + Σ
ws As v(S)
(15)
s
Bounding the error. Consider the ratio
x r. [Asl [o(S) |||=|||
coss xx Favg))
(16)
Bs Un
r cos(r, (r,, Favg)
Using (i) the reverse triangle inequality |||=||| ||x + r,,
1 ||r, II (ii) cos(2, cavg))rav)) 1, and
(iii) the alignment assumption (r,,, ravg)l 2 we obtain
[As [[(S)]
< 1 [avgll - 112
(17)
Bs thn using |ravgll S (average of n-morm vectors). Averaging with weights ws (linear interpolation in case) preserves this bound, yielding
on - Ln th 5 5112
(18)
This concludes the proof.
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Published as a conference paper at ICLR 2026
A.2 PROOF OF COROLLARY
Proof. From Theorem
1 we can write
Re
Φt = + +
Re - IT?.
(19)
Le
Then,
Rn
Rk
2/T²2
on Φx 2 (2/ v/x)
2 (+) - vbk)
(20)
Ln
Lb:
L
Hence, if vi - 1 > 21T L, then on > Ōx.
A.3 PROOF OF LEMMA I
Proof. By independence, Pr[X.] = p² and Pr[Xi] Ex v.. Using dispersion,
K
K
p²
Σ
PK S (maxpi P&)
Σ Pk = (1 p) max Ph S (1
p)
1 = p²
(21)
Strict inequality holds unless all mass concentrates single incorrect option at exactly max; Pk: Hence, agreement is more likely on the correct answer.
This completes the proof.
A.4 PROOF OF LEMMA 2
Proof. Fix n E S. Decompose
(In, Favg) = (Fn, In) + Σ
((n (In, Imm
(22)
meS mæf
ugS
By assumptions (i)-(ii),
(T), = I2
(nn rm) l (m E S {n]).
(nn. ( STB (u $ S).
(23)
Hence
(rns Favg) 2 22 - -N -
12
(24)
Now fix h: & S. Similarly,
(Tk, Favg) = (rK, rx) + Σ rr), + Σ (rk. Tw).
(25)
wES
By assumptions (ii)-(ii),
(rk, Favg) S T + 3 - (N 1N
(26)
Subtracting yields
(nns Favg) (rk, Favg) 2 - 33) 0.
(27)
Since all 1i = cos(r,, ravg) share the same denominator
||>|| |avgll = I |avgll the incquality
implies vin tha
This completes the proof.
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Published as a conference paper at ICLR 2026
B IMPLEMENTATION DETAILS
Baselines.
We use the benchmark authors' implementations where available (Ye et al. 2025a).
2025) is run with 5 agents and the random topology, following the paper's strongest reported configuration.
MacNet (Qian et al.
DyLAN (Liu et al.
2024 uses 4 agents and 3 rounds.
AgentVerse (Chen et al.
2024) and AutoGen (Wu et al. 2024) are run with their public defaults adapted to the benchmark.
G-Designer (Zhang et al., 2025b) is evaluated on Qwen-2.5-1., 1.5.-1.55B-Iss.. Insttrcct we omit larger models because it requires training separate graph generator, and thus latency-inefficient (see Sections T and for discussion).
We include G-Designer (Zhang al. 2025b) in Qwen-1.5B experiments, it is among the most closely related graph-optimizing methods. However, its design differs fundamentally from SELFORG. G-Designer trains separate graph generator that outputs a communication topology conditioned on the query and predefined agent roles. While this is effective with stronger backbones, does not adapt the responses actually produced by weak agents, which are often noisy. As result, its learned graphs fail to amplify correct signals in the low-capacity regime, leading to poor empirical performance (see Table I..
For larger models, do not run G-Designer, since it requires training dedicated graph generator. This introduces substantial overhead and deviates from our goal of ef- ficient, judge-free orchestration. Our design philosophy emphasizes lightweight, response- conditioned oeff-oraanization without external generators or meta-agents, as discussed in Sections I and 4
To compare with single agent execution methods, we incorporate evaluations against sin- gle execution and chain-of-thought (CoT) prompting (Wei et al. 2022)
SELFORG configuration.
Implementation can be found at: htpp://gitubbcoommuute/////ffftt
SELFORG is configured as:
Agent pool: (Assistands Programmer, Mathematician, Economist: st, Psychologist, Historian, Lawyer, Doctor).
Number of agents: for math-based tasks: agents with fixed roles (from the pool), and for science and knowledge: 5 agents up to psychologist.
Neighbor selection: top-2 neighbors per agent (by pairwise cosine similarity S); similarity threshold 0.5 for edge formation.
Rounds and structure: a maximum of 3 rounds (including decentralized initialization); with DAG enforcement.
Contribution estimation: we use a bedding dimension of 384 (a lightweight sentence embedding model).
embedding model with an em-
Aggregation: contribution-weiglee sponse to the centroid.
centroid (Equation 6); final answer is the nearest
Reform policy: reform the DAG in each round based on the updated responses.
