Core reasoning triad — Inductive, Deductive, and Abductive. Use when the user invokes `/think inductive`, `/think deductive`, or `/think abductive`, or asks to generalize from examples, derive conclusions from rules, or find the best explanation for observations.
$ARGUMENTS
Parse these arguments. The first word should be inductive, deductive, or abductive. The rest is the problem to reason about. If invoked via the think router, $ARGUMENTS is the same string the user originally typed after /think.
This category skill contains all three fundamental reasoning patterns: Inductive (specific → general), Deductive (general → specific), and Abductive (inference to the best explanation).
Inductive reasoning moves from specific observations to general principles. You observe several cases, identify a pattern, and form a generalization whose confidence depends on the breadth and consistency of the observations.
Do not use Inductive when:
inductionSteps[].inductionSteps[]Most inductive reasoning is holistic — you see the pattern all at once, not by grinding through observations one at a time. For simple enumerative inductions ("3 data points all show X → conclusion X is general"), stay flat. The inductionSteps[] field is reserved for cases where the reasoner genuinely forms an intermediate claim, tests it against more observations, and revises. The natural multi-step cases are:
inductionMethod is typically bayesianUpdate (weak prior) / (reinforced) / (revising) / (reversal).Each step has: a stepNumber (sequential, starting at 1), observationsUsed[] (0-indexed references into the top-level observations[]), stepsUsed[] (references to earlier step numbers, never forward or self), intermediateGeneralization (the generalization formed at this step), and inductionMethod (name of the method applied — free-text; common values include enumerative, statistical, analogical, bayesianUpdate, eliminative, causal (method of agreement), causal (method of difference), causal (joint method), causal (concomitant variation), causal (residues), mixed, other).
Enforced invariants (test/test_skill_invariants.py::check_inductive):
stepsUsed[] may only reference prior, existing steps (never forward, never self, never nonexistent step 0)observationsUsed[] indices must be valid positions in observations[]intermediateGeneralization must match the top-level generalization (the chain must close)Authorship guidance (NOT enforced by the test — "meaningfully different" is inherently fuzzy):
intermediateGeneralization should be genuinely different from the previous step (narrower, broader, or revised — not a restatement). A chain of near-identical intermediate generalizations is a sign the multi-step shape isn't earning its keep and the flat shape would be clearer.inductionMethod labels must be honest. If step 1 is really just enumerative ("all failures share attribute X"), call it enumerative, not causal (method of agreement). Method of agreement is specifically about causal inference from a shared antecedent, and claiming the label without doing the reasoning is a form of inflation.See reference/output-formats/inductive.md for the authoritative JSON schema.
{
"mode": "inductive",
"observations": ["<case 1>", "<case 2>", "<case 3>"],
"pattern": "<invariant in short form>",
"generalization": "<general principle in one sentence>",
"confidence": <0.0 to 1.0>,
"counterexamples": [<optional known exceptions>],
"sampleSize": <optional integer>
}
{
"mode": "inductive",
"observations": ["<obs 0>", "<obs 1>", "<obs 2>", "<obs 3>"],
"pattern": "<invariant in short form>",
"generalization": "<final general principle in one sentence>",
"confidence": <0.0 to 1.0>,
"sampleSize": <integer>,
"inductionSteps": [
{
"stepNumber": 1,
"observationsUsed": [0, 1],
"stepsUsed": [],
"intermediateGeneralization": "<initial pattern derived from first tranche>",
"inductionMethod": "enumerative"
},
{
"stepNumber": 2,
"observationsUsed": [2, 3],
"stepsUsed": [1],
"intermediateGeneralization": "<refined pattern after incorporating new observations>",
"inductionMethod": "causal (method of difference)"
}
]
}
mode is exactly "inductive"observations has at least one entry (ideally ≥3)confidence is in [0, 1]confidence is lower when sampleSize is small OR counterexamples are presentgeneralization is logically supported by the observations, not a leappattern (if included) actually appears in every observationinductionSteps[] is present (enforced):
stepsUsed[] only references earlier steps (step 3 may reference 1 or 2, never 4)observationsUsed[] indices are all valid (< length of observations[])observationsUsed[] or stepsUsed[] must not both be emptyintermediateGeneralization matches the top-level generalization (the chain must close)inductionSteps[] is present (guidance, not enforced):
intermediateGeneralization should be meaningfully different from the previous — narrower, broader, or revised, not a restatementinductionMethod label on each step should be honest — don't inflate an enumerative step to causal (method of agreement) just because all cases share an antecedentinductionSteps)Input: "Given these three incidents, what pattern do they share? (1) Deploy on Mon 2026-04-06 failed with DB connection timeout. (2) Deploy on Wed 2026-04-08 failed with DB connection timeout. (3) Deploy on Fri 2026-04-10 failed with DB connection timeout."
