Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 11375-11386· 0 citations· 11 references
TL;DR
VCAgent is proposed, a self-evolving framework that optimizes how biological knowledge from external APIs is structured and integrated into LLM reasoning, and introduces a schema-based slot filling mechanism that transforms raw API responses into compact, typed representations, replacing unstructured text concatenation.
Abstract
Large Language Models (LLMs) are increasingly used for agent-based virtual cell modeling, yet existing frameworks rely on unstructured retrieval or static prompt engineering, injecting noisy evidence and wasting inference budget on redundant tool-use trajectories. We propose VCAgent, a self-evolving framework that optimizes how biological knowledge from external APIs is structured and integrated into LLM reasoning. VCAgent introduces a schema-based slot filling mechanism that transforms raw API responses into compact, typed representations, replacing unstructured text concatenation. To efficiently search the high-dimensional instruction space, we develop Mutation-Guided Monte Carlo Tree Search (MG-MCTS), which combines relevance-based priors derived from inter-API semantic similarity with an adaptive gating strategy to balance exploration and exploitation under a constrained evaluation budget. Experiments on single-cell perturbation prediction across five cell lines show that VCAgent improves both reasoning accuracy and search efficiency across multiple backbone LLMs, substantially outperforming existing prompt optimization methods. Codes are available at https://github.com/LZYBUPT/VCAgent.
Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery. However, current approaches are fundamentally constrained by three bottlenecks: structurally blind parent selection, sparse whole-program evaluation rewards, and static mutation operators that fail to adapt during search. We present GAE (Graph-Augmented Evolution), a framework that resolves these limitations through a tightly coupled, three-pillar architecture. First, a relational graph neural network (GNN) parses programs into typed computation graphs, producing structure-aware embeddings. Second, an RL-optimized meta-controller leverages these embeddings to replace blind evolutionary sampling with a directed policy, dynamically selecting optimal parents and mutation directions based on reward history. Third, an online GRPO fine-tuning loop continuously updates the LLM mutation operator at test-time using group-normalized evaluation rewards, directly aligning the model's generation distribution with high-fitness structural edits. We evaluate GAE on a challenging scientific discovery task: symbolic regression for complex nonlinear oscillator systems. By transforming stochastic search into a directed, self-improving trajectory, GAE efficiently discovers closed-form physical equations, consistently matching or outperforming static LLM-driven baselines and achieving state-of-the-art out-of-distribution performance.
Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compresses heterogeneous search failures into a scalar score and a single prompt. We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views. A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory. During search, evaluator feedback characterizes reliability and productivity, updates role-level utilities, and routes elite motifs, failure traces, and validity diagnostics to different agents. The framework self-evolves at two timescales: within a run, it adapts the search process without updating LLM parameters; across runs, recorded trajectories can be distilled into open-source LLMs as role-conditioned proposal priors. Averaged over the four LSR-Synth scientific domains in LLM-SRBench, A-SR improves Acc@0.01 over baselines from 25.79% to 48.30% with Llama3.1-8B, while A-SR-LoRA improves the corresponding Qwen3-4B result from 24.58% to 38.29%. On four real-world scientific discovery tasks, A-SR obtains the best in-distribution or out-of-distribution normalized mean squared error on 7 of 8 reported metrics.
Wenxiao Zhao, Dong Liu, Kaiyi Xu et al.· 2 citations
: Multi-Agent systems powered by Large Language Models have demonstrated remarkable capabilities; however, their effectiveness is limited by rigid, manually designed workflows that do not adapt to varying task complexity. Existing adaptive methods utilize greedy policies that select workflow components without antic-ipating downstream cost-accuracy tradeoffs, often necessitating multiple attempts to identify successful configurations. This work introduces AgentSearch, a cost-aware Monte Carlo Tree Search (MCTS) framework that constructs agentic workflows through deliberative lookahead search. The proposed approach employs a dual-network architecture: a value network that decomposes expected rewards into success probability and remaining cost estimates, and a grammar-constrained policy network that ensures semantically valid constructions. Guided by these networks, MCTS explicitly simulates candidate workflow trajectories, enabling principled reasoning about the cost-accuracy tradeoff prior to decision-making. The networks are trained using a multi-phase protocol that combines stochastic exploration, supervised pre-training, and self-play refinement. Experiments on mathematical reasoning (MATH, AQUA-RAT) and code generation (HumanEval, MBPP) benchmarks demonstrate that the method achieves 80.03% average accuracy, surpassing Chain-of-Thought by 13.78 points and the best multi-agent baseline by 8.87 points. Notably, the approach attains single-episode success while reducing computational costs by up to 47%, thereby eliminating the trial-and-error exploration required by previous adaptive methods.
