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Novel error categorization framework examining failures across hops (diversity), coverage, and overthinking. Combines human annotation with automated metrics to diagnose why reasoning models hallucinate on multi-step tasks.
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| name | hop-skip-overthink-diagnosis |
| title | Hop Skip Overthink - Diagnosing Reasoning Models Multi-Hop |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2508.04699 |
| keywords | ["reasoning-diagnosis","error-analysis","hallucination","multi-hop-qa"] |
| description | Novel error categorization framework examining failures across hops (diversity), coverage, and overthinking. Combines human annotation with automated metrics to diagnose why reasoning models hallucinate on multi-step tasks. |
Reasoning models hallucinate more on multi-hop questions than general models, yet root causes remain unclear. This work introduces a nuanced error categorization framework that goes beyond accuracy metrics to diagnose failure modes. By examining three dimensions—hop diversity/uniqueness, information coverage, and cognitive efficiency—the framework provides actionable guidance for improving reasoning model robustness.
Create structured system for classifying reasoning failures.
from enum import Enum
from typing import Dict, List, Tuple
from dataclasses import dataclass
class ErrorType(Enum):
"""Types of errors in reasoning models."""
HOP_DIVERSITY = "hop_diversity" # Using same sources repeatedly
COVERAGE_GAP = "coverage_gap" # Missing relevant information
OVERTHINKING = "overthinking" # Inefficient reasoning
FACTUAL_ERROR = "factual_error" # Wrong facts
LOGICAL_ERROR = "logical_error" # Wrong reasoning
@dataclass
class ReasoningError:
"""Characterized reasoning error."""
error_type: ErrorType
question: str
model_answer: str
gold_answer: str
evidence: List[str]
severity: float # 0-1
reasoning: str
class ErrorCategorizer:
"""
Categorize reasoning model errors across multiple dimensions.
"""
def __init__(self, model, evidence_retriever):
self.model = model
self.retriever = evidence_retriever
def categorize_failure() -> [ReasoningError]:
errors = []
hop_error = ._analyze_hop_diversity(
question,
model_output,
retrieved_evidence
)
hop_error:
errors.append(hop_error)
coverage_error = ._analyze_coverage(
question,
model_output,
gold_output,
retrieved_evidence
)
coverage_error:
errors.append(coverage_error)
overthink_error = ._analyze_overthinking(
question,
model_output,
gold_output
)
overthink_error:
errors.append(overthink_error)
errors
() -> ReasoningError:
referenced_docs = ._extract_referenced_documents(model_output, retrieved_evidence)
num_unique_docs = ((referenced_docs))
num_total_references = (referenced_docs)
num_total_references == :
diversity_ratio = num_unique_docs / num_total_references
diversity_ratio < :
ReasoningError(
error_type=ErrorType.HOP_DIVERSITY,
question=question,
model_answer=model_output,
gold_answer=,
evidence=retrieved_evidence,
severity= - diversity_ratio,
reasoning=
)
() -> ReasoningError:
gold_facts = ._extract_facts(gold_output)
model_facts = ._extract_facts(model_output)
covered_facts = ((gold_facts) & (model_facts))
total_facts = (gold_facts)
coverage_ratio = covered_facts / total_facts total_facts >
coverage_ratio < :
missing_facts = (gold_facts) - (model_facts)
ReasoningError(
error_type=ErrorType.COVERAGE_GAP,
question=question,
model_answer=model_output,
gold_answer=gold_output,
evidence=retrieved_evidence,
severity= - coverage_ratio,
reasoning=
)
() -> ReasoningError:
reasoning_steps = model_output.count() + model_output.count()
sentences = model_output.split()
unique_sentences = ((sentences))
repetition_ratio = - (unique_sentences / (sentences))
reasoning_steps > repetition_ratio > :
ReasoningError(
error_type=ErrorType.OVERTHINKING,
question=question,
model_answer=model_output,
gold_answer=gold_output,
evidence=[],
severity=(repetition_ratio, reasoning_steps / ),
reasoning=
)
() -> []:
referenced = []
i, doc (documents):
doc text (
key_phrase text.lower()
key_phrase ._extract_key_phrases(doc)[:]
):
referenced.append(i)
referenced
() -> []:
re
sentences = re.split(, text)
facts = []
sent sentences:
sent = sent.strip()
(sent.split()) > (
verb sent.lower() verb [, , , ]
):
facts.append(sent)
facts
() -> []:
words = text.split()
[w w words w[].isupper() (w) > ][:]
Combine automated metrics with human judgment.
class HybridErrorEvaluator:
"""
Hybrid evaluation combining automated and human judgment.
