BLADE uses Bayesian list-wise alignment with dynamic estimation to create a self-evolving target that overcomes limitations of static references in LLM-based recommendation, yielding sustained gains in ranking and complex metrics.
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DynaFix iteratively feeds execution-level dynamic information such as variable states and control flows into LLM prompts to repair 186 bugs on Defects4J, a 10% gain over baselines including 38 previously unrepaired cases.
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VRSafe adds false positive keystrokes to VR typing data to reduce keystroke inference attack accuracy and includes an efficient malicious login detector.
ICL4Decomp applies in-context learning to guide LLMs in generating re-executable decompiled code from binaries, reporting roughly 40% higher re-executability than prior methods across datasets and optimization levels.
Dual-Rerank fuses autoregressive and non-autoregressive generative reranking via knowledge distillation and uses list-wise decoupled RL optimization to improve whole-page utility and cut latency in industrial video search.
citing papers explorer
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Beyond Static Best-of-N: Bayesian List-wise Alignment for LLM-based Recommendation
BLADE uses Bayesian list-wise alignment with dynamic estimation to create a self-evolving target that overcomes limitations of static references in LLM-based recommendation, yielding sustained gains in ranking and complex metrics.
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Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking
Dual-Rerank fuses autoregressive and non-autoregressive generative reranking via knowledge distillation and uses list-wise decoupled RL optimization to improve whole-page utility and cut latency in industrial video search.