๐ฏ Top Personalized Recommendations
University of Exeter
AI Summary
- Researchers analyzed Twitter data from the UK General Election 2017 to identify patterns of coordinated link sharing behavior during Italian elections, demonstrating the influence of external political events on social networks. [2]
- The study examines how football fan communities can be conduits for political influence, highlighting the importance of understanding how divisive viewpoints spread in an era of geopolitical instability, misinformation, and persistent polarization. [1]
Abstract
This paper investigates how political campaigns engaged UK football fan communities on Twitter in the aftermath of the Brexit Referendum (2016-2017). Football fandom, with its strong collective identities and tribal behaviours, offers fertile ground for political influence. Combining social network and content analysis, we examine how political discourse became embedded in football conversations. We show that a wide range of actors -- including parties, media, activist groups, and pseudonymous influencers -- mobilised support, provoked reactions, and shaped opinion within these communities. Through case studies of hashtag hijacking, embedded activism, and political "megaphones", we illustrate how campaigns leveraged fan cultures to amplify political messages. Our findings highlight mechanisms of political influence in ostensibly non-political online spaces and point toward the development of a broader framework in future work.
This paper investigates how political campaigns engaged UK football fan communities on Twitter in the aftermath of the Brexit Referendum (2016-2017). Football fandom, with its strong collective identities and tribal behaviours, offers fertile ground for political influence. Combining social network and content analysis, we examine how political discourse became embedded in football conversations. We show that a wide range of actors -- including parties, media, activist groups, and pseudonymous influencers -- mobilised support, provoked reactions, and shaped opinion within these communities. Through case studies of hashtag hijacking, embedded activism, and political "megaphones", we illustrate how campaigns leveraged fan cultures to amplify political messages. Our findings highlight mechanisms of political influence in ostensibly non-political online spaces and point toward the development of a broader framework in future work.
Why we think this paper is great for you:
This paper explores the intersection of political influence and community engagement, aligning with interests in social movements and political dynamics. Understanding how groups mobilize around shared identities is a core concern within the userโs stated interests.
This paper explores the intersection of political influence and community engagement, aligning with interests in social movements and political dynamics. Understanding how groups mobilize around shared identities is a core concern within the userโs stated interests.
Meta
AI Summary
- SWE-Bench: a comprehensive benchmark to evaluate autonomous code-writing and code-fixing agents on realistic tasks. [3]
- The combination of monorepo development and LLM-based tools like ECO underscores a trend toward holistic scale: treating an entire organizationโs code as a single evolvable system, with AI agents providing the intelligence to manage global changes, dependency analysis, and performance tuning in ways humans alone could not easily scale. [2]
- Large-scale software engineering has driven interest in AI assistance for code discovery, understanding, and consistent changes at scale. [1]
Abstract
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.
Why we think this paper is great for you:
The focus on building robust coding agents with complex toolchains resonates with interests in technological development and its potential impact on political and economic systems. It directly addresses the creation of intelligent systems.
The focus on building robust coding agents with complex toolchains resonates with interests in technological development and its potential impact on political and economic systems. It directly addresses the creation of intelligent systems.
Northeastern University
AI Summary
