Large Language Model
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LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers...
Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, have introduced LLaMA-Omni2, a family of speech-capable large language models (SpeechLMs) now available...
How AI Agents Store, Forget, and Retrieve? A Fresh Look at...
Memory plays a crucial role in LLM-based AI systems, supporting sustained, coherent interactions over time. While earlier surveys have explored memory about LLMs, they...
RWKV-X Combines Sparse Attention and Recurrent Memory to Enable Efficient 1M-Token...
LLMs built on Transformer architectures face significant scaling challenges due to their quadratic complexity in sequence length when processing long-context inputs. Methods like Linear...
Scaling Reinforcement Learning Beyond Math: Researchers from NVIDIA AI and CMU...
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities across diverse tasks, with Reinforcement Learning (RL) serving as a crucial mechanism for refining their...
Multimodal Queries Require Multimodal RAG: Researchers from KAIST and DeepAuto.ai Propose...
RAG has proven effective in enhancing the factual accuracy of LLMs by grounding their outputs in external, relevant information. However, most existing RAG implementations...
Google Researchers Advance Diagnostic AI: AMIE Now Matches or Outperforms Primary...
LLMs have shown impressive promise in conducting diagnostic conversations, particularly through text-based interactions. However, their evaluation and application have largely ignored the multimodal nature...
IBM AI Releases Granite 4.0 Tiny Preview: A Compact Open-Language Model...
IBM has introduced a preview of Granite 4.0 Tiny, the smallest member of its upcoming Granite 4.0 family of language models. Released under the...
From ELIZA to Conversation Modeling: Evolution of Conversational AI Systems and...
TL;DR: Conversational AI has transformed from ELIZA's simple rule-based systems in the 1960s to today's sophisticated platforms. The journey progressed through scripted bots in...
JetBrains Open Sources Mellum: A Developer-Centric Language Model for Code-Related Tasks
JetBrains has officially open-sourced Mellum, a purpose-built 4-billion-parameter language model tailored for software development tasks. Developed from the ground up, Mellum reflects JetBrains’ engineering-first...
Meta and Booz Allen Deploy Space Llama: Open-Source AI Heads to...
In a significant step toward enabling autonomous AI systems in space, Meta and Booz Allen Hamilton have announced the deployment of Space Llama, a...
Training LLM Agents Just Got More Stable: Researchers Introduce StarPO-S and...
Large language models (LLMs) face significant challenges when trained as autonomous agents in interactive environments. Unlike static tasks, agent settings require sequential decision-making, cross-turn...
DeepSeek-AI Released DeepSeek-Prover-V2: An Open-Source Large Language Model Designed for Formal...
Formal mathematical reasoning has evolved into a specialized subfield of artificial intelligence that requires strict logical consistency. Unlike informal problem solving, which allows for...
Microsoft AI Released Phi-4-Reasoning: A 14B Parameter Open-Weight Reasoning Model that...
Despite notable advancements in large language models (LLMs), effective performance on reasoning-intensive tasks—such as mathematical problem solving, algorithmic planning, or coding—remains constrained by model...
Meta AI Introduces ReasonIR-8B: A Reasoning-Focused Retriever Optimized for Efficiency and...
Addressing the Challenges in Reasoning-Intensive Retrieval
Despite notable progress in retrieval-augmented generation (RAG) systems, retrieving relevant information for complex, multi-step reasoning tasks remains a significant...
Multimodal AI on Developer GPUs: Alibaba Releases Qwen2.5-Omni-3B with 50% Lower...
Multimodal foundation models have shown substantial promise in enabling systems that can reason across text, images, audio, and video. However, the practical deployment of...
Mem0: A Scalable Memory Architecture Enabling Persistent, Structured Recall for Long-Term...
Large language models can generate fluent responses, emulate tone, and even follow complex instructions; however, they struggle to retain information across multiple sessions. This...
Exploring the Sparse Frontier: How Researchers from Edinburgh, Cohere, and Meta...