"""
def __init__(self, categorizer: ErrorCategorizer):
self.categorizer = categorizer
self.error_statistics = {}
def evaluate_errors_hybrid(
self,
test_examples: List[Dict],
human_judgments: List[Dict] = None
) -> Dict:
"""
Evaluate errors using hybrid approach.
Args:
test_examples: Test cases with model outputs
human_judgments: Optional human annotation of errors
Returns:
Comprehensive error analysis
"""
automated_errors = []
for example in test_examples:
errors = self.categorizer.categorize_failure(
example["question"],
example["model_output"],
example["gold_output"],
example.get("evidence", [])
)
automated_errors.extend(errors)
# Combine with human judgments if available
if human_judgments:
automated_errors = self._reconcile_with_human_judgment(
automated_errors,
human_judgments
)
# Aggregate statistics
error_stats = self._aggregate_error_statistics(automated_errors)
return error_stats
def _reconcile_with_human_judgment(
self,
automated_errors: List[ReasoningError],
human_judgments: []
) -> [ReasoningError]:
error automated_errors:
matching_human = (
(h h human_judgments h[] == error.question),
)
matching_human:
matching_human.get():
error.severity = (, error.severity * )
:
error.severity = (, error.severity * )
automated_errors
() -> :
stats = {
: (errors),
: {},
: ,
: []
}
error_type ErrorType:
count = ( e errors e.error_type == error_type)
count > :
stats[][error_type.value] = {
: count,
: count / (errors) errors
}
errors:
stats[] = (e.severity e errors) / (errors)
stats[] = ._generate_recommendations(stats)
stats
() -> []:
recommendations = []
error_type, type_stats stats[].items():
percentage = type_stats[]
error_type == percentage > :
recommendations.append(
)
error_type == percentage > :
recommendations.append(
)
error_type == percentage > :
recommendations.append(
)
recommendations
Create human-readable diagnostic reports.
def generate_diagnostic_report(
model,
test_examples: List[Dict],
evidence_retriever
) -> Dict:
"""
Generate comprehensive diagnostic report.
Args:
model: Reasoning model to evaluate
test_examples: Test cases
evidence_retriever: Retrieval system
Returns:
Diagnostic report with analysis and recommendations
"""
categorizer = ErrorCategorizer(model, evidence_retriever)
evaluator = HybridErrorEvaluator(categorizer)
# Evaluate all examples
error_stats = evaluator.evaluate_errors_hybrid(test_examples)
# Build report
report = {
"model_performance": {
"total_examples": len(test_examples),
"errors_detected": error_stats["total_errors"],
"error_rate": error_stats["total_errors"] / len(test_examples) if test_examples else 0
},
"error_breakdown": error_stats["errors_by_type"],
"severity": {
"average": error_stats["avg_severity"],
"critical": sum(1 for e in test_examples if e.get("severity", 0) > 0.8),
"moderate": sum(1 for e in test_examples if 0.4 <= e.get("severity", ) <= )
},
: error_stats[],
: test_examples[:]
}
report
The key insight is that reasoning failures have multiple independent causes, not a single root. By separating hop diversity, coverage, and overthinking diagnostics, the framework enables targeted improvements. Traditional accuracy metrics mask these specific failure modes, making diagnosis difficult.
Hop Skip Overthink: Diagnosing Reasoning Models Multi-Hop (arXiv:2508.04699)
Introduces nuanced error categorization framework examining failures across hops (diversity), coverage (completeness), and overthinking (efficiency). Combines human annotation with automated metrics to provide actionable diagnostics for improving reasoning model robustness.