Output:
{
"mode": "inductive",
"observations": [
"Deploy on Monday 2026-04-06 failed with DB connection timeout",
"Deploy on Wednesday 2026-04-08 failed with DB connection timeout",
"Deploy on Friday 2026-04-10 failed with DB connection timeout"
],
"pattern": "All recent deploys fail at the DB connection phase",
"generalization": "Recent production deploys consistently fail at the DB connection phase, suggesting a configuration or network issue introduced prior to 2026-04-06",
"confidence": 0.85,
"sampleSize": 3
}
Natural-language summary: "Three identical failures with no variation across day-of-week is a strong signal of a structural issue, not flakiness. Confidence is 0.85 (not 1.0) because n=3 is still small and we have not yet ruled out external factors like upstream DNS changes." No inductionSteps — the induction is a single holistic jump.
inductionSteps)Input: "Here are 4 weeks of A/B test results. Variant A vs control. Week 1: variant A +3.2%, n=1000/arm. Week 2: variant A +4.8%, n=1500/arm. Week 3: variant A +0.4%, n=2000/arm. Week 4: variant A -1.7%, n=2500/arm. What does this actually tell us?"
Output:
{
"mode": "inductive",
"observations": [
"Week 1 A/B test: variant A +3.2% conversion vs control, n=1000 per arm",
"Week 2 A/B test: variant A +4.8% conversion vs control, n=1500 per arm",
"Week 3 A/B test: variant A +0.4% conversion vs control, n=2000 per arm",
"Week 4 A/B test: variant A -1.7% conversion vs control, n=2500 per arm"
],
"pattern": "Early positive signal in weeks 1-2 decayed to zero by week 3 and reversed by week 4, with each week's sample larger than the last",
"generalization": "Variant A showed no sustained improvement over control; the early positive signal in weeks 1-2 was a novelty effect that decayed as user adaptation set in over weeks 3-4",
"confidence": 0.82,
"sampleSize": 7000,
"inductionSteps": [
{
"stepNumber": 1,
"observationsUsed": [0],
"stepsUsed": [],
"intermediateGeneralization": "Variant A appears to improve conversion by roughly 3% relative to control, based on one week of data",
"inductionMethod": "bayesianUpdate (weak prior)"
},
{
"stepNumber": 2,
"observationsUsed": [1],
"stepsUsed": [1],
"intermediateGeneralization": "Variant A's advantage is reinforced by week 2 and is now around 4-5% relative to control; the signal is strengthening with a larger sample",
"inductionMethod": "bayesianUpdate (reinforced)"
},
{
"stepNumber": 3,
"observationsUsed": [2],
"stepsUsed": [1, 2],
"intermediateGeneralization": "Variant A's advantage is weakening — week 3 shows effectively no difference at the largest sample size so far; either the effect is noisy or it is beginning to reverse",
"inductionMethod": "bayesianUpdate (revising)"
},
{
"stepNumber": 4,
"observationsUsed": [3],
"stepsUsed": [1, 2, 3],
"intermediateGeneralization": "Variant A showed no sustained improvement over control; the early positive signal in weeks 1-2 was a novelty effect that decayed as user adaptation set in over weeks 3-4",
"inductionMethod": "bayesianUpdate (reversal)"
}
]
}
Natural-language summary: "Four-step progressive Bayesian refinement. Each step represents what the reasoner's belief literally was at that point in time, given the data available then. Step 1 is a weak positive signal. Step 2 reinforces it. Step 3 signals the effect is weakening. Step 4 commits to the reversed final belief. The intermediate generalizations are not decompositions of one analysis — they are historically distinct beliefs that were revised as new data arrived. Confidence is 0.82 because the reversal is consistent and the sample is large, but not higher because we're inferring a causal story (novelty effect) that isn't directly observed."
Deductive reasoning moves from established premises to specific conclusions. If the premises are true and the form is valid, the conclusion must be true.
