Convai Innovations presents Laya, a multilingual, non-autoregressive system 1 decision model designed to provide typed answers with mathematically calibrated probabilities in a single forward pass. Unlike generative models, it does not generate text, thereby eliminating hallucinations and the need for parsing. The framework includes an automated Router that detects language and script to dispatch tasks to the most efficient checkpoint (English or Multilingual) within approximately 35ms on GPU.
- It is trained using Reinforcement Learning with Calibrated Decisions (RLCD) to ensure honest probability reporting.
- Laya can support context lengths of up to 8,192 tokens in its multilingual version.
- The model family includes specialized checkpoints like `laya-typed-decisions` which achieves significantly higher accuracy through fine-tuning on specific workflows.
- Performance benchmarks show it is roughly 6–8× faster than TypeSafe Jev for single question latency on a T4 GPU.
- It is trained using Reinforcement Learning with Calibrated Decisions (RLCD) to ensure honest probability reporting.
- Laya can support context lengths of up to 8,192 tokens in its multilingual version.
- The model family includes specialized checkpoints like `laya-typed-decisions` which achieves significantly higher accuracy through fine-tuning on specific workflows.
- Performance benchmarks show it is roughly 6–8× faster than TypeSafe Jev for single question latency on a T4 GPU.
El Assadi et al. compare ten LLMs (six families) and 26 embedding models (118M - 14B parameters) on 37 tasks, considering cost. In aggregate, the two paradigms are effectively tied (best LLM scores 77.6 versus best embedding model 77.2), yet their strengths diverge by task: LLMs lead on reasoning-heavy retrieval while embedding models lead on classification, and the two match on clustering, STS, and pair classification.
LLMs are significantly more expensive (up to 1,431x) and slower (2.5-736x) than embedding models for certain tasks. The authors suggest using embedding models for similarity, classification, and clustering, and LLMs for reasoning in retrieval.
Reasoning tokens are 28-81% of LLM inference cost; lower budgets maintain or boost retrieval quality for most tested models.
- Only Gemini 3.1 Pro breaks into the Pareto frontier alongside the leading embedding models.
- Accepted to COLM 2026; code, datasets, and results are publicly released on GitHub.
LLMs are significantly more expensive (up to 1,431x) and slower (2.5-736x) than embedding models for certain tasks. The authors suggest using embedding models for similarity, classification, and clustering, and LLMs for reasoning in retrieval.
Reasoning tokens are 28-81% of LLM inference cost; lower budgets maintain or boost retrieval quality for most tested models.
- Only Gemini 3.1 Pro breaks into the Pareto frontier alongside the leading embedding models.
- Accepted to COLM 2026; code, datasets, and results are publicly released on GitHub.
Ashish Vaswani et. al. introduce Transformers and Attention in this classic 2017 paper.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
A post-retrieval temporal layer designed to improve RAG systems by addressing time-blindness in vector searches. This library implements validity filtering, document kind classification, and exponential decay scoring to ensure retrieved information is fresh and accurate. It functions downstream of existing vector search systems without requiring re-indexing or new infrastructure.
This article demonstrates how to perform text summarization using the scikit-llm library, which provides a simple interface for utilizing large language models within a scikit-learn style workflow. The guide walks through installing the necessary dependencies and implementing both extractive and abstractive summarization techniques on sample text data.
Key topics include:
- Introduction to the scikit-llm library
- Implementing abstractive summarization using LLMs
- Using scikit-llm for text classification and clustering tasks
- Practical code examples for integrating LLM capabilities into machine learning pipelines
Key topics include:
- Introduction to the scikit-llm library
- Implementing abstractive summarization using LLMs
- Using scikit-llm for text classification and clustering tasks
- Practical code examples for integrating LLM capabilities into machine learning pipelines
A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required.
Learn how to label text without the need for task-specific training data by using zero-shot text classification. This guide explains how pretrained transformer models, such as BART, reframe classification as a reasoning task where labels are treated as natural language statements.
Key topics include:
* The core concept of zero-shot classification and its advantages for rapid prototyping.
* Using the Hugging Face transformers pipeline with the facebook/bart-large-mnli model.
* Implementing multi-label classification for texts belonging to multiple categories.
* Improving accuracy through custom hypothesis template tuning and clear label wording.
Key topics include:
* The core concept of zero-shot classification and its advantages for rapid prototyping.
* Using the Hugging Face transformers pipeline with the facebook/bart-large-mnli model.
* Implementing multi-label classification for texts belonging to multiple categories.
* Improving accuracy through custom hypothesis template tuning and clear label wording.
This review examines Google’s LangExtract, a library designed to solve the "production nightmare" of inconsistent data extraction from large documents using standard LLM APIs.
* **Source Grounding:** Maps entities back to original text to prevent hallucinations.
* **Smart Chunking:** Splits long text at natural boundaries to preserve context.
* **Parallel Processing:** Uses `max_workers` to reduce latency.
* **Multi-pass Extraction:** Runs multiple cycles and merges results for higher accuracy.
* **Visual Interface:** Provides interactive highlighting of extracted data.
**Result:** The author successfully transformed a messy 15,000-character meeting transcript into clean, structured JSON.
* **Source Grounding:** Maps entities back to original text to prevent hallucinations.
* **Smart Chunking:** Splits long text at natural boundaries to preserve context.
* **Parallel Processing:** Uses `max_workers` to reduce latency.
* **Multi-pass Extraction:** Runs multiple cycles and merges results for higher accuracy.
* **Visual Interface:** Provides interactive highlighting of extracted data.
**Result:** The author successfully transformed a messy 15,000-character meeting transcript into clean, structured JSON.
This is an open, unconventional textbook covering mathematics, computing, and artificial intelligence from foundational principles. It's designed for practitioners seeking a deep understanding, moving beyond exam preparation and focusing on real-world application. The author, drawing from years of experience in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
Large Language Models (LLMs) demonstrate remarkable capabilities, yet their inability to maintain persistent memory in long contexts limits their effectiveness as autonomous agents in long-term interactions. While existing memory systems have made progress, their reliance on arbitrary granularity for defining the basic memory unit and passive, rule-based mechanisms for knowledge extraction limits their capacity for genuine learning and evolution. To address these foundational limitations, we present Nemori, a novel self-organizing memory architecture inspired by human cognitive principles. Nemori's core innovation is twofold: First, its Two-Step Alignment Principle, inspired by Event Segmentation Theory, provides a principled, top-down method for autonomously organizing the raw conversational stream into semantically coherent episodes, solving the critical issue of memory granularity. Second, its Predict-Calibrate Principle, inspired by the Free-energy Principle, enables the agent to proactively learn from prediction gaps, moving beyond pre-defined heuristics to achieve adaptive knowledge evolution. This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks demonstrate that Nemori significantly outperforms prior state-of-the-art systems, with its advantage being particularly pronounced in longer contexts.
