University of Bremen
AI Insights
- Reasoning: The process of drawing conclusions or making decisions based on the knowledge represented in an ontology. (ML: 0.98)
- Knowledge representation and reasoning (KRR): The process of representing knowledge in a way that can be used by machines to reason about it. (ML: 0.98)
- Ontology: A formal representation of knowledge that provides a common understanding of the meaning of terms and concepts. (ML: 0.97)
- They also discuss the importance of explainability and adaptability in KRR for robotics, as well as the need for more research in this area. (ML: 0.95)
- It highlights the potential benefits of using an ontology-based framework for representing and reasoning about knowledge in robotics. (ML: 0.94)
- The paper discusses the challenges of integrating knowledge representation and reasoning (KRR) into robotic systems. (ML: 0.93)
- The paper concludes by emphasizing the need for more research in KRR for robotics, particularly in areas such as explainability and adaptability. (ML: 0.89)
- The authors propose an ontology-based framework for representing and reasoning about knowledge in robotics, which can be used to integrate various components of a robotic system. (ML: 0.87)
- It highlights the need for a more flexible and open approach to KRR in robotics, rather than relying on proprietary or closed systems. (ML: 0.83)
- The authors also discuss the importance of developing more flexible and open approaches to KRR in robotics. (ML: 0.82)
Abstract
This paper introduces KRROOD, a framework designed to bridge the integration gap between modern software engineering and Knowledge Representation & Reasoning (KR&R) systems. While Object-Oriented Programming (OOP) is the standard for developing complex applications, existing KR&R frameworks often rely on external ontologies and specialized languages that are difficult to integrate with imperative code. KRROOD addresses this by treating knowledge as a first-class programming abstraction using native class structures, bridging the gap between the logic programming and OOP paradigms. We evaluate the system on the OWL2Bench benchmark and a human-robot task learning scenario. Experimental results show that KRROOD achieves strong performance while supporting the expressive reasoning required for real-world autonomous systems.
This paper introduces KRROOD, a framework designed to bridge the integration gap between modern software engineering and Knowledge Representation & Reasoning (KR&R) systems. While Object-Oriented Programming (OOP) is the standard for developing complex applications, existing KR&R frameworks often rely on external ontologies and specialized languages that are difficult to integrate with imperative code. KRROOD addresses this by treating knowledge as a first-class programming abstraction using native class structures, bridging the gap between the logic programming and OOP paradigms. We evaluate the system on the OWL2Bench benchmark and a human-robot task learning scenario. Experimental results show that KRROOD achieves strong performance while supporting the expressive reasoning required for real-world autonomous systems.
Why we are recommending this paper?
Due to your Interest in Object Oriented Programming
This paper directly addresses Object Oriented Programming, a key interest, by proposing a framework for integrating it with Knowledge Representation & Reasoning. It offers a valuable approach to applying design patterns within a KR&R system, aligning with your focus on programming paradigms.
Due to your Interest in Object Oriented Programming
This paper directly addresses Object Oriented Programming, a key interest, by proposing a framework for integrating it with Knowledge Representation & Reasoning. It offers a valuable approach to applying design patterns within a KR&R system, aligning with your focus on programming paradigms.
University of Innsbruck
AI Insights
- Substitution: A mapping from variables to terms, used to replace variables in a term with their corresponding values. (ML: 0.93)
- The problem statement is about proving that a certain property holds for deterministic higher-order patterns (DHPs). (ML: 0.85)
- The result may not hold for non-deterministic higher-order patterns (NDHPs). (ML: 0.85)
- Deterministic Higher-Order Patterns (DHP): A set of terms satisfying certain conditions, including being deterministic and having no free variables. (ML: 0.82)
- The proof shows that the property holds for DHPs under certain conditions. (ML: 0.74)
- The property in question is related to the behavior of substitutions on DHPs. (ML: 0.70)
- The proof involves using various lemmas and properties of DHPs to show that the desired property holds. (ML: 0.69)
- The proof relies on several assumptions about the behavior of substitutions on DHPs. (ML: 0.69)
- The proof involves using various lemmas and properties of DHPs to establish the desired result. (ML: 0.68)
- The result has implications for the behavior of substitutions on DHPs. (ML: 0.67)
Abstract
We present a sound and complete unification procedure for deterministic higher-order patterns, a class of simply-typed lambda terms introduced by Yokoyama et al. which comes with a deterministic matching problem. Our unification procedure can be seen as a special case of full higher-order unification where flex-flex pairs can be solved in a most general way. Moreover, our method generalizes Libal and Miller's recent functions-as-constructors higher-order unification by dropping their global condition on variable arguments, thereby losing the property that every solvable problem has a most general unifier. In fact, minimal complete sets of unifiers of deterministic higher-order patterns may be infinite, so decidability of the unification problem remains an open question.