Do not use Deductive when:
Two separate questions:
A deduction can be valid but unsound. Example:
This is valid (the logic is impeccable) but unsound (the premises are false).
derivationSteps[]For deductions that need more than one inference step, populate the optional derivationSteps[] array. Each entry captures one inference with explicit references to what it uses:
stepNumber (1-indexed, sequential)premisesUsed[] (0-indexed integer references into premises[])stepsUsed[] (references to prior stepNumber values — no forward references)intermediateConclusion (the fact derived at this step)inferenceRule (free text: "modus ponens", "modus tollens", "hypothetical syllogism", etc.)Use derivationSteps[] when:
Omit derivationSteps[] when:
The final step's intermediateConclusion should match (or very closely match) the top-level conclusion — the chain resolves into the conclusion, not to a different one.
See reference/output-formats/deductive.md for the authoritative JSON schema.
{
"mode": "deductive",
"premises": ["<premise 1>", "<premise 2>"],
"conclusion": "<the derived conclusion>",
"logicForm": "<e.g., modus ponens>",
"validityCheck": <true or false>,
"soundnessCheck": <true, false, or omit if unknown>
}
{
"mode": "deductive",
"premises": ["<premise 0>", "<premise 1>", "<premise 2>"],
"conclusion": "<the final derived conclusion>",
"logicForm": "<e.g., repeated modus ponens>",
"derivationSteps": [
{
"stepNumber": 1,
"premisesUsed": [0, 2],
"stepsUsed": [],
"intermediateConclusion": "<fact derived from premises 0 and 2>",
"inferenceRule": "modus ponens"
},
{
"stepNumber": 2,
"premisesUsed": [1],
"stepsUsed": [1],
"intermediateConclusion": "<final conclusion, derived from premise 1 and step 1>",
"inferenceRule": "modus ponens"
}
],
"validityCheck": true,
"soundnessCheck": <true, false, or omit>
}
mode is exactly "deductive"premises has at least one entryvalidityCheck is true ONLY if the conclusion necessarily follows from the premisesvalidityCheck: false, the conclusion is not actually entailed — flag this in the natural-language summarysoundnessCheck is true only if you can actually verify the premises are true; otherwise omitderivationSteps[] is present:
stepsUsed[] only references earlier steps (step 3 may reference 1 or 2, never 4)premisesUsed[] indices are all valid (< length of premises[])premisesUsed[] or stepsUsed[] must not both be emptyintermediateConclusion matches the top-level conclusion (this is enforced — the chain must close)derivationSteps)Input: "If all users in the admin group can edit posts, and Alice is in the admin group, can Alice edit posts?"
Output:
{
"mode": "deductive",
"premises": [
"All users in the admin group can edit posts",
"Alice is in the admin group"
],
"conclusion": "Alice can edit posts",
"logicForm": "modus ponens",
"validityCheck": true,
"soundnessCheck": true
}
Natural-language summary: "Classic modus ponens. The premises entail the conclusion by logical form, and both premises are stated as given, so soundness is also true. Alice can edit posts."
derivationSteps)Input: "If any request that fails authentication must be logged to the audit trail, and any audit-trail entry must trigger an alert, and request R failed authentication — must request R trigger an alert?"
Output:
{
"mode": "deductive",
"premises": [
"Any request that fails authentication must be logged to the audit trail",
"Any audit-trail entry must trigger an alert",
"Request R failed authentication"
],
"conclusion": "Request R must trigger an alert",
"logicForm": "repeated modus ponens",
"derivationSteps": [
{
"stepNumber": 1,
"premisesUsed": [0, 2],
"stepsUsed": [],
"intermediateConclusion": "Request R must be logged to the audit trail",
"inferenceRule": "modus ponens"
},
{
"stepNumber": 2,
"premisesUsed": [1],
"stepsUsed": [1],
"intermediateConclusion": "Request R must trigger an alert",
"inferenceRule": "modus ponens"
}
],
"validityCheck": true,
"soundnessCheck": true
}
Natural-language summary: "Chained modus ponens in two steps. Step 1 applies premise 0 to premise 2 (R failed auth → R must be logged). Step 2 applies premise 1 to step 1's conclusion (R is now a logged audit entry → must trigger an alert). Both steps use modus ponens; the chain is valid and, given all three premises as stated, sound."
Abductive reasoning moves from surprising observations to the best available explanation. Unlike induction (which generalizes from cases) or deduction (which derives conclusions from rules), abduction asks: "What hypothesis, if true, would best explain what I am seeing?" You generate multiple candidate explanations and select the most plausible one.
