Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
Authors: Mengru Wang
Xingyu Chen
Yue Wang
Zhiwei He
Jiahao Xu
Tian Liang
Qiuzhi Liu
Yunzhi Yao
Wenxuan Wang
Ruotian Ma
Haitao Mi
Ningyu Zhang
Zhaopeng Tu
Xiaolong Li
Dong Yu
Venue: NeurIPS 2025 poster ()
Keywords: MoE
cognitive experts
thinking
large reasoning model
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: We introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE), designed to improve reasoning depth and efficiency without additional training or complex heuristics.

Abstract: Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs) have achieved impressive reasoning capabilities by selectively activating experts to facilitate structured cognitive processes. Despite notable advances, existing reasoning models often suffer from cognitive inefficiencies like overthinking and underthinking. To address these limitations, we introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE), designed to improve reasoning depth and efficiency without additional training or complex heuristics. Leveraging normalized Pointwise Mutual Information (nPMI), we systematically identify specialized experts, termed cognitive experts that orchestrate meta-level reasoning operations characterized by tokens like <think>. Empirical evaluations with leading MoE-based LRMs (DeepSeek-R1 and Qwen3-235B) on rigorous quantitative and scientific reasoning benchmarks (AIME and GPQA Diamond) demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization. Crucially, our lightweight approach substantially outperforms prevalent reasoning-steering techniques, such as prompt design and decoding constraints, while preserving the model's general instruction-following skills. These results highlight reinforcing cognitive experts as a promising, practical, and interpretable direction to enhance cognitive efficiency within advanced reasoning models.

Two Experts Are All You Need for Steering Thinking: Reinforcing Reasoning in MoE Models Without Additional Training
Mengru Wang* Xingyu Chen*.2, 2.2, Yue Wang*.2, Zhiwei He* Jiahao Xu² Tian Liang", Qiuzhi Liu?, Yunzhi Yao", Wenxuan Wang², Ruotian Ma² Haitao Mi?, Ningyu Zhang Zhaopeng Tul:2 Xiaolong Li and Dong Yu²
'Zhejiang University Tencent
95
85
Shared by NatlhdPhyeiisshhhmmier
Layer Index
Shared by Math&& Physics
Accurccy
75
79.0
75
182,
126, 29
20
All Domains
Marlh
Chemistry Rislogy
45
Math
Physics
Chemistry
Biology
All Domains
64
128
192
256
Expert Index
(a) Cognitive Experts
(b) Reasoning Accuracy
Figure 1: (a) Illustration of cognitive experts identified across domains. (b) Reinforcing only the top
two experts (in red color) can improve reasoning accuracy without additional training.
Abstract
Mixture-of- -f-erttss (MoE) architectures within Large Reasoning Models (LRMs) have achieved impressive reasoning capabilities by selectively activating experts to facilitate structured cognitive processes [1, 2]. Despite notable advances, existing reasoning models often suffer from cognitive inefficiencies like overthinking [3] and underthinking [4]. To address these limitations, we introduce novel inference- time steering methodology called Reinforcing Cognitive Experts (RICE), designed to improve reasoning depth and efficiency without additional training or complex heuristics. Leveraging normalized Pointwise Mutual Information (nPMI), sys- tematically identify specialized experts, termed cognitive experts that orchestrate meta-level reasoning operations characterized by tokens like "<think>", Empiri- cal evaluations with leading MoE-based LRMs (DeepSeek-N) R1 and Qwen3-235h rigorous quantitative and scientific reasoning benchmarks (AIME and GPQA Di- amond) demonstrate noticeable and consistent improvements reasoning accuracy, cognitive efficiency, and cross doossnn generalization. Crucially, our lightweight approach substantially outperforms prevalent reasoning-steering techniques, such prompt design and decoding constraints, while preserving the model's general instruction-following skills. T'esse results highlight reinforcing cognitive experts as promising, practical, and interpretable direction to enhance cognitive efficiency within advanced reasoning models.
'Equal Contribution. Work done when Mengru, Xingyu, Yue, and Zhiwei interning Tencent. Correspondence to: Zhaopeng Tu <zptu@tencent.com and Ningyu Zhang <zhangningyu @ eju.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Models capable extended reasoning, often referred as Large Reasoning Models (LRMs) like OpenAl's ol [5] and DeepSeek-RI 11 [1], have significantly advanced machine intelligence, largely by scaling test-time computation [6. 7]. Despite their impressive capabilities, these LRMs remain susceptible to inefficiencies [8-15]. Prior work has sought to address these issues through approaches such as preference optimization [3], decoding penalties [4], and various other techniques. In this work, we tackle these problems from novel perspective: potential expert specialization in Mixture- of-Experts (MoE) architecture.
Due to the computational resource efficiency brought about by its sparsity, the MoE architecture has been increasingly adopted by state-of-the-art (SOTA) models, such as DeepSeek-RI [1] and Qwen3 [2]. This sparse, specialized activation paradigm bears a conceptual resemblance to functional specialization in the human brain, where targeted interventions can modulate cognitive functions and behaviors [16-19]. Inspired by this principle, we systematically investigate whether undesirable reasoning behaviors
noE-based LRMs correlate with the activation patterns of specific experts, and critically, if strategic manipulation of these experts can ameliorate such issues.
We introduce approach to identify and modulate key experts integral the reasoning process. By analyzing the co-occurrence of explicit linguistic markers of thought (e.g., "<think>" and "</think>") with individual expert activations, we pinpoint subset experts highly correlated with the model's cognitive deliberations. We designate these critical components as cognitive experts. Through extensive experimentation with SOTA MoE-reasoning models DeepScek-RI [1] and Qwen3-235B [2] on challenging math and scientific reasoning benchmarks, we demonstrate that selectively amplifying as few as two cognitive experts can enhance both reasoning depth and efficiency. Notably, our approach achieves marked accuracy improvements while reducing token usage in critical reasoning tasks, outperforming existing steering methods such as prompting and decoding constraints [4].
Moreover, showcase impressive generalization and robustness of cognitive expert modulation ob- serving consistent improvements in unseen and more complex reasoning scenarios while maintaining or even enhancing general instruction-followin capabilities. Our findings provide strong evidence that modulating selective experts responsible for meta-level reasoning is effective, efficient, and broadly applicable across domains, paving the way for lightweight and interpretable model steering in increasingly sophisticated MoE- bos-based reasoning models.
Our main contributions
1. We propose a normalized Pointwise Mutual Information (nPMI) method for identifying cognitive experts within LRMs that are highly correlated with reasoning behavior, requiring only a single forward propagation and no additional training.
2. We introduce a lightweight inference-time steering strategy, named "reinforcing cognitive experts", that effectively enhances reasoning depth and accuracy without requiring any additional training or supervision signals.
3. Through comprehensive experiments on two prevalent MoE reasoning models and rigorous benchmarks, we empirically validate the efficacy, generalizability. and robustness of cogni- tive expert modulation, demonstrating significant improvements in cognitive efficiency and problem-solving accuracy.
2 Identifying Cognitive Experts
In this section, we leverage normalized Pointwise Mutual Information (nPM)) [20] to quantify the correlation between model thinking and each expert in Mixture of Experts (MoE) reasoning model. We hypothesize that there are some "cognitive experts" selected by nPMI metric, which orchestrate meta-level reasoning for complex tasks.
2.1 Expert Specialization in MoE Models
In large reasoning models, deep thinking is manifested through key tokens, such as "<think>" to initiate reasoning. "</think>" to terminate it, and tokens like "recheck" to guide introspection. In

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the MoE framework, these tokens are generated during forward propagation through various model components, including the MoE routing mechanism that assigns them to specialized experts, with weights determining each expert's contribution.
Formally, let consider an MoE framework [21] with N experts, denoted {F1,.... . F ). at each layer. For each input token æ, a gating function selects a subset S C [E Eoo of O experts (O < N), where |S] O, and assigns weights wy (with Lies wi 1) each selected expert E; E S. The output h for token a is computed as:
h1 = Σ 10 Ex(2), where |S| = O, EE
where (x(x) represents the output of expert E,, and wo is the weight of the i-th selected expert. Prior work on MoE models shows that expert routing is often token-dependent [22], but recent study [23, 24] indicates that DeepSeek-RI I's advanced reasoning enables its expert routing focus on semantic specialization, surpassing token poken-dependent methods. We hypothesize that experts with consistently high co-occurrence scores with thinking tokens serve as key "cognitive experts" responsible for meta-level reasoning.
Measuring Correlation of Specialized Experts and Thinking Tokens To examine whether given expert consistently governs the model's reasoning process, we measure the co-occurrence between its activation and specific rassoinngrrelatdd marker tokens, such as "<think>, " "</think>" and others. Formally, let æ represent a token and y denote expert E, We measure their association using pointwise mutual information (PMI). The PMI of and is defined
p(1, y) p(x) p(v)
log2 p(y]z))
PM y) log2
p(y)
where p(x, y) is the joint probability that x and both occur, while p(r) and p(y) are their individual (marginal) probabilities, and p(y|z)) is the conditional probability that y occurs given
For interpretability, we normalize PMI to the range [-1, +1], yielding
nPMI( v) = PMI( (2, y)
logg p(z, v)
Thus, nPMI(2, y) = -1 indicates that events and y never co-occur, nPMI( v) 0 implies independence, and nPMI(x, y) = +1 indicates they appear almost exclusively together (complete co-occurrence).
Let M be the number of instances dataset, and let T the total number of tokens generated over all instances in the test set. We denote by hi the number of times the expert E; is activated specifically when the thinking token (e.g. "<think>>" appears, and by Kn, the total number of times E, is activated across all tokens (including both thinking and non-thinking tokens). Since the reasoning model generally generates one thinking start and end token for each instance, then we can achieve the following functions when denotes "<think>" or "</think>"
kn M
Kn T
kn pla,y= Eg(x)) T
ply E,(x:)
ply E,)
(4)
logz Kn M log2
+ loge( #)
nPMI(I, y = E.)-
(5)
Intuitively, if an expert E, is activated almost exclusively during "<think>" and rarely (or never)
at other tokens, hin ~ K,, s M, nPMI(x <think?, E,) R3 log, 1+log, # Rs +1, logal 1,)
indicating that this expert is effectively tied to the thinking marker. In other words, the expert's entire usage focuses on activating the thinking token. Such specialists are prime candidates for "cognitive experts", given their consistently high co-occurrence with the thinking marker tokens.
2.2 Identify Cognitive Experts
We observe that some experts exhibit high nPMI scores with both "<think>" and "</think>", indicating bimodal association. This suggests their broad involvement in the reasoning process
3