- RAMTN็ณป็ปๆฏไธ็งๅบไบๅ ไบคไบ็ไบบๆบๅไฝ่ฎค็ฅๅขๅผบ่ๅผ๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๅฎ็ฐๆบ่ฝ่พ ๅฉๅ็ฅ่ฏๅ ฑไบซใ ่ฏฅ็ณป็ป็ๆ ธๅฟๆๆณๆฏๅฐไบบ็ฑปไธๅฎถ็่ฎค็ฅ่ฟ็จไธ่ฎก็ฎๆบ็ณป็ป็ไฟกๆฏๅค็่ฝๅ็ปๅ่ตทๆฅ๏ผไป่ๅฎ็ฐ้ซๆ็ๅณ็ญๆฏๆๅ็ฅ่ฏๆจ็ใ RAMTN็ณป็ป็ๅบ็จ้ขๅๅ ๆฌๆ่ตใๅป็ๅๆ่ฒ็ญๅคไธช้ขๅ๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๆ้ซๅณ็ญๅ็กฎๆงๅๆ็ใ ๅ ไบคไบ๏ผMeta-Interaction๏ผ๏ผไธ็งๅฐไบบ็ฑป่ฎค็ฅ่ฟ็จไธ่ฎก็ฎๆบ็ณป็ปไฟกๆฏๅค็่ฝๅ็ปๅ่ตทๆฅ็ๆๆฏ๏ผๆจๅจๅฎ็ฐ้ซๆ็ๅณ็ญๆฏๆๅ็ฅ่ฏๆจ็ใ ไบบๆบๅไฝ่ฎค็ฅๅขๅผบ่ๅผ๏ผHuman-Machine Collaborative Cognition Enhancement Paradigm๏ผ๏ผไธ็งๅบไบๅ ไบคไบ็ๆกๆถ๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๅฎ็ฐๆบ่ฝ่พ ๅฉๅ็ฅ่ฏๅ ฑไบซใ RAMTN็ณป็ปๆฏไธ็งๅๆฐๆง็่งฃๅณๆนๆก๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๆ้ซๅณ็ญๅ็กฎๆงๅๆ็ใ ่ฏฅ็ณป็ป็ๅบ็จ้ขๅๅ ๆฌๆ่ตใๅป็ๅๆ่ฒ็ญๅคไธช้ขๅ๏ผๅ ทๆๅนฟๆณ็ๆฝๅๅๅๆฏใ ่ฏฅ็ณป็ป็ๅผๅๅๅบ็จไพ่ตไบๅคง้็ๆฐๆฎๅไฟกๆฏ่ตๆบ๏ผๅฏ่ฝๅญๅจๆฐๆฎ่ดจ้ๅๅฏ้ ๆง็้ฎ้ขใ ่ฏฅ็ณป็ป็ๅฎๅ จๆงๅ้็งไฟๆค้่ฆ่ฟไธๆญฅ็ ็ฉถๅ่งฃๅณใ ๅ ไบคไบๆๆฏๅจๅณ็ญๆฏๆๅ็ฅ่ฏๆจ็้ขๅๆๅนฟๆณ็ๅบ็จๅ็ ็ฉถใ [3]
Abstract
Currently, there exists a fundamental divide between the "cognitive black box" (implicit intuition) of human experts and the "computational black box" (untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of "human-AI collaborative cognitive enhancement," aiming to transform the dual black boxes into a composable, auditable, and extensible "functional white-box" system through structured "meta-interaction." The core breakthrough lies in the "plug-and-play cognitive framework"--a computable knowledge package that can be extracted from expert dialogues and loaded into the Recursive Adversarial Meta-Thinking Network (RAMTN). This enables expert thinking, such as medical diagnostic logic and teaching intuition, to be converted into reusable and scalable public assets, realizing a paradigm shift from "AI as a tool" to "AI as a thinking partner." This work not only provides the first engineering proof for "cognitive equity" but also opens up a new path for AI governance: constructing a verifiable and intervenable governance paradigm through "transparency of interaction protocols" rather than prying into the internal mechanisms of models. The framework is open-sourced to promote technology for good and cognitive inclusion. This paper is an independent exploratory research conducted by the author. All content presented, including the theoretical framework (RAMTN), methodology (meta-interaction), system implementation, and case validation, constitutes the author's individual research achievements.
Currently, there exists a fundamental divide between the "cognitive black box" (implicit intuition) of human experts and the "computational black box" (untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of "human-AI collaborative cognitive enhancement," aiming to transform the dual black boxes into a composable, auditable, and extensible "functional white-box" system through structured "meta-interaction." The core breakthrough lies in the "plug-and-play cognitive framework"--a computable knowledge package that can be extracted from expert dialogues and loaded into the Recursive Adversarial Meta-Thinking Network (RAMTN). This enables expert thinking, such as medical diagnostic logic and teaching intuition, to be converted into reusable and scalable public assets, realizing a paradigm shift from "AI as a tool" to "AI as a thinking partner." This work not only provides the first engineering proof for "cognitive equity" but also opens up a new path for AI governance: constructing a verifiable and intervenable governance paradigm through "transparency of interaction protocols" rather than prying into the internal mechanisms of models. The framework is open-sourced to promote technology for good and cognitive inclusion. This paper is an independent exploratory research conducted by the author. All content presented, including the theoretical framework (RAMTN), methodology (meta-interaction), system implementation, and case validation, constitutes the author's individual research achievements.
Why we think this paper is great for you:
The exploration of human-AI collaboration and addressing the โblack boxโ problem of AI decision-making aligns directly with interests in political philosophy and the ethical implications of intelligent systems.
The exploration of human-AI collaboration and addressing the โblack boxโ problem of AI decision-making aligns directly with interests in political philosophy and the ethical implications of intelligent systems.