Sparse attention is emerging as a compelling approach to improve the ability of Transformer-based LLMs to handle long sequences. This is particularly important because...
Diagnosing and Self- Correcting LLM Agent Failures: A Technical Deep Dive...
Deploying large language model (LLM)-based agents in production settings often reveals critical reliability issues. Accurately identifying the causes of agent failures and implementing proactive...
Alibaba Qwen Team Just Released Qwen3: The Latest Generation of Large...
Despite the remarkable progress in large language models (LLMs), critical challenges remain. Many models exhibit limitations in nuanced reasoning, multilingual proficiency, and computational efficiency....
Tiny Models, Big Reasoning Gains: USC Researchers Introduce Tina for Cost-Effective...
Achieving strong, multi-step reasoning in LMs remains a major challenge, despite notable progress in general task performance. Such reasoning is crucial for complex problem-solving...
ByteDance Introduces QuaDMix: A Unified AI Framework for Data Quality and...
The pretraining efficiency and generalization of large language models (LLMs) are significantly influenced by the quality and diversity of the underlying training corpus. Traditional...
This AI Paper from China Proposes a Novel Training-Free Approach DEER...
Recent progress in large reasoning language models (LRLMs), such as DeepSeek-R1 and GPT-O1, has greatly improved complex problem-solving abilities by extending the length of...
LLMs Can Now Simulate Massive Societies: Researchers from Fudan University Introduce...
Human behavior research strives to comprehend how individuals and groups act in social contexts, forming a foundational social science element. Traditional methodologies like surveys,...
AgentA/B: A Scalable AI System Using LLM Agents that Simulate Real...
Designing and evaluating web interfaces is one of the most critical tasks in today’s digital-first world. Every change in layout, element positioning, or navigation...
Google DeepMind Research Introduces QuestBench: Evaluating LLMs’ Ability to Identify Missing...
Large language models (LLMs) have gained significant traction in reasoning tasks, including mathematics, logic, planning, and coding. However, a critical challenge emerges when applying...
NVIDIA AI Releases OpenMath-Nemotron-32B and 14B-Kaggle: Advanced AI Models for Mathematical...
Mathematical reasoning has long presented a formidable challenge for AI, demanding not only an understanding of abstract concepts but also the ability to perform...
Meta AI Releases Web-SSL: A Scalable and Language-Free Approach to Visual...
In recent years, contrastive language-image models such as CLIP have established themselves as a default choice for learning vision representations, particularly in multimodal applications...
Sequential-NIAH: A Benchmark for Evaluating LLMs in Extracting Sequential Information from...
Evaluating how well LLMs handle long contexts is essential, especially for retrieving specific, relevant information embedded in lengthy inputs. Many recent LLMs—such as Gemini-1.5,...
LLMs Can Now Learn without Labels: Researchers from Tsinghua University and...
Despite significant advances in reasoning capabilities through reinforcement learning (RL), most large language models (LLMs) remain fundamentally dependent on supervised data pipelines. RL frameworks...
Long-Context Multimodal Understanding No Longer Requires Massive Models: NVIDIA AI Introduces...
In recent years, vision-language models (VLMs) have advanced significantly in bridging image, video, and textual modalities. Yet, a persistent limitation remains: the inability to...
ReTool: A Tool-Augmented Reinforcement Learning Framework for Optimizing LLM Reasoning with...
Reinforcement learning (RL) is a powerful technique for enhancing the reasoning capabilities of LLMs, enabling them to develop and refine long Chain-of-Thought (CoT). Models...
LLMs Can Think While Idle: Researchers from Letta and UC Berkeley...
Large language models (LLMs) have gained prominence for their ability to handle complex reasoning tasks, transforming applications from chatbots to code-generation tools. These models...
LLMs Can Be Misled by Surprising Data: Google DeepMind Introduces New...
Large language models (LLMs) are continually evolving by ingesting vast quantities of text data, enabling them to become more accurate predictors, reasoners, and conversationalists....