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rather than specialization in its initiation. To prioritize experts specialized in initiating (rather than terminating) reasoning, we adopt the following selection strategy:
We define a set of thinking tokens 17 {<think>, </think>, Alteee,,eeeaattt Alternatively, The normalized Pointwise Mutual Information (nPMI) score for expert E, is formulated
nPMT,
Σ Gz. < Ex), #EIl
where is a thinking token in set II, denotes the coefficient associated with the token x, assigned
as Cethink> = 1, Ce/think> = 11 and Cltterattielyy -1
Then, we select the top-l experts based on their nPMI scores to form the cognitive expert set P. The weight adjustment for expert E; governed by the following condition:
W 3 W!s
E, E and E, E.P.
W1:
otherwise,
where {E; nPMLg is among the top I scores) denotes the set of cognitive experts, S is the subset selected by the gating function in Eq. 1, and / is the steering multiplier. In other words, these experts are identified, contribution through the hyperparameter (3.
reinforce reasoning in the MoE model by controlling their
Experiments
Research Questions In this study, we investigate the following research questions:
RQI:
Are there "cognitive experts" specialized in thinking? If so, do these experts differ across domains?
RQ2: Can the identified cognitive experts effectively enhance cognitive effort within MoE models?
RQ3:
Do "cognitive experts" differ across various domains (e.g., math, physics, chemistry, and biology)?
RQ4: Does reinforcing specific cognitive experts negatively impact the general problem-soloe capabilities of MoE models?
3.1
Experimental Setup
MoE-based Reasoning Models Currently available open-source MoE architectures tailored for large reasoning models tasks include DeepSeek-R1 [1] and Qwen3-235B [2]. DeepSeek-RI selects 8 experts from a total of 256 each layer, whereas Qwen3-235B selects experts from a total of 128. We primarily use the DeepSeek-RI (671B) model for our experiments, supplemented by additional evaluations on the Qwen3-235B model to examine the generalizability cognitive experts. Note that we provide more experimental details in $B.
Benchmarks We evaluate our approach on two challenging benchmarks designed specifically to test the reasoning abilities necessary for solving scientific problems across diverse domains:
AIME [25]: a dataset from the American Invitational math Examination, which assesses advanced mathematical problem-solving skills. We two recent test sets, AIME2024 and AIME2025, each comprising 30 problems.
GPQA Diamond [26]: a comprehensive dataset of 198 expert-crafted multiplc-choice questions in biology, chemistry, and physics, designed to test advanced scientific reasoning skills.
3.2 Cognitive Experts
To address RQI, we first identify cognitive experts within two MoE reasoning models DeepSeek- R1 [1] and Qwen3-235B [2] across four scientific domains. Taking math as an illustrative example, we first use DeepSeek-RR to generate answers on the AIME2204 dataset, simultaneously recording the expert selections at each token position during forward propagation. Next, employ the nPMI

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Table 1: Identified cognitive experts of DeepSeek-RI. Each entry (layer ID, expert ID) denotes the DeepSeek-RI model layer ID and expert ID. "All" combines data from all domains.
Identified Experts Ranked by nPMI Score
Domain
1st
2nd
3rd
4th
5th
Math
(39, 182)
(29, 126)
(14, 114)
(27,45)
(16, 129)
Physics
(29, 126)
(39, 182)
(36, 53)
(39,4) 46)
(24, 159)
Chemistry
197)
(39, 182)
(22, 37)
(29, 106)
(29, 126)
Biology
(42, 194)
(22, 37)
(37. 241)
(1)
(39, 188)
All
(39, T82)
(29, T26)
(29, 166)
"4,
(50, 720)
Table 2: Identified cognitive experts of Qwen3-235B. Each entry (layer ID, expert ID) denotes the Qwen3-235B model layer ID and expert ID. "All" combines data from all domains.
Identified Experts
Domain
Top-1
Top-2
Top-3
Top-4
Top-5
Math
(70, 47)
(23, 115)
(19, 47)
(75, 46)
(22, 88)
Physics
(2, 28)
(74, 65)
(4, 44)
(25, 103)
(7,36)
Chemistry
(32, 58)
(26, 30)
(68, 35)
(37, 57)
(25, 103)
Biology
(2, 28)
(26, 30)
(67, 15)
(82, 29)
(25, 103)
All
(25, 103)
(26, 30)
(82, 29)
(67, 15)
57)
metric defined in Eq. 6 identify the top five experts that exhibit the strongest statistical association with reasoning-related marker tokens (c.g., "<think>")) These experts are thus identified as the key cognitive experts specialized for mathematical reasoning. Analogously, we apply this procedure to the biology, chemistry, and physics questions in the GPQA Diamond dataset to identify cognitive experts in these respective domains. In the case of Qwen3-235B, we follow similar procedure but generate Nomain-speciff responses with the Qwen3-235B model itself. This ensures consistent identification signals that correspond directly to the model under examination.
We demonstrate the nPMI distribution of the top 10 experts of Deepseek-RI across four domains (Math, Physics, Chemistry, and Biology) in Fig Across the four domains, the top five experts exhibit nPMI values that are mostly above 0.5. Besides, the top 5 experts also indicate sharply peaked distribution toward the other experts. In particular, the group of top five "thinking- specialized" experts shows significantly higher nPMI scores than the remaining experts, sug- gesting that domain reasoning is largely concen- trated within a few highly specialized compo- nents. This pattern supports our hypothesis that a small number of experts are highly special- ized for cognitive functions. Subsequently, we delve into the effectiveness of the top 5 experts in Table 3.
0.7
Math
Physics
Chemistry
aPMI Score
0.6
Biology
0.5
0.4
6
8
10
Top 10 Expert Rank
Figure 2: The nPMI distribution of top 10 expert of Deepseek-Rl across four domains.
Cognitive Experts Across Domains Cognitive experts identified within DeepSeck-RR are summa- rized in Table 1. An analogous summary for Qwen3-2en3 -25BBBBBBBBBBB is provided and discussed in Table 2. From Table we observe that the top two cognitive experts in the math, physics, and the aggregated "All" domains are remarkably consistent: (39, 182) and (29, 126). This strongly suggests these experts play critical and reliable roles reasoning tasks requiring increased cognitive effort, particularly in quantitative and logic-intensive domains. The significant overlap observed between math and physics further implies shared underlying cognitive strategy and structured logical fiference-whiee the model employs consistently across these domains. Addi-
focusing on symbolic manipulation
5