BenGurion University of
Abstract
During data analysis, we are often perplexed by certain disparities observed between two groups of interest within a dataset. To better understand an observed disparity, we need explanations that can pinpoint the data regions where the disparity is most pronounced, along with its causes, i.e., factors that alleviate or exacerbate the disparity. This task is complex and tedious, particularly for large and high-dimensional datasets, demanding an automatic system for discovering explanations (data regions and causes) of an observed disparity. It is critical that explanations for disparities are not only interpretable but also actionable-enabling users to make informed, data-driven decisions. This requires explanations to go beyond surface-level correlations and instead capture causal relationships. We introduce ExDis, a framework for discovering causal Explanations for Disparities between two groups of interest. ExDis identifies data regions (subpopulations) where disparities are most pronounced (or reversed), and associates specific factors that causally contribute to the disparity within each identified data region. We formally define the ExDis framework and the associated optimization problem, analyze its complexity, and develop an efficient algorithm to solve the problem. Through extensive experiments over three real-world datasets, we demonstrate that ExDis generates meaningful causal explanations, outperforms prior methods, and scales effectively to handle large, high-dimensional datasets.
During data analysis, we are often perplexed by certain disparities observed between two groups of interest within a dataset. To better understand an observed disparity, we need explanations that can pinpoint the data regions where the disparity is most pronounced, along with its causes, i.e., factors that alleviate or exacerbate the disparity. This task is complex and tedious, particularly for large and high-dimensional datasets, demanding an automatic system for discovering explanations (data regions and causes) of an observed disparity. It is critical that explanations for disparities are not only interpretable but also actionable-enabling users to make informed, data-driven decisions. This requires explanations to go beyond surface-level correlations and instead capture causal relationships. We introduce ExDis, a framework for discovering causal Explanations for Disparities between two groups of interest. ExDis identifies data regions (subpopulations) where disparities are most pronounced (or reversed), and associates specific factors that causally contribute to the disparity within each identified data region. We formally define the ExDis framework and the associated optimization problem, analyze its complexity, and develop an efficient algorithm to solve the problem. Through extensive experiments over three real-world datasets, we demonstrate that ExDis generates meaningful causal explanations, outperforms prior methods, and scales effectively to handle large, high-dimensional datasets.
Why we think this paper is great for you:
This paperโs investigation into understanding disparities and causal explanations is relevant to the userโs interest in political economy and the analysis of trends within social and political systems.
This paperโs investigation into understanding disparities and causal explanations is relevant to the userโs interest in political economy and the analysis of trends within social and political systems.
Perplexity
AI Summary
- The agent is used primarily for productivity-related tasks (36% of all queries), followed by learning, media, and shopping. [3]
- Research, document editing, and shopping-related tasks appear consistently across occupation clusters. [3]
- Knowledge-intensive sectors like digital technology, entrepreneurship, finance, and academia tend to use the agent for research and learning-related tasks. [3]
- Productivity and learning topics are the most sticky, while travel is the least sticky. [2]
- Users' first queries often fall into productivity, learning, or media topics, but over time, there's a shift towards more cognitively oriented use cases. [1]
Abstract
This paper presents the first large-scale field study of the adoption, usage intensity, and use cases of general-purpose AI agents operating in open-world web environments. Our analysis centers on Comet, an AI-powered browser developed by Perplexity, and its integrated agent, Comet Assistant. Drawing on hundreds of millions of anonymized user interactions, we address three fundamental questions: Who is using AI agents? How intensively are they using them? And what are they using them for? Our findings reveal substantial heterogeneity in adoption and usage across user segments. Earlier adopters, users in countries with higher GDP per capita and educational attainment, and individuals working in digital or knowledge-intensive sectors -- such as digital technology, academia, finance, marketing, and entrepreneurship -- are more likely to adopt or actively use the agent. To systematically characterize the substance of agent usage, we introduce a hierarchical agentic taxonomy that organizes use cases across three levels: topic, subtopic, and task. The two largest topics, Productivity & Workflow and Learning & Research, account for 57% of all agentic queries, while the two largest subtopics, Courses and Shopping for Goods, make up 22%. The top 10 out of 90 tasks represent 55% of queries. Personal use constitutes 55% of queries, while professional and educational contexts comprise 30% and 16%, respectively. In the short term, use cases exhibit strong stickiness, but over time users tend to shift toward more cognitively oriented topics. The diffusion of increasingly capable AI agents carries important implications for researchers, businesses, policymakers, and educators, inviting new lines of inquiry into this rapidly emerging class of AI capabilities.
