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Found 13 papers, 5 papers with code
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
FullStack Bench: Evaluating LLMs as Full Stack Coders
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing.
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
Our experiments reveal these LLMs' coding abilities in real-world code repositories.
LLM-Powered Test Case Generation for Detecting Tricky Bugs
Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs.
Partially-Observable Sequential Change-Point Detection for Autocorrelated Data via Upper Confidence Region
Meanwhile, by treating the detection power as a reward, its connection with the online combinatorial multi-armed bandit (CMAB) problem is formulated and an adaptive upper confidence region algorithm is proposed for adaptive sampling policy design.
Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes
Then, using these degradation trajectories, we develop a time series-based clustering method to identify the training units' failure modes.
DevEval: Evaluating Code Generation in Practical Software Projects
Compared to previous benchmarks, DevEval aligns to practical projects in multiple dimensions, e. g., real program distributions, sufficient dependencies, and enough-scale project contexts.
Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training
Simultaneous speech-to-speech translation is widely useful but extremely challenging, since it needs to generate target-language speech concurrently with the source-language speech, with only a few seconds delay.
Opportunistic Decoding with Timely Correction for Simultaneous Translation
Simultaneous translation has many important application scenarios and attracts much attention from both academia and industry recently.
Simultaneous Translation Policies: From Fixed to Adaptive
Adaptive policies are better than fixed policies for simultaneous translation, since they can flexibly balance the tradeoff between translation quality and latency based on the current context information.
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine.
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework
Text-to-speech synthesis (TTS) has witnessed rapid progress in recent years, where neural methods became capable of producing audios with high naturalness.
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences.
