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This AI Paper Introduces 'Shortest Majority Vote': An Improved Parallel Scaling Method for Enhancing Test-Time Performance in Large Language Models - MarkTechPost
Large language models (LLMs) use extensive computational resources to process and generate human-like text. One emerging technique to enhance reasoning capabilities in LLMs is test-time scaling, which dynamically allocates computational resources during inference. This approach aims to improve the accuracy of responses by refining the model's reasoning process. As models like OpenAI's o1 series introduced test-time scaling, researchers sought to understand whether longer reasoning chains led to improved performance or if alternative strategies could yield better results. Scaling reasoning in AI models poses a significant challenge, especially in cases where extended chains of thought do not necessarily translate to better
Boosting AI Math Skills: How Counterexample-Driven Reasoning is Transforming Large Language Models - MarkTechPost
Mathematical Large Language Models (LLMs) have demonstrated strong problem-solving capabilities, but their reasoning ability is often constrained by pattern recognition rather than true conceptual understanding. Current models are heavily based on exposure to similar proofs as part of their training, confining their extrapolation to new mathematical problems. This constraint restricts LLMs from engaging in advanced mathematical reasoning, especially in problems requiring the differentiation between closely related mathematical concepts. An advanced reasoning strategy commonly lacking in LLMs is the proof by counterexample, a central method of disproving false mathematical assertions. The absence of sufficient generation and employment of counterexamples hinders LLMs
Stanford Researchers Developed POPPER: An Agentic AI Framework that Automates Hypothesis Validation with Rigorous Statistical Control, Reducing Errors and Accelerating Scientific Discovery by 10x - MarkTechPost
Hypothesis validation is fundamental in scientific discovery, decision-making, and information acquisition. Whether in biology, economics, or policymaking, researchers rely on testing hypotheses to guide their conclusions. Traditionally, this process involves designing experiments, collecting data, and analyzing results to determine the validity of a hypothesis. However, the volume of generated hypotheses has increased dramatically with the advent of LLMs. While these AI-driven hypotheses offer novel insights, their plausibility varies widely, making manual validation impractical. Thus, automation in hypothesis validation has become an essential challenge in ensuring that only scientifically rigorous hypotheses guide future research. The main challenge in hypothesis validation is
Building an Ideation Agent System with AutoGen: Create AI Agents that Brainstorm and Debate Ideas - MarkTechPost
Ideation processes often require time-consuming analysis and debate. What if we make two LLMs come up with ideas and then make them debate about those ideas? Sounds interesting right? This tutorial exactly shows how to create an AI-powered solution using two LLM agents that collaborate through structured conversation. For achieving this we will be using AutoGen for building the agent and ChatGPT as LLM for our agent. 1. Setup and Installation First install required packages: Copy CodeCopiedUse a different Browserpip install -U autogen-agentchat pip install autogen-ext 2. Core Components Let’s explore the key components of AutoGen that make this ideation
Google DeepMind Releases PaliGemma 2 Mix: New Instruction Vision Language Models Fine-Tuned on a Mix of Vision Language Tasks - MarkTechPost
Vision‐language models (VLMs) have long promised to bridge the gap between image understanding and natural language processing. Yet, practical challenges persist. Traditional VLMs often struggle with variability in image resolution, contextual nuance, and the sheer complexity of converting visual data into accurate textual descriptions. For instance, models may generate concise captions for simple images but falter when asked to describe complex scenes, read text from images, or even detect multiple objects with spatial precision. These shortcomings have historically limited VLM adoption in applications such as optical character recognition (OCR), document understanding, and detailed image captioning. Google’s new release aims to
xAI Releases Grok 3 Beta: A Super Advanced AI Model Blending Strong Reasoning with Extensive Pretraining Knowledge - MarkTechPost
