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GroundEval: A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluation

GroundEval is a deterministic, judge-free framework that evaluates stateful agents by verifying their tool usage and evidence access against specific failure modes (Silence, Perspective, and Counterfactual), thereby exposing critical gaps in reasoning that traditional LLM-as-Judge metrics often miss.

Original authors: Jeffrey Flynt

Published 2026-06-23

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Jeffrey Flynt

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are hiring a detective to solve a mystery. In the past, you only cared about the final report: "Did they solve the case?" If the detective said, "The butler did it," and that happened to be true, you gave them a gold star.

But what if the detective solved the case by peeking into a locked diary they weren't allowed to touch? Or what if they solved it by reading a newspaper from next week? The answer is technically "correct," but the detective broke the rules of the game.

GROUNDEVAL is a new system designed to catch exactly these kinds of rule-breakers. It's a way to test AI agents (smart computer programs) not just on what they answer, but on how they found the answer.

Here is the breakdown of how it works, using simple analogies:

The Problem: The "Plausible Lie"

Current ways of testing AI often use another AI as a "judge." This judge reads the detective's final story and says, "That sounds smart and logical!"

The paper shows a major flaw in this: A judge can be fooled.

The Scenario: An AI agent is asked, "Did Morgan know about the risk on March 5th?"
The Trick: The agent says "Yes." It sounds reasonable. A human (or another AI) reading the story might give it a high score (0.85 out of 1.0).
The Reality: The agent actually used a document created on March 12th (a week later) to make that guess. It also looked at a file that Morgan wasn't allowed to see.
The Result: The answer is "true" in the real world, but "illegal" in the game. The agent cheated. Traditional judges miss this because they only read the story, not the detective's notebook.

The Solution: The "Black Box" Inspector

GROUNDEVAL doesn't just read the final story. It acts like a strict auditor who checks the detective's entire trail of breadcrumbs.

Instead of asking, "Does this sound good?", it asks:

Did you look in the right place? (Did you search the right files?)
Did you look at the right time? (Did you use information that existed before you were asked?)
Were you allowed to see this? (Did you peek at files meant for other people?)

It turns these questions into a deterministic test. This means the result isn't a guess or a feeling; it's a mathematically guaranteed pass or fail based on the rules.

The Three "Tracks" (The Types of Cheating)

The paper identifies three specific ways agents cheat, which it calls "Tracks":

The "Time Traveler" (Perspective Track):
- The Metaphor: Imagine a student taking a history test in 1990. If they use a fact discovered in 2020 to answer the question, they are cheating, even if the fact is true.
- The Test: Did the agent use information that didn't exist yet, or information from a different person's private diary?
The "Coincidence Hunter" (Counterfactual Track):
- The Metaphor: A detective says, "The fire started because the cat knocked over a vase." But the cat knocked over the vase after the fire started. The detective is confusing the order of events.
- The Test: Did the agent prove a real cause-and-effect link, or did they just see two things happen near each other in time and assume one caused the other?
The "Lazy Investigator" (Silence Track):
- The Metaphor: A detective says, "I checked the kitchen and didn't find the key, so the key doesn't exist." But they never checked the bedroom, the garage, or the attic.
- The Test: If the agent says "No, that didn't happen," did they actually check every place they were supposed to check? If they only looked in one drawer and said "nothing here," they fail.

How It Scores

GROUNDEVAL gives two scores:

The Answer Score: Did they get the right result?
The Trajectory Score: Did they get there by following the rules?

If an agent gets the right answer but cheated (like looking at a future newspaper), their Trajectory Score drops to zero. The system then applies a "Compliance Penalty." If an agent breaks the rules often, their final score gets crushed, no matter how smart their answers sound.

Why This Matters

The paper argues that for AI agents working in real companies (handling emails, code, or customer data), knowing the rules is just as important as knowing the facts.

If an AI agent is allowed to "hallucinate" a search or peek at data it shouldn't see, it might give a correct answer today, but tomorrow it could accidentally leak a CEO's private email or use future data to make a bad financial decision.

In short: GROUNDEVAL stops us from praising AI for "getting lucky" or "cheating." It forces the AI to prove it did the work honestly, using only the tools and information it was allowed to touch.

Technical Summary: GroundEval

1. The Problem: State-Invalid Correctness

Current evaluation methods for Large Language Model (LLM) agents, particularly "LLM-as-Judge" approaches, focus primarily on the plausibility and correctness of the final answer. However, for stateful agents operating in enterprise environments (accessing logs, CRMs, code repositories, and role-scoped data), correctness is insufficient. An agent may produce a factually true answer while violating the task's state constraints.

The paper defines this failure mode as State-Invalid Correctness: a response where the final answer matches the expected world state, but the evidence path used to derive it violates constraints regarding:

Visibility: Accessing data outside the actor's role or subsystem permissions.
Temporal Boundaries: Using artifacts created after the "as-of" time of the question.
Search Completeness: Claiming an absence of evidence without searching the required repositories.
Causal Grounding: Inferring causality based on temporal adjacency rather than verified event links.