Meta AI Introduces Collaborative Reasoner (Coral): An AI Framework Specifically Designed...
Rethinking the Problem of Collaboration in Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in single-agent tasks such as question answering and structured...
NVIDIA Introduces CLIMB: A Framework for Iterative Data Mixture Optimization in...
Challenges in Constructing Effective Pretraining Data Mixtures
As large language models (LLMs) scale in size and capability, the choice of pretraining data remains a critical...
LLMs Can Now Solve Challenging Math Problems with Minimal Data: Researchers...
Language models have made significant strides in tackling reasoning tasks, with even small-scale supervised fine-tuning (SFT) approaches such as LIMO and s1 demonstrating remarkable...
LLMs Can Now Learn to Try Again: Researchers from Menlo Introduce...
The domain of LLMs has rapidly evolved to include tools that empower these models to integrate external knowledge into their reasoning processes. A significant...
IBM Releases Granite 3.3 8B: A New Speech-to-Text (STT) Model that...
As artificial intelligence continues to integrate into enterprise systems, the demand for models that combine flexibility, efficiency, and transparency has increased. Existing solutions often...
Model Performance Begins with Data: Researchers from Ai2 Release DataDecide—A Benchmark...
The Challenge of Data Selection in LLM Pretraining
Developing large language models entails substantial computational investment, especially when experimenting with alternative pretraining corpora. Comparing datasets...
MIT Researchers Introduce DISCIPL: A Self-Steering Framework Using Planner and Follower...
Language models predict sequences of words based on vast datasets and are increasingly expected to reason and perform complex linguistic manipulations. Yet, despite their...
Transformers Can Now Predict Spreadsheet Cells without Fine-Tuning: Researchers Introduce TabPFN...
Tabular data is widely utilized in various fields, including scientific research, finance, and healthcare. Traditionally, machine learning models such as gradient-boosted decision trees have...
SQL-R1: A Reinforcement Learning-based NL2SQL Model that Outperforms Larger Systems in...
Natural language interface to databases is a growing focus within artificial intelligence, particularly because it allows users to interact with structured databases using plain...
From Logic to Confusion: MIT Researchers Show How Simple Prompt Tweaks...
Large language models are increasingly used to solve math problems that mimic real-world reasoning tasks. These models are tested for their ability to answer...
Reflection Begins in Pre-Training: Essential AI Researchers Demonstrate Early Emergence of...
What sets large language models (LLMs) apart from traditional methods is their emerging capacity to reflect—recognizing when something in their response doesn’t align with...
Traditional RAG Frameworks Fall Short: Megagon Labs Introduces ‘Insight-RAG’, a Novel...
RAG frameworks have gained attention for their ability to enhance LLMs by integrating external knowledge sources, helping address limitations like hallucinations and outdated information....
THUDM Releases GLM 4: A 32B Parameter Model Competing Head-to-Head with...
In the rapidly evolving landscape of large language models (LLMs), researchers and organizations face significant challenges. These include enhancing reasoning abilities, providing robust multilingual...
Small Models, Big Impact: ServiceNow AI Releases Apriel-5B to Outperform Larger...
As language models continue to grow in size and complexity, so do the resource requirements needed to train and deploy them. While large-scale models...
LightPROF: A Lightweight AI Framework that Enables Small-Scale Language Models to...
Large Language Models (LLMs) have revolutionized natural language processing, with abilities on complex zero-shot tasks through extensive training data and vast parameters. However, LLMs...
Google AI Introduce the Articulate Medical Intelligence Explorer (AMIE): A Large...
Developing an accurate differential diagnosis (DDx) is a fundamental part of medical care, typically achieved through a step-by-step process that integrates patient history, physical...
Step by Step Coding Guide to Build a Neural Collaborative Filtering...
This tutorial will walk you through using PyTorch to implement a Neural Collaborative Filtering (NCF) recommendation system. NCF extends traditional matrix factorisation by using...
Moonsight AI Released Kimi-VL: A Compact and Powerful Vision-Language Model Series...