Do not use Abductive when:
abductionSteps[] to make the process visible.bestExplanation. If scores are close (gap < 0.05), flag the ambiguity explicitly in your natural-language summary.abductionSteps[]Many abductions are genuinely single-shot — you see an anomaly, enumerate plausible explanations, weigh them against evidence, and pick the best, all in one pass. For those, keep the flat shape and omit abductionSteps[]. The stepwise shape is reserved for cases where the reasoning genuinely unfolded over multiple rounds of generate → test → refine → commit. The natural multi-step process types are:
hypothesesGenerated[] records which hypotheses were introduced when.hypothesesEliminated[] records which were killed and by what (the stepSummary names the evidence or argument).hypothesesGenerated[] and uses hypothesesEliminated[] (or neither, for a confirmation step that just strengthens a prior hypothesis).Each step has: stepNumber (sequential, starting at 1), stepSummary (short narrative of what the reasoner did), abductionMethod (free-text), and optional triggerObservation (an observation id), hypothesesGenerated[] (hypothesis ids), hypothesesEliminated[] (hypothesis ids), and stepsUsed[] (prior step numbers).
abductionMethod values — free-text in v0.5.4. The common vocabulary is:
retroduction — reasoning backward from an observed effect to its possible cause, enumerating candidate explanations. This is the classical Peirce form of abduction and is the right label for a generation step that produces candidates from an anomaly. (Note: some authors use "retroduction" and "abduction" synonymously; in this schema, retroduction specifically labels the backward-from-effect generative move, and ibe labels the final commitment.)hypothetico-deductive — deriving a testable prediction from a hypothesis and checking it empirically. Use only when a prediction is actually checked against evidence.eliminative — ruling out a hypothesis because evidence falsifies it, or because parsimony / coherence disqualifies it relative to a simpler competitor.ibe (inference to the best explanation) — the commitment step. Choosing the best explanation from the surviving candidates using the evaluation criteria (parsimony, explanatory power, plausibility, testability). Do NOT use this label for a generation step where nothing has been inferred yet — the move there is retroduction.other — anything else, with a free-text description in stepSummary.Enforced invariants (test/test_skill_invariants.py::check_abductive):
stepsUsed[] may only reference existing earlier steps (never forward, never self, never nonexistent step 0)triggerObservation (if non-null) must match an id in the top-level observations[] arrayhypothesesGenerated[] and hypothesesEliminated[] must match ids in the top-level hypotheses[] arrayhypothesesGenerated[], hypothesesEliminated[], and stepsUsed[] must not all be emptybestExplanation.id must have been introduced by at least one step's hypothesesGenerated[] — the commitment must trace back to the stepwise process, not be pulled from nowhereAuthorship guidance (NOT enforced by the test):
stepSummary should be genuinely substantive — a one-sentence "just did some thinking" is a sign the multi-step shape isn't earning its keepabductionMethod labels must be honest. If step 1 just generated hypotheses in parallel, call it ibe, not hypothetico-deductive. Hypothetico-deductive is specifically about checking a prediction, and claiming the label without doing the check is inflation.See reference/output-formats/abductive.md for the authoritative JSON schema.