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Table 3: Effect of Deepseek-RI AIME24 with reinforced cognitive experts, evaluated across different multipliers and varying numbers Math-domain cognitive experts. "Random" denotes two randomly chosen experts. The row with Multiplier I denotes the performance of vanilla DeepSeek-RI.
Multiplier
Top1
Top2
Top3
Top4
Top5
Random
73.3
2
70.0
70.0
76.7
73.3
73.3
70.0
76.7
83.3
73.3
66.7
76.7
73.3
8
76.7
73.3
83.3
73.3
73.3
70.0
16
80.0
80.0
1.7
76.7
73.3
73.3
32
70.0
83.3
73.3
73.3
73.3
76.7
64
80.0
83.3
60.0
53.3
50.0
66.7
128
70.0
83.3
43.3
26.7
13.3
63.3
256
73.3
60.0
10.0
6.7
0.0
73.3
512
63.3
46.7
6.7
3.3
0.0
63.3
tionally, the repeated appearance certain experts multiple domains supports our hypothesis: a subset of experts encodes generalized reasoning capabilities applicable across diverse scientific fields. Therefore, these cross-domain patterns indicate that DeepSeek-R may encode robust domain-general cognitive mechanisms, with some experts serving reusable computational building blocks suitable for abstract reasoning and logical problem-sole solving tasks. Note that even within the same domain, there distinctions. For instance, when comparing the top-5 cognitive experts for AIME24, MATH, and GSM8K, we find both shared and dataset-specific experts. Therefore, we hypothesize that the shared experts are responsible for general mathematical abilities, while the unique experts handle dataset-specific skills.
3.3 Effectiveness of Cognitive Experts
Reinforcing Cognitive Experts To answer RQ 2, we reinforce the identified top 5 cognitive experts from the Math (AIME24) and evaluate their performance under different reinforcement the
configurations on the same benchmark AIME24 (Table 3). The optimal hyperparameters number of cognitive experts and the steering multiplier 3-are selected based on this evaluation and used in all subsequent experiments. We then assess the generalization ability of these reinforced experts on the unseen, more challenging tasks from AIME25 (Table 4).
From Table 3, we observe that reinforcing two top-ranked cognitive experts significantly enhances the model's reasoning ability. Notably, using two experts with steering multiplier of 4, 32, 64, or 128 achieves the highest accuracy of 83.3%. In contrast, applying an excessively large multiplier (e.g.. 512) causes dramatic drop in accuracy, often near This failure mode is characterized by the model repetitively generating meaningless tokens, suggesting that overly aggressive reinforcement disrupts the model's generation dynamics. Overall, moderate reinforcement of well-identified cognitive experts leads to consistent improvements, whereas over-reinforcement or random expert selection results performance degradation. However, reinforcing two randomly selected experts across a wide multiplier range (2 to 512) yields minimal performance variation. Therefore, we use two experts with a steering multiplier 64 for all subsequent experiments
We directly apply the cognitive experts identified from AIME24 to solve unseen and more challenging reasoning problems in AIME25. As shown in Table 4, these cognitive cognitive experts generalize well to the AIME25 test set. For DeepSeek-R1. the accuracy improves from 63.3% to 73.3% when guided by the identified cognitive experts. Similarly, for Qwen3-235B, accuracy increases from 66.7% to 73.3%. Additional pass performance using the model's officially recommended top-p sampling strategy (provided in $C.4) further supports this observation. The above phenomenon demonstrates the transferability and robustness of the expert selection across tasks with higher cognitive demands.
"This setup is designed to test the raw generalization of the math derived setting. However, different domains may require different intensities of cognitive steering. We discuss this in detail in I.

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Table 4: Performance of approach the AIME24 and generalization the unseen AIME25.
Benchmark
Method
Accuracy
Thoughts
#Tokens
DeepSeek-RI
73.3
12.0
9,219
AIME24
+RICE [(39,182), (29,126)
83.3
10.2
8,317
DeepSeek-RT
63.3
17.0
10,875
AIME25
+RICE ((39, (29,126)
73.3
15.2
11,441
Qwen3-235B
86.7
20.1
10,956
AIME24
+RICE {(70,47), (23,115)
86.7
16.2
10,722
Qwen3-235B
66.7
19.7
15,013
AIME25
+RICE {(70,47), (23,115))
73.3
16.8
13,935
Table Effect of cognitive experts of Deepseck- across different domains.
Domain
Math
Physics
Chemistry
Biology
Average
73.3
91.9
49.5
79.0
73.4
Math
83.3
89.5
50.4
79.0
75.6
Physics
83.3
89.5
50.4
79.0
75.6
Chemistry
80.0
95.4
52.7
68.4
74.
Biology
73.3
93.0
47.3
73.9
71.9
All
83.3
89.5
50.4
79..0
75.6
Crucially, the observed accuracy improvements do not necessarily entail increased computational cost in terms of token usage, supporting our hypothesis that method encourages deeper thinking rather than just longer outputs. Our cognitive expert strategy, despite improving average accuracy of Deepseek-RI on AIME24, uses more efficient reasoning thought (10.2 vs 12.0) and tokens (8.317 vs 9,219) on average compared to the baseline. This efficiency phenomenon is also observed in Qwen3- 2-35B, where the substantial accuracy gain (+6.6%) accompanied by notable reduction in thought (16.8 vs 19.7) and token count (13,935 vs 15,013). This suggests that reinforcing cognitive experts helps the model reason more effectively, focusing computational effort more productively within the reasoning process without generating excessive verbosity. The reasoning effectiveness can be clearly observed in Table where our RICE demonstrates deeper and more consistent reasoning. leading directly to the correct answer. In contrast, vanilla DeepSeek-RI exhibits more frequent shifts reasoning and fails to commit to initially correct deductions.
3.4 Performance of Cognitive Experts across Domains
To address RQ3, we evaluate the transferability of domain-specific cognitive experts by applying expert sets identified from domain others. As the top-2 experts selected from Math, Physics, and the All domains are identical, their results are the same across domains. As shown in Table 5, we have several observations:
Cognitive experts generalize well across domains. Our evaluation, summarized in Table 5, clearly illustrates the efficacy of the identified cognitive experts in enhancing the DeepSeek-RR model's reasoning capability across multiple domains. Leveraging cognitive experts identified from aggregated data ("All" domains) shows marked overall improvement, raising the average accuracy from 73.4% to 75.6%. Notably, substantial improvement is observed in the math tasks (from 73.3% 83.3%). Moderate accuracy gains are also seen in Chemistry (from 49.5% to 50.4%) and minor degradation observed in Physies (from 91. 91.9% 89.5%), indicating broad applicability and effectiveness of these general reasoning modulators across diverse problem sets. Biology tasks show stable performance, unaffected by general expert modulation.
'We use the underthinking score from prior work [4] to quantify reasoning efficiency, with lower Thought values indicating greater efficiency.
7

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Domain-specific expert sets provide targeted gains. Further analysis demonstrates the nuanced implications of domain domain-specific cognitive experts. Chemistry-identifeed experts outperform general experts significantly within their native Chemistry domain (49.5% 5% to 52.7%) and notably enhance Physics performance (91.9% to 95.4%), highlighting potential cross-domain synergies between physics and chemistry reasoning processes. However, this specialization lowers the accuracy in math (from 83.3% with general experts to 80.0%) and more substantially limits the Biology domain performance (from 19.0% to 68.4%). Similarly, Biology-derived experts enhance task-specific results (from 91.9% to 93.0% in Physics) but degrade average performance across other domains, indicating further that specialized expert selections may negatively impact general cognitive reasoning by reinforeing overly specialized activations.
No evidence of harmful side-effects on other domains. Our experimental findings clearly confirm that cognitive experts, either chosen from aggregated cross-domain data or specific domains, constitute effective cognitive modulators that enhance model reasoning accuracy and efficiency. General-purpos expert adjustments deliver robust cross-domain improvements, demonstrating their fundamental importance reasoning processes regardless of subject matter. Meanwhile, domain- specialized expert modulation illustrates substantial potential for targeted cognitive improvements, particularly within closely related scientific domains. Together, these insights validate proposed approach as versatile, effective, and immediately deployable for enhancing efficiency, accuracy, and overall reasoning proficiency of existing MoF-hased large reasoning models.
3.5 Impact of Reinforced Cognitive Experts on General Capabilities
To address RQ4, we investigate whether reinforcing cognitive experts negatively impacts the model's general capabilities, such
To this end, we evaluate reinforced models on the ArenaHard benchmark [27] to assess potential adverse impacts on general capabilities. The Arena- Hard benchmark, designed to evaluate folllwoinnn capabilities, comprises 500 challenging user queries spanning diverse scenarios. We randomly select 50 user queries as the test data and employ GPT-44Turbo to judge pairwise comparisons of outputs against the GPT-4-06133 baseline.
Reinforcing cognitive experts maintains or slightly im- proves general instruction-following capabilities. Our experimental evaluation on the ArenaHard benchmark demonstrates that reinforcing the identified cognitive perts does not adversely impact the model's capability to tasks.
Table 6: Effect of reinforced cognitive experts of Deepseek-R1 ArenaHard.
Method
Accuracy
Token
handle general, challenging instruction-followee As shown in Table 6, models modulated by cognitive ex- perts derived from each domain consistently maintain or marginally improve upon the baseline DeepSeek-RI I accu- racy of 91% %. Specifically, the domain-specific cognitive experts from Chemistry and Biology show notable accu- racy enhancements (from 91.0% to 94.0% in Chemistry; AAll from 91.0% to 93.0% in Biology), underscoring the po- tential for positive transfer of reasoning-rich expert reinforcement to general-purpose capabilities. Moreover, the general experts ("AII" domain) also marginally improve performance (to 92.0%) confirming that cognitive expert-control has a cuurral-o-beenffciaa impact on general instruction- following capabilities.
Vanilla
91.0
2,919
Reinforce Experts from different domains
Math
92.0
2,933
Physics
92.0
2,933
Chemistry
94.0
3,332
Biology
93.0
3,072
52.0
2,933
Modulation of cognitive experts results in moderately increased verbosity. An analysis token counts further reveals that cognitive expert modulation moderately increases model verbosity in response generation, suggesting enhanced cognitive thoroughness. For example, Chemistry and Biology models increase average token counts notably (from 2,919 to 3,332 tokens and from 2,919 to 3,072 tokens, respectively), highlighting that the activation of certain nomain-specine cognitive experts may favor more detailed deliberations. Nevertheless, the overall increase in verbosity is moderate, indicating a desirable balance between detail-oriented reasoning and response conciseness.
Overall, reinforcing cognitive experts does not hinder but rather supports general capabilities. These findings collectively confirm our approach as effective and safe for targeted, lightweight interventions. Reinforcing cognitive experts significantly enhances model performance within their
8

