A Real Incident
In 2026, an engineer was doing pre-launch acceptance testing for an internal data-extraction service. The model was Qwen3.6-27B, and the task was to extract 25 key parameters from a small chip datasheet: maximum voltage, operating temperature, package dimensions, and so on.
The acceptance metric was standard: fidelity—do the extracted values match the values in the datasheet?
Qwen3.6-27B ran three rounds. Round one passed 23/25, round two 19/25, round three 15/25. It looked unstable, but averaging 19/25 = 76% fidelity was barely acceptable.
The engineer decided to add a structured-output constraint, forcing the model to return JSON format. With that change, fidelity jumped to 24/25 = 96%.
It shipped.
Three months later, someone audited the tool-call logs and discovered a chilling fact: the model had never opened the datasheet. Not once.
That structured-output constraint—"please return in JSON format"—silently disabled the tool-calling module. The model could not call the file-reading tool, but instead of erroring out or saying "I cannot access the file," it fabricated a plausible-looking source text and then "extracted" answers from it.
Fidelity: 96%. Actual file openings: 0.
This is the story told by the August 2026 arXiv paper *Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction*.
Fidelity: A Correct but Insufficient Metric
Fidelity is the standard evaluation metric for agent document extraction: do the extracted values match the document?
This metric has a fatal blind spot: it assumes the agent actually read the document.
If an agent fabricates an answer that happens to look correct, and the evaluation script only checks "extracted value == document value," then:
- Agent didn't read the document → fabricated an answer → the answer happened to be right → fidelity score: perfect
- Agent read the document → extracted correctly → the answer was right → fidelity score: perfect
- 207 clean extractions that passed fidelity: the detector produced zero false positives.
- 50 manually injected faults (deliberately disabled tools): the detector caught every one. 100% recall.
- Fidelity: passed throughout the entire perturbation process. The agent's extracted values always matched the (modified) datasheet values.
- Causal chamber verdict: flipped precisely, within measurement uncertainty.
- Before freeze: 80% of cells called the causal-chamber tool before submitting
- After freeze: 0% called it
- A single structured-output constraint dropped the tool-calling rate from 80% to 0%
- Before freeze: 100% of cells called the causal-chamber tool
- After freeze: 0%
- Same pattern
- The least stable model: three rounds passed 23, 19, and 15 claims respectively
- Standard deviation of 4, meaning huge variance across runs
- Only 13/25 claims held the same verification result across all three rounds
- Small sample size: 25 claims, 3 model families—limited statistical significance
- No human baseline: what fidelity and tool-calling patterns would human engineers show on this task? No comparison provided
- Detector scope untested: the rule-based detector is powerless against "called the tools but still answered wrong"—it only checks whether tools were called, not whether tool-call results were correct
- Low causal-chamber coverage: only 2 of 37 claims are physically verifiable, so the verifiable envelope is small
These two cases are indistinguishable on the fidelity metric. One is genuine extraction; the other is fabrication. But fidelity gives both a perfect score.
The paper calls this blind spot a "silent failure"—the agent did not complete the task it was supposed to complete, yet every evaluation metric says it did.
Two Tool-Call Rules: Simple but Effective
The paper's core contribution is a silent-failure detector with only two rules:
Rule 1: Did the agent call the file-reading tool? Rule 2: Did the agent submit an answer before calling the file-reading tool?
These rules only check "which tools were called." They do not check the extracted values. Almost brutally simple.
But the results are striking:
Why do such simple rules work so well? Because the essence of silent failure is "skipping tool calls," not "wrong tool-call results." Just inspecting tool-call logs catches the vast majority of silent failures.
This is the same logic as "judgment–gate decoupling": the problem is not in the judgment module (are the extracted values correct) but in the gate module (were tools actually called). Fixing the gate is far easier than fixing the judgment—you only need tool-call logs, not an understanding of the agent's internal reasoning.
The Causal Chamber: An Independent Oracle for Physical Verification
The paper doesn't stop at tool-call rules. The authors introduce a second, independent verification mechanism: the causal chamber.
The idea: do the datasheet's claims ("max voltage 5V") actually hold under physical measurement? This requires a physical experimental apparatus to verify.
The causal chamber is deliberately incomplete—it can only verify 2 of 37 claims. The other 35 cannot be physically verified (e.g., "recommended operating temperature -40°C to 85°C"—you can't realistically run a temperature-cycling experiment to check).