Agent Profiling. We adopt a standard community template for defining agent roles that is widely used in prior multi-agent system benchmarks. In experiments, subset of four/five agents is instantiated per run (default), selected in fixed order unless otherwise specified. Each role assigned a default prompt template (system instruction) from the benchmark community, without additional fine-tuning or hand- -egnndeeiineringg.. This ensures that performance differences arise from orchestration rather than custom role design.
The role descriptions are provided below.
Evaluation. We use a direct scoring approach with a task-specific evaluator (x Verify (Chen et al. 2.25)), which is fine-tuned assess correctness across various domains (Ye al. 2025a).
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Assistant
You are a super-intelligent assistant capable of performing tasks more effectively than humans.
Mathematician
You mathematician.
You are good at math games, arithmetic calculation, and Leng-term planning.
Economist
You are an economist.
You are good. at cconomics, finance, and business. You have experience on understanding charts while interpreting the macroeconomic environment prevailing across world economies.
Psychologist
You are a psychelogist.
You are good at psychology, sociology, and philosophy. You give people scientific suggestions that will make them feel better.
Programmer
You are a programmer.
You are good at computer science, engineering, and physics. You have experience in designing and develeping computer seftware and hardware.
Historian
You are a historian. You research and analyze cultural, eccnorie, political, and social events in the past, collect data from primary sources and use it to develop thecries about what happened during various periods history.
Lawyer
You are a lawyer.
You are good at 1aw, politics, and history.
Doctor
You are a coctor and come up with creative treatments for illnesses or diseases. You are able to recommend conventional medicines, herbal remedies and other natural alternatives. You also consider the patient's s age, lifestyle and medical history when providing your recommencations.
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Published as a conference paper at ICLR 2026
C GRAPH FORMATION FUNCTION
Algorithm 2 Graph Formation
Require: Responses RR 1 n= similarity threshold 7, optional neighbor budget l-
Ensure: Graph G = (V, 8), topological order æ,, contribution scores J
1: Compute embeddings rn f- f(Rn), Vn [M]
2: Form similarity matrix S
3: Get contribution scores {1hn 1N n (Eq.
4: Initialize edge set & {
5: for = 1 to N do
6: ./ { m # n Sn,m r)
7: if' specified then
8: keep top-k in N
9: end if
for m EN do
11: add edge Cm-on to 3
12: end for
13:
end for
14: while & contains a cycle do
15: Identify cycle C
16: Remove edge from lower-ii to higher-th node in C
17:
end while
18: Obtain topological order π of G = EV,
return (G, n, {1))))
D ADDITIONAL EXPERIMENTS
D.1 WEAK AGENT IN A POOL
To test the robustness of SELFORG in setting with a weak agent present, we evaluate configurations where weaker agents are introduced alongside stronger peers. Figure reports the distribution of contribution ranks assigned across two scenarios: (i) three powerful agents backed by the Qwen- 2.5-7B-Instruct model paired with one Wren-2.5-1.5B-Innttre agent, and (ii) two agents of each type.
Table 3 summarizes AQUA-RAT performance under these settings. In case (i), where three strong and one weak agent are present, the single-agent performance is 71.65, while SELFORG raises it to 75.98, approaching the 76.77 level achieved when all four agents are strong. In case (ii), with two strong and two weak agents, SELFORG again yields large gains, improving accuracy from 66.54 in the single baseline 74.80. These results demonstrate that SELFORG is able reliably mitigate the drag introduced by weaker models, often recovering performance close to the all-strong setting.
Table 3: Performance with weak agents in the pool (AQUA-RAT) Comparison SELFORG against single-agent baselines in the (3 strong vs weak) and (2 strong vs 2 weak) settings.
Method
Aa
AA
A
AQUA-RAT
Note
Single
1.5B
51.18
Single agent; Qwen- 1,5B backbone (single weak) Single agent; Qwen- backbones (single strong)
Single
7B
76.77
Single
7B
7B
7B
1.5B
71.65
Backbone assignment is random per query (7B prob. 0.75)
SELFORG
7B
7B
7B
1.5B
75.98
Each agent uses its fixed backbone
Single
7B
7B
1.5B
1.5B
66.54
Backbone assignment random per query (7B prob. 0.5)
SELFORG
7B
7B
1.5B
1.5B
74.80
Each agent uses its fixed backbone
In setting (i), the weak agent is consistently identified as the least contributive, being placed in rank-4 in the majority of runs (68.1%). The stronger 7B models distribute across the higher ranks, demonstrating that the contribution estimation mechanism sharply separates weak from strong par- ticipants. The observation supports the theoretical guarantee in Section 2.6], namely that agreement
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assigned to Published as a conference paper at ICLR 2026
40
78 38.6
26.4
26.4
8.7
7B
40.2
26.8
21.6
11.4
40
75
27.2
34.2
27.2
11.4
30
7B
30.7
35.8
16.9
16.5
30
78
31.5
31.9
24.8
11.8
20
1.58
14.2
18.9
33.5
33.5
1.58
2.8
7.5
21.6
68.1
-10
1.58
15.0
18.5
27.9
38.6
2
3
Rank
Rank
Figure 5: Heatmaps of ranking outcomes with a weak agent in the pool. Each heatmap depicts the percentage (%) of times agents were assigned to contribution ranks (rank = highest contribution, rank 4 = weakest). The y-axis denotes the model type (Qwen-2.5-(7, each agent.
among correct agents amplifies their contribution scores, relegating weaker outliers downstream in the communication graph.