Modern AI systems have made significant strides, yet many still struggle with complex reasoning tasks. Issues such as inconsistent problem-solving, limited chain-of-thought capabilities, and occasional factual inaccuracies remain. These challenges hinder practical applications in research and software development, where nuanced understanding and precision are crucial. The drive to overcome these limitations has prompted a reexamination of how AI models are built and trained, with a focus on improving transparency and reliabilit xAI’s recent release of the Grok 3 Beta marks a thoughtful step forward in AI development. In their announcement, the company outlines how this new model builds on its
Steps to Build an Interactive Text-to-Image Generation Application using Gradio and Hugging Face's Diffusers - MarkTechPost
In this tutorial, we will build an interactive text-to-image generator application accessed through Google Colab and a public link using Hugging Face's Diffusers library and Gradio. You'll learn how to transform simple text prompts into detailed images by leveraging the state-of-the-art Stable Diffusion model and GPU acceleration. We’ll walk through setting up the environment, installing dependencies, caching the model, and creating an intuitive application interface that allows real-time parameter adjustments. Copy CodeCopiedUse a different Browser!pip install diffusers transformers accelerate gradio First, we install four essential Python packages using pip. Diffusers provides tools for working with diffusion models, Transformers offers pretrained
KGGen: Advancing Knowledge Graph Extraction with Language Models and Clustering Techniques - MarkTechPost
Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack essential entity relationships, diminishing their utility in retrieval-augmented generation (RAG) and other machine-learning tasks. Traditional extraction methods are likely to provide sparse graphs with absent important connections or noisy, redundant representations. Therefore it is difficult to obtain high-quality structured knowledge from unstructured text. Overcoming these challenges is critical to enable improved knowledge retrieval, reasoning, and insights with the help of artificial intelligence. State-of-the-art methods for extracting KGs from raw text are Open Information Extraction
Learning Intuitive Physics: Advancing AI Through Predictive Representation Models - MarkTechPost
Humans possess an innate understanding of physics, expecting objects to behave predictably without abrupt changes in position, shape, or color. This fundamental cognition is observed in infants, primates, birds, and marine mammals, supporting the core knowledge hypothesis, which suggests humans have evolutionarily developed systems for reasoning about objects, space, and agents. While AI surpasses humans in complex tasks like coding and mathematics, it struggles with intuitive physics, highlighting Moravec’s paradox. AI approaches to physical reasoning fall into two categories: structured models, which simulate object interactions using predefined rules, and pixel-based generative models, which predict future sensory inputs without explicit abstractions.
Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-Making - MarkTechPost
Multimodal AI agents are designed to process and integrate various data types, such as images, text, and videos, to perform tasks in digital and physical environments. They are used in robotics, virtual assistants, and user interface automation, where they need to understand and act based on complex multimodal inputs. These systems aim to bridge verbal and spatial intelligence by leveraging deep learning techniques, enabling interactions across multiple domains. AI systems often specialize in vision-language understanding or robotic manipulation but struggle to combine these capabilities into a single model. Many AI models are designed for domain-specific tasks, such as UI navigation
Breaking the Autoregressive Mold: LLaDA Proves Diffusion Models can Rival Traditional Language Architectures - MarkTechPost
The field of large language models has long been dominated by autoregressive methods that predict text sequentially from left to right. While these approaches power today’s most capable AI systems, they face fundamental limitations in computational efficiency and bidirectional reasoning. A research team from China has now challenged the assumption that autoregressive modeling is the only path to achieving human-like language capabilities, introducing an innovative diffusion-based architecture called LLaDA that reimagines how language models process information. Current language models operate through next-word prediction, requiring increasingly complex computations as context windows grow. This sequential nature creates bottlenecks in processing speed and
Advancing MLLM Alignment Through MM-RLHF: A Large-Scale Human Preference Dataset for Multimodal Tasks - MarkTechPost