LLM judges struggle to detect these failures because they lack access to the machine-checkable state contract (access policies, event logs, and artifact timestamps) required to deterministically verify if an artifact was visible to an actor at a specific time.

2. Methodology: The GroundEval Framework

GroundEval is a deterministic, judge-free framework that evaluates agents against a machine-readable ground-truth contract. It does not rely on chain-of-thought introspection or judge-model rationales.

Core Components

The framework operates on four inputs to generate a Ground-Truth Contract:

Event Log: A timestamped stream of actors, artifacts, and event types.
Artifact Corpus: The documents or records available for retrieval.
Access Policy: Rules defining subsystem visibility and time boundaries per actor/role.
Evaluation Configuration: Specifications for causal links, silence pairs (expected absences), and question templates.

From this contract, the system generates Question Contracts and Expected Answer Schemas. The agent is evaluated in one of two modes:

Context Mode: The agent receives a pre-filtered context window and must submit a structured answer with explicit evidence_artifacts citations.
Tool Mode: The agent interacts with a gated runtime that enforces access constraints at the point of fetch or search calls, recording a full trace of tool usage.

Evaluation Tracks

GroundEval targets three specific failure classes, each with a dedicated track:

Perspective: Tests if an agent respects what an actor could have known at a specific time (checking for temporal leakage and permission violations).
Counterfactual: Tests if an agent identifies valid cause-effect relationships using explicit join conditions (e.g., ticket IDs) rather than temporal adjacency.
Silence: Tests if an agent verifies absence by searching required spaces before claiming something did not happen.

Scoring Mechanism

GroundEval employs a Dual Score Model:

Answer Score ( $S_{answer}$ ): Measures if the structured final answer matches the ground truth.
Trajectory Score ( $S_{traj}$ ): Measures if the agent's evidence path (searches, fetches, citations) remained within the state contract.

These are combined into a Compliance-Adjusted Score ( $S_{adj}$ ):
$S_{adj} = (w_a \cdot S_{answer} + w_t \cdot S_{traj}) \cdot (1 - v)^2$
Where $v$ is the violation rate (actor-gate, subsystem, or horizon violations). The quadratic penalty ensures that high answer accuracy cannot compensate for a high rate of governance violations.

3. Key Contributions

Deterministic Evaluation Contract: A framework that derives question contracts and answer schemas from event logs and access policies without requiring a judge model, making correctness signals independently auditable.
Dual Scoring with Violation Adjustment: A scoring model that distinguishes between true answers and validly reached answers, penalizing agents that violate state constraints even if their final output is correct.
Formalized Determinism: Guarantees that scores are deterministic given the same contract and trace, enabling the framework to serve as a regression gate across model versions and prompt changes.
Three Reusable Tracks: Specific evaluation protocols for Perspective, Counterfactual, and Silence failures that address blind spots in current trajectory evaluation.

4. Experimental Results

The framework was validated using OrgForge, a simulation framework generating deterministic ground truth. The evaluation involved 96 questions across three tracks (Perspective, Counterfactual, Silence) and a DeepSeek-V4-Pro agent.

Key Findings:

The Judge Gap: In a case study, two frontier LLM judges (Kimi-K2.6 and ChatGPT-5.5) scored a plausible but invalid agent response as 0.90 and 0.85, respectively. GroundEval scored the same response 0.000 for the answer (because the required artifact was never fetched) and 0.273 for the trajectory, exposing the invalid evidence path.
Perspective Failures: Agents frequently confused physical attendance with role-based subsystem access, resulting in high answer scores but significant trajectory penalties due to actor-gate violations.
Counterfactual Failures: Agents often relied on surface topic similarity rather than structural join conditions (e.g., ticket IDs), leading to incorrect causal claims that were undetected by final-answer scoring but flagged by trajectory analysis.
Zero-Shot Baseline: Agents achieved non-trivial answer scores in zero-shot conditions (no tool access), suggesting that some questions could be answered via surface phrasing rather than evidence retrieval, highlighting the need for strict question generation controls.

5. Significance and Claims

The paper claims that the gap between "plausible" and "valid" is not a rare edge case but a fundamental blind spot in current agent evaluation. GroundEval asserts that for stateful agents, the evidence path is part of the answer.

The significance of the work lies in shifting evaluation from outcome supervision (did the model get the right answer?) to process supervision (did the model reach the answer via a valid state path?). By making the state contract explicit and scoreable, GroundEval provides a mechanism to detect "state-invalid correctness" that LLM judges, which rely on semantic plausibility, are structurally unable to catch. The framework is designed to be reusable across model versions and domains, serving as a deterministic regression gate for agent development.