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Archive / page 26 of 49

Latest articles, page 26

  1. The Lottery Ticket Hypothesis in Large Language Models: How Sparse Subnetworks and Iterative Magnitude Pruning Retain Transformer Capacity

    The Lottery Ticket Hypothesis in Large Language Models: How Sparse Subnetworks and Iterative Magnitude Pruning Retain Transformer Capacity Modern large language models operate under extreme overparameterization. Frontier architectures allocate tens or hundreds of billions of parameters to achieve low perplexity and robust generalization across reasoning, code generation, and factual retrieval. Yet empirical pruning consistently demonstrates that post-training models can lose 30% to 50% of their

    AI
    8 min read

  2. Enterprises Curb AI Agent Autonomy Amid 40% Project Cancellation Projections

    Enterprises deploying agentic artificial intelligence are shifting architectures away from open-ended autonomy toward bounded, verifiable execution as projects encounter governance, security, and financial bottlenecks in production environments. Data from industry research firms highlights a widening divergence between model capability and operational control. According to projections from Gartner, more than 40% of current agentic AI initiatives are projected to be canceled by the end of 2027.

    AI
    2 min read

  3. Function Calling Evaluation in Production: AST Matching, Executable Sandboxes, and Multi-Turn Benchmark Architecture

    Production AI systems increasingly rely on Large Language Models not merely as conversational generators, but as deterministic execution routers that select and invoke external software tools. While general-purpose LLM evaluations such as MMLU or Chatbot Arena measure semantic fluency and broad reasoning, they provide little insight into whether a model can reliably format API parameters, adhere to strict JSON schemas, or maintain consistency across multi-step execution graphs. In real-world ag

    AI
    6 min read

  4. Sharpness-Aware Minimization in Large Language Models: How Adversarial Weight Perturbations and Flat Minima Boost Generalization

    In overparameterized deep neural networks, minimizing empirical training loss is insufficient to guarantee optimal generalization on unseen distributions. Modern deep architectures, including vision models and autoregressive Large Language Models (LLMs), operate in regimes where parameter counts far exceed training token counts, producing highly non-convex loss surfaces populated by infinite global minima. Standard optimization via Stochastic Gradient Descent (SGD) or AdamW often converges to sh

    AI
    6 min read

  5. Prefix-Tree KV Cache Management in Production: RadixAttention, Tree-Structured LRU Eviction, and Token-Level Sharing in SGLang and vLLM

    Prefix-Tree KV Cache Management in Production: RadixAttention, Tree-Structured LRU Eviction, and Token-Level Sharing in SGLang and vLLM Autoregressive large language model inference is heavily constrained by memory bandwidth and the computational overhead of the prefill phase. For workloads such as multi-turn conversations, autonomous agent tool loops, few-shot prompt evaluations, and tree-search decoding, consecutive requests often share substantial token prefixes. In a standard multi-turn ses

    AI
    7 min read

  6. Neural Collapse: How Simplex Equiangular Tight Frames Emerge at the Terminal Phase of Training

    In classification tasks, deep neural networks exhibit an unexpected geometric simplicity during late-stage optimization. While the internal activations of early training appear high-dimensional and complex, the penultimate layer representations and linear classifiers converge toward an exact, symmetrical geometric structure known as Neural Collapse (NC). First identified empirically by Papyan, Han, and Donoho (2020), Neural Collapse emerges during the Terminal Phase of Training (TPT). This regi

    edu
    6 min read

  7. Inherent Releases Faraday: 27B Scientific Agent Outperforms Frontier Models on Paper Replication

    London-based AI research startup Inherent has released Faraday, an autonomous AI agent engineered to independently reproduce published scientific research without prior exposure to target solutions. Founded by former Google DeepMind researchers Louis Kirsch, Kaloyan Aleksiev, Tantum Collins, and Edward Hughes, the lab launched Faraday weeks after securing a $50 million seed round. According to benchmark results published by the lab, Faraday outperformed significantly larger frontier systems, in

    AI
    2 min read

  8. CUDA Graph Capture in Production LLM Serving: Dynamic Batch Bucketing, Static Memory Pooling, and Kernel Launch Elimination

    CUDA Graph Capture in Production LLM Serving: Dynamic Batch Bucketing, Static Memory Pooling, and Kernel Launch Elimination In high-throughput large language model serving, the autoregressive generation (decode) phase presents an operational bottleneck distinct from prefill processing. While prefill computation is dominated by compute-bound General Matrix Multiplications (GEMMs) operating across extended sequence lengths, autoregressive decoding processes a single token per sequence at each ite

    Inference
    8 min read

  9. Neural Tangent Kernel: How Infinite-Width Networks Linearize Gradient Descent

    The Neural Tangent Kernel (NTK) describes the behavior of infinitely wide neural networks during gradient descent. In the infinite-width limit, network training reduces to kernel regression with a fixed kernel — the NTK — that stays constant throughout training. This theoretical framework explains why wide networks converge to global minima, why they struggle to learn high-frequency functions early (spectral bias), and why they behave differently from finite-width networks that learn features.

    edu
    4 min read

  10. LLM 0.33 Adds Template Chaining, Per-Call Embedding Keys, and Server Tool Logging

    Simon Willison has released llm 0.33, an update to the open-source command-line tool and Python library for interacting with large language models. The release introduces template composition, stateless per-call embedding credentials, and server-side tool execution visibility in logs, alongside an upgrade to the OpenAI Python 3.x client and httpx2. Template Composition and Parameter Decoupling The primary workflow enhancement in version 0.33 is the ability to repeat the -t or --template flag

    Tooling
    2 min read