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original domains and has either neutral or positive effects on general purpose instruction-following benchmarks. The moderate increase in verbosity indicates richer, more thoughtful reasoning. aligning with the intended goal of encouraging deeper cognitive processing without sacrificing practicality. This highlights the practicality and versatility of our approach improving existing MoE model reasoning efficacy and general cognitive capabilities through strategic expert modulation.
3.6 Comparison with Other Methods
We compare cognitive experts against two prevalent inference-time methods for reasoning tasks: prompt engineering and decoding constraints. Specifically, we analyze two prompt configurations: placing the prompt before the <think> token (Promplctore) and after <think> token (Prompt,nce)) with details outlined in Appendix B.I. For decoding constraints, we adopt a strategy similar to TTP from Wang al. [4], which curtail the generation of alternative solutions foster coherent and focused reasoning. In our work, we penalize the think mark tokens (</think>) rather than the thought switching tokens (e.g., "alternatively"). and name the method as TIP,-
Table 7 compares our cognitive expert mod-
Table 7: Comparison with other steering methods on AIME24 and AIME25.
ulation method against prompting (both be- fore and after the <think> token) and de- coding constraints (TIP) on the challenging AIME benchmarks. Our approach achieves the highest average improvement, boost- ing DeepSeek-RR 's performance from a baseline of 68.3% 78.7%, surpassing the best-performing baseline TIP, by 2.0% in average accuracy. Notably, our method achieves particularly pronounced gains on AIME24 (83.3% compared Prompi (76.7%) and TIP, (80.0%), indicating a superior ability to enhance cogmitive effort and deep reasoning through targeted modulation of expert routing weights. These results validate our hypothesis that strategically steering cognitive experts significantly reinforces appropriate cognitive behavior and enhances reasoning accuracy more effectively than generie prompting and decoding constraints.
Method
AIME24
AIME25
Avg.
DeepSeek-RI
73.3
63.3
68.3
Prompl before
76.7
73.3
75.0
Promptane
76.7
66.7
71.7
TIP:
80.0
73.3
76.7
RICE
83.3
73.3
78.7
Table 8: An example of our RICE corrects wrong reasoning (in red) generated by Deepseek-RR -1 into correct reasoning (in green).
Problem
Let ABCDEF be convex equilateral hexagon in... and EF has side lengths 200, 240, and 300. Find the side length of the hexagon.
DeepSeek-
Okay, so I need to find the side len triangle formed by extending AB, CD, and EF maybe
RI
has sides 200, 240, and 300. Let sketch this Sentally...Aternattii model the hexagon being formed by three pairs parallel Aides...lterraaivvel. think the hexagon as a combination of three maybe each pair of opposite sides of the hexagon is parallel to different side of the triangle... will posit that the answer is 80... Therefore, the answer is 75/2...let me verify this result..Thuss, 1200 / 23. Wait, this contradicts previous I think the answer is base. the thorough step-by-step analysis, 1200/23 the conclusion.
using concept
similar
+RICE
Okay, so I need to find the side length...Lett me draw a rough sketch in my mind...So, the triangle is such that each side is aligned with one of the extended sides of the hexagon... But maybe using coordinate geometry could help here....Not sure, maybe this is getting too complicated...Aleeraattvv
maybe using Aiter...Altrraativeel. after struggling through all these approaches, perhaps the answer is related to the harmonic mean of the triangle's s Sides...Therefoor think the answer is 80. But need to the hcxagon is 80
the side length of
4 Related Work
Large Reasoning Models Large Reasoning Models (LRMss) significantly enhances the reasoning capabilities of large language models (LLMs) [5, 28). Prominent implementations include OpenAl's

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ol [5], QwQ [29], Qwen3 [2], DeepSeck-R1 [1]. Claude 3.7 [30] and Kimi-1.5 [31] achieve human- like reasoning by leveraging scaled test-time computation. In particular, the open-source DeepSeek-RR R utilizes a oixture-of-Experts (MoF) architecture [32] with sparsely activated parameters, selectively activating only out of 256 experts per layer [21]. This MoE architecture has been widely adopted in recent LLMss [33-36], achieving an optimal balance between computational efficiency and competitive performance in complex reasoning tasks.
MoE Models Previous research on Mixture of Experts (MoE) models indicates that expert routing is primarily token-dependent [22]. However, Olson et al. [23] demonstrate that DeepSeek- RI advanced reasoning capabilities enable its routing mechanism to achieve greater semantic specialization and structured cognitive processing, representing substantial advancement over prior MoE models. Subsequently, Hazra et al. [37] train sparse autoencoders (SAEs) on DeepSeek- R11, identifying interpretable features such as backtracking, division, and rapid response patterns within the SAEs space. However, training SAFs is computationally intensive, posing significant resource demands. We employ the normalized Pointwise Mutual Information (nPMI) metric to evaluate expert specialization, requiring only single forward propagation.
Efficient Thinking Despite significant advancements, ol-like models continue to encounter sub- stantial cognitive challenges, such the overthinking [3, 38-40] and underthinking phenomenon [4, 11, 41]. Subsequent efforts address these issues through rule-based stop, decoding constraints [42, 4, 43-49], steering vectors [50-53], and parameters tuning [54, 3, 55-57J. There are also some works specifically designed to improve reasoning capabilities in MoE architectures by re- mixing experts through gradient- based optimization [58] or by expert pruning via sparse dictionary learning [59]. However, the resource-intensiee nature of expert re-mixing algorithms makes them im- practical to scale to large models such as 600B-parameter systems, whereas our method is lightweight and directly applicable to such large-scale settings. Generally, in contrast to the above strategies that primarily rely on crafted rules, extensive labeled data, or computationally expensive parameter training, our reinforcing cognitive experts approach achieves more efficient and deeper reasoning with only a single forward pass, without requiring any supervision signals or additional training.
5 Conclusion and Future Work
In this work, we investigate cognitive experts in MoE-basee language models and propose an efficient nPMI-based method to identify those most relevant to reasoning. We show that steering these experts enables control over the model's reasoning with minimal computational overhead. Notably, these experts exhibit strong transferability across scientific domains, suggesting a generalizable cognitive function. Future directions include deeper investigations into the structural properties and broader applicability of cognitive experts, as well as integration with other cognitive control strategies to further enhance reasoning robustness. By uncovering this hidden layer of functional specialization within MoE models, we may open new avenues for fine-grained control over neural reasoning processes, more closely mirroring the modularity observed in biological cognitive systems.
6 Limitations and Broader Impacts
The internal coordination mechanisms of long-range reasoning models are inherently complex, and our uPMI-based approach may not fully capture all relevant interactions. Future work should explore more sophisticated metrics for expert identification. Besides, our validation was constrained by the current availability of open-source MoE architectures designed for long-range reasoning, limited to DeepSeek-RP [1] and Qwen3-235B [2]. Additional testing across more diverse architectures is warranted. The ability to precisely control reasoning processes in large language models has significant implications for both Al safety and efficiency. Our method's 's minimal computational overhead makes it particularly promising for real-world applications where resource constraints are critical. The observed cross-domain transferability of cognitive experts suggests exciting possibilities for developing more general and adaptable AI systems.
10