Multimodal AI enables machines to process and reason across various input formats, such as images, text, videos, and complex documents. This domain has seen...
Allen Institute for AI (Ai2) Launches OLMoTrace: Real-Time Tracing of LLM...
Understanding the Limits of Language Model Transparency
As large language models (LLMs) become central to a growing number of applications—ranging from enterprise decision support to...
This AI Paper from Salesforce Introduces VLM2VEC and MMEB: A Contrastive...
Multimodal embeddings combine visual and textual data into a single representational space, enabling systems to understand and relate images and language meaningfully. These embeddings...
LLMs No Longer Require Powerful Servers: Researchers from MIT, KAUST, ISTA,...
HIGGS — the innovative method for compressing large language models was developed in collaboration with teams at Yandex Research, MIT, KAUST and ISTA.
HIGGS makes...
Nvidia Released Llama-3.1-Nemotron-Ultra-253B-v1: A State-of-the-Art AI Model Balancing Massive Scale, Reasoning...
As AI adoption increases in digital infrastructure, enterprises and developers face mounting pressure to balance computational costs with performance, scalability, and adaptability. The rapid...
Balancing Accuracy and Efficiency in Language Models: A Two-Phase RL Post-Training...
Recent advancements in LLMs have significantly enhanced their reasoning capabilities, particularly through RL-based fine-tuning. Initially trained with supervised learning for token prediction, these models...
RoR-Bench: Revealing Recitation Over Reasoning in Large Language Models Through Subtle...
In recent years, the rapid progress of LLMs has given the impression that we are nearing the achievement of Artificial General Intelligence (AGI), with...
Together AI Released DeepCoder-14B-Preview: A Fully Open-Source Code Reasoning Model That...
The demand for intelligent code generation and automated programming solutions has intensified, fueled by a rapid rise in software complexity and developer productivity needs....
Boson AI Introduces Higgs Audio Understanding and Higgs Audio Generation: An...
In today’s enterprise landscape—especially in insurance and customer support —voice and audio data are more than just recordings; they’re valuable touchpoints that can transform...
OpenAI Open Sources BrowseComp: A New Benchmark for Measuring the Ability...
Despite advances in large language models (LLMs), AI agents still face notable limitations when navigating the open web to retrieve complex information. While many...
Unveiling Attention Sinks: The Functional Role of First-Token Focus in Stabilizing...
LLMs often show a peculiar behavior where the first token in a sequence draws unusually high attention—known as an "attention sink." Despite seemingly unimportant,...
Salesforce AI Released APIGen-MT and xLAM-2-fc-r Model Series: Advancing Multi-Turn Agent...
AI agents quickly become core components in handling complex human interactions, particularly in business environments where conversations span multiple turns and involve task execution,...
Huawei Noah’s Ark Lab Released Dream 7B: A Powerful Open Diffusion Reasoning Model with...
LLMs have revolutionized artificial intelligence, transforming various applications across industries. Autoregressive (AR) models dominate current text generation, with leading systems like GPT-4, DeepSeek, and...
This AI Paper from ByteDance Introduces MegaScale-Infer: A Disaggregated Expert Parallelism...
Large language models are built on transformer architectures and power applications like chat, code generation, and search, but their growing scale with billions of...
A Code Implementation to Use Ollama through Google Colab and Building...
In this tutorial, we’ll build a fully functional Retrieval-Augmented Generation (RAG) pipeline using open-source tools that run seamlessly on Google Colab. First, we will...
RARE (Retrieval-Augmented Reasoning Modeling): A Scalable AI Framework for Domain-Specific Reasoning...
LLMs have demonstrated strong general-purpose performance across various tasks, including mathematical reasoning and automation. However, they struggle in domain-specific applications where specialized knowledge and...
Transformer Meets Diffusion: How the Transfusion Architecture Empowers GPT-4o’s Creativity
OpenAI’s GPT-4o represents a new milestone in multimodal AI: a single model capable of generating fluent text and high-quality images in the same output...
