The motivation comes from a shift in how artificial intelligence is used in high-energy physics. Machine learning has long been embedded in the field, powering event reconstruction, jet tagging, anomaly detection, fast simulation and statistical inference. More recently, however, agents built on LLMs have begun to move beyond isolated inference tasks toward the orchestration of entire scientific workflows, including code generation, tool invocation, structured context management and human-in-the-loop analysis. Parameter-space exploration, the authors argue, is a natural next target, because a substantial part of the work lies not in choosing a sampling algorithm but in assembling the surrounding computational machinery: connecting external physics programs, modifying input cards point by point, reading output observables, defining likelihoods and constraints, and storing the whole setup alongside its results.

EasyScan_HEP 2 takes a distinctive architectural stance. Rather than improving the scan engine itself, the framework lets AI assist the configuration layer. The scientific content of any scan remains encoded in an explicit .ini configuration file that specifies the scan method, input parameters, external programs, input-output mappings, constraints, plots and result folder. An LLM agent can generate or revise this file from a natural-language request, but the file itself remains the single source of truth, executable by the same backend that powered the original EasyScan_HEP. This design means the AI never silently changes the physics; it only drafts a document that the user can inspect before anything runs.

Several machine-readable interfaces make this workflow practical. The package is now an installable Python tool with a command available from any working directory. A dry-run configuration checker parses the .ini file and reports errors, warnings and informational messages without launching any scan points, catching common failure modes such as unsupported scan methods, wrong paths, missing likelihood constraints, duplicated variable names, invalid numerical ranges or plot variables that no input or output block defines. Crucially, the checker can return machine-readable output, allowing an agent to repair its own mistakes iteratively. The authors are careful to note that the checker verifies only syntactic and operational consistency, not whether the underlying physics model is correct.

The run interface has also been made agent-friendly. An explicit overwrite policy replaces interactive prompts, and a structured JSON report records whether the run succeeded, the return code, the command used, the launch directory, the configuration path, the log path, the result directory and the overwrite action. A separate result-reader command summarizes an existing result directory without rerunning the scan, counting rows, listing generated plots and identifying a representative best row by minimizing chi-squared or minus-two-log-likelihood columns where available. Because this summary comes from a deterministic reader rather than the model’s interpretation of terminal output, users do not have to trust the agent’s reading of raw logs.

To quantify the benefit, the team ran a controlled evaluation with 11 benchmark tasks, each repeated three times under three conditions, giving 99 isolated runs using the gpt-5.6-terra model with medium reasoning effort in the same Codex environment. Condition S used EasyScan_HEP with its documentation and the registered agent skill; condition D used the package without the skill; and condition M had the model implement each scan directly without EasyScan_HEP. Both EasyScan_HEP conditions completed all 33 runs end to end, while the direct-implementation condition completed 31 of 33 and required 77 task executions, including 27 failed launches. The median number of code lines requiring user review was roughly five times larger without the framework.

The skill itself delivered a modest but measurable efficiency gain: first-execution success rose from 22 to 25 out of 30 executable runs, and total task executions fell from 46 to 40. The authors emphasize that the benchmark does not capture broader functions of the skill, such as organizing an end-to-end workflow, guiding software setup, and converting missing information in a user prompt into explicit follow-up questions. That conservative behavior is deliberate: if a user does not specify the location of an external program, the skill asks for the path rather than searching the file system and risking a wrong choice among multiple installed versions, since such details must be checked by the user in any case.

The modular design also made it straightforward to add three new scan methods through an LLM-agent-guided workflow. BESTFIT performs differential-evolution minimization of the configured chi-squared via SciPy, aimed at quickly locating a good-fit point. EMCEE adds ensemble Markov-chain Monte Carlo sampling with a configurable number of walkers, writing a flattened chain file for post-processing. DYNESTY brings Python-based nested sampling, storing log-likelihoods, log-weights and evidence-related quantities without requiring the native MultiNest libraries. Because a new method only needs to decide how points are proposed in parameter space, while the common workflow handles priors, external programs, constraints and plotting, the extension route has been encoded directly into the agent skill.

To demonstrate the framework on real physics, the authors scanned the Z2-symmetric real singlet scalar extension of the Standard Model, a minimal Higgs-portal model in which a new stable scalar can serve as a dark matter candidate. A two-dimensional grid scan over the singlet mass and portal coupling, with the singlet self-coupling fixed, chained together micrOMEGAs 7.1 for the relic dark matter density and PhaseTracer 2 for the electroweak phase transition, including an explicit convention conversion in which the portal coupling passed to PhaseTracer differs by a factor of two. The same scan could be prepared three equivalent ways: by writing the configuration file directly, through the agent skill from a natural-language prompt, or via a local single-user Web interface that loads, edits, checks and runs the same files. A follow-up plot of the transition strength and relic-density contours was produced by simply asking the agent to post-process the saved result table.

The broader significance is that EasyScan_HEP 2 offers a template for how AI agents can enter computationally intensive science without eroding scientific accountability. The agent drafts and repairs configurations, but the checker validates them, the Web interface exposes them, the runner records exactly how they were executed, and the result reader summarizes outputs deterministically. Generated configurations must still be inspected by the user, and no agent replaces physics validation. As LLM-agent workflows mature across the field, from collider analyses to dark-matter phenomenology, this configuration-centered approach, keeping every calculation tied to an explicit, checkable scan description, may prove to be the model that lets physicists embrace autonomous assistants while keeping the final word firmly in human hands.

Subject of Research: LLM-agent-assisted parameter-scan workflows for high-energy physics phenomenology

Article Title: EasyScan_HEP 2: LLM-agent parameter-scan workflows in high energy physics

Article References: Xiao, Y., Yue, Y., & Zhang, Y. (2026). EasyScan_HEP 2: LLM-agent parameter-scan workflows in high energy physics. The European Physical Journal C, 86(9), Article 1101. https://doi.org/10.1140/epjc/s10052-026-16358-9

Image Credits: AI Generated

DOI: 10.1140/epjc/s10052-026-16358-9

Keywords: large language models, AI agents, high-energy physics, parameter scans, EasyScan_HEP, beyond the Standard Model, dark matter, electroweak phase transition, scientific workflows, reproducibility, Markov-chain Monte Carlo, nested sampling