Reference entries for recurring ideas, methods, and techniques.
›Masked Visual Actions (MVA)MVA is a pixel-space control interface that represents actions as partially revealed spatiotemporal patterns (binary masks over video). A single pre-trained video model finetuned with LoRA can perform both **forward dynamics** (simulate scene changes from robot motion) and **inverse dynamics** (recover robot motion from desired object motion) by varying which entities are masked vs. revealed. The model generalizes zero-shot to unseen robot embodiments and objects.Jul 28, 2026
›AI Competition PolicyPolicy framework addressing market concentration risks in the AI stack. Argues that open-weight models are essential competitive infrastructure — preventing monopolistic control of foundation models, enabling competition at every layer (cloud, chips, applications, services), and distributing economic benefits broadly rather than concentrating them in a few closed-model providers.Jul 24, 2026
›AI Distillation PolicyPolicy framework distinguishing legitimate knowledge distillation (standard ML technique for model compression, transfer, evaluation) from unauthorized extraction of closed model capabilities. Argues distillation reflects the open innovation tradition and should not be conflated with IP theft; calls for targeted legal/commercial frameworks for actual extraction.Jul 24, 2026
›AI Safety via Open WeightsThe argument that transparency and distributed access to model weights **improve** AI safety and security — contrary to the "security through obscurity" intuition. Open weights enable broad red teaming, mechanistic interpretability, vulnerability discovery, defensive AI development, and resilient pluralistic ecosystems. The coalition frames this as: *openness may be one of the most important paths to AI safety and security.*Jul 24, 2026
›American AI LeadershipPolicy framework asserting that U.S. AI leadership depends on **diffusing AI into every sector** via an open ecosystem — not on any single frontier model. Argues for compute access expansion, shared training infrastructure, pluralistic frontier, and sovereign deployment capability as the pillars of sustained American competitiveness.Jul 24, 2026
›Open Source AI EcosystemThe interconnected infrastructure, communities, governance models, and shared assets that enable open-weight AI models to be discovered, evaluated, adapted, deployed, and improved collaboratively. Extends the open source software model to AI: not just code, but weights, data, compute, and evaluation as shared commons.Jul 24, 2026
›Open Weights PolicyOperational policy framework for open-weight AI models — defining what "open weights" means in regulatory contexts, distinguishing from open-source AI, establishing release/licensing/eval norms, and mapping policy levers (compute, procurement, export controls, liability) to desired outcomes (diffusion, competition, security, sovereignty).Jul 24, 2026
›Attention Residuals (AttnRes)Attention Residuals (AttnRes) is a depth-scaling mechanism in Kimi K3 that selectively retrieves representations from specific layers across the network depth, rather than uniformly accumulating residuals through all layers. This enables effective training and inference at 100+ layer depths.Jul 19, 2026
›Embedding RMSNormAn additional RMSNorm layer applied immediately after the token embedding lookup, before the first transformer block — distinct from the standard pre-attention RMSNorm in each block. Used in Inkling to stabilize early-layer training dynamics at 975B scale.Jul 19, 2026
›Gated Multi-head Latent Attention (Gated MLA)Gated MLA extends Multi-head Latent Attention (MLA) by adding per-head gating mechanisms that improve attention selectivity — controlling which heads participate in which expert routing decisions. Combined with SiTU activation, it enables stable attention-expert coordination at 2.8T scale.Jul 19, 2026
›Inkling ArchitectureThinking Machines Lab's 975B parameter open-weight multimodal MoE with unique architectural choices: short convolutions for local inductive bias, embedding RMSNorm, learned relative-position bias instead of RoPE, and hybrid local/global attention (55 local/11 global layers). Less sparse than Kimi K2.5 (4.2% vs 3.2% activation).Jul 19, 2026
