Michael Alexander Riegler’s Post

🚀 What if you could prove your work is correct... without showing how you did it? That's the magic of Zero-Knowledge Proofs (ZKPs) 🧙♂️🔒 Imagine solving a Rubik's Cube 🧩 behind your back. Instead of showing the solved cube (which gives away your method), you simply reveal the corner colors. The verifier checks them and confirms ✅ that you solved it; yet your secret technique remains safe. Why does this matter? 🤔 When companies train AI models or process sensitive data in the cloud, they face a serious dilemma: How do you prove results are correct without exposing confidential data or proprietary methods? That's where ZKPs shine — giving mathematical certainty ("I did this correctly") without the risk of leaks or reverse engineering. No secrets spilled, no data exposed. 🧩✨ 🎉 Thrilled to announce our paper 📘 zkMaP: Zero-Knowledge Succinct Non-Interactive Matrix Multiplication Proofs is now published in the journal IACR Communications in Cryptology (CiC), co-authored with Prof. M. Anwar Hasan, University of Waterloo, Canada! This work tackles a foundational computing task: matrix multiplication, the mathematical engine behind AI, recommendation systems, graphics, and scientific simulations. ⚙️ 💡 Why zkMaP rocks: ⚡ 16× faster proof generation: Generate a proof for multiplying two 1024×1024 matrices in ~12 seconds. 📏 Tiny: 320-byte proofs size (96% smaller than prior work) ⏱️ Instant verification: 3.6 ms, verification time remains constant regardless of matrix size 🧮 Batch-friendly: 1,024 proofs using just 24 MB memory 🔐 Built on KZG commitments — strong, trusted crypto In short: fast ✅ secure ✅ scalable ✅ verification for massive computations. 🚀 🔮 Next up: Verifiable machine learning, audit AI models without seeing data or model parameters. Privacy + accountability for the AI era. 🤖✅☁️ 📄 Read the full paper: https://lnkd.in/gUJ4XEAb #ZeroKnowledgeProofs #Cryptography #AI #MachineLearning #zkSNARKs #PrivacyTech #zkMaP #IACR #VerifiableCompute

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝗔𝗚 𝗱𝗲𝗺𝗼 𝗶𝘀 𝗲𝗮𝘀𝘆. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗻𝗲 𝘁𝗵𝗮𝘁'𝘀 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗵𝗮𝗿𝗱. The truth is, most Retrieval-Augmented Generation systems are just brittle prototypes. They hallucinate, miss context, and fail when they meet real-world questions. The difference between a demo and a production-grade system isn't a magic fix—it's a leap in engineering discipline. So, I put together a complete blueprint for building high-performing RAG systems that you can actually trust. Inside, we break down: 🔹 The shift from naive chunking to high-fidelity knowledge bases. 🔹 Why Hybrid Search is non-negotiable for accuracy. 🔹 The two-stage search pattern using Rerankers to find the true signal. 🔹 Prompting techniques that enforce reliability and citations. This is the guide I wish I had when I started. Hope it helps you build better, more reliable AI. Full blueprint in the post here: https://lnkd.in/gpAHRNGX #AI #MachineLearning #RAG #LLM #GenAI #SoftwareArchitecture #Engineering #DataScience

Your RAG system is slow because it's doing unnecessary work. REFRAG just proved that 99% of cross-passage attention is wasted computation. As context windows get longer, time-to-first-token latency explodes quadratically. 𝗥𝗘𝗙𝗥𝗔𝗚 is a newly released framework that achieves 30.85× 𝗧𝗧𝗙𝗧 (Time To First Token) acceleration without losing accuracy - meaning we’re getting faster generations from our LLMs, without having to sacrifice context size or output quality. Instead of feeding raw tokens from retrieved passages into the generative LLM, REFRAG: • Chunks context into fixed-size pieces • Uses a lightweight encoder (like RoBERTa) that’s trained specifically for this task to create compressed chunk embeddings • Feeds these embeddings directly to the decoder alongside the query tokens • Adds an RL policy to selectively expand only the most important chunks With REFRAG, attention now scales with number of chunks rather than number of tokens. So a compression rate of 16 gives a 16.53× speedup while actually improving performance by 9.3% over previous methods. The compressed representations are also precomputable and reusable across queries, which means you could store them in a vector db so you don’t have to compute them at every query. This "compress anywhere" capability means it would be great for multi-turn conversations and agentic applications, so I can see frameworks like DSPy making this available at some point for users. TLDR: Because REFRAG allows more context to be injected in the prompt within the same computational budget, it consistently outperforms other LLMs in RAG use cases. IMHO, this highlights something super important - specialized techniques for RAG can actually outperform generic long-context optimizations. The attention sparsity in retrieved passages is a feature, not a bug, and REFRAG is the first to really exploit it. This has huge potential to become another tool to add to our context engineering systems. Paper: arxiv.org/abs/2509.01092

