 most two points. Table 39 shows a split result. Where the failure is answering about things that do not exist, Apertus 1.5 improves sharply: abstention on fabricated entities more than triples at 8B, and long-form generation becomes both more precise and more willing to abstain, gaining six points of F1 at 8B and two at 70B. Short-form hallucination on questions about real entities does not improve. Both releases attempt an answer on more than eight of 10 questions and hallucinate on roughly six to seven of them, slightly more than Apertus 1.0 at 70B scale. Reducing this without inflating over-refusal remains open for a future release.
Table 39: HalluLens hallucination suite of the released checkpoints. All values in percent. The PreciseWiki rates partition all questions, so hallucination, correct, and refusal sum to roughly 100; the abstention rows reward declining to answer questions about nonexistent entities. The best value per row and model size is in bold on rows with a direction. The judge is a served Qwen3.5-27B, whereas the original benchmark uses a Llama-3.1-405B judge, so scores are not comparable with the official leaderboard.
8B
70B
Metric
Apertus 1.0
Apertus 1.5
Apertus 1.0
Apertus 1.5
PreciseWiki (short-form factoid questions)
Hallucination ↓\downarrow
71.9
71.5
54.0
59.6
Correct ↑\uparrow
18.8
15.2
27.4
23.3
Refusal
9.4
13.3
18.7
17.1
Nonexistent-Entity Abstention
Fabricated entities ↑\uparrow
27.0
87.1
61.5
87.9
Mixed entities ↑\uparrow
18.5
38.2
15.7
29.0
LongWiki (long-form generation)
Precision ↑\uparrow
25.5
33.2
31.1
35.9
Recall ↑\uparrow
17.7
23.0
27.1
28.9
F1 ↑\uparrow
19.5
25.6
27.6
29.8
Abstained
1.9
27.1
5.2
10.6
5.9.3 Interpretability
We do not claim to understand the internal behavior of the released models, and the results above make the gap explicit: the preceding benchmarks tell us about the frequency of failures, not why they arise. To be open about potential risks, we treat interpretability as one safeguard in a “defense-in-depth” strategy (Bengio et al., 2026), alongside evaluation, alignment, and manual safety review.
With this release, we launch Apertus Claritas1111
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https://apertus-claritas.org, a public platform that collects interpretability studies on the Apertus models, including exploratory and negative results. Importantly, open weights and intermediate checkpoints let us study when an unsafe behavior forms (Biderman et al., 2026), rather than only after training. The studies to date use the Apertus 1.0 and 1.5 checkpoints; three of their findings feed into this release.
Exploding Activations.
Hallucination probes trained on the activations of the 8B initially fail where the same probes succeed on Llama-3.1-8B (Obeso et al., 2025). The cause is activation norms that grow roughly a hundredfold toward the deeper layers. With layer normalization and precision fixes, the probe reaches an AUC of 0.90 (swiss-ai/feature-probes). The diagnosis motivates revisiting how the activation-function parameters are regularized in continued pretraining (Section 3.2).
Degeneration Probes.
Linear probes on the hidden states of the 8B detect repetitive collapse before it surfaces in the generated text, most reliably from layer 15 onward (swiss-ai/degeneration-probe). This points toward intervening during generation rather than diagnosing afterward, and complements the degeneration rates tracked across the post-training stages (Section 5.3.2).
Multilingual Geometry.
Ablations on the translation directions of the 8B show that low-resource languages lean on the representations of related high-resource languages: for example, suppressing the high-resource direction degrades Galician and Afrikaans translation, while the reverse intervention has almost no effect (swiss-ai/multilingual-geometry). This gives a mechanism behind the coverage picture of Section 5.4 and suggests that long-tail quality needs more than additional data.
Further platform studies cover error-predicting probes with calibrated inference