Multimodal Large Language Models (MLLMs) have gained significant attention for their ability to handle complex tasks involving vision, language, and audio integration. However, they lack the comprehensive alignment beyond basic Supervised Fine-tuning (SFT). Current state-of-the-art models often bypass rigorous alignment stages, leaving crucial aspects like truthfulness, safety, and human preference alignment inadequately addressed. Existing approaches target only specific domains such as hallucination reduction or conversational improvements, falling short of enhancing the model's overall performance and reliability. This narrow focus raises questions about whether human preference alignment can improve MLLMs across a broader spectrum of tasks. Recent years have witnessed substantial
All You Need to Know about Vision Language Models VLMs: A Survey Article - MarkTechPost
Vision Language Models have been a revolutionizing milestone in the development of language models, which overcomes the shortcomings of predecessor pre-trained LLMs like LLama, GPT, etc. Vision Language Models explore a new territory beyond single modularity to combine inputs from text and image videos. VLMs thus bestow a better understanding of visual-spatial relationships by expanding the representational boundaries of input, supporting a richer worldview. With new opportunities come new challenges, which is the case with VLMs. Currently, researchers across the globe are encountering and solving new challenges to make VLMs better, one at a time. Based on a survey by
OpenAI introduces SWE-Lancer: A Benchmark for Evaluating Model Performance on Real-World Freelance Software Engineering Work - MarkTechPost
Addressing the evolving challenges in software engineering starts with recognizing that traditional benchmarks often fall short. Real-world freelance software engineering is complex, involving much more than isolated coding tasks. Freelance engineers work on entire codebases, integrate diverse systems, and manage intricate client requirements. Conventional evaluation methods, which typically emphasize unit tests, miss critical aspects such as full-stack performance and the real monetary impact of solutions. This gap between synthetic testing and practical application has driven the need for more realistic evaluation methods. OpenAI introduces SWE-Lancer, a benchmark for evaluating model performance on real-world freelance software engineering work. The benchmark is
Microsoft AI Releases OmniParser V2: An AI Tool that Turns Any LLM into a Computer Use Agent - MarkTechPost
In the realm of artificial intelligence, enabling Large Language Models (LLMs) to navigate and interact with graphical user interfaces (GUIs) has been a notable challenge. While LLMs are adept at processing textual data, they often encounter difficulties when interpreting visual elements like icons, buttons, and menus. This limitation restricts their effectiveness in tasks that require seamless interaction with software interfaces, which are predominantly visual. To address this issue, Microsoft has introduced OmniParser V2, a tool designed to enhance the GUI comprehension capabilities of LLMs. OmniParser V2 converts UI screenshots into structured, machine-readable data, enabling LLMs to understand and interact with
DeepSeek AI Introduces NSA: A Hardware-Aligned and Natively Trainable Sparse Attention Mechanism for Ultra-Fast Long-Context Training and Inference - MarkTechPost
In recent years, language models have been pushed to handle increasingly long contexts. This need has exposed some inherent problems in the standard attention mechanisms. The quadratic complexity of full attention quickly becomes a bottleneck when processing long sequences. Memory usage and computational demands increase rapidly, making it challenging for practical applications such as multi-turn dialogues or complex reasoning tasks. Moreover, while sparse attention methods promise theoretical improvements, they often struggle to translate those benefits into real-world speedups. Many of these challenges arise from a disconnect between theoretical efficiency and practical implementation. Reducing computational overhead without losing essential information is
This AI Paper Introduces Diverse Inference and Verification: Enhancing AI Reasoning for Advanced Mathematical and Logical Problem-Solving - MarkTechPost
Large language models have demonstrated remarkable problem-solving capabilities and mathematical and logical reasoning. These models have been applied to complex reasoning tasks, including International Mathematical Olympiad (IMO) combinatorics problems, Abstraction and Reasoning Corpus (ARC) puzzles, and Humanity's Last Exam (HLE) questions. Despite improvements, existing AI models often struggle with high-level problem-solving that requires abstract reasoning, formal verification, and adaptability. The growing demand for AI-driven problem-solving has led researchers to develop novel inference techniques that combine multiple methods and models to enhance accuracy and reliability. The challenge with AI reasoning lies in verifying the correctness of solutions, particularly for mathematical problems