The case (ii) exhibits more competitive dynamic. While the 1.5B agents remain overrepresented in the lower ranks, they also occasionally occupy intermediate positions (ranks and 3), and the separation between strong and weak agents becomes less pronounced (due to the fact that the weak agents occasionally produce correct answers, thus leading to increased variability contribution signals). Nevertheless, the stronger agents still dominate the top positions, ensuring that information flow in the communication graph is largely governed by higher-quality responses.
D.2 TOKEN CONSUMPTION
We compare SELFORG to prior coordination frameworks with respect to both accuracy and token efficiency. Figures 6 and visualize this trade-off, where bubble area corresponds to total token usage. For clarity, only DyL and MacNet are included among the baselines in the plots. Although VeentVerse and AutoGen achieve lower token usage than all other methods, their performance is substantially weaker (Table [], with AutoGen in particular failing across nearly all benchmarks. Since our objective is to highlight the efficiency of coordination methods that remain competitive in accuracy, we restrict the visualization to DyLAN and MacNet.
By contrast, DyLAN and MacNet represent stronger baselines that consume a similar number of tokens as SELFORG. DyLAN exhibits relatively competitive performance on some reasoning tasks, but its overall average lags behind, especially on challenging datasets such as MMLU-Pro. MacNet shows modest efficiency advantages in prompt token usage but suffers from accuracy degradation across nearly all tasks. In both cases, SELFORG outperforms these baselines in accuracy while maintaining comparable token budget, indicating more favorable accuracy-efficiency trade-off.
1e6
AQUA-RAT
MATH
MMLU
1e6
AVG
2.25
1.50
Token consumption
2.10
1.45
1.75
2.05
1.40
2.00
1.7
1.35
1.25
1.95
1.00
1.6
55.0 Accuracy (96)
48
50
42.5 Accuracy (%)
Accuracy (96)
Accuracy 1%6
MacNet
SeltDrg
Figure 6: Visualization of performance and completion token consumption. Each bubble corre- sponds coordination method, with bubble proportional to token consumption. Correspond- ing table: Table
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Published as a conference paper at ICLR 2026
1e6
AQUA-RAT
1e7
MATH
1e6
1e6
AVG
Tikeen consumpttion
3.0
2.5
0.8
4
0.6
1.5
2
1.0
50.0
52.5
55.0
57.5
50 Accuracy (%)
48
45
50 Accuracy (%)
37.5
40.0
42.5
45.0
Accuracy (%)
Accuracy (%)
DyLAN
MacNet
SelfOrg
Figure 7: Visualization of performance and prompt token consumption, Each bubble corre-
sponds to a coordination method, with bubble area proportional to token consumption. Correspond- ing table: Table !
Table 4: Token consumption across coordination methods. Completion tokens (top) and prompt tokens (bottom) consumed on each dataset Dwwe-2.5-1.5B--nsttee model. Corresponding table: Table
Method
MATH
GSM8K
AQUA-RAT GSM-Hard
MMLU
MMLU-P
AIME
completion tokens
DyLAN
2249026
2086972
1312830
2468878
1961663
2786328
238078
MacNet
1056599
1806092
1513390
2238769
2137874
2925015
243205
Verse
1077488
609241
530435
711561
338957
703302
74665
AutoGen
487744
282592
202713
371429
271488
390695
53990
SELFORG
2002530
1858577
1369879
2214019
1934568
1587246
213939
Method
MATH
GSM8K
AQUA-RAT GSM-Hard
MMLU
MMLU-P
AIME
prompt tokens
DyLAN
10391904
4706386
3241463
5719811
6267944
10847226
797505
MacNet
2647651
536500
829202
486320
1149266
1471736
61122
VgentVerse
3309868
2048995
1793383
2283561
1338962
2723973
240881
AutoGen
2026745
1292144
874703
1564267
1442236
2050001
176709
SELFORG
6016239
3836070
2556599
4351062
4038531
4251306
325588
D.3 PROBABILISTIC MODELING, ADDITIONAL CORRELATION RESULTS
To further substantiate the relationship between embedding similarity and answer correctness, extend the empirical analysis beyond the illustrative instance (Figure (2) and evaluate the phe- nomenon across three benchmarks: GSM-Hard, AQUA-RAT, and MMLU in Figure 8 Across
GSM-Hard
AQUA-RAT
MMLU
C1
C2
0.98
C2
0.95
C3
C3
0.96
C3
C4
C4
0.90
0.94
W1
W1
0.92
W1
W2
W2
W2
W3
0.80
W3
0.90
W3
0.80
0.88
0.75
W5
0.75
W5.