Anthropic’s Evaluation of Chain-of-Thought Faithfulness: Investigating Hidden Reasoning, Reward Hacks, and...
A key advancement in AI capabilities is the development and use of chain-of-thought (CoT) reasoning, where models explain their steps before reaching an answer....
Meta AI Just Released Llama 4 Scout and Llama 4 Maverick:...
Today, Meta AI announced the release of its latest generation multimodal models, Llama 4, featuring two variants: Llama 4 Scout and Llama 4 Maverick....
Scalable Reinforcement Learning with Verifiable Rewards: Generative Reward Modeling for Unstructured,...
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs' reasoning and coding abilities, particularly in domains where structured reference answers allow...
Meet Open-Qwen2VL: A Fully Open and Compute-Efficient Multimodal Large Language Model
Multimodal Large Language Models (MLLMs) have advanced the integration of visual and textual modalities, enabling progress in tasks such as image captioning, visual question...
Researchers from Dataocean AI and Tsinghua University Introduces Dolphin: A Multilingual...
Automatic speech recognition (ASR) technologies have advanced significantly, yet notable disparities remain in their ability to accurately recognize diverse languages. Prominent ASR systems, such...
This AI Paper Introduces FASTCURL: A Curriculum Reinforcement Learning Framework with...
Large language models have transformed how machines comprehend and generate text, especially in complex problem-solving areas like mathematical reasoning. These systems, known as R1-like...
Introduction to MCP: The Ultimate Guide to Model Context Protocol for...
The Model Context Protocol (MCP) is an open standard (open-sourced by Anthropic) that defines a unified way to connect AI assistants (LLMs) with external...
UB-Mesh: A Cost-Efficient, Scalable Network Architecture for Large-Scale LLM Training
As LLMs scale, their computational and bandwidth demands increase significantly, posing challenges for AI training infrastructure. Following scaling laws, LLMs improve comprehension, reasoning, and...
This AI Paper Unveils a Reverse-Engineered Simulator Model for Modern NVIDIA...
GPUs are widely recognized for their efficiency in handling high-performance computing workloads, such as those found in artificial intelligence and scientific simulations. These processors...
Snowflake Proposes ExCoT: A Novel AI Framework that Iteratively Optimizes Open-Source...
Text-to-SQL translation, the task of transforming natural language queries into structured SQL statements, is essential for facilitating user-friendly database interactions. However, the task involves...
Salesforce AI Introduce BingoGuard: An LLM-based Moderation System Designed to Predict...
The advancement of large language models (LLMs) has significantly influenced interactive technologies, presenting both benefits and challenges. One prominent issue arising from these models...
Open AI Releases PaperBench: A Challenging Benchmark for Assessing AI Agents’...
The rapid progress in artificial intelligence (AI) and machine learning (ML) research underscores the importance of accurately evaluating AI agents' capabilities in replicating complex,...
Meta AI Proposes Multi-Token Attention (MTA): A New Attention Method which...
Large Language Models (LLMs) significantly benefit from attention mechanisms, enabling the effective retrieval of contextual information. Nevertheless, traditional attention methods primarily depend on single...
Meet Amazon Nova Act: An AI Agent that can Automate Web...
Amazon has revealed a new artificial intelligence (AI) model called Amazon Nova Act. This AI agent is designed to operate and take actions within...
How to Build a Prototype X-ray Judgment Tool (Open Source Medical...
In this tutorial, we demonstrate how to build a prototype X-ray judgment tool using open-source libraries in Google Colab. By leveraging the power of...
This AI Paper Introduces Diversified DPO and ORPO: Post-Training Methods to...
Creative writing is a domain that thrives on diversity and imagination. Unlike fact-based or task-specific writing, where a single correct output may exist, creative...
Advancing Medical Reasoning with Reinforcement Learning from Verifiable Rewards (RLVR): Insights...