This paper presents the first large-scale field study of the adoption, usage intensity, and use cases of general-purpose AI agents operating in open-world web environments. Our analysis centers on Comet, an AI-powered browser developed by Perplexity, and its integrated agent, Comet Assistant. Drawing on hundreds of millions of anonymized user interactions, we address three fundamental questions: Who is using AI agents? How intensively are they using them? And what are they using them for? Our findings reveal substantial heterogeneity in adoption and usage across user segments. Earlier adopters, users in countries with higher GDP per capita and educational attainment, and individuals working in digital or knowledge-intensive sectors -- such as digital technology, academia, finance, marketing, and entrepreneurship -- are more likely to adopt or actively use the agent. To systematically characterize the substance of agent usage, we introduce a hierarchical agentic taxonomy that organizes use cases across three levels: topic, subtopic, and task. The two largest topics, Productivity & Workflow and Learning & Research, account for 57% of all agentic queries, while the two largest subtopics, Courses and Shopping for Goods, make up 22%. The top 10 out of 90 tasks represent 55% of queries. Personal use constitutes 55% of queries, while professional and educational contexts comprise 30% and 16%, respectively. In the short term, use cases exhibit strong stickiness, but over time users tend to shift toward more cognitively oriented topics. The diffusion of increasingly capable AI agents carries important implications for researchers, businesses, policymakers, and educators, inviting new lines of inquiry into this rapidly emerging class of AI capabilities.
Why we think this paper is great for you:
The study of AI agent adoption and usage offers insights into how technology is shaping social interactions and potentially influencing political processes, aligning with the userโs broader interests in activism and political systems.
The study of AI agent adoption and usage offers insights into how technology is shaping social interactions and potentially influencing political processes, aligning with the userโs broader interests in activism and political systems.
Peking University
AI Summary
- Previous research has shown that human-AI collaboration can improve performance in various tasks, including theorem discovery and proof verification. [3]
- The collaboration between human experts and an LLM is organized into three stages, starting from an informal conjecture and ending with a precise theorem and proof. [2]
- Human-AI collaboration can significantly improve mathematical proof and theorem discovery. [1]
Abstract
We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover's quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing.
We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover's quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing.
Why we think this paper is great for you:
The use of LLMs as research tools in mathematical discovery connects to interests in political theory and the nature of knowledge production, particularly concerning the role of technology in shaping intellectual pursuits.
The use of LLMs as research tools in mathematical discovery connects to interests in political theory and the nature of knowledge production, particularly concerning the role of technology in shaping intellectual pursuits.
Abstract
Foundation models (FMs) are increasingly assuming the role of the "brain" of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities -- such as GUI interaction or integrated tool use -- we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions -- spanning dataset construction, evaluation, training paradigms, and safety considerations -- for building FMs with native multi-agent intelligence.
Foundation models (FMs) are increasingly assuming the role of the "brain" of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities -- such as GUI interaction or integrated tool use -- we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions -- spanning dataset construction, evaluation, training paradigms, and safety considerations -- for building FMs with native multi-agent intelligence.
Why we think this paper is great for you:
The exploration of multi-agent foundation models aligns with the user's interest in understanding how intelligent systems can be designed to interact and influence complex social and political landscapes.
The exploration of multi-agent foundation models aligns with the user's interest in understanding how intelligent systems can be designed to interact and influence complex social and political landscapes.
AI Agents
Halmstad University
Abstract
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers operating in closed loops, and show how reliability emerges from principled componentisation (goal manager, planner, tool-router, executor, memory, verifiers, safety monitor, telemetry), disciplined interfaces (schema-constrained, validated, least-privilege tool calls), and explicit control and assurance loops. Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents - and analyse how each pattern reshapes the reliability envelope and failure modes. We distil design guidance on typed schemas, idempotency, permissioning, transactional semantics, memory provenance and hygiene, runtime governance (budgets, termination conditions), and simulate-before-actuate safeguards.
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers operating in closed loops, and show how reliability emerges from principled componentisation (goal manager, planner, tool-router, executor, memory, verifiers, safety monitor, telemetry), disciplined interfaces (schema-constrained, validated, least-privilege tool calls), and explicit control and assurance loops. Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents - and analyse how each pattern reshapes the reliability envelope and failure modes. We distil design guidance on typed schemas, idempotency, permissioning, transactional semantics, memory provenance and hygiene, runtime governance (budgets, termination conditions), and simulate-before-actuate safeguards.
AI Summary
- Multi-agent systems exchange a single 'do-everything' agent for a team of specialised agents that co-operate (or compete) under explicit protocols. [3]
- Planning- and self-improvement agents: A class of AI systems that use search and optimization techniques to solve complex problems. [3]
- Embodied and web agents: AI systems that act in the world, either physically (embodied) or through interactions with untrusted websites and enterprise systems (web). [3]
- Planning- and self-improvement agents can be prone to state explosion, speculative arithmetic errors, and over-confident selection. [3]
- Planning- and self-improvement agents deliver substantial reliability dividends when their power is channelled through explicit controllers, trustworthy verifiers, and disciplined governance of cost and side-effects. [2]
Research Automation with AI
German Cancer Research
Abstract
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana
Deep Learning
Universidad de Guanajuato
Abstract
This document reports the sequence of practices and methodologies implemented during the Big Data course. It details the workflow beginning with the processing of the Epsilon dataset through group and individual strategies, followed by text analysis and classification with RestMex and movie feature analysis with IMDb. Finally, it describes the technical implementation of a distributed computing cluster with Apache Spark on Linux using Scala.
