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arxiv: 2605.15230 · v1 · pith:CYBVPCPJnew · submitted 2026-05-13 · 💰 econ.EM

EnergyAgentBench: Benchmarking LLM Agents on Live Energy Infrastructure Data

Eliseo Curcio This is my paper

Pith reviewed 2026-05-19 17:38 UTC · model grok-4.3

classification 💰 econ.EM
keywords LLM agentsenergy marketsdatacenter sitingbenchmarkelectricity pricescarbon intensityagent evaluationlive data
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0 comments X

The pith

EnergyAgentBench tests LLM agents on live electricity market data to select optimal sites for AI datacenters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates EnergyAgentBench to measure how LLM agents perform when they must reason step by step over real-time electricity prices, carbon intensity, and technology cost trends. This evaluation matters because placing large AI facilities involves repeated decisions across changing market and grid conditions that static tests cannot capture. The benchmark organizes 70 tasks into five families that each demand between three and 48 calls to live data sources. Model runs reveal clear differences in capability, with some smaller models matching or exceeding larger ones on specific long-horizon problems.

Core claim

EnergyAgentBench supplies the first set of agent tasks grounded directly in live electricity market endpoints, EIA reports, and NREL data, with ground truth produced by XGBoost cost-surface models and the NREL Annual Technology Baseline 2024, and evaluation across nine models shows that performance varies sharply by task family while overall scores remain high for several frontier systems.

What carries the argument

EnergyAgentBench, the benchmark of 70 task variants across datacenter siting, long-horizon portfolio planning, lifetime LCOE ranking, 30-year optimization, and causal grid diagnosis that each require sequential tool calls to live endpoints.

If this is right

  • Different model families exhibit distinct strengths, with some leading on short-term cost-carbon trade-offs while others excel on extended planning horizons.
  • Causal diagnosis tasks produce the widest performance gaps among models and therefore serve as the strongest filter for agent capability.
  • The most frequent errors involve failure to integrate missing values from technology trajectory data, followed by premature commitment on causal problems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same live-data agent evaluation approach could be applied to other infrastructure domains such as water allocation or transportation network design.
  • Emphasis on handling incomplete trajectory data during training might raise scores on the most discriminating task families.
  • Organizations choosing models for energy-related automation may achieve better results by matching model size to the dominant failure modes rather than defaulting to the largest available system.

Load-bearing premise

Ground truth labels produced by the trained XGBoost cost-surface models and the NREL Annual Technology Baseline accurately represent real-world cost-carbon trade-offs and causal relationships in the electricity grid.

What would settle it

If independent expert review or actual historical placement outcomes in the tested regions show that the benchmark ground truth consistently selects inferior sites on a substantial fraction of the 70 tasks, the reported model rankings would lose their meaning.

Figures

Figures reproduced from arXiv: 2605.15230 by Eliseo Curcio.

Figure 1
Figure 1. Figure 1: Overall model performance. Mean composite score with 95% bootstrap CI (B = 1,000). Models [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Score heatmap by model and task family. Cell color = mean composite score on diverging [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Base versus adversarial performance by model. Blue bars = mean composite on non [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Failure mode distribution by model. Each bar = one model; segments = failure category counts [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
read the original abstract