Reinforcement Learning from Verifiable Rewards (RLVR) has recently emerged as a promising method for enhancing reasoning abilities in language models without direct supervision. This...
UCLA Researchers Released OpenVLThinker-7B: A Reinforcement Learning Driven Model for Enhancing...
Large vision-language models (LVLMs) integrate large language models with image processing capabilities, enabling them to interpret images and generate coherent textual responses. While they...
Google AI Released TxGemma: A Series of 2B, 9B, and 27B...
Developing therapeutics continues to be an inherently costly and challenging endeavor, characterized by high failure rates and prolonged development timelines. The traditional drug discovery...
Meet Open Deep Search (ODS): A Plug-and-Play Framework Democratizing Search with...
The rapid advancements in search engine technologies integrated with large language models (LLMs) have predominantly favored proprietary solutions such as Google's GPT-4o Search Preview...
This AI Paper Introduces the Kolmogorov-Test: A Compression-as-Intelligence Benchmark for Evaluating...
Compression is a cornerstone of computational intelligence, deeply rooted in the theory of Kolmogorov complexity, which defines the minimal program needed to reproduce a...
Google DeepMind Researchers Propose CaMeL: A Robust Defense that Creates a...
Large Language Models (LLMs) are becoming integral to modern technology, driving agentic systems that interact dynamically with external environments. Despite their impressive capabilities, LLMs...
This AI Paper Introduces PLAN-AND-ACT: A Modular Framework for Long-Horizon Planning...
Large language models are powering a new wave of digital agents to handle sophisticated web-based tasks. These agents are expected to interpret user instructions,...
DeepSeek AI Unveils DeepSeek-V3-0324: Blazing Fast Performance on Mac Studio, Heating...
Artificial intelligence (AI) has made significant strides in recent years, yet challenges persist in achieving efficient, cost-effective, and high-performance models. Developing large language models...
Google AI Released Gemini 2.5 Pro Experimental: An Advanced AI Model...
In the evolving field of artificial intelligence, a significant challenge has been developing models that can effectively reason through complex problems, generate accurate code,...
RWKV-7: Advancing Recurrent Neural Networks for Efficient Sequence Modeling
Autoregressive Transformers have become the leading approach for sequence modeling due to their strong in-context learning and parallelizable training enabled by softmax attention. However,...
Qwen Releases the Qwen2.5-VL-32B-Instruct: A 32B Parameter VLM that Surpasses Qwen2.5-VL-72B...
In the evolving field of artificial intelligence, vision-language models (VLMs) have become essential tools, enabling machines to interpret and generate insights from both visual...
A Coding Implementation of Extracting Structured Data Using LangSmith, Pydantic, LangChain,...
Unlock the power of structured data extraction with LangChain and Claude 3.7 Sonnet, transforming raw text into actionable insights. This tutorial focuses on tracing...
This AI Paper from NVIDIA Introduces Cosmos-Reason1: A Multimodal Model for...
Artificial intelligence systems designed for physical settings require more than just perceptual abilities—they must also reason about objects, actions, and consequences in dynamic, real-world...
TokenSet: A Dynamic Set-Based Framework for Semantic-Aware Visual Representation
Visual generation frameworks follow a two-stage approach: first compressing visual signals into latent representations and then modeling the low-dimensional distributions. However, conventional tokenization methods...
SuperBPE: Advancing Language Models with Cross-Word Tokenization
Language models (LMs) face a fundamental challenge in how to perceive textual data through tokenization. Current subword tokenizers segment text into vocabulary tokens that...
A Unified Acoustic-to-Speech-to-Language Embedding Space Captures the Neural Basis of Natural...
Language processing in the brain presents a challenge due to its inherently complex, multidimensional, and context-dependent nature. Psycholinguists have attempted to construct well-defined symbolic...
Achieving Critical Reliability in Instruction-Following with LLMs: How to Achieve AI...
Ensuring reliable instruction-following in LLMs remains a critical challenge. This is particularly important in customer-facing applications, where mistakes can be costly. Traditional prompt engineering...