W5
C
W1
W2
W5
W2
Wa
W5
W1
W2
WB
Figure Cosine-similarity heatmaps response embeddings (correct vs. incorrect responses) across three benchmarks.
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Table 5: For randomly sampled queries, p denotes the probability of generating the correct answer over 100 runs, max; ps: is the largest probability of incorrect answers.
GSM-Hard
AQUA-RAT
MMLU
max; Pk:
maxp Pk
max; Pk:
1
0.98
0.01
0.60
0.11
0.61
0.14
2
0.35
0.05
0.93
0.02
0.57
0.16
3
0.74
0.02
0.90
0.04
0.42
0.20
4
0.71
0.03
0.84
0.06
0.75
0.10
5
0.39
0.09
0.23
0.21
0.42
0.21
6
0.31
0.07
0.40
0.16
0.80
0.07
7
0.70
0.04
0.70
0.08
0.84
0.07
benchmarks, we observe consistent geometric signature the response-embedding space: em- beddings of correct answers form tight cluster, whereas incorrect answers are comparatively dis- persed, with low cross-similarity between the two groups. This structure persists broadly across both open-ended and multiple choice settings.
all three cases, (i) correct responses exhibit high within-group similarity, (ii) incorrect responses more weakly correlated with each other, and (iii) cross-block similarities remain low. This pattern is consistent with the modeling assumption that correct responses are semantically aligned, while incorrect responses scatter across modes, implying that correct responders tend to align more with the population centroid than incorrect responders.
To complement the qualitative heatmap plots, we randomly sample multiple queries from each task and estimate (over repeated stochastic generations) the probability of producing the correct answer, denoted by p, and the maximum probability of the most frequently occurring incorrect response, denoted by max; Pk- Table 5 reports these values. Across GSM-Hard, AQUA-RAT, and MMLU, p consistently exceeds max; Pk by substantial margin (satisfying the assumption in Lemma indicating that correctness concentrates probability mass more strongly than any competing incorrect alternative.
Notably, the narrowest separation appears in the 5th AQUA-RAT example (highlighted in red), where p Rs 0.23 and max; Pk Rs 0.21. This case consistent with near-random behavior: AQUA- RAT has five answer options, so uninformed guessing concentrates around = 0.2, and no semanti- cally coherent correct cluster is expected to dominate such instances. Outside of these near-chance cases, the observed gaps support the mechanism underlying the contribution dominance.
D.4 EMBEDDING MODEL
In main experiments, employ the all al1-Mim IM- (Reimers & Gurevych) 2019) model. a lightweight embedding model with only 22.7M parameters, to estimate similarity between agent responses. This choice intentional: we aim to keep the method efficient and avoid reliance on large embedding models, even if this introduces some additional noise into similarity estimates.
To validate this design choice, we conduct ablation study in the weak-agent-in---ponn scenario using different embedding models (see Figure In addition to all-MiniLMM, we evaluate -lleM- base-v2 (109M parameters) (Reimers & Gurevych] 2019) and Owen3-0.6B. 0.6--mmeddiing (600M pa- rameters) Zhang et al. 2025d). Across all cases, the embedding models are able to correctly iden- tify the weakest agent: weak participant consistently ranked lowest in the majority of runs. Moreover, both MPNet and Qwen-0.6B provide sharper separation between strong and weak agents compared to MiniLM, reflecting their stronger representational capacity.
Nevertheless, our goal is to design coordination mechanism that remains effective with lightweight embeddings. Despite the noisier similarity signals from all-MiniLMM SELFORG still succeeds in differentiating weak and strong contributors and delivers strong overall performance. This confirms that our approach does not require powerful encoders and can operate effectively under a minimal embedding budget, making it broadly applicable in resource-constrained settings.
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All-MiniLM
All-MPNet
Qwen-0.6B-Emb
78
27.2
34.2
27.2
11.4
7B
29.5
33.9
30.7
5.9
7B
32.7
37.8
23.6
5.9
78
31.5
31.9
24.8
11.8
35.0
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10
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Figure 9: Embedding model comparison in the weak-agent-in-a-pol scenario. Heatmaps show the percentage of times of each agent (rows) being assigned contribution ranks (columns) when using different embedding models for similarity estimation: All All-Minill M--MiniLy LM (22.7M parameters), All-MPNet (109M), and Owen-O, (600M). All models are able correctly identify the weak- est agent (A4), with MPNet and Qwen-0.6B providing sharper separation between strong and weak agents.
D.5 HETEROGENEOUT AGENTS WITH DYNAMIC ROLES
We additionally evaluate dynamic role-switching vari- of the heterogeneous-agent setting from Section 3.3
50.0
24.4
40
where agent roles are dynamic and not pre-fixed. Dy- namic role settings yield small improvement over fixed roles (accuracy: 66.93% vs. 16,,,, Figure 4), sug- gesting that centribution-uuiden routing remains effective without static role prior and can achieve similar perfor- mance with permutation diversity.
T11
23.2
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We present Figure 10 which reports the percentage of times each agents attains contribution ranks (1-4). The distribution indicates systematic separation between stronger and weaker agents: Qwen appears most fre- quently in earlier positions (rank 1), while Mistral is most frequently placed later (rank 4).
