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The paper introduces TrAgent, a tree-based orchestration framework for multiple self-controlled LLM agents that uses a PUCT-style search to coordinate agent actions while preserving per-agent autonomy. The key technical contribution is a parent-level, experience-informed prior shaping mechanism that blends static priors P(s,a) with empirical evidence derived from exponentially-smoothed success scores EXP(s,a), yielding a shaped prior \tilde{P}(s,a) for selection. The method is specified with equations (1)–(6) and Algorithm 1 and is evaluated on GPU GEMM (FP16) kernel optimization under a specification-driven development protocol. The authors claim that TrAgent substantially outperforms a single self-controlled agent and a random baseline and approaches vendor-level performance (cuBLAS), reporting trajectories that improve normalized elapsed time across PUCT rounds.
Cross‑Modal Consistency: [22]/50
Textual Logical Soundness: [18]/30
Visual Aesthetics & Clarity: [16]/20
Overall Score: [56]/100
Detailed Evaluation (≤500 words):
1. Cross‑Modal Consistency
• Major 1: Performance claim conflicts with Fig. 2 (≤cuBLAS vs 80% of cuBLAS). Evidence: “achieving 80% of the performance of the cuBLAS code.” (Abstract) vs “decreases … to 0.015 … converging to the cuBLAS … (2% of baseline)” (Sec 4.2) and green curve below the brown “cublas (≈2%)” line in Fig. 2.
• Major 2: Fig. 2 caption units mismatch the plotted metric. Evidence: “Figure 2: … elapsed time (ms, y-axis)” (caption) while axis reads “Normalized Elapsed Time (baseline = 1)” in Fig. 2.
• Major 3: Claimed scaling with number of agents lacks any visual/table support. Evidence: “exhibits a scaling phenomenon as the number of agents increases.” (Abstract); no agent-count ablation shown.
• Minor 1: Random baseline mentioned but not plotted in Fig. 2. Evidence: “comparing against … a random search baseline” (Sec 4) vs Fig. 2 legend lacking this series.
• Minor 2: Standard deviations claimed, but Fig. 2 has no error bars. Evidence: “averaging results over five runs with standard deviations” (Sec 4).
• Minor 3: Symbol inconsistency between text and pseudocode. Evidence: Eq. 6 uses ρ, ε; Algorithm 1 lists r, e (Lines 31–32).
• Minor 4: Objective defined in cycles while reporting time in figures may confuse. Evidence: “minimizes … Elapsed Cycles” (Sec 4.1) vs “Normalized Elapsed Time” in Fig. 2.
2. Text Logic
• Major 1: Flagship performance/generalization claim insufficiently supported by provided evidence. Evidence: “approaching roughly 80% of a strong vendor library across representative settings.” (Intro/Abstract); only one curve, no multi‑shape/hardware results.
• Minor 1: Missing key experimental details (GPU/CPU model, CUDA version, matrix sizes). Evidence: No hardware or size specification in §4.1/§4.2.
• Minor 2: Several formatting artifacts reduce clarity. Evidence: “operatorname {c l i p} … t i m e (c a n d i d a t e)” (Eq. 3) and “Equation equation 6” phrasing.
3. Figure Quality
• No Major issues found.
• Minor 1: Fig. 1 small labels/icons risk illegibility at print size. Evidence: Fig. 1 contains multiple icon labels (“Character/Function/Workflow”, “TrAgent”) in compact layout.
• Minor 2: Fig. 2 lacks uncertainty depiction and gridlines, hindering quick reading. Evidence: Visual inspection of Fig. 2.
• Minor 3: Blue/green series may be hard for some CVD readers without markers. Evidence: Legend shows “single agent” (blue) and “system codex” (green) lines only.
Key strengths:
Key weaknesses:
Recommendations:
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This paper introduces TrAgent, a novel tree-based orchestration system designed to coordinate self-controlled agents while preserving their autonomy. The core idea is to leverage a PUCT-style search algorithm to dynamically allocate agent actions, enabling efficient exploration of the solution space. Unlike traditional multi-agent systems that rely on explicit role assignments and context passing, TrAgent represents decision states as nodes in a tree and agent proposals/actions as edges. This structure allows for selection-expansion-evaluation-backup cycles, which allocate exploration budgets while preserving per-agent autonomy. The system's effectiveness is demonstrated through a challenging general matrix multiplication (GEMM) kernel optimization task, where TrAgent achieves performance close to 80% of the cuBLAS library. The authors emphasize three key contributions: maintaining full agent autonomy for critical tasks, providing a generalized mechanism for inter-agent experience sharing, and ensuring scalability as the number of agents increases. The experimental results show that TrAgent outperforms single-agent baselines and a random search baseline, highlighting the potential of this approach for complex optimization problems. The paper also includes ablation studies to analyze the impact of various hyperparameters and autonomy features. Overall, the paper presents a promising approach to coordinating self-controlled agents, with potential applications in various domains requiring complex optimization and coordination.
