#constrained decoding
93 related articles
Outlines: The Open-Source Tool for Get…
·2 min
Outlines: The Open-Source Tool for Getting Structured Output from LLMs
Outlines is an open-source Python library for structured LLM output. Using constrained decoding with FSMs, it guarantees JSON, regex, and Pydantic-compliant generation with near-zero overhead.
Read more →
·3 min
lx: 72 Local CLI Tools That Bring Ollama Models Into Your Terminal
lx is a set of 72 single-purpose CLI tools running on local Ollama models — no API key, fully offline. Supports git commit generation, log debugging, and more. Rust binaries with <15ms cold start; 7–8B models work great.
Read more →
·2 min
OpenAI's New Voice Model: A Deep Dive into Real-Time Multilingual Simultaneous Interpretation
OpenAI's new voice model delivers near-zero-latency bidirectional conversation with real-time multilingual simultaneous interpretation across Cantonese, Spanish, and English.
Read more →
BAML: A Type-Safe Programming Language…
·2 min
BAML: A Type-Safe Programming Language Designed for AI Agents
BAML is a domain-specific language for AI Agent development that uses a type system to solve unreliable LLM structured output and unmaintainable prompts.
Read more →
·3 min
Chrome Built-in AI in Action: Six APIs for Building a Smart Blog Editor
Deep dive into Chrome Built-in AI's six core APIs — Summarizer, Prompt API, Translator and more — applied to a real blog editor. Local, private, no server needed.
Read more →
·4 min
DSpark Open Source: DeepSeek's Inference Acceleration Tech That Doubles LLM Speed
DeepSeek and Peking University open-source DSpark, an inference acceleration technology using semi-autoregressive architecture and dynamic scheduling to boost LLM speed by 50%+ and double GPU concurrency without quality loss.
Read more →
·2 min
Training a CPU-Native LLM on Zero Budget: The Architectural Gamble of Ternary Quantization × Sparse MoE
An indie dev attempts to train a CPU-native LLM on $0 budget using ternary quantization, sparsity, and fine-grained MoE — with pre-registered success criteria and full public reporting.
Read more →
·2 min
OpenAI Reportedly Planning Major Price Cuts: A Direct Strike at Anthropic's API Market
OpenAI is reportedly in internal discussions about major API token price cuts targeting Anthropic's user base. We analyze the market logic, developer impact, and where AI infrastructure competition is headed.
Read more →
·2 min
The Reasoning Trap in Diffusion Language Models: A One-Line Fix for the 'Flexibility' Flaw
ICML Outstanding Paper 'The Flexibility Trap' reveals how dLLMs' arbitrary-order generation bypasses logical connectives, breaking reasoning. JustGRPO fixes this with autoregressive training + parallel inference, achieving 89.1% on GSM8K in under 60 lines of code.
Read more →
How DSLs Make LLM Outputs More Reliabl…
·3 min
How DSLs Make LLM Outputs More Reliable: Principles and Practice
LLM output instability is a core production challenge. This article analyzes how DSLs improve LLM reliability through verifiability, semantic convergence, and structural constraints.
Read more →
·3 min
LongCat 2.0: Meituan Trains a 1.6 Trillion Parameter LLM on Domestic Chips
Meituan open-sources LongCat 2.0, a 1.6T-parameter MoE model trained on 50,000+ custom chips without NVIDIA GPUs or Google TPUs, rivaling OpenAI and Google.
Read more →
·2 min
ExLlamaV3 v1.0.0 Official Release: Faster Inference, Lossless KV Quantization, and Streamlined Dependencies
ExLlamaV3 v1.0.0 releases with lossless KV cache quantization via kernel fusion, removal of flash-attention-2/xformers, major GEMM/GEMV gains, and broader tensor parallelism support.
Read more →
·4 min
Six Layers of Defense: Engineering Reliable Structured Output from LLMs
LLM JSON output unstable in your Agent? This guide covers 6 engineering layers: constrained decoding, validation retry, fake tool calls, Logit Masking, Schema contracts, and anti-pattern locking.
Read more →
·3 min
iOS 27 In-Depth Review: Five Major New Features Tested and Analyzed
iOS 27 deep dive: AI photo Extend & Spatial Reframe, a rebuilt Siri with personal data access, 30%+ system-wide speed gains, and long-overdue quality-of-life fixes — all tested and explained.
Read more →
·4 min
Two Paths to Better AI Reasoning: Test-Time Scaling and Formal Verification
This article synthesizes two MSR India Summit talks, exploring two key paths to better AI reasoning: test-time scaling with variable granularity search, and a formal verification framework for trustworthy agent execution.
Read more →
Hardware-Software Co-Design: A Guide t…
·2 min
Hardware-Software Co-Design: A Guide to Building Hardware-Friendly LLM Architectures
Explore AI Model Co-Design principles and how hardware-friendly LLM architecture design — covering MoE, GQA, and FP8 quantization — optimizes the accuracy, throughput, and latency trade-off.
Read more →
Latent Reasoning: The Next-Generation …
·3 min
Latent Reasoning: The Next-Generation AI Reasoning Paradigm Beyond Chain of Thought
Is CoT really AI 'thinking'? This deep dive covers latent reasoning's rise — Coconut, HRM, BDH — and the core trade-offs between interpretability, efficiency, and governance in high-stakes AI.
Read more →
Structured Information Extraction with…
·3 min
Structured Information Extraction with Local 7B Quantized Models: Root Cause Analysis and Engineering Optimization
Using Qwen 2.5 7B quantized locally to extract 60+ fields from insurance/financial contracts? Learn why it struggles and how task splitting, RAG, GBNF, and smarter chunking can fix it.
Read more →
·2 min
15-Year-Old Trains 200M MoE Language Model from Scratch: Full Architecture & Hands-On Breakdown
A 15-year-old trained Tiny-MoE, a 200M-parameter MoE language model from scratch using free Kaggle GPUs, featuring MLA attention, RoPE+YaRN, and native PyTorch.
Read more →
·3 min
The Rise of Diffusion Language Models: DiffusionGemma Crushes Autoregressive Models in Speed Tests
Diffusion language model DiffusionGemma dramatically outpaces autoregressive Deepseek Flash in speed tests. Explore the tech behind diffusion vs. autoregressive models and their challenges.
Read more →