Mistral AI Introduces Mistral Saba: A New Regional Language Model Designed to Excel in Arabic and South Indian-Origin Languages such as Tamil - MarkTechPost
As artificial intelligence (AI) continues to gain traction across industries, one persistent challenge remains: creating language models that truly understand the diversity of human languages, including regional dialects and local cultural contexts. While advancements in AI have primarily focused on English, many languages, particularly those spoken in the Middle East and South Asia, remain underserved. Arabic, for example, has various regional dialects, while South Indian languages such as Tamil have their own distinct characteristics. Most existing AI models struggle to grasp these linguistic subtleties, resulting in responses that often lack relevance or depth. Furthermore, the computational costs and large-scale models
Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention Mechanism - MarkTechPost
Efficiently handling long contexts has been a longstanding challenge in natural language processing. As large language models expand their capacity to read, comprehend, and generate text, the attention mechanism—central to how they process input—can become a bottleneck. In a typical Transformer architecture, this mechanism compares every token to every other token, resulting in computational costs that scale quadratically with sequence length. This problem grows more pressing as we apply language models to tasks that require them to consult vast amounts of textual information: long-form documents, multi-chapter books, legal briefs, or large code repositories. When a model must navigate tens or
A Stepwise Python Code Implementation to Create Interactive Photorealistic Faces with NVIDIA StyleGAN2‑ADA - MarkTechPost
In this tutorial, we will do an in-depth, interactive exploration of NVIDIA's StyleGAN2‑ADA PyTorch model, showcasing its powerful capabilities for generating photorealistic images. Leveraging a pretrained FFHQ model, users can generate high-quality synthetic face images from a single latent seed or visualize smooth transitions through latent space interpolation between different seeds. With an intuitive interface powered by interactive widgets, this tutorial is a valuable resource for researchers, artists, and enthusiasts looking to understand and experiment with advanced generative adversarial networks. Copy CodeCopiedUse a different Browser!git clone https://github.com/NVlabs/stylegan2-ada-pytorch.git First, we clone the NVIDIA StyleGAN2‑ADA PyTorch repository from GitHub into your current
Meet Fino1-8B: A Fine-Tuned Version of Llama 3.1 8B Instruct Designed to Improve Performance on Financial Reasoning Tasks - MarkTechPost
Understanding financial information means analyzing numbers, financial terms, and organized data like tables for useful insights. It requires math calculations and knowledge of economic concepts, rules, and relationships between financial terms. Although sophisticated AI models have shown excellent general reasoning ability, their suitability for financial tasks is questionable. Such tasks require more than simple mathematical calculations since they involve interpreting domain-specific vocabulary, recognizing relationships between financial points, and analyzing structured financial data. Generally, reasoning approaches like chain-of-thought fine-tuning and reinforcement learning boost performance on multiple tasks but collapse with financial rationale. They improve logical reasoning but cannot replicate the complexity
ViLa-MIL: Enhancing Whole Slide Image Classification with Dual-Scale Vision-Language Multiple Instance Learning - MarkTechPost
Whole Slide Image (WSI) classification in digital pathology presents several critical challenges due to the immense size and hierarchical nature of WSIs. WSIs contain billions of pixels and hence direct observation is computationally infeasible. Current strategies based on multiple instance learning (MIL) are effective in performance but considerably dependent on large amounts of bag-level annotated data, whose acquisition is troublesome, particularly in the case of rare diseases. Moreover, current strategies are strongly based on image insights and encounter generalization issues due to differences in the data distribution across hospitals. Recent improvements in Vision-Language Models (VLMs) introduce linguistic prior through large-scale