I find the core concept of TrAgent, which is to orchestrate self-controlled agents using a tree-based search while preserving their autonomy, to be a significant strength of this paper. The PUCT-style search mechanism is well-suited for dynamically allocating agent actions, and the representation of decision states as nodes and agent proposals/actions as edges is a clever way to structure the exploration process. The paper clearly articulates the limitations of existing multi-agent systems, such as fine-grained top-down control, limitations in coordination through shared prompts, and scalability issues, which motivates the need for a new approach like TrAgent. The empirical results on the GEMM kernel optimization task are compelling, demonstrating that TrAgent can achieve performance close to the highly optimized cuBLAS library. This is a strong indication of the system's effectiveness in a complex optimization problem. Furthermore, the inclusion of ablation studies provides valuable insights into the impact of various hyperparameters and autonomy features, which helps to understand the system's behavior and robustness. The paper's focus on maintaining agent autonomy while enabling inter-agent experience sharing is a crucial aspect that distinguishes it from other multi-agent systems. The authors have clearly identified a gap in the existing literature and have proposed a novel solution that addresses the challenges of organizing self-controlled agents. The potential for scalability, as highlighted by the authors, is another important strength, suggesting that TrAgent could be applied to larger and more complex systems. The paper is also well-written and easy to follow, making the core ideas accessible to a broad audience.
After a thorough examination of the paper, I've identified several weaknesses that warrant careful consideration. First, the paper's reliance on a single experimental task, GEMM kernel optimization, is a significant limitation. While GEMM is a complex task, it is not representative of all optimization problems, and the paper lacks evidence to support the claim that TrAgent would perform well in other contexts. This is a crucial weakness because the paper aims to present a general orchestration method for self-controlled agents, and the lack of diverse experimental validation undermines this claim. The paper states in the conclusion section, "Our results are limited to GEMM and do not exhaust all hardware or kernel classes; future work should evaluate broader operator suites and heterogeneous devices." This explicit acknowledgement of the limitation confirms the concern. Second, the paper lacks a detailed analysis of the computational overhead introduced by the tree search mechanism. While the paper mentions the budget T for the search, it does not provide a breakdown of the time spent on different stages of the algorithm, such as selection, expansion, evaluation, and backup. This makes it difficult to assess the practical efficiency of TrAgent, especially when compared to simpler methods. The paper also does not discuss the sensitivity of the algorithm to the exploration constant 'c' in the PUCT algorithm, which is a critical parameter that can significantly impact performance. The paper mentions ablating the exploration constant 'c' in the connection to experiments section, but it does not provide a detailed analysis of its impact. Third, the paper does not provide a detailed explanation of how the agents propose actions and how the tree structure is initially constructed. The paper states that "each outgoing edge corresponds to an agent-proposed action," but it does not elaborate on the criteria used by agents to propose actions or the initial state of the tree. This lack of clarity makes it difficult to understand the inner workings of the algorithm and its potential limitations. Fourth, the paper does not include a comparison with other state-of-the-art multi-agent orchestration methods. While the paper compares against single-agent baselines and random search, it lacks a comparison with other established multi-agent systems, which would provide a better understanding of TrAgent's relative performance. The paper mentions existing multi-agent systems in the introduction, but it does not use them as direct experimental comparisons. Fifth, the paper does not provide a detailed analysis of the scalability of the approach. While the paper claims that TrAgent scales with the number and strength of agents, it does not provide empirical evidence to support this claim. The experiments are conducted with a limited number of agents, and the paper lacks a detailed analysis of how the performance of TrAgent changes as the number of agents increases. The paper also does not discuss the potential bottlenecks that may arise when scaling to a larger number of agents. Sixth, the paper does not provide a detailed analysis of the impact of the autonomy features on the performance of the system. While the paper mentions including ablations on autonomy features, it does not provide a detailed analysis of how these features affect the performance of the system. The paper also does not discuss the potential trade-offs between autonomy and performance. Finally, the paper does not provide a detailed explanation of how the agents interact with each other and how the tree structure facilitates this interaction. While the paper describes the tree structure and the PUCT mechanism, it could benefit from a more concrete example illustrating the interaction process. The paper states that the system preserves agent autonomy, but it does not provide a detailed analysis of how this autonomy is maintained while still allowing for effective collaboration. These weaknesses, which I have verified through direct examination of the paper, significantly impact the overall conclusions and limit the generalizability of the proposed approach.