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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 62576307), Ningbo Natural Science Foundation (2024J020), Yongjiang Talent Introduction Programme (202 IA 156-G), Tencent Al Lab Rhino-Bird Focused Research Program (RBFR2024003), and Infor- mation Technology Center and State Key Lab of CAD&CG, Zhejiang University.
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A Renormalization
We investigate the DeepSeek Mixture-of-Expeee Experts (MoE) architecture, where each token selects 8 of 256 experts, with weights normalized to sum to We examine steering specific expert weights under two conditions: with and without renormalization. The effects of the steering coefficient (reinforce factor) are presented in Table 9, with generalization performance analyzed in Table 10.
Table evaluates the reinforce factor's s effect on two cognitive experts. Without renormalization, accuracy peaks 83.3% (factors 4, 32, 64, 128) but drops 3.3% at 2048, with erratic token counts (e.g., 16,836). With renormalization, accuracy remains stable (73.3--83.3) across most factors, with token counts varying moderately (8.383-9,508), though it declines to 66.7% factor 256. Renormalization thus enhances robustness at higher steering coefficients.
We evaluate the generalization performance of cognitive experts, identified using normalized Point- wise Mutual Information (nPMI) within Mixture-of-Expee -fperts (MoE)-based large reasoning models, comparing three strategies: Vanilla R1, Renormalized, and Without Renormalized (wo/Renormalized), Table 10 reports performance across AIME25, Physics, Chemistry, Biology, and their average for experts selected from AIME24.
The wo/Renormalized strategy demonstrates superior generalization, achieving an average score of 73.1, compared to 70.9 for Vanilla RI and 68.8 for Renormalized, This 4.3-point improvement over Renormalized is driven by notable gains AIME25 (73.3 vs. 63.3) and Biology (79.0 vs. 68.4). In Physics, Vanilla R1 (91.9) outperforms wo/Renormalized (89.5, -2.4), while in Chemistry, Renormalized (52.7) surpasses wo/Renormalized (50.4), indicating domain-specific trade-offs.
nonrreoormalization avoids the computational overhead of normalization (c.g., softmax scaling of expert weights), aligning with its reported efficiency. These results confirm that non-renormalization enhances generalization in cognitive experts, offering a computationally lightweight approach to optimizing reasoning MoE architectures.
Table Reinforce factor effects of two cognitive experts with/without renormalization
wo/Renormalization
Renormalization
Reinforce Factor
Acc
Token
Acc
Token
(R1)
73.3
9,291
73.3
9,291
2
70.0
9,103
80.0
8,463
4
83.3
8,145
80.0
8,383
8
73.3
9,502
70.0
8,818
16
80.0
8,493
73.3
9,133
32
83.3
8,337
83.3
8,956
64
83.3
8,317
80.0
9,508
128
83.3
9,490
73.3
9,091
256
60.0
7,986
66.7
8,719
512
46.7
6,270
80.0
8.786
1024
23.3
4,378
73.3
8,564
Table 10: Generalization capacity of two cognitive experts selected from AIME24, with or without renormalization.
Strategy
AIME25
Physies
Chemistry
Biology
Average
Vanilla RI
63.3
91.9
49.5
70.9 52.7
79.0
Renormalized
63.3
90.7
68.4
68.8
wo/Renormalized
73.3
89.5
50.4
79.0
73.1
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Table 11: Pass @k performance of cognitive experts Deepseek-R and Qwen3-235B-A22 For each problem, we generated 16 responses with a temperature of 0.6 and a top value of 0.95.
Accuracy
Model
Strategy
Tokens
Pass@)
Pass@8
AIME24
Vanilla
74.8
88.3
9,219
Deepseek- RI
Our
76.0
89.2
8,317
AIME25
Vanilla
68.5
84.7
10,875
Our
67.7
86.3
11,441
AIME24
Vanilla
84.0
93.0
10,946
Qwen3-235B -A22B
Our
85.0
91.6
10,706
AIME25
Vanilla
82.7
88.3
12,546
Our
82.1
89.7
12,373
B Experiment Setup
B.1 Baselines
We evaluate cognitive experts in comparison with two widely used inference-time techniques for reasoning tasks: prompt engineering. In particular, we consider two types of prompt placements in analysis one positioned before the <think> token (Promptbefore) and the other placed after (Promptafter), defined as follows:
Prompt before <think>
<lbegin of <entencel><<IUsert>>> <context>>
You are an expert math-solving assistant who prioritizes clear, concise solutions. You solve problems in single thought process, ensuring accuracy and efficiency. You seek clarification when needed and respect user preferences even if they are unconventional. </context>
holving__uless
Try to complete every idea you think of and don't 't give up halfway
Don't skip steps
Display solution process clearly
Ask for clarification on ambiguity
</solving rules>>
format <format_rules>>>>>> rules>>
Use equations and explanations for clarity
Keep responses brief but complete
Provide step-by-step reasoning needed </format_rules
PROBLEM: (problem)
OUTPUT: Please think carefully and follow above rules to get the correct answer for PROBLEM. Focus on clear, concise solutions while maintaining a helpful, accurate style. < <think> In
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Table 12: Performance of domain-specifee steering multipliers across scientific domains, The math domain is evaluated using the AIME25 benchmark.
Model
Math
Physics
Chemistry
Biology
Average
DeepSeek-BR R1
63.3
91.9
49.5
79.0
70.9
DeepSeek-RI RICE
73.3
93.0
54.8
79.0
75.0
Owen-235B
66.7
90.7
49.5
78.9
71.5
Qwen-235B + RICE
73.3
95.3
49.5
84.2
75.6
Prompt after <think>
<lbegin timegin_of_nie <entencel>><<Useee <context>>
You are an expert math-solving assistant who prioritizes clear, concise solutions. You solve problems in a single thought process, ensuring accuracy and efficiency. You seek clarification when needed and respect user preferences even if they are unconventional. </context>>
PROBLEM: {problem)
<think> \n
Please think carefully and follow these rules to find the correct answer for PROBLEM.
<solving -solving_nules>>>>
Try to complete every idea you think of and don't give up halfway
Don't 't skip steps
Display solution process clearly
Ask for clarification on ambiguity
</solving rules>
formattruless rules>>
Use equations and explanations for clarity
Keep responses brief but complete
Provide step-by-step reasoning if needed
</format_nles>>>>>>>>
Focus on clear, concise solutions while maintaining a helpful and accurate style.
OUTPUT:
B.2 Experiments Compute Resources
We conduct our DeepSeek-R1 experiments 16 H20 GPUs using v1lm==0 7..0. is worth noting that for experiments on the Qwen3-235B-A22) model, we use v1lm==0.8 8... because the recently released Qwen3-235B--A22 models are only compatible with vllm versions 2 0.8.5.
C Experiment Details and Results
C.1 Steering Multiplier
We use a simple, domain-specific steering multiplier (selected from small of 16, 32, 64), RICE delivers consistent and significant improvements across all domains.
25

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Table 13: Performance comparison of Deepseek- R1 with and without RICE across different datasets.
Dataset
Model
Accuracy
#Token
Deepseek-RI R1
95.9
1028
GSM8K
Deepseek- R1 + RICE (Ours)
96.0
1001
Deepseek-Ri
95.0
3282
MATH-500
Deepseek-Fe + RICE (Ours)
96.4
3204
Deepseek-RT
4.0
9943
HLE
Deepseek-RR + RICE (Ours)
6.0
9445
C.2 Effects on Other Datasets
We delve into the effect of these two experts identified by AIME24 on three additional diverse benchmarks: GSM8K (grade-school math), MATH (competition math), and HLE (Humanity's Last Exam, covering social science, CS, etc.). Note that we randomly sampled 100 text instances as the test set due to resource constraints. As shown in Table 13, RICE consistently improves Deepseek-RR 1 across all datasets, providing modest gains high-perlorming tasks (GSM8K, MATH-500) and larger relative improvements on more challenging tasks (HLE).
Moreover, compare the differences with and without RICE. Specifically, we focus on the token distribution during model decoding. We observe that tokens related "think," " "best," "good," and similar concepts are ranked higher (positioned closer to the top 1) during decoding after expert reweighting.
C.3
Cognitive Experts of Qwen3-235B
As case study in math, we employ Qwen3-235B to generate responses on the AIME2024 dataset, while recording the expert assignments each token during the forward pass. Subsequently, we apply the nPMI measure defined in Eq. 6 to identify the top five experts that exhibit the highest statistical dependence on reasoning-re
indicators, such as the "<think>" token. These selected experts are thus regarded as the core cognitive components specialized in mathematical reasoning, Due to computational constraints, our quantitative analysis in Table focuses specifically on math-domain experts. This focused approach allows for deeper investigation of expert specialization patterns while maintaining feasible resource requirements.
C.4 Pass @k Performance of Cognitive Experts
Table 11 presents the Pass @ass@k k performance of our cognitive expert modulation approach compared vanilla baselines across two model architectures. On DeepSeek-RI, our method demonstrates consistent improvements in Pass @8 accuracy (+0.9% on AIME24 and +1.6% on AIME25) despite showing marginal variations Pass@) performance. Notably, we observe 9.8% reduction in token consumption for AIME24 while maintaining superior accuracy, suggesting improved reasoning efficiency. For Qwen3-235B, 235B-A22B. our approach achieves higher Pass@) accuracy (+1.0% on AIME24) while showing competitive Pass @8 performance (+1..4%, across datasets), with consistent reductions computational cost (2.2% fewer tokens on AIME24 and 1.4% fewer on AIME25). The observed trade-offs between Pass @ and Pass@8 8 metrics suggest that our method enhances reliable reasoning (as reflected Pass (8) more than peak performance (Pass 1), particularly the more challenging AIME25 benchmark. These results substantiate hypothesis that targeted expert modulation can improve reasoning efficiency without compromising solution quality.
26