This document reports the sequence of practices and methodologies implemented during the Big Data course. It details the workflow beginning with the processing of the Epsilon dataset through group and individual strategies, followed by text analysis and classification with RestMex and movie feature analysis with IMDb. Finally, it describes the technical implementation of a distributed computing cluster with Apache Spark on Linux using Scala.
AI Summary
- In the big data era, data completeness can be as important as algorithm sophistication. [3]
- Big Data Analytics Distributed Computing Scalability Algorithm Sophistication Data Completeness The chronological progression demonstrates that mastering big data requires a systematic approach. [3]
- The choice between local and distributed architectures is not merely about computational resources, but about the quality and completeness of the data available to the model. [2]
National University of
Abstract
Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter (PM$_{2.5}$, PM$_{10}$) in Beijing, China. Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits. Both models were trained with pollutant and precursor regressors, with NP additionally leveraging lagged dependencies. For context, two machine learning baselines (LSTM, LightGBM) and one traditional statistical model (SARIMAX) were also implemented. Performance was evaluated on a 7-day holdout using MAE, RMSE, and $R^2$. Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R^2$ above 0.94 for both pollutants. These findings demonstrate that interpretable additive models remain competitive with both traditional and complex approaches, offering a practical balance of accuracy, transparency, and ease of deployment.
Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter (PM$_{2.5}$, PM$_{10}$) in Beijing, China. Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits. Both models were trained with pollutant and precursor regressors, with NP additionally leveraging lagged dependencies. For context, two machine learning baselines (LSTM, LightGBM) and one traditional statistical model (SARIMAX) were also implemented. Performance was evaluated on a 7-day holdout using MAE, RMSE, and $R^2$. Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R^2$ above 0.94 for both pollutants. These findings demonstrate that interpretable additive models remain competitive with both traditional and complex approaches, offering a practical balance of accuracy, transparency, and ease of deployment.
AI Summary
- The study also explores the impact of different input features on the performance of the models and finds that using both air quality index and weather data improves the predictive power of the models. [3]
- AQI: Air Quality Index MAE: Mean Absolute Error The study demonstrates the effectiveness of machine learning models in predicting AQIs and highlights the importance of using both air quality index and weather data for improved predictive power. [3]
- The results of this study can be used to inform policy decisions related to air pollution control and mitigation strategies. [3]
- The study only evaluates the performance of different models on a single dataset and does not explore the generalizability of the results to other locations or datasets. [3]
- The authors do not provide any discussion on the limitations of the study, such as the potential impact of data quality issues or the lack of consideration for non-linear relationships between input features. [3]
- The paper presents a comparative study of various machine learning models for predicting air quality indices (AQIs) in Beijing, China. [2]
- The results show that the Prophet model outperforms other models in terms of accuracy, with a mean absolute error (MAE) of 4.35 ฮผg/mยณ. [1]
We did not find tons of content matching your interests we've included some additional topics that are popular. Also be aware that if the topics is not present in arxiv we wont be able to recommend it.
AI Agents
Halmstad University
Abstract
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers operating in closed loops, and show how reliability emerges from principled componentisation (goal manager, planner, tool-router, executor, memory, verifiers, safety monitor, telemetry), disciplined interfaces (schema-constrained, validated, least-privilege tool calls), and explicit control and assurance loops. Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents - and analyse how each pattern reshapes the reliability envelope and failure modes. We distil design guidance on typed schemas, idempotency, permissioning, transactional semantics, memory provenance and hygiene, runtime governance (budgets, termination conditions), and simulate-before-actuate safeguards.
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers operating in closed loops, and show how reliability emerges from principled componentisation (goal manager, planner, tool-router, executor, memory, verifiers, safety monitor, telemetry), disciplined interfaces (schema-constrained, validated, least-privilege tool calls), and explicit control and assurance loops. Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents - and analyse how each pattern reshapes the reliability envelope and failure modes. We distil design guidance on typed schemas, idempotency, permissioning, transactional semantics, memory provenance and hygiene, runtime governance (budgets, termination conditions), and simulate-before-actuate safeguards.
