By skyforbes LLMs can give the right answer with completely wrong logic.
VeriCoT fixes this by converting each reasoning step into formal logic, checking whether it’s entailed, ungrounded, or a contradiction, and then asking the model to minimally revise only the failing steps.
VeriCoT fixes this by converting each reasoning step into formal logic, checking whether it’s entailed, ungrounded, or a contradiction, and then asking the model to minimally revise only the failing steps.
It’s basically:
Give me your reasoning — now justify every step.
Minimal VeriCoT-style example
Question: Jamie is 17 and lives with a parent. Do they qualify as a dependent?
Provide reasoning in steps.
For each step:
1. List the premise(s) used
2. Label each premise as Context, Commonsense, or Derived
3. Mark the step as Entailed / Ungrounded / Contradiction
4. Revise only failing steps and re-check until fully valid
Possible VeriCoT-style Output
Answer: Yes, Jamie qualifies.
Step 1:
Claim: "Jamie is under 18."
Premise: age=17 → under18 (Context)
Status: Entailed
Step 2:
Claim: "Children living with a parent may qualify as dependents."
Premise: "Minors living with parents may be dependent" (Commonsense)
Status: Entailed
Step 3:
Claim: "Therefore, Jamie qualifies as a dependent."
Premises: Step1 (Derived), Step2 (Derived)
Status: Entailed
Final chain is fully supported; no ungrounded assumptions.
This reflects the original VeriCoT pipeline:
- explicit premises
- premise classification
- solver-like entailment check
- error-type identification
- minimal revision loop
No hand-waving – actual formal justification.
Full breakdown with more examples here:
👉 https://www.instruction.tips/post/vericot-neuro-symbolic-cot-validation