OpenAI Introduces Deep Research: An AI Agent that Uses Reasoning to Synthesize Large Amounts of Online Information and Complete Multi-Step Research Tasks - MarkTechPost
OpenAI has introduced Deep Research, a tool designed to assist users in conducting thorough, multi-step investigations on a variety of topics. Unlike traditional search engines, which return a list of links, Deep Research synthesizes information from multiple sources into detailed, well-cited reports. This feature is particularly useful for professionals in fields such as finance, science, policy, and engineering who require structured, in-depth insights. Deep Research helps users conduct structured research by autonomously collecting, analyzing, and summarizing information from various sources. The tool provides detailed reports with citations, helping users save time and improve the quality of their research. Key Features
Creating a Medical Question-Answering Chatbot Using Open-Source BioMistral LLM, LangChain, Chroma’s Vector Storage, and RAG: A Step-by-Step Guide - MarkTechPost
In this tutorial, we’ll build a powerful, PDF-based question-answering chatbot tailored for medical or health-related content. We’ll leveRAGe the open-source BioMistral LLM and LangChain’s flexible data orchestration capabilities to process PDF documents into manageable text chunks. We’ll then encode these chunks using Hugging Face embeddings, capturing deep semantic relationships and storing them in a Chroma vector database for high-efficiency retrieval. Finally, by employing a Retrieval-Augmented Generation (RAG) system, we’ll integrate the retrieved context directly into our chatbot’s responses, ensuring clear, authoritative answers for users. This approach allows us to rapidly sift through large volumes of medical PDFs, providing context-rich, accurate,
Dendritic Neural Networks: A Step Closer to Brain-Like AI - MarkTechPost
Artificial Neural Networks (ANNs) have their roots established in the inspiration developed from biological neural networks. Although highly efficient, ANNs fail to embody the neuronal structures in their architects truly. ANNs rely on vast training parameters, which lead to their high performance, but they consume a lot of energy and are prone to overfitting. Due to the continuous increase in the complexity and depth of ANNs, there has been an exponential growth in energy usage, which is becoming increasingly difficult to sustain. Therefore, researchers from Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece have
Google AI Introduces Parfait: A Privacy-First AI System for Secure Data Aggregation and Analytics - MarkTechPost
Protecting user data while enabling advanced analytics and machine learning is a critical challenge. Organizations must process and analyze data without compromising privacy, but existing solutions often struggle to balance security with functionality. This creates barriers to innovation, limiting collaboration and the development of privacy-conscious technologies. A solution that ensures transparency minimizes data exposure, preserves anonymity, and allows external verification is needed. Addressing these challenges makes it possible to unlock new opportunities for secure and privacy-first computing, enabling businesses and researchers to collaborate effectively while maintaining strict data protection standards. Recent research has explored various privacy-preserving techniques for data aggregation,
Bio-xLSTM: Efficient Generative Modeling, Representation Learning, and In-Context Adaptation for Biological and Chemical Sequences - MarkTechPost
Modeling biological and chemical sequences is extremely difficult mainly due to the need to handle long-range dependencies and efficient processing of large sequential data. Classical methods, particularly Transformer-based architectures, are limited by quadratic scaling in sequence length and are computationally expensive for processing long genomic sequences and protein modeling. Moreover, most existing models have in-context learning constraints, limiting their ability to generalize to new tasks without retraining. Overcoming these challenges is central to accelerating applications in genomics, protein engineering, and drug discovery, where sequence modeling with precision can lead to precision medicine and molecular biology breakthroughs. Existing methods are mainly
Transformer-Based Modulation Recognition: A New Defense Against Adversarial Attacks - MarkTechPost
The fast development of wireless communication technologies has increased the application of automatic modulation recognition (AMR) in sectors such as cognitive radio and electronic countermeasures. With their various modulation types and signal changes, modern communication systems provide significant obstacles to preserving AMR performance in dynamic contexts. Deep learning-based AMR algorithms have emerged as the leading technology in wireless signal recognition due to their higher performance and automated feature extraction capabilities. Unlike previous techniques, deep learning models excel at managing complicated signal input while maintaining high identification accuracy. However, these models are sensitive to adversarial attacks, where little changes in input