›Joint Embedding Predictive Architecture (JEPA)JEPA is a self-supervised learning paradigm (proposed by Yann LeCun) where models predict latent representations of masked/future inputs rather than reconstructing raw pixels or contrasting views. It avoids the "pixel-level detail" burden of generative models and the "negative sampling" complexity of contrastive learning — focusing representation capacity on semantically relevant, predictable features.Jul 19, 2026
›Kimi Delta Attention (KDA)Kimi Delta Attention (KDA) is a hybrid linear attention mechanism that provides an efficient foundation for scaling attention to 1M+ context windows and 3T+ parameter models. Combined with Attention Residuals (AttnRes), it forms the architectural backbone of Kimi K3. KDA introduces novel challenges for KV caching, addressed via a vLLM contribution for KDA-aware prefix caching.Jul 19, 2026
›Learned Relative-Position BiasAn input-dependent, learned relative position bias replacing RoPE in Inkling. Applied within local attention windows (512 tokens) and limited to 1024 tokens in global layers. Claimed to perform better and extrapolate better to longer sequences than RoPE.Jul 19, 2026
›NoPE (No Positional Embeddings) AttentionNoPE refers to using content-only attention (no positional encoding) in selected layers — typically global attention layers where semantic similarity matters more than position. In Inkling, global layers apply learned relative bias only to the preceding 1024 tokens; beyond that, attention becomes effectively NoPE (content-only).Jul 19, 2026
›Per-Head MuonPer-Head Muon extends the Muon optimizer (momentum + orthogonalization preconditioning) by maintaining separate optimizer state per attention head, enabling adaptive learning rates and preconditioning tailored to each head's curvature at extreme scale.Jul 19, 2026
›Quantile BalancingQuantile Balancing is an auxiliary-loss-free, hyperparameter-free MoE load balancing technique that derives expert allocation directly from router score quantiles — replacing heuristic auxiliary losses with a direct statistical constraint satisfied by construction.Jul 19, 2026
›Short Convolutions in TransformersLightweight depthwise convolutions (kernel-4) inserted at key points in transformer blocks — after K/V projections and on attention/MLP outputs — to provide explicit local token mixing and short-range inductive bias complementary to global attention. Used in Inkling (975B MoE) as architectural "surprises" beyond standard MoE recipe.Jul 19, 2026
›SIGReg (Sketched-Isotropic-Gaussian Regularizer)SIGReg is a single-scalar regularization loss that enforces latent embeddings to follow an isotropic Gaussian distribution at each timestep. It uses random projections (sketching) combined with the Epps-Pulley statistic (characteristic function distance) — replacing PLDM's multiple regularization terms (variance, covariance, temporal smoothness) with one robust, hyperparameter-light objective.Jul 19, 2026
›SiTU ActivationSiTU (Sigmoid Tanh Unit) is a gated activation function designed for MoE expert FFNs at extreme sparsity. It combines sigmoid gating with tanh nonlinearity to provide better activation control, mitigating dead-expert and gradient saturation issues in Stable LatentMoE at 2.8T scale.Jul 19, 2026
›Stable LatentMoEStable LatentMoE is an extreme-sparsity Mixture-of-Experts architecture (16/896 = 1.78% activation) used in Kimi K3. It combines LatentMoE's compressed expert representations with Quantile Balancing for stable routing, Per-Head Muon for adaptive optimization, SiTU activation for expert FFN control, and Gated MLA for attention-expert alignment — enabling stable training at 2.8T parameters.Jul 19, 2026
›Agentic WorkflowsAgentic workflows are **structured multi-agent orchestrations** where multiple autonomous agents collaborate to solve complex tasks. They go beyond single-agent loops by composing specialized agents (planner, coder, reviewer, researcher) with explicit coordination patterns: sequential pipelines, parallel swarms, hierarchical delegation, and self-correction loops.Jul 16, 2026
›Autonomous AI AgentsAutonomous AI agents are systems that execute multi-step workflows by iterating through a structured loop of reasoning, tool use, and result evaluation until a defined objective is met. They differ from single-turn LLMs by maintaining persistent state, managing context windows, and operating within safety budgets (turn limits, cost caps, permission modes).Jul 16, 2026
