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A Developer’s Guide to Systematic Prompting: Mastering Negative Constraints, Structured JSON Outputs, and Multi-Hypothesis Verbalized Sampling

Most developers treat prompting as an afterthought—write something reasonable, observe the output, and iterate if needed. That approach works until reliability becomes critical. As LLMs

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Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill

bill era For years, making a model smarter meant increasing parameters during

What is Tokenization Drift and How to Fix It?

words = for p in pairs] ids_ws = for w in words]

Sakana AI Introduces KAME: A Tandem Speech-to-Speech Architecture That Injects LLM Knowledge in Real Time

The fundamental tension in conversational AI has always been a binary choice:

Build a Multi-Agent AI Workflow for Biological Network Modeling, Protein Interactions, Metabolism, and Cell Signaling Simulation

class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal.max()) peak_kinase

Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations

Authors: Ahsaas Bajaj and Benjamin S Knight ? We ran 134,400 simulations

How a 2021 Quantization Algorithm Quietly Outperforms Its 2026 Successor

, an online vector quantization method, drew wide public attention at ICLR

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