UVMarvel automatically constructs subsystem-level UVM testbenches for mainstream bus protocols using LLMs, an IR, and supporting libraries, reaching 95.65% average code coverage in 4.5 hours of automated runtime.
Alves, José Pombal, Nuno M
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New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
RouteLMT learns to route MT requests to large or small LLMs by predicting marginal quality gain from small-model token representations, yielding a better quality-budget Pareto frontier than baselines.
KIT's IWSLT submission uses segment concatenation, LLM label generation and cross-lingual translation to create >1M long-form training instances and shows that likelihood re-ranking harms semantic tasks unless combined with Minimum Bayes Risk decoding.
citing papers explorer
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UVMarvel: an Automated LLM-aided UVM Machine for Subsystem-level RTL Verification
UVMarvel automatically constructs subsystem-level UVM testbenches for mainstream bus protocols using LLMs, an IR, and supporting libraries, reaching 95.65% average code coverage in 4.5 hours of automated runtime.
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Model Merging: Foundations and Algorithms
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
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RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment
RouteLMT learns to route MT requests to large or small LLMs by predicting marginal quality gain from small-model token representations, yielding a better quality-budget Pareto frontier than baselines.
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Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026
KIT's IWSLT submission uses segment concatenation, LLM label generation and cross-lingual translation to create >1M long-form training instances and shows that likelihood re-ranking harms semantic tasks unless combined with Minimum Bayes Risk decoding.