Artificial Intelligence, Machine Learning & Computing Research · Est. 2007
MLRC becomes an official NeurIPS track in 2026, routed through TMLR acceptance. What the two-stage process signals about reproducibility as a research output.
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How open-vocabulary detection and segmentation models become grounding infrastructure for AI agents — and where that grounding silently fails.
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A practical guide to sequencing inference optimizations — quantization, batching, KV cache, speculative decoding — against your workload profile.
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The EU AI Act's high-risk deadline moved to December 2027 under the 2026 omnibus deferral. Teams still face a runtime evidence gap most AI stacks lack.
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Benchmark contamination makes MMLU and HumanEval unreliable for frontier models. What a credible, contamination-resistant evaluation pipeline requires.
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Multi-agent deployments fail mostly on orchestration, not weak models — race conditions, memory gaps, timeouts. Evaluation must look beyond task success.
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A technical comparison of grid search, random search, Bayesian optimization, and population-based training for tuning machine learning hyperparameters.
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How LoRA, QLoRA, and adapter modules cut fine-tuning cost by orders of magnitude, with the memory/quality trade-offs engineers actually hit in practice.
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Transformer architecture explained for ML practitioners: attention, self-attention, Q/K/V, multi-head attention, positional encoding, and why transformers …
Read more →MLRC becomes an official NeurIPS track in 2026, routed through TMLR acceptance. What the two-stage process signals about reproducibility as a research output.
Read more →
A technical survey of feature attribution, probing, and mechanistic interpretability methods, and an honest look at what they can and can't explain.
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How RLHF actually works: the three-stage pipeline, why it exists, its failure modes, and why DPO is displacing it in production alignment pipelines.
Read more →Multi-agent deployments fail mostly on orchestration, not weak models — race conditions, memory gaps, timeouts. Evaluation must look beyond task success.
Read more →Benchmark contamination makes MMLU and HumanEval unreliable for frontier models. What a credible, contamination-resistant evaluation pipeline requires.
Read more →
A technical guide to LLM evaluation: what MMLU, HELM, GSM8K, and HumanEval actually measure, benchmark contamination, and how to build reliable task-specific …
Read more →How open-vocabulary detection and segmentation models become grounding infrastructure for AI agents — and where that grounding silently fails.
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A practitioner's guide to segmentation model choice and deployment: SAM vs. U-Net/Mask R-CNN, latency, post-processing, domain shift, and evaluation metrics.
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A technical comparison of vision transformers and CNNs — patch embeddings, attention, inductive bias, data efficiency, and when to use each architecture.
Read more →A practical guide to sequencing inference optimizations — quantization, batching, KV cache, speculative decoding — against your workload profile.
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A technical guide to building a robust MLOps pipeline — covering data versioning, experiment tracking, CI/CD for models, deployment patterns, and drift …
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A technical guide to model inference optimization: quantization, batching, KV-cache, distillation, speculative decoding, and hardware trade-offs for ML …
Read more →The EU AI Act's high-risk deadline moved to December 2027 under the 2026 omnibus deferral. Teams still face a runtime evidence gap most AI stacks lack.
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A technical and policy analysis of AI model evaluation standards, covering NIST AI RMF, ISO/IEC 42001, model cards, and red-teaming practices.
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How AI safety benchmarks measure jailbreak resistance, refusal rates, and dangerous capabilities — and why static test suites keep falling short.
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