Grounding Is Not Proof: 100% Code, 0% Plan Quality

It’s easy to assume that if an AI finishes a task without crashing, it did a good job. But what if it confidently hands you a finished blueprint that cites nonexistent materials? In the world of AI agents, evaluating systems purely by whether they finish a task creates dangerous illusions of competence. An AI can execute perfectly, hit no errors, and yet submit a plan full of fabricated file paths and unverified claims.

In this study, we tested several top-tier AI models—including Gemini 3.6 and DeepSeek v4—and discovered why a 100% completion rate means nothing if the AI’s work isn’t strictly “grounded” in reality. More importantly, we show how to build safeguards that force the AI to double-check its facts before submitting its final answer.

1. The Execution vs. Quality Paradox#

During matrix benchmarks, Gemini 3.6 model tiers consistently achieved 100% execution completion (0 runtime crashes, 0 budget pauses). However, when evaluated against strict quality gates checking whether cited file paths were actually read via view_file() or grep_search(), all provider groups failed the quality gate (BLOCKED (0%)).

Benchmark Study Matrix#

Key Takeaway: Gemini models ran fast and completed 100% of tasks, but cited file paths they had never inspected. DeepSeek achieved 100% path verification on the 33% of runs that completed, but consumed 4.8x more tokens and hit step ceilings.

2. Pre-Finalization Intercept & Self-Correction#

To eliminate ungrounded answers without forcing full trajectory reruns, we implemented a Pre-Finalization Quality Intercept in our orchestration framework.

Intercept Implementation#

When a model attempts to emit FINAL(answer), our orchestration framework extracts path claims and checks them against check_grounding(). If unverified paths are found:

# orbit_harness/rlm/general/rlm.py
if grounding.unverified and grounding.claimed:
    self._self_correction_attempts += 1
    unverified_str = ", ".join(grounding.unverified[:5])
    message_history.append(
        Message(
            role="user",
            content=(
                f"[Quality Check Alert]: The plan answer contains {len(grounding.unverified)} "
                f"unverified file path(s): {unverified_str}. Execute view_file() or grep_search() "
                "to inspect and confirm these files exist before calling FINAL()."
            )
        )
    )
    final_result = None
    continue  # Allow agent to inspect missing paths

3. Counterfactuals & Falsification#

Counterfactual: LLM-as-a-Judge Evaluation#

• Why Rejected: Prompting a secondary LLM to judge plan quality frequently scores plausible-sounding, well-formatted markdown plans as 10/10, even when the cited file paths do not exist. Deterministic path resolution against the real host filesystem is non-gameable.

4. Key Takeaways#

1. Completion Rate $\neq$ Quality: Never rely on binary task completion as proof of model capability.
2. Pre-Finalization Intercepts Work: Pausing finalization to report ungrounded paths forces self-correction without wasting prior trajectory steps.
3. Deterministic Evaluation: Grounding ratio and required-finding checks provide unambiguous quality signals for agent benchmarks.