Abstract
This paper develops a model-free framework for static fixed-income pricing and the replication of liability cash flows. We show that the absence of static arbitrage across a universe of fixed-income instruments is equivalent to the existence of a strictly positive discount curve that reproduces all observed market prices. We then study the replication and super-replication of liabilities and establish conditions ensuring the existence of least-cost super-replicating portfolios, including a rigorous interpretation of swap--repo replication within this static framework. The results provide a unified foundation for discount-curve construction and liability-driven investment, with direct relevance for economic capital assessment and regulatory practice.

Why we are recommending this paper?
Due to your Interest in: Economic Inequality

Abstract
Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.

AI Insights

• The four adaptation paradigms in agentic AI are A1 (agent adaptation with tool-execution result as signal), A2 (agent adaptation with agent output as signal), T1 (tool adaptation with agent output as signal), and T2 (tool adaptation with agent output as signal). [3]
• A1 methods use the actual outcomes of external tool invocations as feedback to refine an agent's behavior. [3]
• Recent A1 methods include Toolformer, TRICE, Gorilla, ToolAlpaca, and others, which have achieved state-of-the-art performance on various tasks such as question-answering, math reasoning, and web search. [3]
• The RLVR (Reinforcement Learning with Value Regularization) framework is a key component of many recent A1 methods, allowing for more efficient learning and better generalization. [3]
• A2 methods focus on evaluating an agent's own outputs, rather than relying on tool execution results as feedback. [3]
• The development timeline of A1 methods shows a shift from earlier methods such as SFT (Self-Modifying Task) and DPO (Dynamic Policy Optimization) to more recent RLVR-based methods. [3]
• Recent A1 methods have achieved state-of-the-art performance on various tasks, including question-answering, math reasoning, web search, and text-to-SQL. [3]
• The development timeline of A1 methods shows a rapid growth in research, with many new methods being proposed between 2023 and 2025. [2]
• T1 and T2 methods involve adapting tools based on the agent's output, which can be useful in scenarios where the agent needs to interact with multiple tools or environments. [1]

Why we are recommending this paper?
Due to your Interest in: AI Agents

Abstract
This essay explores a techno-artistic experiment that reanimates a 1980s East German typewriter using a contemporary AI language model. Situated at the intersection of media archaeology and speculative design, the project questions dominant narratives of progress by embedding generative AI in an obsolete, tactile interface. Through public exhibitions and aesthetic intervention, we demonstrate how slowness, friction, and material render artificial intelligence not only visible but open to critical inquiry. Drawing on concepts such as zombie media, technostalgia, and speculative design, we argue that reappropriating outdated technologies enables new forms of critical engagement. Erika - the AI-enabled typewriter - functions as both interface and interruption, making space for reflection, irony, and cultural memory. In a moment of accelerated digital abstraction, projects like this foreground the value of deliberate slowness, experiential materiality, and historical depth. We conclude by advocating for a historicist design sensibility that challenges presentism and reorients human-machine interaction toward alternative, perceived futures.

AI Insights

• The article discusses a project called Erika that embeds AI in an obsolete device, reframing it as a conversation rather than a tool. [3]
• Erika's materiality and historical context evoke histories of control, collectivity, and latency, making it a unique interface for interacting with AI. [3]
• The project challenges the trajectory of AI becoming imperceptible and opaque by making visible what has become hidden. [3]
• Technostalgia: a nostalgic longing for past technologies, often used to critique the present and imagine alternative futures. [3]
• Material friction: the idea that material objects can deepen engagement and foster critical awareness by introducing obstacles or challenges in interaction design. [3]
• The next decade of 'things' will not be defined by novelty, but by recognition, with interfaces that slow us down and demand listening. [3]
• Technostalgia as critique is presented as an active disruption that reframes AI as contested terrain where form matters and history lingers. [2]

Why we are recommending this paper?
Due to your Interest in: AI and Society

Abstract
The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

AI Insights

• ORKG (Open Research Knowledge Graph): A large-scale knowledge graph that integrates various sources of research information. [3]
• The paper discusses the development of an AI-supported research platform called Tib Aissistant, which aims to facilitate research across various life cycles. [2]
• Tib Aissistant's architecture is based on a modular design, with components for prompt engineering, tool integration, and knowledge graph-based search. [1]