D. Attota, Ying Xie· Proceedings of the 15th Inte...· 0 citations
Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inference (TI) is critical. However, existing methods require extensive manual intervention and proficiency in heterogeneous tools, posing a significant barrier to efficient TI analysis. To bridge this gap, we propose SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation. SpaCellAgent utilizes a multi-agent architecture for strategic workflow planning, a dynamic tool-orchestration engine for adaptive algorithm selection, and a self-evolution module that iteratively refines performance through feedback. We evaluate SpaCellAgent on six heterogeneous datasets encompassing complex temporal developmental trajectories, diverse sequencing platforms, and spatially-resolved tissue architectures. SpaCellAgent consistently demonstrates over 40% improvement in analytical efficiency while maintaining expert-aligned performance. By converting natural language specifications into optimized analytical workflows and fully automating the pipeline, SpaCellAgent democratizes advanced spatiotemporal modeling and establishes a scalable, agent-driven paradigm for computational biology. The code and materials are available at https://github.com/LittleXH-shw/SpaCellAgent.
Songhan Wang, Haoang Chi, He Li et al.· Proceedings of the 32nd ACM...· 1 citation
We investigate the use of large language models (LLMs) as evolutionary operators for optimizing the architecture of multi-agent systems (MAS) - a representative instance of LLM-driven optimization over attributed-graph artifacts that arise in generative design. Given a task description, an initial MAS configuration is generated and then refined on a per-task basis through two optimization strategies: (1) a population-based evolutionary approach where LLMs serve as mutation, crossover, and selection operators, with an LLM-as-a-Judge ensemble providing the surrogate fitness signal; and (2) a simpler iterative regeneration scheme that uses textual feedback from LLM judges to guide single-solution improvement without maintaining a population. We evaluate both approaches on the GAIA benchmark. Our experiments reveal a critical sensitivity to the judge model's quality: a capable judge (Claude 4 Sonnet) yields +14.3 and +6.67 percentage-point accuracy improvements for evolutionary and iterative approaches, respectively, whereas a weaker judge (Gemini-2.5-Flash) provides no benefit or even degrades performance below the unoptimized baseline. Both approaches incur substantial computational costs, raising questions about practical cost-effectiveness. These results highlight fundamental challenges of surrogate fitness functions in LLM-driven evolutionary optimization and offer practical insights for applying evolutionary methods to MAS architecture design.
V. Akhmerov, Dmitry Gilemkhanov, Jerzy Kamiński et al.· Proceedings of the Genetic a...· 0 citations
Self-improving coding agents that iteratively rewrite their own source code have demonstrated impressive performance on coding tasks. However, existing solutions generally derive self-modification from a single failure trajectory at a time, overlooking rich comparative signals available in the agent's expanding archive of past attempts. According to Mendelian principles of controlled inheritance, we introduce Mendel G\"odel Machine (MGM). In addition to the general single-trajectory clonal mutation, MGM includes two new types of self-modification that better utilizes evidences accumulated: the reaction-norm mutation edits an agent based on its trajectories on multiple tasks simultaneously, and the cross-lineage hybridization edits an agent using the trajectory of a reference agent from another lineage on the same task. Under an additive fitness landscape model, we prove theoretically and demonstrate via controlled surrogate simulation that the new strategies facilitate a faster and better convergence over single-trajectory baselines. Experiments on SWE-bench and Polyglot confirm MGM's consistent improvement in performance, efficiency, and generalizability.
Changzhi Liu, Yilun Liu, Sikuan Yan et al.· 0 citations