10
i
Rank
Figure 10: Dynamic role switching in SELFORG with heterogeneous agents.
E EFFICIENT SELFORG
While the main pipeline of SELFORG proceeds through multiple rounds, not all rounds are equally necessary. In practice, if the agents already achieve strong agreement, further refinement may waste tokens without improving accuracy. To address this, we introduce an early-stopping mechanism based natural consensus among peers.
Consensus Criterion. Let the similarity matrix S E [-1,1] be defined as in Section 2.4 where
Sn,m cos (m) encodes the pairwise agreement between agents n and m. We define the min- imum consensus across all pairs as Smin min.zmm Sn,m Intuitively, Smin captures the weakest agreement within the system. this minimum exceeds a predefined threshold ? [0, 1]. then agents are deemed to have reached sufficient consensus.
Formally, the system halts further rounds if Smin 2 7, where 7 is the consensus parameter con- trolling strictness of agreement. For example, 7 0.9 requires that all pairs of responses have at least 90% cosine similarity. When satisfied, the system outputs the centroid-based final response (Equation 6) without additional rounds.
This mechanism directly builds upon the communication graph formation step (Section 2.4 Since
embeddings and similarities already computed, evaluating Smia incurs negligible overhead. By stopping once consensus achieved, SELFORG avoids redundant propagation and aggregation, yielding substantial token efficiency. In scenarios where weak agents initially diverge, multiple
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Published as a conference paper at ICLR 2026
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Figure 11: Visualization of performance and completion token consumption across benchmarks (AQUA-RAT, MATH, MMLU, and overall average). Each point corresponds to a method, with bubble size proportional to token usage. Methods include original SELFORG and efficient SELFORG with early stopping at y = (0.9, 0.95]. Early stopping variants show improved efficiency (fewer tokens) while maintaining comparable accuracy.
rounds remain valuable; however, when natural agreement arises early, Efficient SELFORG prevents unnecessary computation.
Experimental Results. Figure 11 compares the bascline SELFORG with its carly-stopping vari- ants under consensus thresholds y E {0.9, 0.95) on AQUA-RAT, MATH, and MMLU. All experi- ments were run with N 4 agents, each selecting its top-2 neighbors, and 3 rounds. We report both accuracy and completion token consumption. Bubble sizes reflect token usage, with smaller bubbles denoting higher efficiency.
Baseline SELFORG achieves accuracies of 58.27% (AQUA-RAT), 52.40% (MATH), and 53.80% (MMLU). Under y 0.95, accuracy slightly drops AQUA-RAT (57.87%), MMLU (51.60%), and MATH (52.2%). With looser threshold = 0.9, performance closely matches or even ex- ceeds the baseline on AQUA-RAT (59.06%), while remaining comparable on MATH (52.00%) and MMLU (51.20%) This indicates that early stopping preserves task quality and, in some cases, improves it by preventing over-refinement.
The key advantage lies in efficiency. Both early-stopping settings consistently reduce token usage compared to the baseline. The stricter 7 0.95 yields moderate savings, while achieves the largest reductions. In relative terms, token usage decreases substantially while accuracy remains stable, with savings on the order of 10 15% across benchmarks.
the looser ? 0.9
Summary. Efficient SELFORG demonstrates that natural peer consensus serve a reliable carly-stopping signal. By halting once strong agreement is reached, the system avoids redundant message-passing rounds, improving token efficiency while preserving accuracy. Unlike prior MAS
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Accuracy 1%6)
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Figure 12: Visualization of performance and completion token consumption across benchmarks (AQUA-RAT, MATH, MMLU, and overall average). Each point corresponds to a method, with bubble size proportional to token usage. Methods include DyLAN, MacNet, SELFORG and efficient SELFORG with early stopping y 0.95).
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Published as a conference paper at ICLR 2026
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Accuracy 19%)
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Figure 13: Visualization of performance and prompt token consumption across benchmarks (AQUA-RAT, MATH, MMLU, and overall average). Each point corresponds to a method, with bubble size proportional to token usage. Methods include DyLAN, MacNet, SELFORG and efficient SELFORG with early stopping at 7 = {0.9, 0.95).
approaches such DyLAN, which require explicit answer extraction from responses measure consensus (and may fail if the LLM deviates from formatting instructions), our method operates purely in the embedding space and thus avoids brittle dependencies on response parsing. Similarly, works that rely on external LLM judges to check consensus introduce additional computational and monetary overhead. contrast, Efficient SELFORG is lightweight, agnesssg, and robust: no answer extraction is needed, no external judge is invoked, and consensus is measured semantically rather than syntactically. This makes especially suitable for scaling to large agent pools and diverse task domains.
For completeness, we provide Figures 12 and 13 which include efficient SEL.FRG along with the other bascline methods and depict the performance and complction/prompt token consumption.
E.1 TERMINATION BREAKDOWN.