BM-25 is a popular retrieval algorithm in RAG, but I will talk about that in a future post. First let’s decode some exciting document processing ideas! It all starts off with one-hot encoding idea, to convert categorical features to a numerical scale for computing weighted combinations of inputs. Documents have variable length. Words are categorical variables with many values(=vocabulary size) Out of vocabulary words? All these causes a new invention. The “bag of words” model or “vector space” model. But this idea, places emphasis on “counts” which have exacerbating aspects. Does too much count always mean too much importance? Answer is No. So we need to log the effect(diminish) of term frequencies. Also terms that are specific to a document should have higher emphasis(hence inverse document frequency). But, we do not capture the “semantics”? A man and a banana are semantically unrelated, compared to a man and a woman. So, we learn a word embedding matrix, that captures these semantics. The resultant dense representation can better aid in feeding the resultant embedding to a logistic regression classifier! For OOV(Out of Vocabulary words) we come up with all the fancy tokenization ideas like Byte-Pair Encoding and many other new encodings.

OpenTSLM is probably the most interesting take I’ve read on applying LLMs to time series data. You MUST read it. The authors propose two novel architectures that blend text prompts with time series data to produce Chain-of-Thought (CoT) classification capabilities: • OpenTSLM-SoftPrompt models time series implicitly by concatenating learnable time-series tokens with text tokens via soft prompting. • OpenTSLM-Flamingo integrates time series and text through cross-attention, applying a Flamingo-style multimodal fusion to temporal data. Results show that small LLM backbones (Gemma 270M, Llama 3.2B) achieve comparable or even superior results to models four orders of magnitude larger (eg gpt-4o). This paper is not only solid technical work but also an example of how to write a paper. In an era of epsilon-loss improvements getting top conference slots, it’s refreshing to see genuinely novel work presented so clearly. Link below.

��� Context Compression: Rethinking Attention at Scale 👉Everyone’s racing to extend context windows — 128K, 1M, even “infinite.” But scaling context is like scaling storage: capacity means nothing without structure. ✅The real breakthrough isn’t longer context — it’s smarter context. ⚙️ State of the Art Modern frontier models don’t read everything equally; they prioritize: 1️⃣ Retrieval-Adaptive Attention (RAA): adjusts attention weights dynamically using relevance entropy — letting the model “zoom in” on salient spans and ignore filler tokens. 2️⃣ Context Distillation: compresses long documents into dense, task-specific “summary embeddings” that preserve semantics, not syntax. 3️⃣ Hierarchical Memory: uses multi-scale attention — local layers handle details, higher layers retain long-range concepts. 4️⃣ Neural Caches: trainable memory banks that store persistent embeddings across sessions, enabling recall without re-reading. ✅Together, these transform token processing from O(n²) brute force to near-logarithmic efficiency. 🚀 Why It Matters We’re entering the era of context intelligence, not just context length. Models will soon decide what to remember, compress, or forget — the same way humans manage cognitive load. The payoff is tangible: 🕒 Lower latency & inference cost 📈 Long-horizon reasoning without drift 🔁 Real-time adaptability to retrieval updates By 2026, success won’t be “1M-token context.” It’ll be achieving Gemini-class coherence with a fraction of the tokens. 💡 My Take Context compression is becoming the new scaling law. Architecture defines capacity. Compression defines intelligence. 🎯 #GenAI #LLM #AttentionMechanisms #ContextCompression #AIResearch #Efficiency

