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Scopri le migliori risorse per approfondire l'intelligenza artificiale, dal machine learning alle tecnologie emergenti.

Models & Architectures

19/19

The paper introduces DS-STAR, a novel data science agent designed to automate complex data analysis tasks by iteratively planning and verifying analysis steps. It addresses challenges with heterogeneous data formats and sub-optimal analysis plans by using a data file analysis module, an LLM-based verification step, and a sequential planning mechanism. DS-STAR demonstrates state-of-the-art performance across several benchmarks, particularly excelling in tasks that require processing multiple data files with diverse formats.

Models & Architectures Link
17/19

The paper 'Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks' introduces a novel framework for managing working memory in large language models (LLMs) during long-horizon tasks. The authors propose treating memory management as an intrinsic capability of the agent, allowing it to actively curate its working memory through explicit editing operations. This approach, called Memory-as-Action, is integrated into the agent's policy and optimized using a new algorithm, Dynamic Context Policy Optimization. The framework aims to improve task performance by balancing memory curation with long-term objectives, addressing challenges such as trajectory fractures that disrupt standard policy gradient methods.

Models & Architectures Link
15/19

The paper introduces PIKE-RAG, a novel approach to enhance Retrieval-Augmented Generation (RAG) systems by incorporating specialized knowledge and rationale augmentation. This method aims to improve the performance of large language models (LLMs) in industrial applications by addressing their limitations in handling domain-specific queries and complex tasks. PIKE-RAG focuses on extracting and applying specialized knowledge while constructing coherent rationale, offering a systematic evaluation framework for RAG systems. The paper also proposes knowledge atomizing and task decomposition techniques to enhance knowledge extraction and application.

Models & Architectures Link
14/19

The paper introduces Agentic Context Engineering (ACE), a framework for evolving contexts in large language models (LLMs) to improve their performance in applications such as agents and domain-specific reasoning. ACE addresses issues like brevity bias and context collapse by treating contexts as evolving playbooks that accumulate and refine strategies. The framework optimizes contexts both offline and online, outperforming strong baselines in various benchmarks without requiring labeled supervision.

Models & Architectures Link
12/19

The paper 'Small Language Models are the Future of Agentic AI' argues that small language models (SLMs) are more suitable and economical for agentic AI systems compared to large language models (LLMs). It discusses the capabilities of SLMs, their fit within agentic systems, and the economic benefits of using SLMs. The authors propose that heterogeneous agentic systems, which use multiple models, are ideal when general-purpose conversational abilities are needed. The paper also outlines potential barriers to adopting SLMs and suggests a conversion algorithm from LLMs to SLMs.

Models & Architectures Link
11/19

The paper introduces SEER, a self-enhancing framework for compressing Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs). SEER aims to improve reasoning efficiency by reducing the length of CoT without compromising performance, particularly in software engineering and mathematical reasoning tasks. The framework uses adaptive filtering to dynamically control verbosity, reducing computational costs and improving accuracy by minimizing truncation and infinite reasoning loops.

Models & Architectures Link
10/19

The paper introduces 'Memory Decoder', a novel plug-and-play pretrained memory component designed to enable efficient domain adaptation for large language models (LLMs) without modifying their original parameters. This approach addresses the challenges of domain-specific adaptation, such as high computational costs and inference latency, associated with existing methods like Domain Adaptive Pretraining (DAPT) and Retrieval-Augmented Generation (RAG). The Memory Decoder uses a small transformer decoder to mimic external non-parametric retrievers, allowing seamless integration with any pretrained language model that shares the same tokenizer. Experimental results show significant improvements in adapting models to specialized domains like biomedicine, finance, and law.

Models & Architectures Link
9/19

The paper 'SSRL: Self-Search Reinforcement Learning' explores the use of large language models (LLMs) as simulators for agentic search tasks in reinforcement learning, aiming to reduce reliance on external search engines. The authors introduce Self-Search RL (SSRL), which enhances LLMs' intrinsic search capabilities through structured prompting and reinforcement learning techniques. The study demonstrates that SSRL-trained models can effectively utilize internal knowledge to achieve high performance in search-driven tasks, facilitating robust sim-to-real transfer.

Models & Architectures Link
8/19

The paper introduces AgentGym-RL, a new framework designed to train large language model (LLM) agents for long-horizon decision making through multi-turn reinforcement learning. The framework is modular and supports a variety of real-world scenarios and mainstream RL algorithms. It also proposes a training approach called ScalingInter-RL, which balances exploration and exploitation to optimize RL training. The framework and approach are validated through extensive experiments, showing that the trained agents can match or surpass commercial models on various tasks. The authors plan to open-source the framework to aid further research.