Why we are recommending this paper?
Due to your Interest in: Research Automation with AI

Abstract
Agricultural regions in rural areas face damage from climate-related risks, including droughts, heavy rainfall, and shifting weather patterns. Prior research calls for adaptive risk-management solutions and decision-making strategies. To this end, artificial intelligence (AI), particularly agentic AI, offers a promising path forward. Agentic AI systems consist of autonomous, specialized agents capable of solving complex, dynamic tasks. While past systems have relied on single-agent models or have used multi-agent frameworks only for static functions, there is a growing need for architectures that support dynamic collaborative reasoning and context-aware outputs. To bridge this gap, we present AgroAskAI, a multi-agent reasoning system for climate adaptation decision support in agriculture, with a focus on vulnerable rural communities. AgroAskAI features a modular, role-specialized architecture that uses a chain-of-responsibility approach to coordinate autonomous agents, integrating real-time tools and datasets. The system has built-in governance mechanisms that mitigate hallucination and enable internal feedback for coherent, locally relevant strategies. The system also supports multilingual interactions, making it accessible to non-English-speaking farmers. Experiments on common agricultural queries related to climate adaptation show that, with additional tools and prompt refinement, AgroAskAI delivers more actionable, grounded, and inclusive outputs. Our experimental results highlight the potential of agentic AI for sustainable and accountable decision support in climate adaptation for agriculture.

AI Insights

• ChatGPT: A conversational AI model that provides general information on a wide range of topics. [3]
• The system's ability to analyze historical weather data and provide specific recommendations for farmers in Kitui, Kenya demonstrates its effectiveness in adapting to local climate conditions. [3]
• The AgroAskAI system provides a detailed and practical agricultural adaptation strategy tailored to the region of Kitui, Kenya. [2]
• CROPWAT: A software tool used for crop water management and irrigation planning. [1]

Why we are recommending this paper?
Due to your Interest in: AGI: Artificial General Intelligence

Abstract
Cryogenic electron microscopy (Cryo-EM) has become an essential tool for capturing high-resolution biological structures. Despite its advantage in visualizations, the large storage size of Cryo-EM data file poses significant challenges for researchers and educators. This paper investigates the application of deep learning, specifically implicit neural representation (INR), to compress Cryo-EM biological data. The proposed approach first extracts the binary map of each file according to the density threshold. The density map is highly repetitive, ehich can be effectively compressed by GZIP. The neural network then trains to encode spatial density information, allowing the storage of network parameters and learnable latent vectors. To improve reconstruction accuracy, I further incorporate the positional encoding to enhance spatial representation and a weighted Mean Squared Error (MSE) loss function to balance density distribution variations. Using this approach, my aim is to provide a practical and efficient biological data compression solution that can be used for educational and research purpose, while maintaining a reasonable compression ratio and reconstruction quality from file to file.

AI Insights

• The project establishes Implicit Neural Representation (INR) as a promising framework for Cryo-EM data compression, balancing efficiency and fidelity. [3]
• The method achieves a compression ratio of approximately 10:1, reducing file sizes from 414 MB to around 40 MB, outperforming traditional GZIP compression. [3]
• Experimental results demonstrate notable progress in surpassing GZIP's compression ratio and achieving high reconstruction quality for structurally significant areas. [3]
• GZIP: a file format used for data compression that typically yields lower ratios on complex Cryo-EM data. [3]
• INR (Implicit Neural Representation): a framework for representing scenes or data using neural networks, allowing for efficient and accurate reconstruction. [3]
• Future work may focus on automating hyperparameter tuning and refining the INR architecture to reduce low-density errors. [3]
• Limitations persist in low-density regions, where mean errors exceed 1000% due to noise and sparsity. [3]
• The project establishes INR as a promising tool for Cryo-EM data management, particularly in resource-limited settings. [2]
• Cryo-EM (Cryogenic Electron Microscopy): a technique used to determine the three-dimensional structure of macromolecules, such as proteins. [1]