The pre-training step of an LLM involves several important steps. This includes collecting and preparing the training data by filtering out unwanted content, designing a neural network architecture that yields desirable properties and performance. This requires several iterations and tweaking of hyperparameters and measuring the loss function to ensure that the trained model performs well on real data. https://lnkd.in/ddqtwY6H

Large Language Models (LLMs) are built on the Transformer architecture, but not all LLMs are the same. At a high level: 🔹 LLM Basics : 👉 LLMs are probabilistic models of text they predict the next token based on context. 👉 They can be steered through prompting (no retraining) or training methods (fine-tuning, RLHF, domain-specific optimization). 🔹 Model Architectures: 👉 Encoders → Best for embeddings & representation (BERT, RoBERTa). 👉 Decoders → Powerhouses for text generation (GPT-4, LLaMA, BLOOM). 👉 Encoder-Decoders → Hybrid balance of both (T5, FLAN, BART). #AI #LLM #MachineLearning #DataScience #GenerativeAI #PromptEngineering

Large Language Models (LLMs) have shown impressive capabilities in solving mathematical problems using Chain-of-Thought (CoT) prompts. But here's the question: do these successes come from genuine reasoning, or are they just leveraging search and rote processes? A recent study introduces an innovative framework that models CoT as a rule-based stochastic process over directed acyclic graphs (DAGs). This approach not only represents the intermediate states and rules of reasoning but also introduces a new metric—logical closeness—to evaluate how closely a model's output follows the expected structure of the DAG. By doing so, researchers have developed the DAG-MATH CoT format and a corresponding benchmark, revealing significant differences in reasoning fidelity among various LLM families. This has profound implications, showing that final-answer accuracy doesn't always reflect true reasoning quality. Such advancements bridge the gap between free-form reasoning and formal proof systems, paving the way for more rigorous evaluations of LLM capabilities. Read more at: https://lnkd.in/eHcUXQqd #ArtificialIntelligence #MachineLearning #NaturalLanguageProcessing #Research #DataScience

RAG that survives production: 8 takeaways from a 2025 systematic review A new systematic review of Retrieval-Augmented Generation maps what actually moves the needle in real systems. Here are some key upgrades worth considering: 1. Hybrid retrieval as default. Combine sparse (BM25) + dense to improve first-pass coverage before reranking. The review flags hybrid retrieval as a key emerging solution for reliability. 2. Rerank + iterate. Use a cross-encoder reranker and, for hard queries, iterative retrieval with reasoning-led sub-queries (multi-hop / CoT) to pull the right evidence, not just the nearest neighbor. 3. Latency budgets by design. Back your vector store with HNSW for fast ANN, add result + embedding caches (e.g., “RAGCache/Proximity cache” patterns), and use adaptive retrieval so easy queries stay cheap. 4. Tighter generator–retriever coupling. If you can, train end-to-end (shared signals). If you can’t, keep a black-box LLM but integrate retrieval via prompts—and consider RETRO-style cross-attention for deeper fusion. 5. Hallucination control is a loop, not a metric. Penalize ungrounded claims, add Self-RAG (self-critique + revise against retrieved docs), and require citations in outputs. 6. Eval what users feel. Track retrieval accuracy and generation fluency, plus end-to-end latency + efficiency. Benchmarking all three levels is table stakes for enterprise deployments. 7. Privacy + governance first. Treat private corpora, provenance, and security as first-class constraints; the paper highlights these as persistent challenges in enterprise RAG. 8. Agentic RAG is coming. Expect loops that reformulate queries, verify, and curate context—pushing RAG beyond single-shot retrieval toward systems that continuously improve. Source: “A Systematic Review of Key RAG Systems: Progress, Gaps, and Future Directions” (Jul 2025) — arXiv:2507.18910. #RAG #LLM #MLOps #EnterpriseAI #InformationRetrieval #AIEngineering