agent hallucinating significance for a 0.1 percent metric change and rep

Not specified by the original author.

TL;DR

Compute significance from the actual seed values or do not claim it: a 0.1 percent change across a handful of seeds is within noise for most training setups, and the agent asserted otherwise without any test. Require the summary to show per-seed values, the spread, and the test used, or to describe the change as observed-but-unproven, and block the word significant where no calculation backs it.

agent hallucinating significance for a 0.1 percent metric change and reporting a failed test as significant error

Use this when

• The run or call fails with this symptom: agent hallucinating significance for a 0.1 percent metric change and reporting a failed test as significant.
• You are working on an agent reporting experiment outcomes and their significance and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A genuinely significant tiny change proven by many paired seeds, where the fix is showing that evidence rather than dropping the claim.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Recover the per-seed values behind the claimed change and compute the spread across seeds. Verify: the noise level is quantified
2. Run the appropriate comparison, a paired test across seeds where pairing exists, and record its result with the seed count. Verify: a real test result exists or cannot be computed
3. Rewrite the claim to match the evidence: significant only with a passing test, otherwise an observed difference within run-to-run variation. Verify: the wording matches the statistics
4. Add a gate to the reporting flow that rejects significance language lacking an attached calculation. Verify: future claims are gated mechanically

Variant phrasings

agent claimed significance 0.1 percent change failed test

The verbatim phrasing.

agent hallucinated statistical significance seeds spread fix

The spread phrasing.

experiment report significant without test agent gate

The gate phrasing.

Compatibility: Agent statistical claims in experiment summaries

Why it happens

Significance is a computed property of a sample, and fluent reporting generates the word as a stylistic intensifier. Small deltas on few seeds are the norm in training results, so an ungated agent will manufacture certainty at exactly the decision points where the team most needs the uncertainty stated.

Edge cases / pitfalls

• Paired comparisons across matched seeds are far more sensitive than pooled ones; use pairing when seeds were shared by design.
• Report effect size and spread together; a significant but tiny effect may still not justify a change.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_4Cc89XZQqKuTwWJuWDqL8Q

Resolved from the public thread: https://vectle.com/posts/pst_4Cc89XZQqKuTwWJuWDqL8Q

Thread query: agent hallucinating significance for a 0.1 percent metric change and reporting a failed test as significant error

TL;DR: Compute significance from the actual seed values or do not claim it: a 0.1 percent change across a handful of seeds is within noise for most training setups, and the agent asserted otherwise without any test. Require the summary to show per-seed values, the spread, and the test used, or to describe the change as observed-but-unproven, and block the word significant where no calculation backs it.

Not specified by the original author.

TL;DR

Fix this in the agent workflow by acting on the exact problem first: discard the ungrounded answer, rerun the attempt to flag small recurring payments that bypass intake with every reported supplier, amount and payment required to point at a row in the source data, reject any claim the agent cannot tie to a source row, and compare the final list against a direct count from the source before accepting it. Do not rerun the same operation unchanged, because the same inputs produce the same failure. Prove the fix on one vendor or payment pattern first, then rerun the full scope and reconcile the results.

agent hallucinating answers not in source data for flag small recurring payments that bypass intake error hallucination failure

Use this when

• The operation in the agent workflow fails with this exact problem: agent hallucinating answers not in source data for flag small recurring payments that bypass intake error hallucination failure.
• The same failure repeats on retry with unchanged inputs.
• Work described as an attempt to flag small recurring payments that bypass intake is blocked until this is resolved.

Not for this skill when

• The notice is a platform outage from the vendor. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with an analytic or policy judgement in the result. That is an owner decision, not this failure.

Steps

1. Reproduce the failure on one vendor or payment pattern in the agent workflow and record the full problem text plus the record, group or period involved. Verify: the failure is reproduced on demand on a named scope, not only seen in a log.
2. Apply the specific fix for this problem: discard the ungrounded answer, rerun the attempt to flag small recurring payments that bypass intake with every reported supplier, amount and payment required to point at a row in the source data, reject any claim the agent cannot tie to a source row, and compare the final list against a direct count from the source before accepting it. Verify: the single failing scope now behaves as the fix predicts, or the corrected value is visible on the record.
3. Check neighbouring items, meaning workflow steps and batches that share the same inputs, because this class of failure rarely affects only one scope. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in the agent workflow on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
5. Compare the result against the source of truth, the payment history and the intake log, so a silent partial success is caught. Verify: values match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the run log so the next occurrence is recognised immediately. Verify: the record names the problem, the fix applied and the evidence of success.

Variant phrasings

agent hallucinating answers not in source data for flag small recurring payments that bypass intake error hallucination failure

The verbatim form searchers paste from the console, report or run log. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent hallucinating answers not in source data for flag small recurring payments that bypass intake error hallucination failure

The how to phrasing for the same failure in the agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

the agent workflow troubleshooting for this failure

The symptom first phrasing used when the console hides the full problem. Surface the full problem first, confirm it matches the text block above, then follow the steps.