Efficient SELFORG halts in one of two ways: (i) it reaches the pre-specified maximum number of communication rounds, or (ii) it stops early once semantic consensus is achieved, i.e., when the min- imum pairwise cosine similarity among agent responses satisfies Smin 2 (consensus threshold; as described above) before exhausting the round budget.
Table reports the breakdown across datasets under two thresholds. With the looser consensus requirement& 7 0.9, roughly 30-39% of sessions terminate early; tightening the threshold to = 0.95 reduces early terminations to about 11-15%. This behavior is consistent with the role of y as an efficiency parameter: stricter value of y demands stronger agreement (fewer early stops), while looser values trigger early stopping more often, reducing token usage while keeping accuracy comparable aggregate.
Table 6: Fraction of runs terminating by round limit ("All rounds") versus early stopping due to consensus (7) under two thresholds.
0.9
=
0.95
Dataset
All rounds
Early stopped
All rounds
Early stopped
AQUA-RAT
0.669
0.331
0.846
0.154
MATH
0.612
0.388
0.852
0.
MMLU
0.700
0.300
0.894
0.106
F ABLATION STUDY
F.1 NUMBER OF AGENTS
We conduct an ablation study to analyze the effect of the number of agents on both accuracy and efficiency. Figure 14pports results for 1.wen-2.-1.5B--Instrne the AQUA-RAT benchmark. The
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Figure 14: Ablation on the number of agents. Results for Qwen-2.5. 2.5-1,,5--Instrucc on AQUA-RAT. The blue line (left axis) shows accuracy as the number of agents N increases, while orange bars (right axis) show token consumption. Latency (s) annotated above each bar. Accuracy improves with more agents, but at the cost of higher latency and token usage, illustrating the trade-off between performance and efficiency multi-agent coordination.
left y-axis shows accuracy, while the right y-axis shows token consumption; latency (in seconds) is annotated above each bar.
We observe that increasing the number of agents improves accuracy, from 53.54% with = agents to 59.84% with N 10. However, this gain comes at the cost of both higher token usage (scaling from 1.07M to 3.53M tokens) and longer latency (from 145s to 581s). Interestingly, accu- racy improvements
not strictly monotonic with N: performance plateaus 58.27% for N and N = 7, before rising again at N = 10. This suggests diminishing returns when adding ad- ditional weak agents, with benefits re-emerging only when coordination capacity (via K) increases sufficiently.
Overall, the ablation highlights the trade-off between accuracy and efficiency: more agents improve reliability but induce significant computational overhead, pointing to the importance of balancing scale against efficiency in multi-agent design.
F.2 TO REFORM OR NOT TO REFORM
An important design choice in SELFORG is whether to reform the communication graph between rounds of interaction. Reforming allows agents dynamically update their information flow struc- ture based on the latest responses, while static graph keeps the initial topology fixed throughout. We conduct an ablation study on benchmarks, GSM8K and MMLU, using N 5 agents and neighbor budget of K = 3, to evaluate the impact of graph reform.
Table 7: Ablation reforming the communication graph across rounds.
Dataset
Reform
N
K
Accuracy
True
5
3
73.8
GSM8K
False
5
3
73.2
True
5
3
52.8
MMLU
False
5
3
51.4
As shown Table 7 reforming the graph consistently improves performance, though the absolute gains modest. This suggests that while the initial communication structure already captures useful alignment among agents, dynamically restructuring the graph allows the system to consolidate correct signals more effectively, especially more challenging knowledge-intensiv tasks. The relatively small gap also indicates that SELFORG robust whether reform is applied but benefits from most in settings where agent responses more diverse and noisy.
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F.3 AGGREGATION STRATEGIES
We evaluate the sensitivity of SELFORG to the final-answer aggregation rule by substituting several alternative methods while keeping the rest of the method unchanged. Table reports the perfor- mance on MMLU over runs. The contribution-weighted centroid used in SELFORG is competitive with selecting the highest contributor across all rounds, and both outperform the uniform centroid and voting- based baselines. Naive majority voting suffers a large drop and high variance due to option-letter parsing errors; using an LLM to adjudicate the majority recovers most of this gap but introduces additional variance.
Table 8: Ablation study on the aggregation methods on MML.U (mean std over 3 runs). "Naive majority voting over extracted option letters; parsing erros materially degrade performance and increase variance.
Aggregation method
MMLU [mean + std]
Contribution weabbbuttoo-wiggheeddd centroid (SELFORG, default) Uniform centroid
53.60 1.27
52.70 + 1.31
Majority voting"
46.17 5.35
Majority voting (with LLM deciding)
51.90 + 2.33
Highest contributor across all rounds
53.20 + 1.19
F.4 SELFORG WITH MEMORY AND TOOLS
All main-table results are reported in a tool-agnostic setting to isolate orchestration quality. Here we show that SELFORG's response-conditione routing remains effective when agents are augmented with (i) per-agent memory (a scratchpad persisted across rounds) and (ii) a lightweight tool (sand- boxed Python REPL for arithmetic checks: no external access). The orchestration mechanism unchanged: contributions are still computed from the semantic content of agent responses and used form the response-conden conditione DAG.
