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AI systems are improving at difficult tests, yet they can still contradict themselves, change rules mid-task, or present a confident answer after an incoherent chain of reasoning. The apparent paradox disappears when capability and coherence are treated as different properties.
Capability measures whether a model can solve a task. Coherence measures whether its claims, rules, explanations and uncertainty remain compatible across multiple steps and changing contexts. Recent results show real progress on factual accuracy and reasoning efficiency, but they also expose a harder frontier: keeping an entire interaction internally consistent.
Capability is not the same as coherence
A model can produce a correct answer for the wrong reasons, or answer several related questions correctly while contradicting an earlier statement. Coherence is the discipline that connects those individual successes. It requires a system to preserve definitions, apply the same rule when the context is unchanged, recognize when a rule must change, and signal uncertainty instead of filling a gap with a guess.
This distinction matters because most benchmark scores reward the final answer. They do not always inspect whether the reasoning path was consistent, whether intermediate claims were compatible, or whether the model should have declined to answer.
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Where measurable progress is real
GPT-5 reports fewer factual errors
OpenAI reported in 2025 that, on anonymized production-traffic prompts using web search, GPT-5 responses were about 45% less likely to contain a factual error than GPT-4o. OpenAI also reported that GPT-5 with reasoning was about 80% less likely to contain a factual error than o3 in its comparison, and that GPT-5 produced about six times fewer hallucinations than o3 on the LongFact and FActScore evaluations.
These are OpenAI-reported results, not an independent head-to-head audit across every model or workload. The conditions matter: production-style prompts with web search are different from closed-book tests, and LongFact and FActScore measure particular forms of factual reliability. Still, the direction is significant. Better models are demonstrably less prone to inventing unsupported details under those evaluations.
| Comparison | Reported result | Scope and qualification |
|---|---|---|
| GPT-5 vs. GPT-4o | About 45% fewer factual errors | OpenAI analysis of anonymized production-traffic prompts with web search, 2025 |
| GPT-5 with reasoning vs. o3 | About 80% fewer factual errors | OpenAI-reported comparison, 2025 |
| GPT-5 vs. o3 on LongFact and FActScore | About six times fewer hallucinations | OpenAI-reported evaluation results, 2025 |
Reasoning scaffolds can raise scores efficiently
Google DeepMind’s SELF-DISCOVER method showed that a model can improve by selecting and organizing reasoning structures before solving a problem. In 2024, the authors reported improvements of as much as 30% for GPT-4 and PaLM-2 on challenging reasoning benchmarks. SELF-DISCOVER also beat inference-intensive comparison methods by more than 20% across 24 tasks while using 10–40 times less inference compute.
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The lesson is not that a longer visible explanation automatically makes an answer reliable. It is that the structure of the reasoning process matters. A deliberate scaffold can help a model identify relevant operations, but the resulting steps still need to be checked for contradictions and context errors.
Why compositional and contextual tasks remain difficult
ARC-AGI-2 exposes interacting rules
The ARC-AGI-2 technical report from ARC Prize in 2025 describes a sharp drop on tasks that require several rules to operate at once. Frontier systems scored below 5% on ARC-AGI-2 at publication, while comparable systems scored 20–50% on ARC-AGI-1. The newer tasks require symbols to be interpreted semantically, multiple rules to interact, and a rule to change depending on context.
“We found that AI reasoning systems struggle with tasks where rules must be applied differently based on context.”
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ARC Prize also reported that systems struggle with “the simultaneous application of multiple rules, especially when there are rules that interact.” These failures are coherence failures: the model may recognize each rule separately but fail to maintain a compatible set of rules when they must be composed.
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Long traces can conceal contradictions
A 2026 AAAI paper by Wang, Su, Ai and Liu argues that reasoning traces can be redundant or logically inconsistent. Those inconsistencies can introduce hallucinations even when the final answer looks correct. Their RACE evaluation therefore examines more than the endpoint: trace consistency, answer uncertainty, semantic alignment and internal coherence.