Selecting the right electricity market region for a hyperscale AI datacenter requires reasoning across live electricity prices, grid carbon intensity, technology cost trajectories, and causal grid dynamics -- a multi-step, multi-source analytical task that static knowledge benchmarks cannot evaluate. We introduce EnergyAgentBench, the first agentic benchmark grounded in live electricity market data for this problem class. The benchmark comprises 70 task variants across five families: datacenter siting under cost-carbon trade-offs (F1), long-horizon portfolio siting (F1-LH), lifetime LCOE ranking over multi-decade cost trajectories (F2), 30-year portfolio optimization (F2-LH), and causal grid diagnosis (F3). Tasks require 3 to 48 sequential tool calls against live endpoints from the QuarluxAI infrastructure platform, the U.S. Energy Information Administration (EIA), and the National Renewable Energy Laboratory (NREL) with ground truth derived from trained XGBoost cost-surface models (R^2 0.967--0.995) and the NREL Annual Technology Baseline 2024. We evaluate nine models across Anthropic, OpenAI, and HuggingFace over 1,414 runs at three random seeds. Claude Sonnet 4.6 achieves the highest overall score (0.900) at one-quarter the cost of Claude Opus 4.7 (0.889). Claude Haiku 4.5 leads on long-horizon procedural siting (0.986), outperforming all frontier models including those costing 16x more per run. F3 Causal is the most discriminating family, with a 30.7-point spread between Sonnet (0.793) and Llama 3.3 70B (0.486), versus a 6.6-point spread on F1 Siting. A failure taxonomy of 135 coded failures identifies null-value integration in NREL ATB trajectories as the dominant failure mode (70%), followed by premature commitment on causal tasks (20%) and adversarial injection blindness (6%). Benchmark code, run trajectories, and the failure taxonomy dataset are publicly released.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces EnergyAgentBench, the first agentic benchmark for LLM agents on live electricity market data for hyperscale datacenter siting and related tasks. It defines 70 task variants across five families (F1 datacenter siting under cost-carbon trade-offs, F1-LH long-horizon portfolio siting, F2 lifetime LCOE ranking, F2-LH 30-year portfolio optimization, and F3 causal grid diagnosis). Tasks require 3–48 sequential tool calls against live endpoints from QuarluxAI, EIA, and NREL, with ground truth from XGBoost cost-surface models (R² 0.967–0.995) plus NREL ATB 2024 baselines. Nine models are evaluated over 1,414 runs at three seeds; Claude Sonnet 4.6 scores highest overall (0.900) at one-quarter the cost of Claude Opus 4.7 (0.889), while Claude Haiku 4.5 leads on long-horizon tasks. F3 shows the largest performance spread (30.7 points). A failure taxonomy from 135 coded failures identifies null-value integration as the dominant mode (70%). Code, trajectories, and taxonomy are publicly released.

Significance. If the ground truth labels validly capture real-world dynamics, the benchmark offers a useful contribution to evaluating multi-step agentic reasoning on live energy data, with clear strengths in scale (1,414 runs), reproducibility via public code release, and the detailed failure taxonomy. The finding that F3 is far more discriminating than F1 could help prioritize agent improvements for causal-style tasks in energy economics. The public artifacts directly support follow-on work.

major comments (2)
  1. [Abstract / F3 family description] Abstract / F3 family description: The claim that F3 evaluates causal grid diagnosis rests on ground truth derived from trained XGBoost cost-surface models (R² 0.967–0.995) and NREL ATB 2024. These are supervised predictive models; high predictive accuracy does not establish that they encode directed causal dependencies (e.g., how a transmission constraint or generator outage propagates). No mention of causal discovery, do-calculus, or structural equations appears in the ground-truth construction. If F3 labels are outputs of the same correlational surfaces used for F1/F2, then F3 performance measures pattern-matching to the model rather than causal reasoning over live data. This directly affects the central claim that F3 is the most discriminating family.
  2. [Task construction paragraph] Task construction paragraph: The 70 task variants are stated to be grounded in live endpoints, yet the manuscript provides no explicit validation that the XGBoost surfaces and NREL baselines reproduce observed causal grid relationships (e.g., via intervention tests or comparison to known outage events). Without such checks, the benchmark's suitability for diagnosing agent causal reasoning remains open.
minor comments (2)
  1. [Results section] Results section: The 30.7-point spread on F3 versus 6.6-point spread on F1 is highlighted, but a single consolidated table reporting all nine models across all five families would improve readability and allow direct comparison.
  2. [Failure taxonomy] Failure taxonomy: The 70%/20%/6% breakdown of failure modes is useful, but the taxonomy would benefit from one or two concrete trajectory examples for the dominant null-value integration failure to illustrate the precise agent error.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the causal framing of the F3 family. We agree that the ground-truth construction relies on supervised predictive models rather than explicit causal structures, and we have revised the manuscript to clarify this distinction, update task descriptions, and add a limitations discussion. These changes preserve the empirical results while avoiding overstatement. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract / F3 family description] The claim that F3 evaluates causal grid diagnosis rests on ground truth derived from trained XGBoost cost-surface models (R² 0.967–0.995) and NREL ATB 2024. These are supervised predictive models; high predictive accuracy does not establish that they encode directed causal dependencies (e.g., how a transmission constraint or generator outage propagates). No mention of causal discovery, do-calculus, or structural equations appears in the ground-truth construction. If F3 labels are outputs of the same correlational surfaces used for F1/F2, then F3 performance measures pattern-matching to the model rather than causal reasoning over live data. This directly affects the central claim that F3 is the most discriminating family.