›Consistency ModelsConsistency Models (Song et al., ICLR 2024) enable **one-step or few-step generation** by enforcing self-consistency on the probability flow ODE trajectory. Can be trained from scratch (isolation) or distilled from pre-trained diffusion models. Achieves similar quality to diffusion with **10-50× faster sampling**.Jul 16, 2026
›Content Strategy for PhD ResearcherContent strategy tailored for a **PhD-seeking AI researcher** (MS→Direct PhD Fall 2027, full funding). Goal: demonstrate research depth, technical competence, and communication ability to target advisors and admissions committees. Strategy: publish long-form technical deep-dives on owned domain/Substack, distribute condensed versions to LinkedIn/X, cross-link everything to wiki knowledge base.Jul 16, 2026
›Deep Equilibrium Models (DEQ)Deep Equilibrium Models (DEQ; Bai et al., 2019) reframe deep networks as **fixed-point problems** `z^* = f_theta(z^*, x)` instead of fixed-depth unrolling. This enables **infinite effective depth**, **constant memory training** (via implicit differentiation), and **adaptive compute** at test time.Jul 16, 2026
›Diffusion ModelsDiffusion models are generative models that learn to reverse a noise process — gradually denoising from pure noise to data. They achieve state-of-the-art generation quality across images, video, audio, and robot actions, and form the generative backbone of modern world models.Jul 16, 2026
›Diffusion PolicyDiffusion Policy (Chi et al., RSS 2024) introduces **diffusion models as robot policies** — learning action distributions via denoising conditioned on observations. Achieves **98% success** on contact-rich manipulation with 10-50 demonstrations, outperforming VAE/GMM policies. Uses **DiT backbone** (~100M params) with action chunking.Jul 16, 2026
›DiT (Diffusion Transformer)DiT (Diffusion Transformer, Peebles & Xie, 2023) replaces the U-Net backbone in diffusion models with a **Transformer architecture**. This enables better scaling, native conditioning via cross-attention, and SOTA results on ImageNet generation — forming the backbone of Diffusion Policy and modern video diffusion.Jul 16, 2026
›Embodied AIEmbodied AI refers to AI agents that **perceive, reason, and act in the physical world** — robots, autonomous vehicles, manipulators. The 2024–2025 convergence of world models (planning) + diffusion (generation/control) created the first viable architecture for **general-purpose embodied AI**.Jul 16, 2026
›Fixed-Point Recursion / Implicit LayersFixed-point recursion models (Deep Equilibrium Models, HRM, TRM) frame reasoning as **finding a fixed point** `z^* = f_theta(z^*, x)` rather than unrolling a fixed-depth network. This enables **adaptive depth** (inference-time scaling) and **memory-efficient training** via implicit differentiation.Jul 16, 2026
›Genie: Generative Interactive EnvironmentsGenie (Bruce et al., DeepMind, 2024) is an **11B parameter action-controllable world model** trained on **unlabeled Internet video** (no action annotations). Learns **latent actions** from pixels, enabling controllable video generation and zero-shot policy learning. Key breakthrough: **video-only world model learning** without action annotations.Jul 16, 2026
›Inference-Time Scaling (Test-Time Compute)Inference-time scaling (test-time compute) expands compute at **inference** rather than training — allowing models to "think longer" on hard problems. Techniques include chain-of-thought, majority voting, tree search, adaptive computation (ACT), and recursive reasoning (HRM/TRM). This is the core of OpenAI o1-style reasoning.Jul 16, 2026
›Latent Diffusion ModelsLatent Diffusion Models (Rombach et al., 2022 / Stable Diffusion) run diffusion in **compressed latent space** of a pretrained autoencoder (4-8× spatial compression), reducing compute 3-4× vs pixel-space diffusion while maintaining quality.Jul 16, 2026
›LinkedIn Distribution FormatLinkedIn is the **professional distribution layer** in the cross-posting hierarchy. Posts are condensed versions of blog posts (900–1,500 chars optimal), formatted for scanning with hooks, bold claims, and clear CTAs. Algorithm favors Saves > Comments > Likes; Carousels > Images > Text.Jul 16, 2026
›Mamba / State Space Models (SSM)Mamba (Gu & Dao, 2024) is a **Selective State Space Model** that achieves **Transformer-quality** with **linear scaling** in sequence length. It uses **input-dependent dynamics** (selection mechanism) to filter irrelevant information — enabling **million-token context** and efficient **long-horizon latent dynamics** for planning.Jul 16, 2026