AI Summary
- Multi-agent systems exchange a single 'do-everything' agent for a team of specialised agents that co-operate (or compete) under explicit protocols. [3]
- Planning- and self-improvement agents: A class of AI systems that use search and optimization techniques to solve complex problems. [3]
- Embodied and web agents: AI systems that act in the world, either physically (embodied) or through interactions with untrusted websites and enterprise systems (web). [3]
- Planning- and self-improvement agents can be prone to state explosion, speculative arithmetic errors, and over-confident selection. [3]
- Planning- and self-improvement agents deliver substantial reliability dividends when their power is channelled through explicit controllers, trustworthy verifiers, and disciplined governance of cost and side-effects. [2]
Perplexity
Abstract
This paper presents the first large-scale field study of the adoption, usage intensity, and use cases of general-purpose AI agents operating in open-world web environments. Our analysis centers on Comet, an AI-powered browser developed by Perplexity, and its integrated agent, Comet Assistant. Drawing on hundreds of millions of anonymized user interactions, we address three fundamental questions: Who is using AI agents? How intensively are they using them? And what are they using them for? Our findings reveal substantial heterogeneity in adoption and usage across user segments. Earlier adopters, users in countries with higher GDP per capita and educational attainment, and individuals working in digital or knowledge-intensive sectors -- such as digital technology, academia, finance, marketing, and entrepreneurship -- are more likely to adopt or actively use the agent. To systematically characterize the substance of agent usage, we introduce a hierarchical agentic taxonomy that organizes use cases across three levels: topic, subtopic, and task. The two largest topics, Productivity & Workflow and Learning & Research, account for 57% of all agentic queries, while the two largest subtopics, Courses and Shopping for Goods, make up 22%. The top 10 out of 90 tasks represent 55% of queries. Personal use constitutes 55% of queries, while professional and educational contexts comprise 30% and 16%, respectively. In the short term, use cases exhibit strong stickiness, but over time users tend to shift toward more cognitively oriented topics. The diffusion of increasingly capable AI agents carries important implications for researchers, businesses, policymakers, and educators, inviting new lines of inquiry into this rapidly emerging class of AI capabilities.
This paper presents the first large-scale field study of the adoption, usage intensity, and use cases of general-purpose AI agents operating in open-world web environments. Our analysis centers on Comet, an AI-powered browser developed by Perplexity, and its integrated agent, Comet Assistant. Drawing on hundreds of millions of anonymized user interactions, we address three fundamental questions: Who is using AI agents? How intensively are they using them? And what are they using them for? Our findings reveal substantial heterogeneity in adoption and usage across user segments. Earlier adopters, users in countries with higher GDP per capita and educational attainment, and individuals working in digital or knowledge-intensive sectors -- such as digital technology, academia, finance, marketing, and entrepreneurship -- are more likely to adopt or actively use the agent. To systematically characterize the substance of agent usage, we introduce a hierarchical agentic taxonomy that organizes use cases across three levels: topic, subtopic, and task. The two largest topics, Productivity & Workflow and Learning & Research, account for 57% of all agentic queries, while the two largest subtopics, Courses and Shopping for Goods, make up 22%. The top 10 out of 90 tasks represent 55% of queries. Personal use constitutes 55% of queries, while professional and educational contexts comprise 30% and 16%, respectively. In the short term, use cases exhibit strong stickiness, but over time users tend to shift toward more cognitively oriented topics. The diffusion of increasingly capable AI agents carries important implications for researchers, businesses, policymakers, and educators, inviting new lines of inquiry into this rapidly emerging class of AI capabilities.
AI Summary
- The agent is used primarily for productivity-related tasks (36% of all queries), followed by learning, media, and shopping. [3]
- Research, document editing, and shopping-related tasks appear consistently across occupation clusters. [3]
- Knowledge-intensive sectors like digital technology, entrepreneurship, finance, and academia tend to use the agent for research and learning-related tasks. [3]
- Productivity and learning topics are the most sticky, while travel is the least sticky. [2]
- Users' first queries often fall into productivity, learning, or media topics, but over time, there's a shift towards more cognitively oriented use cases. [1]
AI and Society
Northeastern University
Abstract
Currently, there exists a fundamental divide between the "cognitive black box" (implicit intuition) of human experts and the "computational black box" (untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of "human-AI collaborative cognitive enhancement," aiming to transform the dual black boxes into a composable, auditable, and extensible "functional white-box" system through structured "meta-interaction." The core breakthrough lies in the "plug-and-play cognitive framework"--a computable knowledge package that can be extracted from expert dialogues and loaded into the Recursive Adversarial Meta-Thinking Network (RAMTN). This enables expert thinking, such as medical diagnostic logic and teaching intuition, to be converted into reusable and scalable public assets, realizing a paradigm shift from "AI as a tool" to "AI as a thinking partner." This work not only provides the first engineering proof for "cognitive equity" but also opens up a new path for AI governance: constructing a verifiable and intervenable governance paradigm through "transparency of interaction protocols" rather than prying into the internal mechanisms of models. The framework is open-sourced to promote technology for good and cognitive inclusion. This paper is an independent exploratory research conducted by the author. All content presented, including the theoretical framework (RAMTN), methodology (meta-interaction), system implementation, and case validation, constitutes the author's individual research achievements.