Models & Architectures Link
7/19

The paper titled 'SpikingBrain Technical Report: Spiking Brain-inspired Large Models' introduces SpikingBrain, a new family of brain-inspired models designed for efficient long-context training and inference. It addresses efficiency bottlenecks in mainstream Transformer-based large language models by leveraging MetaX GPU clusters and focusing on model architecture, algorithmic optimizations, and system engineering. The study presents two models, SpikingBrain-7B and SpikingBrain-76B, which demonstrate significant improvements in training efficiency and inference performance on non-NVIDIA platforms.

Models & Architectures Link
6/19

This document is a survey paper that examines the development and application of specialized large language models (LLMs) across various professional domains such as healthcare, finance, legal, and technical fields. It discusses the transition from general-purpose LLMs to domain-specific models, highlighting innovations like domain-native architectures, parameter efficiency, and multimodal capabilities. The paper also explores how these specialized models address the limitations of general LLMs in professional applications and their performance on domain-specific benchmarks.

Models & Architectures Link
5/19

The paper introduces the NANDA index architecture, designed to enhance the discoverability, identifiability, and authentication of AI agents on the Internet. It proposes a system that supports dynamic, cryptographically verifiable AgentFacts, enabling secure and efficient collaboration among AI agents. The architecture aims to address the limitations of DNS in handling the rapid growth and dynamic nature of AI agents by providing a scalable, privacy-preserving, and trust-aware infrastructure.

Models & Architectures Link

Tools & Frameworks

44/44
A multi-level diagram titled 'Agentic RAG Tech Stack' displaying various tools and frameworks categorized into levels from 0 to 8. Each level represents a different aspect of AI technology, such as Deployment & Infrastructure, Evaluation & Monitoring, Foundation Models, Orchestration Frameworks, Vector Databases, Embedding Models, Data Ingestion & Extraction, Memory & Context Management, and Safety & Governance. Each level lists specific tools and technologies relevant to that category.

A multi-level diagram titled 'Agentic RAG Tech Stack' displaying various tools and frameworks categorized into levels from 0 to 8. Each level represents a different aspect of AI technology, such as Deployment & Infrastructure, Evaluation & Monitoring, Foundation Models, Orchestration Frameworks, Vector Databases, Embedding Models, Data Ingestion & Extraction, Memory & Context Management, and Safety & Governance. Each level lists specific tools and technologies relevant to that category.

Tools & Frameworks Immagine
42/44

Deep Agents is a Python package developed by langchain-ai that allows users to create advanced AI agents capable of planning and executing complex tasks. It combines planning tools, sub-agents, file system access, and detailed prompts to overcome limitations of simpler agent architectures. The package supports customization and integration with LangChain models, enabling features like task decomposition, context management, subagent spawning, and long-term memory.

Tools & Frameworks Link
40/44

PaddleOCR is a powerful and lightweight OCR toolkit developed by PaddlePaddle, designed to convert PDF or image documents into structured data formats like JSON and Markdown. It supports over 100 languages and is used to bridge the gap between images/PDFs and large language models (LLMs). The toolkit is widely adopted by developers for building intelligent document applications, offering high accuracy and integration with projects such as MinerU, RAGFlow, and OmniParser.

Tools & Frameworks Link
39/44

The VRAM Calculator is a tool designed to estimate the VRAM and performance requirements for running large language models (LLMs) on Nvidia GPUs and Apple Silicon. It allows users to configure various parameters such as model quantization, sequence length, and batch size to simulate inference scenarios and assess memory usage and generation speed. The tool provides insights into memory allocation and performance metrics, helping users optimize their hardware configurations for LLM inference.

Tools & Frameworks Link
38/44

RAGLight is a modular framework designed for Retrieval-Augmented Generation (RAG), allowing easy integration of different large language models, embeddings, and vector stores. It provides a flexible and lightweight Python library to enhance the capabilities of LLMs by combining document retrieval with natural language inference. The framework includes features like seamless integration with external tools and data sources through MCP, and offers tools for building context-aware AI solutions.

Tools & Frameworks Link
32/44

MiniCPM-V is an open-source project on GitHub that presents a series of efficient multimodal large language models (MLLMs) designed for ON-DEVICE deployment. The latest version, MiniCPM-V 4.5, is capable of understanding single images, multiple images, and high-FPS videos on mobile devices. It features 8 billion parameters and claims to outperform other models like GPT-4o-latest in vision-language capabilities. The project also includes MiniCPM-o 2.6, which supports audio inputs and provides high-quality speech outputs. The repository includes links to technical reports, cookbooks, and community channels for further engagement.

Tools & Frameworks Link
29/44
The image is an infographic titled 'The Open Source RAG Stack' by ByteByteGo. It categorizes various open-source tools and frameworks into different stages of a data processing and AI pipeline. The categories include Ingest/Data Processing, Embedding Model, Retrieval & Ranking, Vector Database, LLMs, LLM Frameworks, and Frontend Frameworks. Each category lists specific tools or frameworks such as Kubeflow, HuggingFace Transformers, FAISS, Weaviate, LLaMA, LangChain, and NextJS.

