Swortal

  What is complexity? property: Complex collective behavior: each typically following relatively simple rules with no central control or leader. It is the collective actions of vast numbers of components that give rise to the complex, hard-to-predict, and changing patterns of behavior Signaling and information processing: Adaptation: All these systems adapt—that is, change their behavior to improve their chances of survival or success—through learning or evolutionary processes an alternative definition of a complex system: a system that exhibits nontrivial emergent and self-organizing behaviors. How to measure complexity effective complexity Gell-Mann proposed that any given entity is composed of a combination of regularity and randomness. To calculate the effective complexity, first one figures out the best description of the regularities of the entity; the effective complexity is defined as the amount of information contained in that description, or equivalent...

  https://iclr.cc/virtual/2025/workshop/24000 highlights world model is a model given states and actions, predict next states world model can be used for simulation and training RL agent short horizon task: combine local reward and global value function diffusion model can be combined with/replace transformer or VAE to speed up inference post training Robotics one model for high level instruction generation and one for low level motion generation use vision language model simulate with world model and pick best trajectory before execute in real environment World model can be pure video frame prediction encode video into latent space and apply diffusion model or transformer model for generation one component of model to infer latent action from video frames could also concatenate video token with action token and make next frame prediction Quality diversity algorithms find a diverse set of high performing agent(policies) and combine them exploration and goal s...

Recently I built an LLM app which summarize news in a period for a user. This app contains following features. RSS Subscription:  Users can subscribe to RSS channels. Daily News Crawl:  Automatically crawls news entries from RSS channels daily. Preference Survey:  LLM surveys users to understand their news preferences. News Summaries: Generates daily or weekly summaries based on user preferences. Users can expand summary entries to fetch and summarize content from reference URLs. Updates user preferences based on click history. Question Answering:  LLM answers user questions based on crawled news (e.g., "What are the latest AI trends?"). My tech stack is Frontend:  NextJs + custom ExpressJs server + Tailwind CSS Avoid server components to simplify client-side state interaction logic. Use server components only when components are mostly independent (e.g., signin/signup page vs news summary app page). Custom ExpressJs server for flexible middle...

 Source:  https://youtu.be/yBL7J0kgldU?si=koiBhKpq3Cp1M8G7 research methodology deconstruct into building blocks, structure, knowledge, reasoning etc.. study in controlled way, idealized environment, control the data, tweak the params highly repeatable experiments 100m size model, universal laws 1xH100 within a day probe inner working knowledge extraction 2 types of data biography of N individuals QA data to extract the fact of the N individuals based on Biography Training data: N biographies, + N/2 QA data Test data: the other N/2 QA data If the model can perform well on the other N/2 individuals’ biography questions, then it has knowledge extraction capability Option 1: Pre train with both N biographies and N/2 QA result: good knowledge extraction Option 2: Pre train with biography data only, fine tune with QA result: bad knowledge extraction Option 3: augment the biography data for each person, pretrain with biography and fine tune with QA ...

  Personal highlights Memory movement is more expensive than computation Network latency is more significant than computation with same memory consumption, we want the network to have as much computation as possible to increase accuracy Common technique: Pruning, Quantization, Distillation different level of grouping and granularity used in pruning, quantization, parallel execution Common evaluation and optimization criteria weight significance, activation significance, tensor wise, channel wise, batch wise … l2 loss, KL divergence, accuracy, latency, number of computation, memory usage Common ideas to optimize a neural network structure using above techniques architecture option as a trainable parameter and additional loss or KD divergence Optimize the architecture params with regular weights either together or freeze one and optimize the other iteratively iteratively prune/ quantize / distill and evaluate after fine tune in each round abrasion study, delete...