Table 9: Effect of memory and tools on SELFORG with Qwen- 1.5e backbone.
Method (Qwen-1.5B)
GSM-Hard
MATH
Single Belf-Consistency,
36.2
49.2
37.2
50.2
SELFORG (base; no memory/tooss)
38.0
52.4
SELFORG + memory
37.8
53.4
SELFORG + memory + tools
40.0
54.2
Table 9 compares single call, self-consistency (Wang et al., 2023), base SELFORG and SELF- OrG with memory/tools under Twenn11.BB Base SELFORG improves upon both single and self- consistency methods. Adding memory yields comparable performance on GSM-Hard and a small gain MATH, while adding both memory and tools provides the largest gains both tasks.
G ADDITIONAL. CODING EXPERIMENTS
To evaluate whether SELFORG extends beyond AQ/reasoning benchmarks, we add two coding evaluations using a weak backbone (Qwen-1.5B), where orchestration effects are expected to be most pronounced. We report HumanEval (Chen et al. 2021) with execution-based pass@1 and (ii) SRDD Qian et al., 2024b) using the benchmark's composite score (completeness, executability, consistency) since SRDD has no single ground-truth solution.
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Table 10: Collaborative coding results with Qwen-1.5B backbone model (mean std). HvaanEval is pass@t; SRDD is the benchmark composite score.
Method
HumanEval
SRDD
Single
47.31 + 0.91
55.40 + 0.72
CoT
46.34 + 0.75
55.07 0.76
Self-Consistency
45.63 + 1.41
53.33 = 0.76
DyLAN
12.37:3..4
37.40 0.80
MacNet
44.67
56.00 0.40
SELFORG
48.97 = 0.89
60.13 + 0.90
SELFORG is best on both HumanEval and SRDD, indicating that its contribution-guide communi- cation improves not only reasoning but also collaborative coding.
30


Reviews:
[{"review_id": "g5uMArokJW", "reviewer": "Reviewer_TXyD", "summary": "This paper proposes SELFORG, a training-free framework for stochastic self-organization in multi-agent systems that dynamically constructs communication graphs using an embedding-based contribution metric approximating Shapley values. By structuring information flow as a Directed Acyclic Graph to enhance valuable signals and suppress noise, SELFORG achieves consistent improvements on eight reasoning and QA benchmarks, especially when applied to weaker language models.", "questions": "1. Could you comment on the robustness of the chosen hyperparameters? Specifically, how sensitive is the model's performance to adjustments in the cosine similarity threshold and the maximum number of rounds?\n2. Can you provide a breakdown of the termination triggers? Specifically, how frequently did the dialogue stop due to reaching the round limit versus achieving consensus based on the variance threshold?\n3. The final answer is selected as the response with the highest contribution score in the very last round. Have you considered alternative aggregation methods, such as selecting the response with the highest contribution score across all rounds? Is there a risk that the collective converges on a high-agreement answer in the final round, while a more brilliant but initially less understood answer from an earlier round gets discarded?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Simple, training-free mechanism that works across LLM sizes.\n2. Novel approach to constructing the communication DAG by leveraging cosine similarity for its decision logic.\n3. Extensive experiments across a wide array of reasoning benchmarks demonstrate consistent and significant improvements over strong baselines, especially when employing smaller-scale language models.", "weaknesses": "1. The framework's performance appears sensitive to key hyperparameters, such as the similarity threshold \\tau and the termination variance. These variables may require tuning per dataset or model, which the paper does not address, leaving the impression that the chosen values are empirically derived heuristics rather than principled parameters.\n2. The paper highlights that SELFORG is a lightweight framework requiring no pre-training or reinforcement learning, contrasting it with complex task-specific topologies. However, the token consumption table in the appendix shows that the method does not possess a significant advantage in resource usage over other baselines, particularly AutoGen.\n3. The paper's claim of a positive correlation between the cosine similarity of LLM agent embeddings and answer correctness is supported by only a single heatmap from one case, which is not sufficiently convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "knnKINGB6c", "reviewer": "Reviewer_F87g", "summary": "This paper introduces SELFORG, a framework for orchestrating multi-agent systems (MAS) based on Large Language Models (LLMs) that dynamically constructs communication graphs without relying on external judges, pretrained topology generators, or reinforcement learning.", "questions": "1. Why no comparison with \"Who's the MVP? A Game-Theoretic Evaluation Benchmark for Modular Attribution in LLM Agents\"? It also uses Shapley values for agent evaluation.\n\n2. Algorithm 2 removes cycles arbitrarily - how sensitive is performance to different cycle removal orders?\n\n3. How does this scale to 20-50 agents? The O(N²) similarity computation could be a bottleneck.\n\n4. Section D.3: How is consensus threshold ξ chosen? Is it task-specific? What if wrong agents agree?\n\n5. Why only test on tasks where single LLMs already work well? What about negotiation, collaborative design, or other inherently multi-agent problems? such as MultiAgentBench (Zhu et al. 2025)\n\n6. The heterogeneous agent experiment (Section 3.3) uses fixed role assignment. What happens with dynamic role switching?\n\n7. How many times was each experiment run? Standard deviations?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "**Dynamic topology based on actual responses**: Unlike fixed graphs or query-specific topologies, SELFORG adapts to the stochastic agent outputs in real-time (Section 2.4), which is more realistic.