Why we are recommending this paper?
Due to your Interest in: Deep Learning

Abstract
This paper provides a review of deep learning applications in scene understanding in autonomous robots, including innovations in object detection, semantic and instance segmentation, depth estimation, 3D reconstruction, and visual SLAM. It emphasizes how these techniques address limitations of traditional geometric models, improve depth perception in real time despite occlusions and textureless surfaces, and enhance semantic reasoning to understand the environment better. When these perception modules are integrated into dynamic and unstructured environments, they become more effective in decisionmaking, navigation and interaction. Lastly, the review outlines the existing problems and research directions to advance learning-based scene understanding of autonomous robots.

Why we are recommending this paper?
Due to your Interest in: Deep Learning

Abstract
Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.

AI Insights

• The four adaptation paradigms in agentic AI are A1 (agent adaptation with tool-execution result as signal), A2 (agent adaptation with agent output as signal), T1 (tool adaptation with agent output as signal), and T2 (tool adaptation with agent output as signal). [3]
• A1 methods use the actual outcomes of external tool invocations as feedback to refine an agent's behavior. [3]
• Recent A1 methods include Toolformer, TRICE, Gorilla, ToolAlpaca, and others, which have achieved state-of-the-art performance on various tasks such as question-answering, math reasoning, and web search. [3]
• The RLVR (Reinforcement Learning with Value Regularization) framework is a key component of many recent A1 methods, allowing for more efficient learning and better generalization. [3]
• A2 methods focus on evaluating an agent's own outputs, rather than relying on tool execution results as feedback. [3]
• The development timeline of A1 methods shows a shift from earlier methods such as SFT (Self-Modifying Task) and DPO (Dynamic Policy Optimization) to more recent RLVR-based methods. [3]
• Recent A1 methods have achieved state-of-the-art performance on various tasks, including question-answering, math reasoning, web search, and text-to-SQL. [3]
• The development timeline of A1 methods shows a rapid growth in research, with many new methods being proposed between 2023 and 2025. [2]
• T1 and T2 methods involve adapting tools based on the agent's output, which can be useful in scenarios where the agent needs to interact with multiple tools or environments. [1]

Why we are recommending this paper?
Due to your Interest in: AI Agents

Abstract
The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegate multiple tasks to different agents, and even write a full piece of software purely out of natural language. In reality, what roles agents play in professional software development remains in question. This paper investigates how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments. Through field observations (N=13) and qualitative surveys (N=99), we find that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes, employing strategies for controlling agent behavior leveraging their expertise. In addition, experienced developers feel overall positive about incorporating agents into software development given their confidence in complementing the agents' limitations. Our results shed light on the value of software development best practices in effective use of agents, suggest the kinds of tasks for which agents may be suitable, and point towards future opportunities for better agentic interfaces and agentic use guidelines.

AI Insights

• The findings on this topic were largely from observations. [3]
• They also use their own expertise to make sure the AI's suggestions are good ones. [3]
• Experienced software developers control the software design and implementation by prompting and planning with clear context and explicit instructions, and letting agents work on only a few tasks at a time. [2]
• Agentic task suitability: The degree to which AI-powered tools are suitable for a particular software development task. [1]

Why we are recommending this paper?
Due to your Interest in: AI Agents

Abstract
This essay explores a techno-artistic experiment that reanimates a 1980s East German typewriter using a contemporary AI language model. Situated at the intersection of media archaeology and speculative design, the project questions dominant narratives of progress by embedding generative AI in an obsolete, tactile interface. Through public exhibitions and aesthetic intervention, we demonstrate how slowness, friction, and material render artificial intelligence not only visible but open to critical inquiry. Drawing on concepts such as zombie media, technostalgia, and speculative design, we argue that reappropriating outdated technologies enables new forms of critical engagement. Erika - the AI-enabled typewriter - functions as both interface and interruption, making space for reflection, irony, and cultural memory. In a moment of accelerated digital abstraction, projects like this foreground the value of deliberate slowness, experiential materiality, and historical depth. We conclude by advocating for a historicist design sensibility that challenges presentism and reorients human-machine interaction toward alternative, perceived futures.

