#LLM

OpenAI's latest research reveals LLMs can confess errors, enhancing AI trustworthiness.

Matrix enhances synthetic data generation efficiency by leveraging decentralized control, improving token throughput significantly.

Create an AI framework that analyzes literature, generates hypotheses, plans experiments, simulates results, and reports findings.

C2S-Scale 27B turns scRNA-seq profiles into rank-ordered 'cell sentences' so LLMs can analyze cell states. The model predicted and bench-validated that CK2 inhibition combined with low-dose interferon increases MHC-I antigen presentation by about 50% in vitro.

'Google released an experimental Python MCP server that exposes read-only Google Ads API tools (search via GAQL and list_accessible_customers) for LLM agents to query campaign data without custom SDKs.'

IBM released Granite 4.0, a hybrid Mamba-2/Transformer LLM family that cuts serving memory by over 70% for long-context inference while keeping strong instruction-following and tool-use performance.

'Learn how asyncio lets you run LLM API calls concurrently to cut waiting times and improve AI app performance in real scenarios.'

'Google's RLM treats regression as language modeling, letting compact LLMs predict cluster performance directly from serialized logs and configs with high accuracy and uncertainty estimates.'

'Learn how JSON prompting turns vague instructions into precise, machine-readable requests for LLMs, with Python examples comparing free-form and JSON outputs to show the gains in consistency and integration.'

'For banks and insurers in 2025, prefer SLMs for latency-sensitive extraction and internal workflows and reserve LLMs for long-context synthesis and complex multi-step reasoning; governance and NIST-aligned controls are mandatory.'

'Hugging Face released AI Sheets, a free open-source no-code spreadsheet that integrates with open-source LLMs for building, cleaning, and enriching datasets, available in-browser or for local deployment.'

'Mixture-of-Agents (MoA) arranges specialized LLM agents in layered pipelines to produce more accurate and interpretable results on multi-step tasks, outperforming single monolithic models on benchmarks.'

Anthropic AI proposes a novel method using persona vectors to detect and control personality shifts in large language models, enhancing their reliability and safety.

Google AI introduces LangExtract, a powerful open-source Python library that extracts structured and traceable data from unstructured text using LLMs like Gemini.

ByteDance introduces Seed-Prover, a novel lemma-centric system that achieves breakthrough results in automated mathematical theorem proving, solving 5 out of 6 IMO 2025 problems and excelling across multiple benchmarks.

Discover how context engineering advances large language models beyond prompt engineering with innovative techniques, system architectures, and future research directions.

Anthropic's new research reveals that activating 'evil' behavior patterns during training can prevent large language models from adopting harmful traits, improving safety without compromising performance.

TransEvalnia leverages prompting-based reasoning with large language models to provide detailed, human-aligned translation evaluations, outperforming traditional metrics on multiple language pairs.

This tutorial walks through building a modular text analysis pipeline with LangGraph, incorporating classification, entity extraction, summarization, sentiment analysis, and advanced conditional flow control.

A new study reveals that longer reasoning in large language models can degrade performance by causing distraction, overfitting, and alignment issues, challenging the idea that more computation always leads to better results.

This tutorial demonstrates building a medical knowledge graph from unstructured patient logs using GPT-4o-mini and Python, enabling efficient extraction and visualization of medical insights.

TikTok researchers have launched SWE-Perf, the pioneering benchmark designed to assess LLMs' ability to optimize code performance across entire repositories, revealing current AI limitations compared to human experts.

Master-RM is a new reward model designed to fix vulnerabilities in LLM-based evaluators by reducing false positives caused by superficial cues, ensuring more reliable reinforcement learning outcomes.

MemAgent introduces a reinforcement learning-based memory agent that allows large language models to process ultra-long documents efficiently, maintaining high accuracy with linear computational costs.

FlexOlmo introduces a modular framework that allows training large language models on private datasets without data sharing, achieving strong performance while respecting data governance and privacy constraints.

EG-CFG introduces real-time execution feedback into code generation, significantly improving performance on major benchmarks and surpassing leading models like GPT-4.