\n\n**Efficient Shapley value approximation**: Reduces complexity from O(2^N) to O(N) with theoretical bounds (Theorem 1) and ranking stability guarantees (Corollary 1).\n\n**Strong results with weak models**: Achieves 45% accuracy with Qwen-1.5B vs 33-37% for other multi-agent methods (Table 1), proving the value of the approach when agents are unreliable.", "weaknesses": "**Limited to QA/reasoning tasks**: Only evaluates on MATH, GSM8K, MMLU etc. With strong models (Table 2), improvements are marginal (3-4%). No evaluation on genuine multi-agent tasks like MultiAgentBench or collaborative coding (SRDD).\n\n**Missing key baseline**: MAS-GPT is mentioned (page 1) but never compared experimentally despite being the most similar approach.\n\n**No statistical analysis**: Temperature=0.5 but no error bars or multiple runs reported. Only Figure 2 shows 100 runs for one problem.\n\n**Strong assumptions**: Assumption 1 (correct answers cluster, wrong answers scatter) is only validated on one example (Figure 2b). All theory depends on this.\n\n**Small scale only**: Tests use 4-5 agents. Scalability unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "TzR49CV4Nx", "reviewer": "Reviewer_SZuJ", "summary": "The paper proposes a decentralized, response-conditioned multi-agent communication framework named SELFORG. Under the framework, agents first independently generate answers to the user query and use a Shapley-value–inspired approximation to assess peer contributions. A directed acyclic graph (DAG) is then constructed to govern information flow among agents, ensuring stable and efficient message transmission from high-contribution agents to others. Compared with prior approaches, SELFORG requires no pretrained topology generator, no edge-level reinforcement learning, and no external judge. The communication structure self-organizes round by round and is especially robust in the weak-backend regime. The paper further provides a theoretical analysis showing that increasing the number of agents raises the probability of obtaining the correct answer, and that correct responses naturally dominate the information flow, explaining the method’s effectiveness.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Unlike prior task-level and query-level communication frameworks, SELFORG performs round-level construction of the communication graph with low method complexity.\n2. It uses a Shapley-inspired contribution estimate to build a DAG, placing high-contribution agents upstream to enable stable and efficient information propagation.\n3. Both theory and experiments demonstrate the effectiveness of the proposed method.", "weaknesses": "1. The proposed method rests on the assumption that correct answers cluster more tightly in the embedding space while incorrect ones are more dispersed. The assumption may break down on tasks with multiple equivalent answer forms.\n\n2. The construction of the communication graph relies on several heuristics—e.g., the similarity threshold $\\tau$ and the number of neighbors $k$ per node—but the paper provides limited systematic robustness analysis of these hyperparameters.\n\n3. The final output is selected as the response closest to the contribution-weighted centroid. This strategy may favor the dominant mode. When multiple valid solutions exist, the aggregation method may potentially suppresses minority—yet superior—reasoning paths. The paper does not provide comparisons with alternative aggregation methods like voting.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "GbxGSuh0zE", "reviewer": "Reviewer_gZmw", "summary": "This paper proposes using response embeddings and similarity to the group opinion as a self-supervised signal to compute an approximate shapley value for attribution, and then constructing a graph heuristically based on node contributions to achieve better answer aggregation.", "questions": "1. Have the authors tried other tasks? Currently, the datasets appear to be the common ones that have already been heavily overfit and are prone to data contamination.\n2. Have you compared against baselines such as self-consistency? As it stands, this looks less like a multi-agent task setting and more like aggregating multiple responses. A single node lacks memory, tool use, and multi-turn interaction, and nodes may differ only by LLM or prompt.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper’s motivation is well organized; a response-level, on-the-fly self-organizing topology for multi-agent systems is indeed compelling.\n2. The overall methodological chain is clear and reasonable. Obtain signals in a self-supervised way, then use graph construction to optimize aggregation of those signals. There is theoretical support, and the approach aligns with intuition.", "weaknesses": "1. I think the premise “the correct answer appears repeatedly, while wrong answers scatter” only holds when the gap between model capability and task difficulty is not large. In that regime, SELFORG can be viewed as an improved self-consistency method.\n2. When tasks are harder (e.g., on difficult mathematical reasoning where pass@1024 is low), correct responses are more likely to be a minority. In contrast, wrong responses may become the center of a conformity cluster. In that case, your self-supervised conformity signal may drown out the correct signal, and I don’t believe multiple rounds will reverse this failure mode.\n3. There are many heuristic parameters; various thresholds and hyperparameters require manual tuning.\n4. The graph construction is overly heuristic. Although the topology is not hard-coded, it is still generated by rules based on node contributions, which is insufficient to claim true emergence or self-organization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)