Reviews:
[{"review_id": "vhJwcy6jda", "reviewer": "Reviewer_RpVC", "summary": "This paper introduces Reinforcing Cognitive Experts (RICE), an inference-time steering method for Mixture-of-Experts (MoE) large reasoning models (LRMs) to enhance reasoning depth and efficiency without additional training. Using normalized Pointwise Mutual Information (nPMI), the approach identifies \"cognitive experts\" highly correlated with reasoning markers, and then add weight on the identified experts. Experiments on DeepSeek-R1 and Qwen3-235B across mathematical (AIME) and scientific (GPQA Diamond) benchmarks show that reinforcing just the top two cognitive experts significantly improves reasoning accuracy, reduces token usage, and demonstrates cross-domain generalization to some extend.", "questions": "See weakness.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The proposed RICE method demonstrates significant performance improvements across reasoning benchmarks.\n2. It is lightweight, requiring no additional training or complex heuristics.\n3. The paper features clear and well-structured writing, enhancing readability.\n\n\nWeakness:\n1. The results rely on only two benchmarks (AIME and GPQA Diamond) and two models (DeepSeek-R1 and Qwen3-235B), which limits the generalizability of the findings.\n2. As shown in Table 4, activating domain-specific experts does not consistently improve performance. For example, physics experts slightly degrade performance on physics tasks, and biology experts show reduced accuracy in biology. In contrast, math experts demonstrate the most robust effectiveness, highlighting potential domain-specific inconsistencies.\n3. Despite impressive performance gains, the paper lacks in-depth explanations for why RICE works. Visualizing and analyzing feature distributions or activation patterns after expert reweighting could provide crucial insights, requiring further analysis.\n4. What is the data for identifying the cognitive experts, and could there be some data leakage? \nTable 3 shows that the cognitive experts identified using AIME24 are applied to AIME25; however, the similarity between these datasets raises questions on data leakage. Could the author adopt some other benchmarks are evaluate the activated experts?\n5. eq(6): the expression \"t ∈ set Π\" should be \"x ∈ set Π\"?", "was_revised": true, "final_justification": "I have read the rebuttal and my concerns are addressed. I have raised my score.", "rebuttal": "Thank you for your valuable feedback and positive assessment of our method's novelty and clarity.\n\n> **W1:** The results rely on only two benchmarks (AIME and GPQA Diamond) and two models (DeepSeek-R1 and Qwen3-235B), which limits the generalizability of the findings.\n\nWe address concerns about the number of models and benchmarks used.\n\n- **Models:** Our work focuses on identifying cognitive experts in large *reasoning* models with MoE architectures. Concurrent work [1] has shown that these models exhibit semantic expert specialization, which is distinct from the more syntactic specialization found in general-purpose MoEs [2]. At present, **DeepSeek-R1 and Qwen3-235B are the only publicly available, state-of-the-art LRMs with an MoE architecture.** We are committed to extending our evaluation as more such models become open-source.\n- **Benchmarks:** We acknowledge the request for broader evaluation. To supplement our original results on AIME and GPQA Diamond, we have run new experiments on three additional diverse benchmarks: **GSM8K** (grade-school math), **MATH** (competition math), and **HLE** (Humanity's Last Exam, covering social science, CS, etc.). As shown below, RICE consistently provides benefits. These results will be added to the revised paper.\n\n| Dataset         | Model                    | Accuracy   | #Token  |\n|-----------------|--------------------------|:----------:|:-------:|\n| GSM8K (1319)    | Deepseek-R1              | 95.9%      | 1028    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.0%**  | **1001** |\n| MATH-500 (500)  | Deepseek-R1              | 95.0%      | 3282    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.4%**  | **3204** |\n| HLE (100)       | Deepseek-R1              | 4.0%       | 9433    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **6.0%**   | **9445** |\n\n\nThe number in parentheses after each dataset indicates the number of test examples. For HLE, we randomly sampled 100 examples as the test set. Due to resource and time constraints, we will conduct more extensive evaluations for the camera-ready version.\n\n> **W2:** As shown in Table 4, activating domain-specific experts does not consistently improve performance.\nAs you noted, applying RICE sometimes degraded performance in non-math domains (e.g., Physics, Biology in Table 4). This was the result of a specific experimental choice to test generalization. In the original manuscript, we used hyperparameters (steering multiplier = 64) tuned only on the math domain (AIME) and applied them directly to all other domains.\n\nThis setup was designed to test the raw generalization of the math-derived setting. However, different domains may require different intensities of cognitive steering. When we use a simple, domain-specific steering multiplier (selected from a small set of {16, 32, 64}), RICE delivers **consistent and significant improvements across all domains**.\n\n**DeepSeek-R1 with Domain-Tuned Multipliers**\n\n| Model                       | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:----------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Deepseek-R1                |     63.3      |  91.9   |   49.5    |  79.0   |  70.9   |\n| Deepseek-R1 + RICE (Our)   |     **73.3**      |  **93.0**   |   **54.8**    |  79.0   |  **75.0**   |\n\n---\n\n**Qwen3-235B with Domain-Tuned Multipliers**\n\n| Model                   | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Qwen-235B              |     66.7      |  90.7   |   49.5    |  78.9   |  71.5   |\n| Qwen-235B + RICE (Our) |     **73.3**      |  **95.3**   |   49.5    |  **84.2**   |  **75.6**   |\n\nOur hypothesis is that math problems demand more intensive reasoning, hence the higher optimal multiplier. We will clarify this in the paper to avoid misinterpretation.\n\n> **W3:** Despite impressive performance gains, the paper lacks in-depth explanations for why RICE works.\n\nWe agree that a deeper analysis is valuable. Visualizing feature distributions to understand how re-weighting steers the model is an excellent suggestion. Given the scale of these models and the short rebuttal period, we were unable to complete this analysis in time. We have begun this investigation and commit to including visualizations of activation patterns in the camera-ready version to provide these crucial insights.\n\n>**W4:** What is the data for identifying the cognitive experts, and could there be some data leakage? Could the author adopt some other benchmarks are evaluate the activated experts?\n\nThis is a critical point that we are happy to clarify.\n\n- **No Data Leakage:** - Our method for identifying cognitive experts is **fully unsupervised**. It relies only on the co-occurrence of expert activations with reasoning tokens (`<think>`) from input queries, *not* on any ground-truth labels. Therefore, the concept of data leakage in the supervised sense does not apply.\n- **Expert Generalization:** To explicitly test generalization, we identified experts on AIME24 and applied them to new domains. As shown in our **General Response (Sec 1)**, these same experts (identified on math problems) improve performance on GSM8K, MATH, and HLE, demonstrating strong task generalization.\n\n\n| Dataset         | Model                    | Accuracy   | #Token  |\n|-----------------|--------------------------|:----------:|:-------:|\n| GSM8K (1319)    | Deepseek-R1              | 95.9%      | 1028    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.0%**  | **1001** |\n| MATH-500 (500)  | Deepseek-R1              | 95.0%      | 3282    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.4%**  | **3204** |\n| HLE (100)       | Deepseek-R1              | 4.0%       | 9433    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **6.0%**   | **9445** |\n\nThe number in parentheses after each dataset indicates the number of test examples. For HLE, we randomly sampled 100 examples as the test set due to the limited time available for author response.\n\n> **W5:** Modification of formula tokens in Eq. (6)\nThank you for catching this. We have corrected the typo and have performed another thorough proofread of the manuscript.\n\nReference\n\n[1] Semantic Specialization in MoE Appears with Scale: A Study of DeepSeek R1 Expert Specialization, arxiv 2025.\n\n[2] OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models, ICML 2024."}, {"review_id": "YUquC3gMt9", "reviewer": "Reviewer_9D19", "summary": "This paper proposes a inference time model steering method for Mixture of Experts (MoE) models. They use pointwise mutual information to get a correlation between the generation of <think> token and activation of a particular expert in a MoE model. The experts which have a high mutual information are then considered as the ones contributing to the reasoning of the generated solution. The activation weight of such experts can be controlled to steer more reasoning and their results show that this helps with the end task on AIME and GPQA diamond datasets.", "questions": "1. Could you provide insights into the distribution of the nPMI for experts?\n2. There is a qualitative example of how reasoning changes. What are some other ways to evaluate if the reasoning/thinking tokens improved?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Lightweight activation steering of models is an active area of research and such steering without training has broad applicability.\n2. The paper is well written with clear experiments.\n3. The steering method is interpretable and easy to control.\n4. Results on math problems are encouraging.\n\nWeaknesses:\n1. The evaluation sets are small.\n2. Results on math are clear and convincing, but results on other domains are not very significant and the writing does not call it out. For example from line 214 and 215: it is misleading to say 0.9% gain is moderate and 2.4% drop is minor.\n3. Evaluation is done only on 2 models. \n4. It is not clear if the method generalizes to other reasoning tasks beyond QA.", "was_revised": true, "final_justification": "The paper proposed a clear and simple but interesting idea. The rebuttal addressed some of the concerns, but the overall merit of the paper remains the same. Keeping the rating to 4.", "rebuttal": "Thank you for your encouraging review and for highlighting our method's interpretability and strong performance on math problems.