SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.
Root cause analysis for microservice systems via hierarchical reinforcement learning from human feedback
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A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
A survey reviewing benchmark data contamination in LLMs, its impact on evaluation, and alternative assessment approaches.
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SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions
SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.
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Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
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Benchmark Data Contamination of Large Language Models: A Survey
A survey reviewing benchmark data contamination in LLMs, its impact on evaluation, and alternative assessment approaches.