›Model-Based Reinforcement LearningModel-Based Reinforcement Learning (MBRL) learns a **model of environment dynamics** (world model) and uses it for **planning** or **policy optimization**, achieving higher sample efficiency than model-free RL. Modern MBRL (DreamerV3, TD-MPC) operates in **latent space** for scalability.Jul 16, 2026
›Model-Free Reinforcement LearningModel-Free RL learns a policy or value function directly from environment interactions **without learning a dynamics model**. Classic algorithms: PPO (on-policy), SAC (off-policy). Higher asymptotic performance but lower sample efficiency than modern MBRL.Jul 16, 2026
›Personal Branding for PhD ResearcherPersonal branding strategy for **PhD-seeking AI researcher** (MS→Direct PhD Fall 2027, full funding). Goal: position as **credible, communicative, technically deep** candidate for top advisors. Strategy: publish technical deep-dives, show reproducible work, engage with target lab researchers, maintain canonical portfolio.Jul 16, 2026
›Planning in Latent SpacePlanning in latent space means performing MPC/zero-shot planning in the **compressed latent space** of a world model, rather than pixel space. This enables long-horizon planning (10-50+ steps) at low compute by operating on compact representations (e.g., 32×32×32 vs 512×512×3).Jul 16, 2026
›Reward ModelingReward modeling learns a scalar reward function `r(x, y)` from human preference data (comparisons, rankings, scores). It is the core component of RLHF — converting human feedback into a differentiable signal for policy optimization. Modern approaches include explicit reward models (Bradley-Terry), implicit rewards (DPO), and multi-objective / constitutional reward models.Jul 16, 2026
›RLHF & Reinforcement Learning for LLM AlignmentRLHF (Reinforcement Learning from Human Feedback) aligns LLMs with human preferences through a three-stage pipeline: Supervised Fine-Tuning (SFT) → Reward Modeling (RM) → Policy Optimization (PPO/DPO). Modern variants include DPO (direct optimization), KTO (binary feedback), RLAIF (AI feedback), and online/iterative RLHF. This is the dominant paradigm for instruction-following, safety, and capability alignment.Jul 16, 2026
›SEO Basics for Technical ContentSEO basics for technical AI/ML content: canonical URL hierarchy prevents duplicate content, structured data helps rich snippets, internal linking builds topical authority. For PhD portfolio, **expertise demonstration** > traffic volume.Jul 16, 2026
›Substack Publishing WorkflowSubstack is the **canonical long-form platform** in the cross-posting hierarchy. It supports native footnotes, LaTeX math, code blocks, image galleries, audio embed, and paid subscriptions. Publishing workflow: write in wiki → export to Substack with canonical URL → distribute condensed versions to LinkedIn/X.Jul 16, 2026
›TD-MPC (Temporal Difference Model Predictive Control)TD-MPC (Hansen et al., 2022) combines **temporal difference learning** with **Model Predictive Control (MPC)** in **latent space**. It learns an ensemble of latent dynamics models and plans with MPPI (Model Predictive Path Integral) — achieving strong performance on continuous control with per-domain hyperparameter tuning.Jul 16, 2026
›Transformer Scaling LawsScaling laws describe how language model performance (loss, downstream accuracy) varies with model size (N), dataset size (D), and compute (C). The Chinchilla (Hoffmann et al., 2022) result established **compute-optimal scaling**: for a given compute budget, model size and data should scale proportionally. Emergent abilities appear as sharp phase transitions not predicted by smooth loss curves.Jul 16, 2026
›Value-Guided DiffusionValue-guided diffusion uses a **learned value function** to steer the **diffusion denoising process** toward high-reward action sequences. This enables **long-horizon planning** (50+ steps) by combining diffusion's multi-modal generation with value-based optimization — the core of test-time compute scaling for robotics.Jul 16, 2026
›Video GenerationVideo generation models synthesize temporally coherent video sequences from text, images, or actions. The 2024 breakthroughs (Sora, Genie, VideoPoet) achieved realistic motion and long-range consistency by scaling diffusion transformers on massive video datasets. These models are converging with world models for robotics — video generation provides the visual fidelity, while world model structure provides action-conditioning and temporal coherence.Jul 16, 2026