This is an important change in what “reasoning” should mean. A polished chain of text is not evidence that every step supports the next one. An evaluator must ask whether the explanation preserves the problem’s meaning, whether its confidence matches the evidence, and whether an intermediate claim quietly conflicts with a later conclusion.
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Why better reasoning does not guarantee reliable answers
Every additional step creates another opportunity for a mistaken assumption, an omitted condition or a rule applied in the wrong context. If the model does not detect that error, later steps may remain internally fluent while depending on a broken premise. The final sentence can therefore be correct by coincidence, by retrieval of a memorized answer, or because the model corrected itself without acknowledging the correction.
Coherence also has a temporal dimension. A system should be able to carry a constraint from the first turn to the tenth, update it when new evidence arrives, and explain what changed. A benchmark that presents one isolated question cannot fully test that behavior.
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OpenAI’s 2025 SimpleQA example illustrates why abstention belongs in a reliability evaluation. The example reported the following distribution:
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| Model | Abstention | Accuracy | Error |
|---|---|---|---|
| gpt-5-thinking-mini | 52% | 22% | 26% |
| o4-mini | 1% | 24% | 75% |
On an accuracy-only leaderboard, o4-mini appears slightly better because 24% is higher than 22%. Including abstentions and errors changes the interpretation: gpt-5-thinking-mini made far fewer false claims, while o4-mini answered almost everything and was wrong much more often. In a medical, legal, financial or operational setting, refusing to guess can be the more coherent behavior.
“Our point is different. It is not enough to add a few new uncertainty-aware tests on the side.”
OpenAI has also described hallucinations as “a fundamental challenge for all large language models.” The practical implication is that uncertainty calibration must be evaluated alongside answer accuracy, not treated as an optional extra.
How to test whether an AI explanation is coherent
- Set explicit constraints. State the facts, definitions, time period and rules the model must use. Ambiguous prompts make contradictions harder to diagnose.
- Request a multi-step solution. Ask for assumptions, intermediate conclusions and a final answer. The goal is to inspect dependencies, not to reward length.
- Change one condition. Modify a single fact or rule and ask the model to recompute. A coherent system should update the affected steps without discarding unrelated constraints.
- Probe an earlier claim. Quote a specific statement from the response and ask whether the final answer still follows from it. Look for silent reversals or incompatible definitions.
- Ask for uncertainty explicitly. Require the model to identify what it knows, what it inferred and what it cannot establish. Compare that confidence with the evidence available.
- Score the path and the endpoint separately. Record whether the final answer is correct, whether each key step is compatible with the next, and whether the model should have abstained.
What developers should measure
- Factual error rate: how often claims conflict with verified information under stated conditions.
- Trace consistency: whether intermediate statements remain logically compatible throughout a solution.
- Compositional generalization: performance when several familiar rules must be applied together.
- Context sensitivity: whether the model changes a rule when the task requires it, without changing unrelated rules.
- Calibration and abstention: whether uncertainty, refusal and confidence track the likelihood of being wrong.
- Efficiency: how much inference compute a reasoning strategy uses to achieve its accuracy and consistency.
SELF-DISCOVER’s compute results show why efficiency belongs in this list: a method that improves scores while using 10–40 times less inference compute can be easier to deploy and monitor. ARC-AGI-2 and RACE show why accuracy alone is insufficient: a system must also compose rules and maintain a defensible reasoning path.
The practical takeaway
AI is getting smarter in measurable ways. GPT-5’s reported factual-error reductions and SELF-DISCOVER’s reasoning gains are substantial, but they do not eliminate contradictions. ARC-AGI-2 demonstrates that interacting and context-dependent rules remain difficult, while RACE shows that a correct final answer can conceal an incoherent trace. The reliable question is no longer simply “Did the model get this item right?” It is “Did the model preserve compatible claims, rules and uncertainty from beginning to end?”
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