\n\n> **W1&W3:** Limited Evaluation \nWe address concerns about the number of models and benchmarks used.\n\n- **Models:** Our work focuses on identifying cognitive experts in large *reasoning* models with MoE architectures. Concurrent work [1] has shown that these models exhibit semantic expert specialization, which is distinct from the more syntactic specialization found in general-purpose MoEs [2]. At present, **DeepSeek-R1 and Qwen3-235B are the only publicly available, state-of-the-art LRMs with an MoE architecture.** We are committed to extending our evaluation as more such models become open-source.\n- **Benchmarks:** We acknowledge the request for broader evaluation. To supplement our original results on AIME and GPQA Diamond, we have run new experiments on three additional diverse benchmarks: **GSM8K** (grade-school math), **MATH** (competition math), and **HLE** (Humanity's Last Exam, covering social science, CS, etc.). As shown below, RICE consistently provides benefits. These results will be added to the revised paper.\n\n| Dataset         | Model                    | Accuracy   | #Token  |\n|-----------------|--------------------------|:----------:|:-------:|\n| GSM8K (1319)    | Deepseek-R1              | 95.9%      | 1028    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.0%**  | **1001** |\n| MATH-500 (500)  | Deepseek-R1              | 95.0%      | 3282    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.4%**  | **3204** |\n| HLE (100)       | Deepseek-R1              | 4.0%       | 9433    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **6.0%**   | **9445** |\n\nThe number in parentheses after each dataset indicates the number of test examples. For HLE, we randomly sampled 100 examples as the test set. Due to resource and time constraints, we will conduct more extensive evaluations for the camera-ready version.\n\nReference \n\n[1] Semantic Specialization in MoE Appears with Scale: A Study of DeepSeek R1 Expert Specialization, arxiv 2025.\n\n[2] OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models, ICML 2024.\n\n> **W2:** Insignificant/misleading non-math results.\nAs you noted, applying RICE sometimes degraded performance in non-math dmains (e.g., Physics, Biology in Table 4). This was the result of a specific experimental choice to test generalization. In the original manuscript, we used hyperparameters (steering multiplier = 64) tuned only on the math domain (AIME) and applied them directly to all other domains.\n\nThis setup was designed to test the raw generalization of the math-derived setting. However, different domains may require different intensities of cognitive steering. When we use a simple, domain-specific steering multiplier (selected from a small set of {16, 32, 64}), RICE delivers **consistent and significant improvements across all domains**.\n\n**DeepSeek-R1 with Domain-Tuned Multipliers**\n\n| Model                       | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:----------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Deepseek-R1                |     63.3      |  91.9   |   49.5    |  79.0   |  70.9   |\n| Deepseek-R1 + RICE (Our)   |     **73.3**      |  **93.0**   |   **54.8**    |  79.0   |  **75.0**   |\n\n---\n\n**Qwen3-235B with Domain-Tuned Multipliers**\n\n| Model                   | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Qwen-235B              |     66.7      |  90.7   |   49.5    |  78.9   |  71.5   |\n| Qwen-235B + RICE (Our) |     **73.3**      |  **95.3**   |   49.5    |  **84.2**   |  **75.6**   |\n\nOur hypothesis is that math problems demand more intensive reasoning, hence the higher optimal multiplier. We will clarify this in the paper to avoid misinterpretation.\n\n> **W4:** It is not clear if the method generalizes to other reasoning tasks beyond QA.\n\nTo address this, we evaluated on **GSM8K** (math word problems), **MATH** (competition math), and **HLE** (multi-domain problem-solving), which are not simple QA tasks. Our method shows clear benefits on these diverse reasoning formats.\n\n| Dataset         | Model                    | Accuracy   | #Token  |\n|-----------------|--------------------------|:----------:|:-------:|\n| GSM8K (1319)    | Deepseek-R1              | 95.9%      | 1028    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.0%**  | **1001** |\n| MATH-500 (500)  | Deepseek-R1              | 95.0%      | 3282    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **96.4%**  | **3204** |\n| HLE (100)       | Deepseek-R1              | 4.0%       | 9433    |\n|                 | **Deepseek-R1 + RICE (Ours)** | **6.0%**   | **9445** |\n\nThe number in parentheses after each dataset indicates the number of test examples. For HLE, we randomly sampled 100 examples as the test set due to the limited time available for author response.\n\nIf \"beyond QA\" refers to other task families (e.g., code generation, summarization), we believe this is an exciting direction for future work, but our current focus is on complex reasoning.\n\n> **Q1:** Could you provide insights into the distribution of the nPMI for experts?\n\nThis is an excellent question. The distribution is **sharply peaked**, which strongly supports our hypothesis that a small number of experts are highly specialized for cognitive functions. We list the nPMI of top 10 experts for Deepseek-R1. The group of top five “thinking-specialized” experts have significantly higher nPMI scores than the rest.\n\n| Rank | Math | Physics | Chemistry | Biology |\n|:----:|:----:|:-------:|:---------:|:-------:|\n| 1    | 0.61 | 0.65    | 0.61      | 0.63    |\n| 2    | 0.60 | 0.61    | 0.61      | 0.60    |\n| 3    | 0.57 | 0.56    | 0.59      | 0.58    |\n| 4    | 0.56 | 0.55    | 0.59      | 0.55    |\n| 5    | 0.54 | 0.55    | 0.58      | 0.54    |\n| 6    | 0.47 | 0.49    | 0.53      | 0.48    |\n| 7    | 0.47 | 0.48    | 0.53      | 0.48    |\n| 8    | 0.46 | 0.47    | 0.53      | 0.48    |\n| 9    | 0.46 | 0.46    | 0.52      | 0.48    |\n| 10   | 0.45 | 0.46    | 0.51      | 0.48    |\n\n> **Q2:** There is a qualitative example of how reasoning changes. What are some other ways to evaluate if the reasoning/thinking tokens improved?\n\nWe evaluate the improvement in reasoning through a combination of methods:\n\n1.  **Final Accuracy (Quantitative):** The most critical metric, as improved reasoning should lead to more correct answers (Tables in main paper and General Response).\n\n2.  **Qualitative Analysis:** We provide examples (Table 7) showing how RICE leads to more structured and effective reasoning paths.\n\n3.  **Efficiency (#Tokens):** On reasoning tasks, RICE often achieves higher accuracy with fewer tokens, suggesting more direct and less wasteful thinking.\n\n4.  **Future Analysis:** As promised to reviewer R-RpVC, we will add analysis of activation patterns in the final version for a deeper look at the mechanism."}, {"review_id": "8lRybtZOXh", "reviewer": "Reviewer_tFnP", "summary": "For MoE models, this paper uses normalized pointwise mutual information to identify experts related to reasoning tokens, which are called cognitive experts. This paper proposes a method to improve the model's reasoning ability by increasing the weight of cognitive experts during routing. Experiments on some domains, especially math, show that the proposed method could improve the model's reasoning ability and outperform other training-free steering methods", "questions": "Whether the proposed method is useful for all domains or only MATH? Please refer to the weaknesses for detailed comments.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "This finds an interesting phenomenon in MoE models, that is, the reasoning ability might be highly correlated with some specific experts. What's more, simply increasing the weights of these experts during routing could bring significant improvements in some math benchmarks. \n\nHowever, I think the experiments are not solid enough to support the authors' claim. Here are some of my concerns.\n\n1. The cognitive experts seem to be domain-specific. As shown in Table 2, the top-2 identified experts in Chemistry and Biology are different from those in Math and Physics. I wonder whether different datasets in the same domain would result in different cognitive experts? For example, will math-500 and AIME have the same cognitive experts?\n\n 2. The reinforcing method seems to only work in math, instead of in any other domains. As shown in Table 4, when reinforcing the cognitive experts in physics will make the performance in physics degrades from 91.9 to 89.5, and when reinforcing the cognitive experts in biology will make the performance in biology degrades from 79.0 to 73.9. The experiment in Chemistry is positive, but the improvement is not as significant as in the math domain. I also want to know the results of section 3.5 using other domains. Whether the proposed method still outperform other steering methods?\n\n3. Whether reinforcing cognitive experts could help the model to reason more effectively is in doubt. The experiment in Section 3.2 mainly supports this claim. However, as shown in Table 5, all models using the reinforce trick generate more tokens compared with the vanilla model. This contradicts the claim.", "was_revised": true, "final_justification": "One of my major concerns is that the proposed method only works in math. During rebuttal, the authors provide further results with different hyperparameters, which show the proposed method could bring improvement in nearly all the domains studied in this paper. \nThis concern havs been addressed, and thus I improve my score to 4.", "rebuttal": "Thank you for your review and for acknowledging the interesting phenomenon we identified.\n\n> **W1:** The cognitive experts seem to be domain-specific. I wonder whether different datasets in the same domain would result in different cognitive experts? For example, will MATH-500 and AIME have the same cognitive experts?\n\nYou are correct that the *exact set* of top experts can vary between datasets, even within the same domain. For instance, comparing the top-5 cognitive experts for AIME24, MATH, and GSM8K, we find both shared and dataset-specific experts. For example, the 182nd expert at layer 39 appear in the top-5 across all three math datasets, suggesting they support general mathematical reasoning. In contrast, other experts may focus on patterns unique to each dataset. We'll add this observation to the paper.\n\n> **W2&Q1:** The reinforcing method seems to only work in math, instead of in any other domains. Whether the proposed method still outperform other steering methods?