SciAgent: AI Agent-Based Scientific Evaluation

• AI Agent-Based Model for Scientific Evaluation is an architecture that employs a hierarchy of autonomous agents to coordinate domain-specific scientific problem solving.
• The model utilizes specialized Worker systems and adaptive sub-agent pipelines to integrate symbolic, numerical, and multimodal reasoning strategies.
• Empirical evaluations in mathematics, physics, and chemistry demonstrate that the system can approach or exceed gold-medalist performance benchmarks.

The AI Agent-Based Model for Scientific Evaluation is an integrated architecture employing a hierarchy of specialized autonomous agents for science problem-solving and benchmarking across multiple disciplines. These systems instantiate generalistic scientific reasoning—the capacity to adapt strategies and assemble domain-specific pipelines—by orchestrating fine-grained, feedback-driven modules under meta-control. SciAgent epitomizes this paradigm through a three-tier design that enables coherent, expert-level performance across mathematics, physics, and chemistry Olympiads, validated against gold-medalist standards (Li et al., 11 Nov 2025).

1. Hierarchical Architecture of Scientific Agent Systems

SciAgent’s architecture is structured into three meta-levels:

• Meta Level: Coordinator Agent Parses incoming problem text and infers the domain, modality (symbolic, multimodal, numerical), and problem complexity using a classifier $P(d \mid \text{problem text})$ and a neural difficulty estimator. Based on these, it dynamically routes the task to the most suitable Worker System.
• Domain Level: Worker Systems
  • Math Olympiad Worker: symbolic deduction, proof verification
  • Physics Olympiad Worker: conceptual modeling, quantitative derivation, image analysis
  • Chemistry Olympiad Worker: reaction modeling, molecular perception, symbolic/SMILES verification
  • General Exam Worker: mid-level reasoning across mixed domains
  • Workers autonomously assemble pipelines of Sub-agents tailored to the particular scientific task.
• Execution Level: Sub-agents These fine-grained modules implement individual reasoning operations (Generator, Reviewer, Improver, Image Analyser, etc.). Execution follows an adaptive feedback loop:

$$R_{i} \gets A_{i}(R_{i-1}), \text{ with Reviewer and Improver steps applied conditionally}$$

The pipeline structure is fully dynamic, allowing injection of critic/verification modules for failed stages.

2. Taxonomy and Interaction of Sub-agents

SciAgent defines four principal classes of Sub-agents, instantiated per-task:

Sub-agent Class	Core Functions (cell)	Example Role in Math Worker
Symbolic Deduction Agents	Generate/repair proofs, split lemmas	Generator (initial proof), Reviewer (flag gaps), Improver (patch logic)
Conceptual Modeling Agents	Model physical/cognitive abstractions	Conceptual Modeler builds diagrams, Summarizer clarifies intermediate steps
Numerical Computation Agents	Execute code, verify numeric outputs	Code Executor (Python/Mathematica), Numeric Verifier checks tolerances
Verification Agents	Consistency/unit checking across domain	Reviewer (proof correctness), Image Analyser, SMILES verification

These agents communicate via structured critique messages. Repeated Reviewer failures trigger replanning, pipeline changes, or escalation to more capable LLMs.

3. Dynamic Pipeline Assembly and Feedback Orchestration

The Coordinator formalizes pipeline assembly as:

$$\begin{aligned} \text{domain} &= \arg\max_{d}\;P(d\mid \text{problem text}) \ \text{difficulty} &= f_{\text{diff}}(\text{problem text}) \ W^* &= \arg\max_{W}\;\mathrm{suitability}(W,\text{domain},\text{difficulty}) \ \mathcal{P} &= \mathrm{assemblePipeline}(W^*,\,\text{domain},\,\text{difficulty}) \end{aligned}$$

Runtime feedback (number of review passes, stage timing, convergence of intermediate states) enables dynamic reconfiguration—adding numeric verifiers, triggering Breakdown agents, or switching symbolic to mixed reasoning pipelines.

4. Algorithmic Foundations and Verification Strategies

Key formal components underpinning SciAgent include:

• Scoring Metric (Olympiad benchmarks): $S_{\mathrm{total}} = \sum_{i=1}^k S_i$ (points per sub-problem)
• Worker Routing:

$$W^* = \arg\max_W [\alpha\,\mathrm{sim}(W,\mathrm{domain}) + \beta\,\mathrm{cap}(W,\mathrm{difficulty})]$$

• Verification Workflow:

$$\mathrm{Verified}(R) = \begin{cases} \text{accept} & \text{if } \forall\,C_j(R)=\mathrm{True},\ \text{reject} & \text{otherwise.} \end{cases}$$

• ReAct Loop (Physics/Chemistry):

$$\begin{aligned} \text{Thought}_t & \gets G_t(\text{history}_{<t}) \ \text{Action}_t & \gets \mathrm{Actuator}(\text{Thought}_t) \ \text{Observation}_t & \gets \mathrm{Perceptor}(\text{Action}_t) \ \text{history}_t & \gets \text{history}_{t-1} \cup \{\text{Thought}_t, \text{Action}_t, \text{Observation}_t\} \end{aligned}$$

These mechanisms allow SciAgent to self-correct, analyze reasoning steps, and escalate errors for downstream improvement.

5. Empirical Performance and Benchmarking

SciAgent has been systematically evaluated on:

• Mathematics: IMO 2025 (36/42), IMC 2025 (100/100)
• Physics: IPhO 2024 (27.6/30), IPhO 2025 (25.0/30), CPhO 2025 (264/320)
• Chemistry: IChO 2025 pilot (strong qualitative results)
• General scientific reasoning: HLE benchmark subset (high-quality cross-domain answers)

The scoring methodology leverages initial LLM-based rubric evaluators, cross-validated by human experts to ensure strict adherence to competition standards. SciAgent consistently matches or exceeds average gold-medalist performance; for IMC and CPhO, it ties or surpasses the best recorded human results.

6. Domain Generality, Limitations, and Prospects

• Domain Generality:

The Coordinator–Worker–Sub-agent hierarchy supports instantiation in new scientific fields, mixing symbolic, numeric, and multimodal reasoning (e.g., adaptation from math proofs to chemical synthesis or image interpretation tasks).

• Limitations:

Current chemistry pipelines do not achieve gold-medalist performance; further domain-specific knowledge and mechanistic model fine-tuning are required. Biology Worker evaluation is currently blocked by data access constraints. Misrouting by the domain/difficulty classifier can occur on highly mathematical physics questions.

• Extensions and Future Work:

The architecture is amenable to expansion in biology, earth sciences, and interdisciplinary evaluation. Prospective improvements include persistent inter-agent memory, enhanced multimodal reasoning (e.g., table parsing, diagram-to-model conversion), hypothesis generation for research workflows, and interactive collaborator-style deployments.

7. Significance and Outlook

SciAgent exemplifies scalable, generalistic scientific intelligence achieved through agent hierarchy, dynamic routing, pipeline self-assembly, and integrated verification loops (Li et al., 11 Nov 2025). Its mechanism supports adaptive reasoning strategies, cross-disciplinary transfer, and robust evaluation on complex benchmarks. While gold-standard human-level generality remains a challenge in some domains, the demonstrated system marks a concrete step toward automated, expert-level scientific reasoning deployable across diverse research contexts.