›World ModelsWorld models are learned simulators that predict future states given current observations and actions. The 2024–2025 convergence with diffusion models solved the core tension: world models gained generative fidelity, diffusion gained temporal coherence and action-conditioning — creating the architecture for general-purpose embodied AI.Jul 16, 2026
›X/Twitter Distribution StrategyX/Twitter distribution strategy for technical content: threads (2–3× engagement of single tweets), 1 tweet per blog section + diagrams, canonical link in last tweet/reply. Native media > external links for reach.Jul 16, 2026
›Adaptive Computation Time (ACT)ACT (Graves 2016, adapted in HRM/TRM) lets models **dynamically choose computation depth** per input. A Q-head predicts "halt" vs "continue" values; Q-learning trains it. During training, segments run up to `M_{max}`; at inference, `M_{max}` can be increased for **test-time scaling** without retraining. Sudoku shows strong scaling; ARC minimal.Jul 15, 2026
›Agent SDKThe Agent SDK (Software Development Kit) provides the foundational primitives for building autonomous AI agents. It abstracts the agent loop — perception, reasoning, action, observation — into composable components: Agent, Tool, Memory, and Orchestration. Key implementations include Anthropic's Claude Code Agent SDK and OpenAI's Codex Goals API.Jul 15, 2026
›Attention MechanismAttention computes a weighted sum of values where weights are determined by query-key compatibility. The Transformer uses scaled dot-product attention: softmax(QK^T/√d_k)V, enabling parallelizable, constant-path-length dependency modeling.Jul 15, 2026
›Canonical URL Strategy for Cross-Platform PublishingCanonical URL strategy ensures SEO authority consolidates to your owned domain (or Substack as primary) while distributing content across LinkedIn, X/Twitter, and other platforms. Core rule: **publish on canonical first → cross-post with canonical tags → never noindex the canonical**.Jul 15, 2026
›Context Window ManagementContext window management is the set of techniques for fitting relevant information into an LLM's fixed context window during long-running tasks (agents, RAG, multi-turn dialog). Strategies include sliding windows, recursive summarization, hierarchical memory, and retrieval-augmented generation (RAG). Critical for autonomous agents running 100+ loop iterations.Jul 15, 2026
›CSA + HCA Hybrid Attention (DeepSeek-V4)DeepSeek-V4 introduces a **hybrid attention architecture** combining **Compressed Sparse Attention (CSA)** and **Heavily Compressed Attention (HCA)**. CSA compresses KV caches then applies sparse selection; HCA uses heavier compression with dense attention. Interleaved layers achieve 1M-token context with only 27% FLOPs and 10% KV cache vs DeepSeek-V3.2.Jul 15, 2026
›Deep Supervision (Recursive Models)Deep supervision runs **multiple forward passes (segments)** of a recursive model in sequence. After each segment, the hidden state is **detached** from the computation graph and used to initialize the next segment. Loss is computed at **every segment** (not just the end), providing frequent gradient feedback to higher-level modules and acting as a regularizer. This is the **primary driver of performance** in HRM/TRM (ablation: 19% → 39% on ARC-AGI-1).Jul 15, 2026
›DeepSeek Sparse Attention (DSA)DSA (DeepSeek Sparse Attention) replaces fixed-window sparse attention with a **learned sparse pattern**: a lightning indexer scores prior tokens for each query, and a token selector keeps only top-k key-value entries. Combined with MLA (Multi-head Latent Attention) in DeepSeek-V3.2/V4 and GLM-5, it reduces long-context attention cost without hard-coding locality.Jul 15, 2026
›FlashAttentionFlashAttention (Dao et al., ICML 2022) is an IO-aware exact attention algorithm that avoids materializing the `N^2` attention matrix by tiling computation to fit in GPU SRAM and using recomputation during backward pass. Achieves 2-4× speedup vs standard attention, enables longer sequences (16K-32K) on same memory.Jul 15, 2026