\n\n- **Performance in Other Domains:** Applying RICE sometimes degraded performance in non-math dmains (e.g., Physics, Biology in Table 4). This was the result of a specific experimental choice to test generalization. In the original manuscript, we used hyperparameters (steering multiplier = 64) tuned only on the math domain (AIME) and applied them directly to all other domains.\n    \n    This setup was designed to test the raw generalization of the math-derived setting. However, different domains may require different intensities of cognitive steering. When we use a simple, domain-specific steering multiplier (selected from a small set of {16, 32, 64}), RICE delivers **consistent and significant improvements across all domains**.\n\n**DeepSeek-R1 with Domain-Tuned Multipliers**\n\n| Model                       | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:----------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Deepseek-R1                |     63.3      |  91.9   |   49.5    |  79.0   |  70.9   |\n| Deepseek-R1 + RICE (Our)   |     **73.3**      |  **93.0**   |   **54.8**    |  79.0   |  **75.0**   |\n\n---\n\n**Qwen3-235B with Domain-Tuned Multipliers**\n\n| Model                   | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Qwen-235B              |     66.7      |  90.7   |   49.5    |  78.9   |  71.5   |\n| Qwen-235B + RICE (Our) |     **73.3**      |  **95.3**   |   49.5    |  **84.2**   |  **75.6**   |\n\nOur hypothesis is that math problems demand more intensive reasoning, hence the higher optimal multiplier. We will clarify this in the paper to avoid misinterpretation.\n\n- **Comparison with Other Steering Methods:** We ran a new comparison against a strong baseline, Contrastive Activation Addition (CAA), on AIME 24. RICE not only performs better but also avoids the negative side effects on general instruction-following in Arena that CAA exhibits.\n\n| Model                      | Accuracy ↑ | #Token ↓ | Side Effect (Arena) ↓ |\n|:---------------------------|:----------:|:--------:|:----------------------:|\n| Deepseek-R1               |  73.3%    |  9219   |          0.0%             |\n| Deepseek-R1 + CAA         |  80.0%    |  8408    |        2.1%           |\n| Deepseek-R1 + RICE (Our)  |  **83.3%**   | **8317**    |        **-1.0%**            |\n\n\nLower is better for Side Effects; RICE improves general capabilities.\n\n> **W3:** Whether reinforcing cognitive experts could help the model to reason more effectively is in doubt. The experiment in Section 3.2 mainly supports this claim. However, as shown in Table 5, all models using the reinforce trick generate more tokens compared with the vanilla model. This contradicts the claim.\n\nThis is a misunderstanding of the purpose of Table 5.\n\n- The experiment in **Table 5 (Arena-Hard)** is **NOT** meant to measure reasoning efficiency. Its purpose is to check for **negative side effects** on general instruction-following capabilities.\n- Arena-Hard includes tasks like creative writing, where longer, more detailed answers are often better. The slight increase in token count here is not a sign of inefficiency but reflects the nature of the tasks.\n- Our efficiency claims are based on reasoning benchmarks like AIME, where RICE often **reduces** token count while improving accuracy (see table above).\n\nReference:\n\n[1] Steering Llama 2 via Contrastive Activation Addition, ACL 2023."}, {"review_id": "OJkWkSOE3T", "reviewer": "Reviewer_yV5E", "summary": "This paper studies Mixture-of-Experts (MoE) models and proposes to identify which experts are most associated with “thinking” behavior. To achieve this, the authors use a mutual information metric measuring how strongly each expert activates on <think> </think> tokens compared to other tokens. They then demonstrate that stimulating (via steering) these identified experts can yield performance gains on reasoning tasks.", "questions": "- What does the distribution of nPMI scores across experts look like? Is it sharply peaked for the top experts, or does it decrease more gradually?\n- Why does stimulating the “biology experts” fail to improve performance in the biology domain?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths:\n\nThis is an enjoyable paper to read, with a clear, simple idea and a good execution. The finding that manipulating just a few experts can improve reasoning performance is interesting. The experimental setup is strong, covering different domains and testing generalization to another dataset.\n\n\n# Weaknesses:\n\nIn answering RQ1, the paper focuses on the top 5 experts. It is unclear why the number 5 was chosen over other possible thresholds. It would be much more informative to examine the entire distribution of normalized pointwise mutual information (nPMI) across experts. Is there a clear group of “thinking-specialized” experts that stand out with very high nPMI scores, or does the distribution decline more gradually?\n\nOverall, the experimental section has great potential, but the current structure and reporting fall somewhat short of fully realizing it. I think the research questions (RQs) could be structured and answered in a more logical and cumulative order to better support the paper’s core claims. For example:\n\n- RQ1: Are there “cognitive experts” specialized in thinking? That is, do a few experts exhibit peak nPMI on thinking tokens? This would require showing the distribution of nPMI over all experts, to see whether the top experts have a clear spike in nPMI scores or whether the distribution declines slowly.\n- RQ2: Do these experts differ across domains? (Current RQ2 / Table 1)\n- RQ3: Can we enhance performance by steering them? (Current RQ1 / Tables 2 and 3)\n- RQ4: Does stimulation of these experts generalize to other domains? (Current RQ2 / Table 4)\n- RQ5: Does stimulating these experts negatively impact general abilities? (Current RQ3 / Table 5)\n\nFinally, the paper does not report any uncertainty estimates alongside its results (e.g., standard deviations or confidence intervals). Including such measures is important for rigor.", "was_revised": true, "final_justification": "After discussion with authors, I preserve my positive assessment and encourage the authors to include the newly discussed results and clarifications.", "rebuttal": "Thank you for your very positive and constructive review. We are delighted you found the paper enjoyable and the idea clear and well-executed.\n\n> **W1&Q1:** The distribution of nPMI distribution\n\nThis is an excellent question. We list the nPMI of top 10 experts for Deepseek-R1. The distribution is **sharply peaked**, which strongly supports our hypothesis that a small number of experts are highly specialized for cognitive functions. The group of top five “thinking-specialized” experts have significantly higher nPMI scores than the rest.\n\n| Rank | Math | Physics | Chemistry | Biology |\n|:----:|:----:|:-------:|:---------:|:-------:|\n| 1    | 0.61 | 0.65    | 0.61      | 0.63    |\n| 2    | 0.60 | 0.61    | 0.61      | 0.60    |\n| 3    | 0.57 | 0.56    | 0.59      | 0.58    |\n| 4    | 0.56 | 0.55    | 0.59      | 0.55    |\n| 5    | 0.54 | 0.55    | 0.58      | 0.54    |\n| 6    | 0.47 | 0.49    | 0.53      | 0.48    |\n| 7    | 0.47 | 0.48    | 0.53      | 0.48    |\n| 8    | 0.46 | 0.47    | 0.53      | 0.48    |\n| 9    | 0.46 | 0.46    | 0.52      | 0.48    |\n| 10   | 0.45 | 0.46    | 0.51      | 0.48    |\n\n> **W2:** Paper structure\n> \n\nYour suggestions for restructuring the research questions are excellent and create a more logical narrative flow. We will adopt this structure for the camera-ready version. Thank you!\n\n> **W3:** No uncertainty estimates\n> \n\nThis is a fair point. Our results are currently based on single, deterministic runs due to the high computational cost. For the final version, we will perform multiple runs with different seeds to report standard deviations, adding to the rigor of our findings.\n\n> **Q2:** Why does stimulating the “biology experts” fail to improve performance in the biology domain?\n\nThis is an excellent question with a two-part answer:\n\n- **Hyperparameter Mismatch:**\n    \n    As you noted, applying RICE sometimes degraded performance in non-math dmains (e.g., Biology in Table 4). This was the result of a specific experimental choice to test generalization. In the original manuscript, we used hyperparameters (steering multiplier = 64) tuned only on the math domain (AIME) and applied them directly to all other domains.\n    \n    This setup was designed to test the raw generalization of the math-derived setting. However, different domains may require different intensities of cognitive steering. When we use a simple, domain-specific steering multiplier (selected from a small set of {16, 32, 64}), RICE delivers **consistent and significant improvements across all domains**.\n\n**DeepSeek-R1 with Domain-Tuned Multipliers**\n\n| Model                       | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:----------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Deepseek-R1                |     63.3      |  91.9   |   49.5    |  79.0   |  70.9   |\n| Deepseek-R1 + RICE (Our)   |     **73.3**      |  **93.0**   |   **54.8**    |  79.0   |  **75.0**   |\n\n---\n\n**Qwen3-235B with Domain-Tuned Multipliers**\n\n| Model                   | Math (AIME25) | Physics | Chemistry | Biology | Average |\n|:------------------------|:-------------:|:-------:|:---------:|:-------:|:-------:|\n| Qwen-235B              |     66.7      |  90.7   |   49.5    |  78.9   |  71.5   |\n| Qwen-235B + RICE (Our) |     **73.3**      |  **95.3**   |   49.5    |  **84.2**   |  **75.6**   |\n\nOur hypothesis is that math problems demand more intensive reasoning, hence the higher optimal multiplier. We will clarify this in the paper to avoid misinterpretation.\n\n- **Failure Mode Analysis:** We also found that many errors in the biology domain stem from a lack of core factual knowledge in the base model (e.g., \"What protein is encoded by gene X?\"). Our method, RICE, is designed to enhance the *reasoning process* given a set of facts, not to inject missing knowledge. We will add this important clarification about the scope of our method to the paper."}]

Author rebuttal:

Meta-review: This paper studies a novel approach to identify key experts (referred to as cognitive experts) corresponding to reasoning process via pointwise mutual information and specifically amplifying them to improve the reasoning power and efficiency of the model. The reviews for the paper are uniformly positive. There were a few concerns about evaluations, which were addressed by the authors in the rebuttal. I recommend acceptance and ask the the authors address the reviewers concerns in the final version of the paper.
Decision: Accept (poster)