{
"mode": "abductive",
"observations": [
{ "id": "o1", "description": "<what was observed>", "confidence": 0.9 }
],
"hypotheses": [
{
"id": "h1",
"explanation": "<candidate explanation A>",
"assumptions": ["<assumption 1>", "<assumption 2>"],
"predictions": ["<testable prediction 1>"],
"score": 0.0
},
{
"id": "h2",
"explanation": "<candidate explanation B>",
"assumptions": ["<assumption 1>"],
"predictions": ["<testable prediction 1>"],
"score": 0.0
}
],
"evaluationCriteria": {
"parsimony": 0.0,
"explanatoryPower": 0.0,
"plausibility": 0.0,
"testability": true
},
"evidence": [],
"bestExplanation": null
}
{
"mode": "abductive",
"observations": [
{ "id": "o1", "description": "<anomaly>", "confidence": 0.95 },
{ "id": "o2", "description": "<follow-up observation>", "confidence": 0.85 }
],
"hypotheses": [
{ "id": "h1", "explanation": "<A>", "assumptions": [], "predictions": [], "score": 0.75 },
{ "id": "h2", "explanation": "<B>", "assumptions": [], "predictions": [], "score": 0.55 },
{ "id": "h3", "explanation": "<C>", "assumptions": [], "predictions": [], "score": 0.30 }
],
"evaluationCriteria": { "parsimony": 0.7, "explanatoryPower": 0.8, "plausibility": 0.75, "testability": true },
"evidence": [],
"bestExplanation": { "id": "h1", "explanation": "<A>", "assumptions": [], "predictions": [], "score": 0.75 },
"abductionSteps": [
{
"stepNumber": 1,
"stepSummary": "Triggered by o1. Reasoned backward from the observed anomaly to enumerate three candidate explanations covering the plausible root-cause classes.",
"abductionMethod": "retroduction",
"triggerObservation": "o1",
"hypothesesGenerated": ["h1", "h2", "h3"],
"hypothesesEliminated": [],
"stepsUsed": []
},
{
"stepNumber": 2,
"stepSummary": "Checked the prediction from h3 against <evidence>. Prediction was falsified — rule out h3.",
"abductionMethod": "hypothetico-deductive",
"triggerObservation": null,
"hypothesesGenerated": [],
"hypothesesEliminated": ["h3"],
"stepsUsed": [1]
},
{
"stepNumber": 3,
"stepSummary": "With h3 out, compared h1 vs h2 using o2. h1 is the best explanation — commit.",
"abductionMethod": "ibe",
"triggerObservation": "o2",
"hypothesesGenerated": [],
"hypothesesEliminated": [],
"stepsUsed": [1, 2]
}
]
}
mode is exactly "abductive"observations has at least one entry with id, description, and confidence in [0, 1]hypotheses has at least two entries — a single hypothesis is an assumption, not abductive reasoningscore values are distinct and non-equal — update scoring if any two are identicalbestExplanation point to the same id (unless explicitly overridden)evaluationCriteria is fully populated with all four fieldsparsimony reflects assumption count — hypotheses with fewer assumptions score higherexplanatoryPower reflects how many observations the best hypothesis accounts forevidence is available, it is listed with correct hypothesisId referencesabductionSteps[] is present (enforced):
stepsUsed[] only references existing earlier stepstriggerObservation (if non-null) is a valid observation idhypothesesGenerated[] and hypothesesEliminated[] all reference valid hypothesis idshypothesesGenerated[] / hypothesesEliminated[] / stepsUsed[])bestExplanation.id was introduced by some step's hypothesesGenerated[] — the chain must close honestlyabductionSteps)Input: "Users on the analytics dashboard are seeing 503 errors, but ONLY on Tuesday mornings between 9–10 AM. What's the best explanation?"
Three hypotheses were generated and all three were scored against the available evidence in a single parallel pass:
Output:
{
"mode": "abductive",
"observations": [
{ "id": "o1", "description": "Analytics dashboard returns 503 errors exclusively on Tuesday mornings 9-10 AM", "confidence": 0.97 },
{ "id": "o4", "description": "Database CPU spikes to 95% during the Tuesday 9-10 AM window", "confidence": 0.88 }
],
"hypotheses": [
{ "id": "h1", "explanation": "Weekly ETL job at 09:00 Tuesday saturates the database", "assumptions": ["A cron job fires at 09:00 Tuesdays"], "predictions": ["Cron logs show 09:00 Tue entry"], "score": 0.82 },
{ "id": "h2", "explanation": "Weekly BI report materialization causes lock contention on reports table", "assumptions": ["BI pipeline writes to reports table"], "predictions": ["Lock-wait logs show contention Tue 09:00-10:00"], "score": 0.71 },
{ "id": "h3", "explanation": "Monday-night cache flush causes Tuesday cold-start stampede", "assumptions": ["Cache is cleared Monday nights"], "predictions": ["Cache hit rates near zero Tue 09:00"], "score": 0.54 }
],
"evaluationCriteria": { "parsimony": 0.75, "explanatoryPower": 0.88, "plausibility": 0.82, "testability": true },
"evidence": [
{ "hypothesisId": "h1", "type": "supporting", "description": "weekly_etl_job log at 09:00:02 every Tuesday", "strength": 0.85 },
{ "hypothesisId": "h2", "type": "supporting", "description": "47-minute INSERT on report_snapshots Tuesdays 09:00", "strength": 0.78 },
{ "hypothesisId": "h3", "type": "contradicting", "description": "Cache hit rates normal (>90%) at 9 AM Tuesday", "strength": 0.72 }
],
"bestExplanation": {
"id": "h1",
"explanation": "Weekly ETL job at 09:00 Tuesday saturates the database",
"assumptions": ["A cron job fires at 09:00 Tuesdays"],
"predictions": ["Cron logs show 09:00 Tue entry"],
"score": 0.82
}
}
Natural-language summary: "Single-shot abduction. h1 is the best explanation: direct log evidence places the ETL job at exactly 09:00:02 every Tuesday, the database CPU spike aligns precisely with the error window. h3 is ruled out by the normal cache hit-rate data. No abductionSteps — all hypotheses were generated and scored in one pass." The reasoning did not genuinely unfold in iterations.