Researchers from University of Waterloo and CMU Introduce Critique Fine-Tuning (CFT): A Novel AI Approach for Enhancing LLM Reasoning with Structured Critique Learning - MarkTechPost
Traditional approaches to training language models heavily rely on supervised fine-tuning, where models learn by imitating correct responses. While effective for basic tasks, this method limits a model’s ability to develop deep reasoning skills. As artificial intelligence applications continue to evolve, there is a growing demand for models that can generate responses and critically evaluate their own outputs to ensure accuracy and logical consistency. A serious limitation of traditional training methods is that they are based on imitation of responses and restrict models from critical analysis of responses. As a result, imitation-based techniques fail to present proper logical depth when
Can AI Understand Subtext? A New AI Approach to Natural Language Inference - MarkTechPost
Understanding implicit meaning is a fundamental aspect of human communication. Yet, current Natural Language Inference (NLI) models struggle to recognize implied entailments—statements that are logically inferred but not explicitly stated. Most current NLI datasets are focused on explicit entailments, making the models insufficiently equipped to deal with scenarios where meaning is indirectly expressed. This limitation bars the development of applications such as conversational AI, summarization, and context-sensitive decision-making, where the ability to infer unspoken implications is crucial. To mitigate this shortcoming, a dataset and approach that systematically incorporates implied entailments in NLI tasks are needed. Current NLI benchmarks like SNLI,
An In-Depth Exploration of Reasoning and Decision-Making in Agentic AI: How Reinforcement Learning RL and LLM-based Strategies Empower Autonomous Systems - MarkTechPost
Agentic AI gains much value from the capacity to reason about complex environments and make informed decisions with minimal human input. The first article of this five-part series focused on how agents perceive their surroundings and store relevant knowledge. This second article explores how that input and context are transformed into purposeful actions. The Reasoning/Decision-Making Module is the system’s dynamic “mind,” guiding autonomous behavior across diverse domains, from conversation-based assistants to robotic platforms navigating physical spaces. This module can be viewed as the bridge between observed reality and the agent’s objectives. It takes preprocessed signals, images turned into feature vectors,
Researchers from Stanford, UC Berkeley and ETH Zurich Introduces WARP: An Efficient Multi-Vector Retrieval Engine for Faster and Scalable Search - MarkTechPost
Multi-vector retrieval has emerged as a critical advancement in information retrieval, particularly with the adoption of transformer-based models. Unlike single-vector retrieval, which encodes queries and documents as a single dense vector, multi-vector retrieval allows for multiple embeddings per document and query. This approach provides a more granular representation, improving search accuracy and retrieval quality. Over time, researchers have developed various techniques to enhance the efficiency and scalability of multi-vector retrieval, addressing computational challenges in handling large datasets. A central problem in multi-vector retrieval is balancing computational efficiency with retrieval performance. Traditional retrieval techniques are fast but frequently fail to retrieve
Creating an AI-Powered Tutor Using Vector Database and Groq for Retrieval-Augmented Generation (RAG): Step by Step Guide - MarkTechPost
Currently, three trending topics in the implementation of AI are LLMs, RAG, and Databases. These enable us to create systems that are suitable and specific to our use. This AI-powered system, combining a vector database and AI-generated responses, has applications across various industries. In customer support, AI chatbots retrieve knowledge base answers dynamically. The legal and financial sectors benefit from AI-driven document summarization and case research. Healthcare AI assistants help doctors with medical research and drug interactions. E-learning platforms provide personalized corporate training. Journalism uses AI for news summarization and fact-checking. Software development leverages AI for coding assistance and debugging.