We present a sound and complete unification procedure for deterministic higher-order patterns, a class of simply-typed lambda terms introduced by Yokoyama et al. which comes with a deterministic matching problem. Our unification procedure can be seen as a special case of full higher-order unification where flex-flex pairs can be solved in a most general way. Moreover, our method generalizes Libal and Miller's recent functions-as-constructors higher-order unification by dropping their global condition on variable arguments, thereby losing the property that every solvable problem has a most general unifier. In fact, minimal complete sets of unifiers of deterministic higher-order patterns may be infinite, so decidability of the unification problem remains an open question.
Why we are recommending this paper?
Due to your Interest in Design Patterns
This work investigates deterministic higher-order patterns, a sophisticated topic relevant to programming language design and pattern matching. The unification procedure presented aligns with your interest in formalizing and understanding programming paradigms.
Due to your Interest in Design Patterns
This work investigates deterministic higher-order patterns, a sophisticated topic relevant to programming language design and pattern matching. The unification procedure presented aligns with your interest in formalizing and understanding programming paradigms.
Timaeus
AI Insights
- patterning is a method for controlling neural network training by identifying which data shapes which internal structures and intervening accordingly the mathematical framework of patterning is based on linear response theory, where susceptibilities measure how observables respond to infinitesimal shifts in the data distribution experiments demonstrate that susceptibility measurements can be used to steer circuit formation in a small language model patterning has potential applications to AI alignment, where the goal is to control how models generalize beyond their training distribution patterning: the dual problem to interpretability, where given a desired form of generalization, one determines what training data produces it susceptibilities: measures of how observables respond to infinitesimal shifts in the data distribution linear response theory: a framework for understanding how systems respond to small perturbations patterning provides a principled approach to steering generalization, with potential applications to AI alignment and other areas the ability to identify which data shapes which internal structures and intervene accordingly offers a promising direction for controlling neural network training experiments use small models (3M parameters) and simple tasks (ML: 0.97)
Abstract
Mechanistic interpretability aims to understand how neural networks generalize beyond their training data by reverse-engineering their internal structures. We introduce patterning as the dual problem: given a desired form of generalization, determine what training data produces it. Our approach is based on susceptibilities, which measure how posterior expectation values of observables respond to infinitesimal shifts in the data distribution. Inverting this linear response relationship yields the data intervention that steers the model toward a target internal configuration. We demonstrate patterning in a small language model, showing that re-weighting training data along principal susceptibility directions can accelerate or delay the formation of structure, such as the induction circuit. In a synthetic parentheses balancing task where multiple algorithms achieve perfect training accuracy, we show that patterning can select which algorithm the model learns by targeting the local learning coefficient of each solution. These results establish that the same mathematical framework used to read internal structure can be inverted to write it.
Mechanistic interpretability aims to understand how neural networks generalize beyond their training data by reverse-engineering their internal structures. We introduce patterning as the dual problem: given a desired form of generalization, determine what training data produces it. Our approach is based on susceptibilities, which measure how posterior expectation values of observables respond to infinitesimal shifts in the data distribution. Inverting this linear response relationship yields the data intervention that steers the model toward a target internal configuration. We demonstrate patterning in a small language model, showing that re-weighting training data along principal susceptibility directions can accelerate or delay the formation of structure, such as the induction circuit. In a synthetic parentheses balancing task where multiple algorithms achieve perfect training accuracy, we show that patterning can select which algorithm the model learns by targeting the local learning coefficient of each solution. These results establish that the same mathematical framework used to read internal structure can be inverted to write it.
Why we are recommending this paper?
Due to your Interest in Design Patterns
This paper explores the concept of patterning, directly related to the design and implementation of patterns in software. It offers a novel perspective on generalization and interpretability, aligning with your interest in design patterns and programming language design.
Due to your Interest in Design Patterns
This paper explores the concept of patterning, directly related to the design and implementation of patterns in software. It offers a novel perspective on generalization and interpretability, aligning with your interest in design patterns and programming language design.
