Presents a diagnostic framework for semantic ID tokenizer failures using overlap and capacity metrics and proposes DRQ to separate geometry from distribution matching.
Autocoder: Autonomous code generation with large language models,
3 Pith papers cite this work. Polarity classification is still indexing.
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Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
LLM coding agents show cross-architecture divergence: higher GPU throughput with Gemini but prompt-sensitivity, while GPT is steadier; on Cerebras, producing runnable code dominates over performance tuning, with GPT succeeding where Gemini fails.
citing papers explorer
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Decoupled Residual Quantization for Robust Semantic IDs in Recommendation
Presents a diagnostic framework for semantic ID tokenizer failures using overlap and capacity metrics and proposes DRQ to separate geometry from distribution matching.
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Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
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Evaluating LLM Coding Agents on SZ-Family Lossy Compression Across Architectures
LLM coding agents show cross-architecture divergence: higher GPU throughput with Gemini but prompt-sensitivity, while GPT is steadier; on Cerebras, producing runnable code dominates over performance tuning, with GPT succeeding where Gemini fails.