Currently, there exists a fundamental divide between the "cognitive black box" (implicit intuition) of human experts and the "computational black box" (untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of "human-AI collaborative cognitive enhancement," aiming to transform the dual black boxes into a composable, auditable, and extensible "functional white-box" system through structured "meta-interaction." The core breakthrough lies in the "plug-and-play cognitive framework"--a computable knowledge package that can be extracted from expert dialogues and loaded into the Recursive Adversarial Meta-Thinking Network (RAMTN). This enables expert thinking, such as medical diagnostic logic and teaching intuition, to be converted into reusable and scalable public assets, realizing a paradigm shift from "AI as a tool" to "AI as a thinking partner." This work not only provides the first engineering proof for "cognitive equity" but also opens up a new path for AI governance: constructing a verifiable and intervenable governance paradigm through "transparency of interaction protocols" rather than prying into the internal mechanisms of models. The framework is open-sourced to promote technology for good and cognitive inclusion. This paper is an independent exploratory research conducted by the author. All content presented, including the theoretical framework (RAMTN), methodology (meta-interaction), system implementation, and case validation, constitutes the author's individual research achievements.
AI Summary
- RAMTN็ณป็ปๆฏไธ็งๅบไบๅ ไบคไบ็ไบบๆบๅไฝ่ฎค็ฅๅขๅผบ่ๅผ๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๅฎ็ฐๆบ่ฝ่พ ๅฉๅ็ฅ่ฏๅ ฑไบซใ ่ฏฅ็ณป็ป็ๆ ธๅฟๆๆณๆฏๅฐไบบ็ฑปไธๅฎถ็่ฎค็ฅ่ฟ็จไธ่ฎก็ฎๆบ็ณป็ป็ไฟกๆฏๅค็่ฝๅ็ปๅ่ตทๆฅ๏ผไป่ๅฎ็ฐ้ซๆ็ๅณ็ญๆฏๆๅ็ฅ่ฏๆจ็ใ RAMTN็ณป็ป็ๅบ็จ้ขๅๅ ๆฌๆ่ตใๅป็ๅๆ่ฒ็ญๅคไธช้ขๅ๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๆ้ซๅณ็ญๅ็กฎๆงๅๆ็ใ ๅ ไบคไบ๏ผMeta-Interaction๏ผ๏ผไธ็งๅฐไบบ็ฑป่ฎค็ฅ่ฟ็จไธ่ฎก็ฎๆบ็ณป็ปไฟกๆฏๅค็่ฝๅ็ปๅ่ตทๆฅ็ๆๆฏ๏ผๆจๅจๅฎ็ฐ้ซๆ็ๅณ็ญๆฏๆๅ็ฅ่ฏๆจ็ใ ไบบๆบๅไฝ่ฎค็ฅๅขๅผบ่ๅผ๏ผHuman-Machine Collaborative Cognition Enhancement Paradigm๏ผ๏ผไธ็งๅบไบๅ ไบคไบ็ๆกๆถ๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๅฎ็ฐๆบ่ฝ่พ ๅฉๅ็ฅ่ฏๅ ฑไบซใ RAMTN็ณป็ปๆฏไธ็งๅๆฐๆง็่งฃๅณๆนๆก๏ผๆจๅจ้่ฟๆๅไธๅฎถๅณ็ญๆกๆถๆฅๆ้ซๅณ็ญๅ็กฎๆงๅๆ็ใ ่ฏฅ็ณป็ป็ๅบ็จ้ขๅๅ ๆฌๆ่ตใๅป็ๅๆ่ฒ็ญๅคไธช้ขๅ๏ผๅ ทๆๅนฟๆณ็ๆฝๅๅๅๆฏใ ่ฏฅ็ณป็ป็ๅผๅๅๅบ็จไพ่ตไบๅคง้็ๆฐๆฎๅไฟกๆฏ่ตๆบ๏ผๅฏ่ฝๅญๅจๆฐๆฎ่ดจ้ๅๅฏ้ ๆง็้ฎ้ขใ ่ฏฅ็ณป็ป็ๅฎๅ จๆงๅ้็งไฟๆค้่ฆ่ฟไธๆญฅ็ ็ฉถๅ่งฃๅณใ ๅ ไบคไบๆๆฏๅจๅณ็ญๆฏๆๅ็ฅ่ฏๆจ็้ขๅๆๅนฟๆณ็ๅบ็จๅ็ ็ฉถใ [3]
Research Automation with AI
Peking University
Abstract
We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover's quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing.
We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover's quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing.