The image is an infographic titled 'The Open Source RAG Stack' by ByteByteGo. It categorizes various open-source tools and frameworks into different stages of a data processing and AI pipeline. The categories include Ingest/Data Processing, Embedding Model, Retrieval & Ranking, Vector Database, LLMs, LLM Frameworks, and Frontend Frameworks. Each category lists specific tools or frameworks such as Kubeflow, HuggingFace Transformers, FAISS, Weaviate, LLaMA, LangChain, and NextJS.

Tools & Frameworks Immagine

Foundations & Theory

19/19

This paper provides a survey on data agents, which are autonomous systems designed to manage and analyze data using AI. It introduces a hierarchical taxonomy for data agents, inspired by the SAE J3016 standard, to clarify the levels of autonomy from manual operations to fully autonomous systems. The survey reviews existing research on data agents, focusing on their evolution and technical gaps, particularly the transition from procedural execution to autonomous orchestration.

Foundations & Theory Link
18/19

This paper investigates how large language models (LLMs) utilize their depth during inference. The authors propose a 'Guess-then-Refine' framework to explain the structured use of depth in LLMs, where early layers make statistical guesses that are refined in deeper layers. The study uses intermediate representations of several models to analyze prediction dynamics across layers, providing insights into computational efficiency and prediction accuracy. The paper includes case studies on part-of-speech analysis, fact recall tasks, and multiple-choice tasks to illustrate the dynamic usage of layer depth.

Foundations & Theory Link
17/19

The paper 'From Mimicry to True Intelligence (TI) -- A New Paradigm for Artificial General Intelligence' discusses a new framework for Artificial General Intelligence (AGI) that moves beyond performance-based definitions to focus on foundational cognitive architectures. It introduces the concept of True Intelligence (TI) characterized by six core components and proposes a five-level taxonomy for AGI development. The work aims to provide a clear path for developing genuinely intelligent systems by addressing the qualitative nature of intelligence and the potential emergence of consciousness.

Foundations & Theory Link
16/19

The paper 'LIMI: Less is More for Agency' introduces a novel approach to developing AI systems with autonomous agency. It challenges the conventional belief that more data leads to better agency, demonstrating that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations. The study presents LIMI, a model that achieves superior performance on the AgencyBench benchmark with significantly fewer training samples compared to state-of-the-art models. This work proposes the Agency Efficiency Principle, suggesting that machine autonomy is better cultivated through quality data rather than quantity.

Foundations & Theory Link
14/19

This paper provides a comprehensive survey of recent advances in the application of Reinforcement Learning (RL) to Large Reasoning Models (LRMs), particularly focusing on the integration with Large Language Models (LLMs) for complex reasoning tasks. It discusses foundational components, challenges in scaling RL for LRMs, and explores strategies for enhancing RL's scalability towards Artificial SuperIntelligence. The survey also covers RL's role in improving reasoning abilities in LLMs, foundational problems, training resources, and various applications.

Foundations & Theory Link
12/19

The paper “On the Theoretical Limitations of Embedding-Based Retrieval” (Google DeepMind) explores the theoretical limitations of embedding-based retrieval systems, particularly focusing on the constraints imposed by the dimensionality of embeddings in handling retrieval tasks. The authors demonstrate that even simple queries can encounter these limitations, challenging the assumption that larger models and better data can overcome these issues. They introduce a dataset called LIMIT to empirically test these theoretical findings and observe that state-of-the-art models struggle with it. The work calls for new methods to address these fundamental limitations in embedding models.

Foundations & Theory Link
10/19

The paper, titled “OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows”, presents a new benchmark to assess the capabilities of LLM-based autonomous agents in handling intricate, multi-step tasks typical of real-world productivity environments. Existing benchmarks, such as WebArena, AgentBench, and OfficeBench, primarily evaluate agents on isolated tasks, which do not reflect the contextual persistence and coordination required in realistic workflows. OdysseyBench fills this gap by introducing tasks that span multiple applications and require agents to maintain and leverage long-term context from user-agent interactions.

Foundations & Theory Link

Evaluation & Alignment

8/8

The paper titled 'Why Did Apple Fall To The Ground: Evaluating Curiosity In Large Language Model' explores the concept of curiosity in large language models (LLMs). It uses the Five-Dimensional Curiosity scale Revised (5DCR) to evaluate various dimensions of curiosity such as Information Seeking and Social Curiosity in LLMs. The study finds that while LLMs show a strong desire for knowledge, they tend to make conservative choices in uncertain situations. The research suggests that curiosity can enhance reasoning and active learning abilities in LLMs, indicating potential for future development in curiosity-driven learning.