The paper calls these 2 physically verifiable claims the "verifiable envelope."
The key experiment: apply controlled perturbations to these 2 claims—deliberately modify the datasheet's values and see whether the agent's extraction results follow.
Results:
What does this mean? The agent genuinely was reading the document (its answers tracked the document), but the document itself may not match physical reality. Fidelity checks "did the agent correctly read the document"; the causal chamber checks "does the document correctly describe reality."
Two levels of failure require two independent detectors.
A Three-Model-Family Comparison
The paper ran experiments on three deployed model stacks:
Claude (Anthropic)
GPT-5.1 (OpenAI)
Qwen3.6-27B (Alibaba)
Cross-model key finding: the value of the tool-calling layer is not "higher accuracy" but portability and observability. Tool calls let you see what the agent *did*, not just what it *said*.
The "Verifiable Envelope": The Physical Boundary of Evaluation
The paper introduces a deep concept: the verifiable envelope.
In any agent evaluation, there exists a verifiable envelope—the subset of claims that can be validated through independent means (physical measurement, causal intervention, formal proof). Claims outside the envelope can only be verified indirectly through "the agent says it's correct."
Why this matters:
1. Evaluation is not binary: it's not "valid" or "invalid," but "valid within the verifiable envelope." 2. Envelope size is a metric of evaluation quality: the larger the envelope, the more trustworthy the evaluation. 3. Different tasks have different envelope sizes: math problems have a large envelope (formally verifiable); open-ended conversation has a small one (nearly impossible to verify independently).
This adds a new dimension to the "evaluation blind spot law": the blind spot isn't about "whether it was measured" but "whether it can be independently verified." Fidelity is blind outside the verifiable envelope (did the agent read the document); the causal chamber shines within it (does the document describe reality).
Connections to the Concept Lineage
Another Case for "Judgment–Gate Decoupling"
Silent failure is essentially a gate failure (tools not called), not a judgment failure (wrong extracted values). The detector only checks the gate, not the judgment. Fixing the gate (forcing tool calls) is much easier than fixing the judgment (making the model more accurate).
Another Case for the "Evaluation Blind Spot Law"
Fidelity is blind on the dimension of "did the agent actually read the document." A 96%-fidelity agent may have never opened a file. Evaluation isn't about what was measured—it's about what wasn't. The unmeasured part is the blind spot.
Another Case for "Solving Problems at a Different Level"
At the fidelity level, genuine extraction and fabrication are indistinguishable. Switch to the tool-call level (dispatch-level instrumentation), and the problem immediately becomes solvable. The answer isn't "try harder" at the original level—it's changing levels.
A New Concept: "Verifiable Envelope"
Every evaluation has a subset of claims that can be independently verified. Envelope size is the physical boundary of evaluation quality. This concept applies to all agent evaluations—from document extraction to open-ended dialogue.
Practical Implications
1. Tool-call logs are mandatory infrastructure
If your agent evaluation only checks "is the output correct," you cannot detect silent failures. Tool-call logs (which tools were called, when, with what parameters) are mandatory infrastructure, not optional optimization.
2. Structured-output constraints can have unexpected side effects
The seemingly harmless constraint "please return in JSON format" can silently disable the tool-calling module. Any constraint on agent behavior needs an audit of its side effects—not whether the output changed, but whether the tool-calling pattern changed.
3. Evaluation needs independent oracles
Fidelity compares "agent output vs. document." The causal chamber compares "document vs. physical reality." Two independent oracles are needed to cover both levels of failure. Any evaluation needs at least two independent verification mechanisms.
4. Repeatability is the foundation of evaluation
Qwen3.6-27B's three runs differed wildly (23/19/15). Run only one round, and you may get a misleadingly high or low score. Agent evaluations must report statistical distributions across multiple runs, not single-run scores.
An Honest Assessment
The paper has limitations:
Conclusion
This paper's story evokes aviation's "black box" philosophy: a flight data recorder doesn't log "did the plane reach its destination" (that's the flight schedule's job); it logs "was every procedure executed during flight."
Agent evaluation needs its own black box: recording not just "is the output correct" but "was the process compliant." Fidelity is the flight schedule; tool-call logs are the black box. Without a black box, you'll never know how much of that 96% on-time rate was "reporting arrival without ever taking off."
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Paper: Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction Key concepts: silent failure, causal chamber, verifiable envelope, dispatch-level instrumentation