NVIDIA's Canary-Qwen-2.5B model sets a new benchmark in speech recognition with a record low Word Error Rate and fast processing speed. This open-source, commercially licensed hybrid ASR-LLM model enables advanced audio transcription and language understanding.

Discover how to leverage Mirascope and OpenAI's GPT-4o model to identify and remove semantically duplicate customer reviews, enhancing feedback clarity.

ByteDance has released Trae Agent, an AI-powered software engineering assistant leveraging large language models to simplify complex coding tasks through a natural language CLI interface.

Context engineering enhances AI performance by optimizing the input data fed to large language models, enabling more accurate and context-aware outputs across various applications.

AbstRaL uses reinforcement learning to teach LLMs abstract reasoning, significantly improving their robustness and accuracy on varied GSM8K math problems compared to traditional methods.

Thought Anchors is a new framework that improves understanding of reasoning processes in large language models by analyzing sentence-level contributions and causal impacts.

DeepSeek-TNG introduces R1T2 Chimera, a new Assembly-of-Experts LLM that delivers twice the speed of R1-0528 and improved reasoning, available now under MIT license.

Baidu releases ERNIE 4.5, a series of open-source large language models scaling from 0.3 billion to 424 billion parameters, featuring advanced architectures and strong multilingual capabilities.

A new study reveals that large reasoning models, while powerful, expose sensitive information through their reasoning traces, highlighting significant privacy risks in AI personal assistants.

ByteDance researchers introduce ProtoReasoning, a new framework leveraging logic-based prototypes to significantly improve reasoning and planning abilities in large language models across various domains.

VERINA introduces a holistic benchmark for evaluating LLMs on verifiable code generation, combining code, formal specifications, and proofs across diverse difficulty levels.

New research from Apple reveals why Large Language Models tend to overthink simple puzzles but struggle and give up on complex ones, highlighting challenges in AI reasoning capabilities.

Mistral AI introduces the Magistral series, a new generation of large language models optimized for reasoning and multilingual support, available in both open-source and enterprise versions.

Meta introduces Llama Prompt Ops, a Python package that automates the conversion and optimization of prompts for Llama models, easing transition from proprietary LLMs and improving prompt performance.

Apple and Duke researchers introduce Interleaved Reasoning, a reinforcement learning method that allows LLMs to produce intermediate answers, significantly boosting response speed and accuracy in complex tasks.

Meta introduces KernelLLM, an 8-billion-parameter model that automates converting PyTorch modules into efficient Triton GPU kernels, outperforming larger models in kernel generation benchmarks.

Salesforce Research introduces UAEval4RAG, a new benchmark framework that evaluates RAG systems' ability to reject unanswerable queries across diverse categories, enhancing the reliability of AI responses.

DeepSeek-V3 introduces innovative architecture and hardware co-design strategies that drastically improve efficiency and scalability in large language models, making high-performance AI more accessible.

JetBrains has open-sourced Mellum, a 4-billion-parameter language model specialized for programming tasks, aiming to improve AI-assisted software development.

Researchers have introduced SICA, a novel coding agent capable of iteratively improving its own code and performance, demonstrating significant gains on software engineering benchmarks.

OpenPipe’s ART·E uses reinforcement learning to deliver faster, cheaper, and more accurate email question-answering, outperforming OpenAI’s o3 agent in key metrics.

Alibaba's Qwen3 introduces a new generation of large language models that excel in hybrid reasoning, multilingual understanding, and efficient scalability, setting new standards in AI performance.

Discover a practical tutorial on implementing the Model Context Protocol to manage context effectively for large language models using semantic chunking and dynamic token management.

ByteDance unveils QuaDMix, a unified framework that enhances large language model pretraining by jointly optimizing data quality and diversity, leading to significant performance gains.

Google DeepMind introduces QuestBench, a benchmark designed to evaluate how well large language models identify missing information in complex reasoning tasks and generate necessary clarifying questions.

Researchers from Tsinghua University and Shanghai AI Lab introduce TTRL, a novel method allowing large language models to improve their performance without labeled data by leveraging self-generated pseudo-rewards during inference.