AI Summary
- Previous research has shown that human-AI collaboration can improve performance in various tasks, including theorem discovery and proof verification. [3]
- The collaboration between human experts and an LLM is organized into three stages, starting from an informal conjecture and ending with a precise theorem and proof. [2]
- Human-AI collaboration can significantly improve mathematical proof and theorem discovery. [1]
German Cancer Research
Abstract
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana
AGI: Artificial General Intelligence
Meta
Abstract
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.
AI Summary
- SWE-Bench: a comprehensive benchmark to evaluate autonomous code-writing and code-fixing agents on realistic tasks. [3]
- The combination of monorepo development and LLM-based tools like ECO underscores a trend toward holistic scale: treating an entire organizationโs code as a single evolvable system, with AI agents providing the intelligence to manage global changes, dependency analysis, and performance tuning in ways humans alone could not easily scale. [2]
- Large-scale software engineering has driven interest in AI assistance for code discovery, understanding, and consistent changes at scale. [1]
Abstract
Foundation models (FMs) are increasingly assuming the role of the "brain" of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities -- such as GUI interaction or integrated tool use -- we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions -- spanning dataset construction, evaluation, training paradigms, and safety considerations -- for building FMs with native multi-agent intelligence.
Foundation models (FMs) are increasingly assuming the role of the "brain" of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities -- such as GUI interaction or integrated tool use -- we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions -- spanning dataset construction, evaluation, training paradigms, and safety considerations -- for building FMs with native multi-agent intelligence.
Deep Learning
Universidad de Guanajuato
Abstract
This document reports the sequence of practices and methodologies implemented during the Big Data course. It details the workflow beginning with the processing of the Epsilon dataset through group and individual strategies, followed by text analysis and classification with RestMex and movie feature analysis with IMDb. Finally, it describes the technical implementation of a distributed computing cluster with Apache Spark on Linux using Scala.
This document reports the sequence of practices and methodologies implemented during the Big Data course. It details the workflow beginning with the processing of the Epsilon dataset through group and individual strategies, followed by text analysis and classification with RestMex and movie feature analysis with IMDb. Finally, it describes the technical implementation of a distributed computing cluster with Apache Spark on Linux using Scala.
AI Summary
- In the big data era, data completeness can be as important as algorithm sophistication. [3]
- Big Data Analytics Distributed Computing Scalability Algorithm Sophistication Data Completeness The chronological progression demonstrates that mastering big data requires a systematic approach. [3]
- The choice between local and distributed architectures is not merely about computational resources, but about the quality and completeness of the data available to the model. [2]
National University of
Abstract
Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter (PM$_{2.5}$, PM$_{10}$) in Beijing, China. Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits. Both models were trained with pollutant and precursor regressors, with NP additionally leveraging lagged dependencies. For context, two machine learning baselines (LSTM, LightGBM) and one traditional statistical model (SARIMAX) were also implemented. Performance was evaluated on a 7-day holdout using MAE, RMSE, and $R^2$. Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R^2$ above 0.94 for both pollutants. These findings demonstrate that interpretable additive models remain competitive with both traditional and complex approaches, offering a practical balance of accuracy, transparency, and ease of deployment.
Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter (PM$_{2.5}$, PM$_{10}$) in Beijing, China. Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits. Both models were trained with pollutant and precursor regressors, with NP additionally leveraging lagged dependencies. For context, two machine learning baselines (LSTM, LightGBM) and one traditional statistical model (SARIMAX) were also implemented. Performance was evaluated on a 7-day holdout using MAE, RMSE, and $R^2$. Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R^2$ above 0.94 for both pollutants. These findings demonstrate that interpretable additive models remain competitive with both traditional and complex approaches, offering a practical balance of accuracy, transparency, and ease of deployment.
AI Summary
- The study also explores the impact of different input features on the performance of the models and finds that using both air quality index and weather data improves the predictive power of the models. [3]
- AQI: Air Quality Index MAE: Mean Absolute Error The study demonstrates the effectiveness of machine learning models in predicting AQIs and highlights the importance of using both air quality index and weather data for improved predictive power. [3]
- The results of this study can be used to inform policy decisions related to air pollution control and mitigation strategies. [3]
- The study only evaluates the performance of different models on a single dataset and does not explore the generalizability of the results to other locations or datasets. [3]
- The authors do not provide any discussion on the limitations of the study, such as the potential impact of data quality issues or the lack of consideration for non-linear relationships between input features. [3]
- The paper presents a comparative study of various machine learning models for predicting air quality indices (AQIs) in Beijing, China. [2]
- The results show that the Prophet model outperforms other models in terms of accuracy, with a mean absolute error (MAE) of 4.35 ฮผg/mยณ. [1]
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