Based on the identified weaknesses, I recommend several concrete improvements for this paper. First, the authors should significantly expand the experimental evaluation to include a more diverse set of tasks beyond GEMM kernel optimization. This should include tasks with varying levels of complexity, different types of search spaces, and different types of agent interactions. For example, the authors could consider tasks from the domains of symbolic reasoning, planning, or resource allocation. This would provide a more robust assessment of the generalizability of the proposed approach. Second, the authors should provide a detailed analysis of the computational overhead introduced by the tree search mechanism. This should include a breakdown of the time spent on different stages of the algorithm, such as selection, expansion, evaluation, and backup. The authors should also compare the computational cost of TrAgent with other multi-agent orchestration methods. Furthermore, the authors should conduct a sensitivity analysis of the exploration constant 'c' in the PUCT algorithm, showing how different values affect the performance and convergence of the system. Third, the authors should provide a more detailed explanation of how the agents propose actions and how the tree structure is initially constructed. This should include a clear description of the criteria used by agents to propose actions and the initial state of the tree. The authors should also discuss the potential limitations of the action proposal mechanism and how it might affect the performance of the system. Fourth, the authors should include a comparison with other state-of-the-art multi-agent orchestration methods. This would provide a better understanding of TrAgent's relative performance and its advantages and disadvantages compared to existing approaches. The authors should also discuss the potential trade-offs between TrAgent and other methods. Fifth, the authors should provide a more detailed analysis of the scalability of the approach. This should include experiments with a larger number of agents and a detailed analysis of how the performance of TrAgent changes as the number of agents increases. The authors should also discuss the potential bottlenecks that may arise when scaling to a larger number of agents and how these bottlenecks can be addressed. Sixth, the authors should provide a more detailed analysis of the impact of the autonomy features on the performance of the system. This should include a discussion of the potential trade-offs between autonomy and performance and how these trade-offs can be managed. The authors should also provide a more concrete example of how the agents interact with each other and how the tree structure facilitates this interaction. Finally, the authors should provide a more detailed discussion of the limitations of the proposed approach and potential directions for future research. This should include a discussion of the potential challenges of applying TrAgent to real-world problems and how these challenges can be addressed. By addressing these weaknesses, the authors can significantly strengthen the paper and make a more compelling case for the effectiveness and generalizability of TrAgent.
After reviewing the paper, I have several questions that I believe are crucial for a deeper understanding of the proposed approach. First, how does the action proposal mechanism of the agents affect the overall performance of TrAgent? Specifically, what are the criteria used by the agents to propose actions, and how does the quality of these proposals impact the efficiency of the tree search? Second, how does the tree search mechanism handle situations where the agents propose similar or redundant actions? Is there a mechanism to encourage diversity in the proposed actions, and how does this mechanism affect the exploration of the solution space? Third, what is the impact of the exploration constant 'c' on the performance of TrAgent? The paper mentions ablating this parameter, but it does not provide a detailed analysis of its impact. How does the choice of 'c' affect the trade-off between exploration and exploitation, and what are the guidelines for selecting an appropriate value for 'c'? Fourth, how does the system handle situations where the agents have conflicting goals or preferences? The paper assumes that the agents are working towards a common goal, but in real-world scenarios, agents may have different objectives. How does TrAgent handle such situations, and what are the potential limitations of the approach in the presence of conflicting goals? Fifth, how does the system ensure that the agents do not get stuck in local optima? The paper uses a tree-based search mechanism, but it does not provide a detailed analysis of how it avoids local optima. What are the mechanisms in place to encourage exploration and prevent the agents from converging to suboptimal solutions? Sixth, how does the system handle situations where the evaluation of the actions is noisy or uncertain? The paper assumes that the evaluation of the actions is accurate, but in real-world scenarios, the evaluation may be noisy or uncertain. How does TrAgent handle such situations, and what are the potential limitations of the approach in the presence of noisy evaluations? Finally, how does the system handle the trade-off between agent autonomy and the need for coordination? The paper emphasizes the importance of maintaining agent autonomy, but it also requires the agents to coordinate their actions. How does TrAgent balance these two competing requirements, and what are the potential trade-offs between autonomy and coordination? These questions target core methodological choices and assumptions, and I believe that addressing them would significantly enhance the paper's clarity and impact.