Evaluation & Alignment Link
7/8

The paper titled 'Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History' presents a study on the behavioral consistency of large language models (LLMs). It introduces PERSIST, an evaluation framework that tests 25 open-source models across over 2 million responses using both traditional and novel LLM-adapted personality questionnaires. The study finds significant variability in personality measurements due to factors like question reordering, model scaling, reasoning modes, and conversation history, challenging assumptions about LLM stability. The findings suggest that current LLMs lack the architectural foundations for consistent behavior, posing challenges for safety-critical applications.

Evaluation & Alignment Link
6/8

This paper explores new experimental methods for assessing the welfare of language models by integrating verbal and behavioral tests. The authors investigate how language models express preferences through verbal reports and behaviors in virtual environments, and examine the consistency of these preferences across different conditions. The study highlights the potential for measuring AI welfare but also notes uncertainties and inconsistencies in the findings.

Evaluation & Alignment Link
5/8

The paper titled 'Why Do Multi-Agent LLM Systems Fail?' presents a systematic analysis of the challenges faced by Multi-Agent LLM Systems (MAS). The authors introduce MAST (Multi-Agent System Failure Taxonomy), an empirically grounded taxonomy designed to understand MAS failures. The study involves analyzing seven popular MAS frameworks across over 200 tasks and identifies 14 unique failure modes. The paper highlights the need for complex solutions to address these failures and provides a roadmap for future research in MAS development.

Evaluation & Alignment Link
4/8

This paper explores the comparison between human annotators and large language models (LLMs) in terms of sensitivity to prompt modifications. It investigates whether the brittleness observed in LLM outputs due to prompt changes is also present in human annotations. The study systematically examines the effects of prompt variations on both humans and LLMs in text classification tasks, finding that both exhibit increased brittleness to certain prompt modifications, though humans are less affected by typographical errors and label order changes.

Evaluation & Alignment Link

Experiments & Applications

26/27

The article discusses a study comparing the abilities of specialized deep neural networks and general large language models (LLMs) in predicting correlations between personality questionnaire items. The study finds that AI models, including PersonalityMap and LLMs like GPT-4o and Claude 3 Opus, outperform most humans, including laypeople and academic experts, in this task. The research highlights the strengths of both specialized AI systems and general LLMs in understanding human personality, while emphasizing the continued relevance of human expertise in personality research.

Experiments & Applications Link
25/27

This paper explores the implications of generative AI on financial stability, focusing on how AI agents affect herd behavior in trading. Through experiments with large language models, the study finds that AI agents tend to make more rational decisions than humans, potentially reducing asset price bubbles. However, AI agents can also be guided to engage in optimal herding, which has its own implications for market stability. The research highlights the dual nature of AI in financial markets, where it can both mitigate and contribute to financial vulnerabilities.

Experiments & Applications Link
22/27

The article 'Smol2Operator: Post-Training GUI Agents for Computer Use' on Hugging Face's blog discusses a lightweight vision-language model that can acquire GUI-grounded skills and evolve into an agentic GUI coder. It details the process of transforming data, creating a unified action space, and training the model in phases to achieve perception and cognition. The article provides training recipes, data-processing tools, and datasets to enable reproducibility and further research.

Experiments & Applications Link
21/27

The paper presents an AI system designed to assist scientists in writing expert-level empirical software. The system utilizes a combination of a Large Language Model (LLM) and Tree Search (TS) to enhance the quality of scientific software by exploring and integrating complex research ideas. It demonstrates effectiveness across various domains, including bioinformatics, epidemiology, geospatial analysis, and more, by outperforming existing human-developed methods. The work aims to accelerate scientific progress by automating the creation of high-quality computational tools.

Experiments & Applications Link
20/27

This paper presents a large-scale empirical study on the motivations behind code refactoring in open-source projects. It uses Large Language Models (LLMs) to analyze version control data and identify developers' motivations for refactoring. The study compares LLM-derived motivations with those reported in the literature and finds that while LLMs align with human judgment in many cases, they often provide more detailed rationale. The research highlights the potential of combining LLMs with software metrics to improve the systematic prioritization of refactoring efforts.

Experiments & Applications Link

Society & Ethics

3/5

This paper explores the impact of AI and automation on the intergenerational transmission of tacit knowledge in the workplace. It presents a model analyzing how advanced automation affects entry-level job opportunities and the acquisition of essential skills by younger workers. The study highlights a trade-off between immediate productivity gains and potential long-term economic growth challenges, suggesting that AI-driven automation could reduce the growth rate of U.S. per capita output. The paper also discusses the role of AI co-pilots in mitigating skill acquisition issues, while noting potential downsides.

Society & Ethics Link

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