AI Insights

• The article discusses a project called Erika that embeds AI in an obsolete device, reframing it as a conversation rather than a tool. [3]
• Erika's materiality and historical context evoke histories of control, collectivity, and latency, making it a unique interface for interacting with AI. [3]
• The project challenges the trajectory of AI becoming imperceptible and opaque by making visible what has become hidden. [3]
• Technostalgia: a nostalgic longing for past technologies, often used to critique the present and imagine alternative futures. [3]
• Material friction: the idea that material objects can deepen engagement and foster critical awareness by introducing obstacles or challenges in interaction design. [3]
• The next decade of 'things' will not be defined by novelty, but by recognition, with interfaces that slow us down and demand listening. [3]
• Technostalgia as critique is presented as an active disruption that reframes AI as contested terrain where form matters and history lingers. [2]

Why we are recommending this paper?
Due to your Interest in: AI and Society

Abstract
Artificial intelligence systems are increasingly deployed in domains that shape human behaviour, institutional decision-making, and societal outcomes. Existing responsible AI and governance efforts provide important normative principles but often lack enforceable engineering mechanisms that operate throughout the system lifecycle. This paper introduces the Social Responsibility Stack (SRS), a six-layer architectural framework that embeds societal values into AI systems as explicit constraints, safeguards, behavioural interfaces, auditing mechanisms, and governance processes. SRS models responsibility as a closed-loop supervisory control problem over socio-technical systems, integrating design-time safeguards with runtime monitoring and institutional oversight. We develop a unified constraint-based formulation, introduce safety-envelope and feedback interpretations, and show how fairness, autonomy, cognitive burden, and explanation quality can be continuously monitored and enforced. Case studies in clinical decision support, cooperative autonomous vehicles, and public-sector systems illustrate how SRS translates normative objectives into actionable engineering and operational controls. The framework bridges ethics, control theory, and AI governance, providing a practical foundation for accountable, adaptive, and auditable socio-technical AI systems.

AI Insights

• The Social Responsibility Stack (SRS) is a framework for ensuring that AI systems are designed and deployed in a responsible manner. [2]

Why we are recommending this paper?
Due to your Interest in: AI and Society

Abstract
The rapidly growing popularity of adopting Artificial Intelligence (AI), and specifically Large Language Models (LLMs), is having a widespread impact throughout society, including the academic domain. AI-supported research has the potential to support researchers with tasks across the entire research life cycle. In this work, we demonstrate the TIB AIssistant, an AI-supported research platform providing support throughout the research life cycle. The AIssistant consists of a collection of assistants, each responsible for a specific research task. In addition, tools are provided to give access to external scholarly services. Generated data is stored in the assets and can be exported as an RO-Crate bundle to provide transparency and enhance reproducibility of the research project. We demonstrate the AIssistant's main functionalities by means of a sequential walk-through of assistants, interacting with each other to generate sections for a draft research paper. In the end, with the AIssistant, we lay the foundation for a larger agenda of providing a community-maintained platform for AI-supported research.

Why we are recommending this paper?
Due to your Interest in: Research Automation with AI

Abstract
The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

AI Insights

• ORKG (Open Research Knowledge Graph): A large-scale knowledge graph that integrates various sources of research information. [3]
• The paper discusses the development of an AI-supported research platform called Tib Aissistant, which aims to facilitate research across various life cycles. [2]
• Tib Aissistant's architecture is based on a modular design, with components for prompt engineering, tool integration, and knowledge graph-based search. [1]

Why we are recommending this paper?
Due to your Interest in: Research Automation with AI