Exploration Challenges in LLMs: Balancing Uncertainty and Empowerment in Open-Ended Tasks - MarkTechPost
LLMs have demonstrated impressive cognitive abilities, making significant strides in artificial intelligence through their ability to generate and predict text. However, while various benchmarks evaluate their perception, reasoning, and decision-making, less attention has been given to their exploratory capacity. Exploration, a key aspect of intelligence in humans and AI, involves seeking new information and adapting to unfamiliar environments, often at the expense of immediate rewards. Unlike exploitation, which relies on leveraging known information for short-term gains, exploration enhances adaptability and long-term understanding. The extent to which LLMs can effectively explore, particularly in open-ended tasks, remains an open question. Exploration has
This AI Paper from the Tsinghua University Propose T1 to Scale Reinforcement Learning by Encouraging Exploration and Understand Inference Scaling - MarkTechPost
Large language models (LLMs) are developed specifically for math, programming, and general autonomous agents and require improvement in reasoning at test time. Various approaches include producing reasoning steps in response to some prompt or using sampling and training models to generate the same step. Reinforcement learning is more likely to give self-exploration and the ability to learn from feedback; however, their impact on complex reasoning has remained limited. Scaling LLMs at test time is still an issue because increased computational efforts do not necessarily translate to better models. Deep reasoning and longer responses can potentially improve performance, but it has
Creating an AI Agent-Based System with LangGraph: Adding Persistence and Streaming (Step by Step Guide) - MarkTechPost
In our previous tutorial, we built an AI agent capable of answering queries by surfing the web. However, when building agents for longer-running tasks, two critical concepts come into play: persistence and streaming. Persistence allows you to save the state of an agent at any given point, enabling you to resume from that state in future interactions. This is crucial for long-running applications. On the other hand, streaming lets you emit real-time signals about what the agent is doing at any moment, providing transparency and control over its actions. In this tutorial, we’ll enhance our agent by adding these powerful
Memorization vs. Generalization: How Supervised Fine-Tuning SFT and Reinforcement Learning RL Shape Foundation Model Learning - MarkTechPost
Modern AI systems rely heavily on post-training techniques like supervised fine-tuning (SFT) and reinforcement learning (RL) to adapt foundation models for specific tasks. However, a critical question remains unresolved: do these methods help models memorize training data or generalize to new scenarios? This distinction is vital for building robust AI systems capable of handling real-world variability. Reference: https://arxiv.org/pdf/2501.17161 Prior work suggests SFT risks overfitting to training data, making models brittle when faced with new task variants. For example, an SFT-tuned model might excel at arithmetic problems using specific card values (e.g., treating ‘J’ as 11) but fail if the rules
Light3R-SfM: A Scalable and Efficient Feed-Forward Approach to Structure-from-Motion - MarkTechPost
Structure-from-motion (SfM) focuses on recovering camera positions and building 3D scenes from multiple images. This process is important for tasks like 3D reconstruction and novel view synthesis. A major challenge comes from processing large image collections efficiently while maintaining accuracy. Several approaches rely on the optimization of camera poses and scene geometry. However, these have usually increased computational costs substantially, and scaling SfM for large datasets remains challenging due to the sensitivity of balancing speed, accuracy, and memory consumption. Currently, SfM methods follow two main approaches: incremental and global. Incremental methods build 3D scenes step by step, starting from two
Mistral AI Releases the Mistral-Small-24B-Instruct-2501: A Latency-Optimized 24B-Parameter Model Released Under the Apache 2.0 License - MarkTechPost
Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, making them inaccessible for many users and organizations with limited hardware capabilities. Additionally, there is a growing demand for methods that can handle diverse tasks, support multilingual communication, and provide accurate responses efficiently without sacrificing quality. Balancing performance, scalability, and accessibility is crucial, particularly for enabling local deployments and ensuring data privacy. This highlights the need for innovative approaches to create smaller, resource-efficient models that deliver capabilities comparable to their larger counterparts while remaining versatile and cost-effective. Recent advancements in
Meet RAGEN Framework: The First Open-Source Reproduction of DeepSeek-R1 for Training Agentic Models via Reinforcement Learning - MarkTechPost
Developing AI agents capable of independent decision-making, especially for multi-step tasks, is a significant challenge. DeepSeekAI, a leader in advancing large language models and reinforcement learning, focuses on enabling AI to process information, predict outcomes, and adjust actions as situations evolve. It underlines the importance of proper reasoning in dynamic settings. The new development from DeepSeekAI captures state-of-the-art methods in reinforcement learning, large language models, and agent-based decision-making to ensure that it stays on top of the current AI research and applications. It deals with many common problems, such as decision-making inconsistencies, long-term planning issues, and the inability to adapt