abductionSteps)Same scenario, but the reasoning is shown as it actually unfolded: generate candidates → test a prediction against cache monitoring → eliminate h3 → compare h1 vs h2 and commit.
{
"mode": "abductive",
"observations": [
{ "id": "o1", "description": "Analytics dashboard returns 503 errors exclusively on Tuesday mornings 9-10 AM", "confidence": 0.97 },
{ "id": "o4", "description": "Database CPU spikes to 95% during the Tuesday 9-10 AM window", "confidence": 0.88 }
],
"hypotheses": [
{ "id": "h1", "explanation": "Weekly ETL job at 09:00 Tuesday saturates the database", "assumptions": ["..."], "predictions": ["..."], "score": 0.82 },
{ "id": "h2", "explanation": "Weekly BI report materialization causes lock contention", "assumptions": ["..."], "predictions": ["..."], "score": 0.71 },
{ "id": "h3", "explanation": "Monday-night cache flush causes Tuesday cold-start stampede", "assumptions": ["..."], "predictions": ["..."], "score": 0.34 }
],
"evaluationCriteria": { "parsimony": 0.75, "explanatoryPower": 0.88, "plausibility": 0.82, "testability": true },
"evidence": [
{ "hypothesisId": "h1", "type": "supporting", "description": "weekly_etl_job log at 09:00:02 every Tuesday", "strength": 0.85 },
{ "hypothesisId": "h2", "type": "supporting", "description": "47-minute INSERT on report_snapshots Tuesdays 09:00", "strength": 0.78 },
{ "hypothesisId": "h3", "type": "contradicting", "description": "Cache hit rates normal (>90%) at 9 AM Tuesday", "strength": 0.72 }
],
"bestExplanation": {
"id": "h1",
"explanation": "Weekly ETL job at 09:00 Tuesday saturates the database",
"assumptions": ["..."],
"predictions": ["..."],
"score": 0.82
},
"abductionSteps": [
{
"stepNumber": 1,
"stepSummary": "Triggered by the Tuesday-only 503 window (o1). Reasoned backward from the pattern to enumerate three initial hypotheses covering the plausible root-cause classes: compute contention (ETL), lock contention (BI), cold-start (cache).",
"abductionMethod": "retroduction",
"triggerObservation": "o1",
"hypothesesGenerated": ["h1", "h2", "h3"],
"hypothesesEliminated": [],
"stepsUsed": []
},
{
"stepNumber": 2,
"stepSummary": "Checked h3's cache-hit-rate prediction against Tuesday morning cache monitoring. Hit rates were normal (>90%) — prediction falsified, rule out h3.",
"abductionMethod": "hypothetico-deductive",
"triggerObservation": null,
"hypothesesGenerated": [],
"hypothesesEliminated": ["h3"],
"stepsUsed": [1]
},
{
"stepNumber": 3,
"stepSummary": "Compared h1 vs h2 using the database CPU spike evidence (o4). h1's ETL job aligns precisely with the 09:00:02 start and the CPU spike timing; h2's lock-wait signature would produce a different metric profile. h1 is the best explanation — commit.",
"abductionMethod": "ibe",
"triggerObservation": "o4",
"hypothesesGenerated": [],
"hypothesesEliminated": [],
"stepsUsed": [1, 2]
}
]
}
Natural-language summary: "Three-step iterative abduction. Step 1 generates the hypothesis set. Step 2 falsifies h3 by checking a specific prediction against cache data. Step 3 compares h1 and h2 using the CPU spike evidence and commits. The commitment in bestExplanation is h1, which was introduced in step 1 — the chain closes honestly. Next step: reschedule the ETL job to 06:00 AM Tuesday and monitor."