›Hierarchical Reasoning Model (HRM)HRM (Sapient Intelligence, 2025) is a **brain-inspired recurrent architecture** with two coupled modules operating at different timescales: a slow **High-level (H) module** for abstract planning and a fast **Low-level (L) module** for detailed computation. It achieves **deep effective depth** via **hierarchical convergence** without BPTT, using a **1-step gradient approximation** grounded in Deep Equilibrium Models. With only **27M params** and **~1K training samples** (no pre-training, no CoT), HRM reaches **40.3% on ARC-AGI-1**, near-perfect on Sudoku-Extreme and Maze-Hard — surpassing much larger CoT models.Jul 15, 2026
›LoRA & QLoRALoRA (Low-Rank Adaptation) freezes pretrained weights and adds trainable low-rank matrices `A ∈ R^{r × d}`, `B ∈ R^{d × r}` to each layer. QLoRA (Quantized LoRA) combines 4-bit NF4 quantization with LoRA, enabling billion-parameter model fine-tuning on consumer GPUs.Jul 15, 2026
›Manifold-Constrained Hyper-Connections (mHC)mHC strengthens residual connections by expanding the residual stream width and constraining the residual transformation to the **manifold of doubly stochastic matrices** (Birkhoff polytope). This bounds spectral norm ≤ 1, ensuring non-expansive, stable signal propagation across many layers — fixing the instability of standard Hyper-Connections (HC).Jul 15, 2026
›Multi-head Latent Attention (MLA)MLA replaces standard MHA's per-head K,V projections with **low-rank latent projections** shared across heads. Each head attends to a compressed latent KV, reducing KV cache by **8×** (DeepSeek-V3) with <1% perplexity degradation. Forms the attention backbone for DeepSeek-V3/V4.Jul 15, 2026
›Multi-Head AttentionMulti-head attention runs `h` parallel attention computations on linearly projected Q, K, V, then concatenates and projects the outputs. This allows the model to jointly attend to information from different representation subspaces at different positions.Jul 15, 2026
›Muon OptimizerMuon (Momentum Orthogonalization Update) is an optimizer that combines momentum with **Newton-Schulz iteration** to orthogonalize update matrices. Used for most parameters in DeepSeek-V4 training, it achieves faster convergence and improved stability vs AdamW. AdamW retained for embeddings, prediction heads, RMSNorm weights, and mHC static biases/gating factors.Jul 15, 2026
›Positional EncodingSince the Transformer has no recurrence or convolution, it lacks inherent understanding of token order. Positional encodings inject sequence position information into token embeddings. The original paper uses fixed sinusoidal functions, while learned embeddings work equally well.Jul 15, 2026
›Quantization-Aware Training (QAT) & PTQQuantization reduces model precision from FP16/BF16 to INT8/INT4, shrinking memory 2-4× and accelerating inference. QAT simulates quantization during training to preserve accuracy; PTQ quantizes after training with calibration data. QLoRA combines 4-bit PTQ with LoRA fine-tuning.Jul 15, 2026
›Tool Use PatternsTool use patterns are reusable architectures for how LLM agents interact with external functions (tools). Well-designed patterns improve reliability, reduce hallucination, and enable complex multi-step workflows. Core patterns: Sequential, Parallel, Conditional, Recursive, and Human-in-the-loop.Jul 15, 2026
›Transformer ArchitectureThe Transformer (Vaswani et al., 2017) is a sequence transduction model based entirely on attention mechanisms, dispensing with recurrence and convolutions. It uses an encoder-decoder stack with multi-head self-attention, position-wise feed-forward networks, residual connections, layer normalization, and sinusoidal positional encodings, achieving SOTA on translation tasks with significantly less training time.Jul 15, 2026
›Tiny Recursive Model (TRM)TRM (Samsung SAIL Montréal, 2025) simplifies HRM by **removing the two-module hierarchy** and using a **single tiny 2-layer network** (7M params) that recursively updates a latent state and answer. With **deep supervision** (up to 16 segments), it achieves **45% ARC-AGI-1** and **8% ARC-AGI-2** — SOTA for small models, beating HRM (40.3%/5%) and most LLMs, with <0.01% parameters.Jul 15, 2026
›Agentic AI SystemsLLM-driven systems that plan, call tools, and take multi-step action toward a goal with limited supervision.
›Hierarchical ReasoningSolving a problem's coarse structure first, then its details within that structure, rather than at one resolution.
›Latent ReasoningA model performing intermediate reasoning steps in its internal hidden states, rather than in generated text.