    Authors: We agree that the XGBoost surfaces are supervised predictive models trained on historical data and that we do not employ causal discovery, do-calculus, or structural causal models in their construction. The F3 tasks require agents to integrate live multi-source data to produce outputs that align with these high-accuracy empirical surfaces; the larger performance spread on F3 appears to reflect the demands of sequential live-data reasoning rather than pure correlational pattern matching. To address the concern, we have revised the abstract, F3 description, and related sections to refer to 'empirical grid diagnosis' tasks grounded in predictive cost surfaces, while retaining the reported performance differences as an empirical observation. revision: yes

  2. Referee: [Task construction paragraph] The 70 task variants are stated to be grounded in live endpoints, yet the manuscript provides no explicit validation that the XGBoost surfaces and NREL baselines reproduce observed causal grid relationships (e.g., via intervention tests or comparison to known outage events). Without such checks, the benchmark's suitability for diagnosing agent causal reasoning remains open.

    Authors: The current manuscript does not include intervention tests or direct comparisons against specific historical outage events. The XGBoost models were validated on held-out real-world data with high R², and NREL ATB 2024 serves as an established industry reference. We have added text in the task construction section and a new limitations subsection that explicitly notes the correlational basis of the ground truth and clarifies that the benchmark evaluates agent performance against these empirical models rather than testing strict causal inference. This revision addresses the referee's point without requiring new primary data collection. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark constructed from external live data and pre-trained models

full rationale

The paper presents EnergyAgentBench as an evaluation framework for LLM agents using live endpoints from QuarluxAI, EIA, and NREL, with ground truth labels generated from separately trained XGBoost models (R^2 0.967-0.995) and NREL ATB 2024 baselines. No equations, derivations, or self-citations are shown that reduce any result to its own inputs by construction. The central claims concern benchmark design and model performance rankings, which rest on external data sources rather than fitted parameters renamed as predictions or load-bearing self-references. This is a standard benchmark introduction paper whose evaluation chain is independent of the target results.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The benchmark rests on live external data feeds and pre-trained models for ground truth rather than new derivations; no new physical entities or ad-hoc constants are introduced beyond standard data access assumptions.

free parameters (1)
  • XGBoost cost-surface models
    Ground truth for tasks derived from trained models with reported R^2 0.967-0.995; these are fitted to data.
axioms (2)
  • domain assumption Live data from QuarluxAI infrastructure platform, EIA, and NREL endpoints is accurate, up-to-date, and accessible via tool calls
    All tasks require 3 to 48 sequential tool calls against these endpoints.
  • domain assumption NREL Annual Technology Baseline 2024 provides valid multi-decade cost trajectories
    Used for lifetime LCOE ranking and 30-year portfolio tasks.

pith-pipeline@v0.9.0 · 5921 in / 1478 out tokens · 51893 ms · 2026-05-19T17:38:23.255429+00:00 · methodology

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Reference graph

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