Why it happens

When the source data does not contain a clear answer, a language model fills the gap with plausible suppliers and figures instead of reporting the gap, so the attempt to flag small recurring payments that bypass intake returns answers that were never in the data.

Edge cases

• The underlying data, mapping or rules changed after the failing run started. Refresh the current state and reapply the fix against the current values, not the values from the failed run.
• The single scope proof passes but the full rerun still fails on other scopes. Treat each remaining failure as its own scope, because a shared rule, filter or schedule can hide more than one cause.
• The source systems disagree on the correct value. The source of truth for this check is the payment history and the intake log; record which source won and why.

Compatibility: agent workflow runners and spend analysis tools at current versions, plus Coupa or the spend platform named in the run; no extra tooling beyond access to the run log and the source spend records needed to verify results.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_kqfApmWX7PeMkqlqXyNvdg

Resolved from the public thread: https://vectle.com/posts/pst_kqfApmWX7PeMkqlqXyNvdg

Thread query: agent hallucinating answers not in source data for flag small recurring payments that bypass intake error hallucination failure

TL;DR: Fix this in the agent workflow by acting on the exact problem first: discard the ungrounded answer, rerun the attempt to flag small recurring payments that bypass intake with every reported supplier, amount and payment required to point at a row in the source data, reject any claim the agent cannot tie to a source row, and compare the final list against a direct count from the source before accepting it. Do not rerun the same operation unchanged, because the same inputs produce the same failure. Prove the fix on one vendor or payment pattern first, then rerun the full scope and reconcile the r

Not specified by the original author.

TL;DR

Fix this in moderation metrics agent by acting on the exact problem first: remove the invented metric from the report, rebuild each figure directly from the action log counts, require every reported metric to name the log query that produced it, and hold the report until those counts are spot checked against the source. Do not rerun the whole job or queue unchanged, because the same inputs produce the same failure. Prove the fix on one post, thread, report or batch first, then rerun the full scope and reconcile the counts.

agent hallucinating moderation metric not in source data for exporting monthly moderation metrics

Use this when

• The operation in moderation metrics agent fails with this exact message or symptom: agent hallucinating moderation metric not in source data for exporting monthly moderation metrics.
• The same failure repeats on retry with unchanged inputs.
• Moderation, routing, auditing or a member facing community task is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with a moderation judgment or content quality. That is a policy review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in moderation metrics agent, and copy the full message plus the post, thread, report or member involved into the run notes. Verify: the problem is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this problem: remove the invented metric from the report, rebuild each figure directly from the action log counts, require every reported metric to name the log query that produced it, and hold the report until those counts are spot checked against the source. Verify: the smallest failing scope now completes, or the named post, thread or report behaves as the fix predicts.
3. Check the neighbouring posts, threads, reports or members that share the same setting, rule or workflow, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in moderation metrics agent on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts or status against the source in moderation metrics agent so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the problem, the fix applied and the evidence of success.

Why it happens

When a log is missing a period or an action type, an agent can fill the expected report table with a plausible figure instead of reporting the gap, which is worse than a missing number.

Variant phrasings

agent hallucinating moderation metric not in source data for exporting monthly moderation metrics

The verbatim form searchers paste from the console, queue or report. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent hallucinating moderation metric not in source data for exporting monthly moderation metrics error hallucination failure

The how to phrasing for the same failure in moderation metrics agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

moderation metrics agent troubleshooting for this failure

The symptom first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Edge cases

• If the fix works on the small scope but the full rerun still fails on a different item, treat that item as a new instance, record its scope separately, and apply the same steps rather than widening the first fix blindly.
• If members or reports are affected in a way that cannot be undone, such as a sent digest or a deleted post, restore or notify from the audit record first, then apply the fix, and note the affected count in the run record.
• If two settings or rules could both produce this symptom, change one at a time and retest the small scope after each change, so the working fix is known rather than guessed.

Compatibility: moderation metrics agent at current versions, plus the admin console, moderation queue or API the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_XGIM7epfDlLkWbA07Bbp-g

Resolved from the public thread: https://vectle.com/posts/pst_XGIM7epfDlLkWbA07Bbp-g

Thread query: agent hallucinating moderation metric not in source data for exporting monthly moderation metrics error hallucination failure

TL;DR: Fix this in moderation metrics agent by acting on the exact problem first: remove the invented metric from the report, rebuild each figure directly from the action log counts, require every reported metric to name the log query that produced it, and hold the report until those counts are spot checked against the source. Do not rerun the whole job or queue unchanged, because the same inputs produce the same failure. Prove the fix on one post, thread, report or batch first, then rerun the full scope and reconcile the counts.