Abstract
Agricultural regions in rural areas face damage from climate-related risks, including droughts, heavy rainfall, and shifting weather patterns. Prior research calls for adaptive risk-management solutions and decision-making strategies. To this end, artificial intelligence (AI), particularly agentic AI, offers a promising path forward. Agentic AI systems consist of autonomous, specialized agents capable of solving complex, dynamic tasks. While past systems have relied on single-agent models or have used multi-agent frameworks only for static functions, there is a growing need for architectures that support dynamic collaborative reasoning and context-aware outputs. To bridge this gap, we present AgroAskAI, a multi-agent reasoning system for climate adaptation decision support in agriculture, with a focus on vulnerable rural communities. AgroAskAI features a modular, role-specialized architecture that uses a chain-of-responsibility approach to coordinate autonomous agents, integrating real-time tools and datasets. The system has built-in governance mechanisms that mitigate hallucination and enable internal feedback for coherent, locally relevant strategies. The system also supports multilingual interactions, making it accessible to non-English-speaking farmers. Experiments on common agricultural queries related to climate adaptation show that, with additional tools and prompt refinement, AgroAskAI delivers more actionable, grounded, and inclusive outputs. Our experimental results highlight the potential of agentic AI for sustainable and accountable decision support in climate adaptation for agriculture.

AI Insights

• ChatGPT: A conversational AI model that provides general information on a wide range of topics. [3]
• The system's ability to analyze historical weather data and provide specific recommendations for farmers in Kitui, Kenya demonstrates its effectiveness in adapting to local climate conditions. [3]
• The AgroAskAI system provides a detailed and practical agricultural adaptation strategy tailored to the region of Kitui, Kenya. [2]
• CROPWAT: A software tool used for crop water management and irrigation planning. [1]

Why we are recommending this paper?
Due to your Interest in: AGI: Artificial General Intelligence

Abstract
Cryogenic electron microscopy (Cryo-EM) has become an essential tool for capturing high-resolution biological structures. Despite its advantage in visualizations, the large storage size of Cryo-EM data file poses significant challenges for researchers and educators. This paper investigates the application of deep learning, specifically implicit neural representation (INR), to compress Cryo-EM biological data. The proposed approach first extracts the binary map of each file according to the density threshold. The density map is highly repetitive, ehich can be effectively compressed by GZIP. The neural network then trains to encode spatial density information, allowing the storage of network parameters and learnable latent vectors. To improve reconstruction accuracy, I further incorporate the positional encoding to enhance spatial representation and a weighted Mean Squared Error (MSE) loss function to balance density distribution variations. Using this approach, my aim is to provide a practical and efficient biological data compression solution that can be used for educational and research purpose, while maintaining a reasonable compression ratio and reconstruction quality from file to file.

AI Insights

• The project establishes Implicit Neural Representation (INR) as a promising framework for Cryo-EM data compression, balancing efficiency and fidelity. [3]
• The method achieves a compression ratio of approximately 10:1, reducing file sizes from 414 MB to around 40 MB, outperforming traditional GZIP compression. [3]
• Experimental results demonstrate notable progress in surpassing GZIP's compression ratio and achieving high reconstruction quality for structurally significant areas. [3]
• GZIP: a file format used for data compression that typically yields lower ratios on complex Cryo-EM data. [3]
• INR (Implicit Neural Representation): a framework for representing scenes or data using neural networks, allowing for efficient and accurate reconstruction. [3]
• Future work may focus on automating hyperparameter tuning and refining the INR architecture to reduce low-density errors. [3]
• Limitations persist in low-density regions, where mean errors exceed 1000% due to noise and sparsity. [3]
• The project establishes INR as a promising tool for Cryo-EM data management, particularly in resource-limited settings. [2]
• Cryo-EM (Cryogenic Electron Microscopy): a technique used to determine the three-dimensional structure of macromolecules, such as proteins. [1]

Why we are recommending this paper?
Due to your Interest in: Deep Learning

Abstract
This paper provides a review of deep learning applications in scene understanding in autonomous robots, including innovations in object detection, semantic and instance segmentation, depth estimation, 3D reconstruction, and visual SLAM. It emphasizes how these techniques address limitations of traditional geometric models, improve depth perception in real time despite occlusions and textureless surfaces, and enhance semantic reasoning to understand the environment better. When these perception modules are integrated into dynamic and unstructured environments, they become more effective in decisionmaking, navigation and interaction. Lastly, the review outlines the existing problems and research directions to advance learning-based scene understanding of autonomous robots.

Why we are recommending this paper?
Due to your Interest in: Deep Learning