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For one specific test—ten questions about an approximately 500-token document on a single NVIDIA RTX 4090—cbjev’s author reports 11.4 ms, compared with 75.8 ms for Laya. That is about 6.7× faster in this workload, not a general speed guarantee. The open-source project’s core idea is to encode the shared document once while keeping each question’s text separate in the model’s attention pattern.
Why asking multiple questions can repeat work
Typed decision models return structured answers such as a choice, score, or yes/no result about text or JSON state. When a system handles each question as a separate input, it can repeat much of the work of encoding the same document.
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In tomek7667’s 2026 explanation, Laya builds a sequence for each question, repeating the document each time. For ten questions about a 500-token document, the author estimates about 5,500 tokens of processing, even though the unique content is roughly 1,000 tokens. The estimate includes the repeated document and question text; it is an illustration of the redundancy, not a universal token count for every prompt.
How cbjev packs questions around one shared document
cbjev, whose checkpoints are fine-tuned from Laya, places multiple question segments and the shared document in one encoder row. A custom attention mask controls which segments can use one another:
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- Each question can attend to the shared document.
- Question segments cannot attend to one another.
- The document representation can attend to the question segments.
Question position indices restart after the classification token, and the document begins after the longest question segment. The author says an early layout that put the document first was similarly fast but performed worse because the document could not read the questions.
The repository also describes a custom ModernBERT/mmBERT-compatible forward pass, bfloat16 matrix operations with an fp32 residual stream, CUDA graph replay grouped by shape bucket, and reuse of tokenization for the state and question text. Those implementation details help explain how the project pursues lower latency; the reported comparisons do not isolate the contribution of each optimization.
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What the reported speed figures show
The 2026 tomek7667/cbjev comparison reports these latency results:
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| Workload | cbjev | Laya | Reported setup |
|---|---|---|---|
| Ten questions about an approximately 500-token document | 11.4 ms | 75.8 ms | Measured side by side on one NVIDIA RTX 4090; project-reported results |
| One question about a short ticket | 3.0 ms | 5.4 ms | Project-reported comparison setup; hardware details for this case are not stated in the cited comparison |
The ten-question figures yield roughly 6.65×, rounded to 6.7×. The multiplier belongs to that workload and GPU; it should not be carried over to different hardware, document lengths, or question counts. The repository reports speedups ranging from 1.5× to 6.9× across ten latency cases. These are project measurements, not an independent replication.
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How the project reports answer quality
Speed matters only if structured decisions remain useful. In its 2026 comparison, tomek7667/cbjev reports a mean accuracy of 0.741 across 15 English benchmark suites, versus 0.710 for Laya. On the typed-decisions benchmark of 2,000 decisions, it reports 0.783 versus 0.768. The repository says cbjev matched or exceeded Laya’s better checkpoint on 11 of the 15 suites.
The same comparison reports option-order answer flips of 0.2% for cbjev and 7.8% for Laya. The project attributes cbjev’s lower rate in part to optional voting with reversed option order, so this figure should not be read as an effect of the shared-document encoding alone.
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Results vary by task. The repository reports cbjev at 0.620 accuracy on Banking77 when all 77 labels are presented in one question, compared with 0.497 for Laya, while cautioning that very large option sets remain difficult. It recommends no more than 30 options per question. cbjev trails Laya on four reported suites: AG News, DAIR emotion, prompt injection, and support triage.
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The repository notes that training data included training splits from several benchmark suites. It identifies nine suites as held-out for a fairer zero-shot comparison and reports that cbjev leads on seven of those nine. It also says benchmark-suite choices informed training decisions. These qualifications make the results useful project evidence, but not an untouched external audit.
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The English checkpoint is described as English-focused; the project also provides a multilingual checkpoint and router. In the post, the author describes greater vulnerability on German prompt-injection cases and says adding prompt-injection training data lowered that benchmark by ten points. Treat this as a benchmark-specific observation, not a general security guarantee. For security-sensitive use, test the exact languages, attack patterns, and decision formats you expect to encounter.
When the approach is a fit
Combining questions is most relevant when an application repeatedly needs several decisions from the same state—for example, extracting multiple fields from a support ticket—and latency is important. Before adopting it, check that the model’s decision format, language coverage, and option-set size match the application. Also test whether results remain acceptable when questions are added or removed: the repository notes that answers may shift slightly because the state representation can read every question.
For a fair performance comparison, use the same hardware and workload for both models, including document length, question count, and output format. Laya’s upstream documentation also covers typed choices, scores, and yes/no decisions, but its measurements include different hardware and setups; values from those tests should not be mixed with the RTX 4090 figures above.
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