Yonsei University
AI Insights
- It outperforms other methods in most cases, especially when dealing with large networks. (ML: 0.95)
- The results show that there are significant differences in the brain network structure between the two groups. (ML: 0.94)
- The paper presents a new method called f-SGM (functional structural graph model) to analyze functional brain networks. (ML: 0.89)
- It is used to compare the brain structure between children with ADHD and control group. (ML: 0.89)
- f-SGM method is used to analyze functional brain networks and compare the brain structure between children with ADHD and control group. (ML: 0.88)
- The f-SGM method is a powerful tool for analyzing functional brain networks. (ML: 0.88)
- The brain network structure between children with ADHD and control group shows significant differences. (ML: 0.87)
- The brain network structure between children with ADHD and control group shows significant differences. (ML: 0.87)
- High computational cost of FGGM for high dimensional networks. (ML: 0.82)
- The f-SGM method outperforms other methods in most cases. (ML: 0.82)
- f-SGM is robust and can handle large networks with high dimensionality. (ML: 0.80)
- Difficulty in choosing tuning parameters for f-SGM and FAPO methods. (ML: 0.66)
Abstract
Functional graphical models have undergone extensive development during the recent years, leading to a variety models such as the functional Gaussian graphical model, the functional copula Gaussian graphical model, the functional Bayesian graphical model, the nonparametric functional additive graphical model, and the conditional functional graphical model. These models rely either on some parametric form of distributions on random functions, or on additive conditional independence, a criterion that is different from probabilistic conditional independence. In this paper we introduce a nonparametric functional graphical model based on functional sufficient dimension reduction. Our method not only relaxes the Gaussian or copula Gaussian assumptions, but also enhances estimation accuracy by avoiding the ``curse of dimensionality''. Moreover, it retains the probabilistic conditional independence as the criterion to determine the absence of edges. By doing simulation study and analysis of the f-MRI dataset, we demonstrate the advantages of our method.
Functional graphical models have undergone extensive development during the recent years, leading to a variety models such as the functional Gaussian graphical model, the functional copula Gaussian graphical model, the functional Bayesian graphical model, the nonparametric functional additive graphical model, and the conditional functional graphical model. These models rely either on some parametric form of distributions on random functions, or on additive conditional independence, a criterion that is different from probabilistic conditional independence. In this paper we introduce a nonparametric functional graphical model based on functional sufficient dimension reduction. Our method not only relaxes the Gaussian or copula Gaussian assumptions, but also enhances estimation accuracy by avoiding the ``curse of dimensionality''. Moreover, it retains the probabilistic conditional independence as the criterion to determine the absence of edges. By doing simulation study and analysis of the f-MRI dataset, we demonstrate the advantages of our method.
Why we are recommending this paper?
Due to your Interest in Functional Programming
Focusing on functional graphical models, this paper aligns with your interest in functional programming. The research explores techniques for learning and representing functional data structures, a core concept in functional programming.
Due to your Interest in Functional Programming
Focusing on functional graphical models, this paper aligns with your interest in functional programming. The research explores techniques for learning and representing functional data structures, a core concept in functional programming.
The University of Edinburgh
AI Insights
- Further research is needed to explore the limitations of the current implementation and to develop more sophisticated methods for integrating LLMs into EDA workflows. (ML: 0.96)
- The LaMDA framework is a tool that enables human-Large Language Model (LLM) interaction for electronic design automation (EDA). (ML: 0.93)
- The LaMDA framework demonstrates the potential for LLMs to assist in EDA tasks, improving efficiency and accuracy. (ML: 0.93)
- LLM (Large Language Model): a type of artificial intelligence model that can generate human-like text based on input prompts. (ML: 0.93)
- LaMDA (Large Language Model-based Design Automation): a tool that enables human-Large Language Model interaction for electronic design automation. (ML: 0.90)
- The authors evaluated the LaMDA framework using three case studies across three LLM models: OpenAI GPT-4o, GPT-4o-mini, and o1. (ML: 0.89)
- EDA (Electronic Design Automation): the process of designing and developing electronic systems using computer-aided tools. (ML: 0.84)
- LaMDA uses a graph-based representation of netlists as an intermediate circuit diagram for error handling in complex designs. (ML: 0.81)
- The graph-based netlist representation shows promise for error handling in complex designs. (ML: 0.81)
- The framework includes pre-processing steps to extract netlist blocks from LLM responses and ensure tool-ready formatting. (ML: 0.81)
- LaMDA supports three EDA domains: analogue, radio-frequency (RF), and field-programmable gate array (FPGA). (ML: 0.60)
Abstract
Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place-and-route. Existing methods primarily focus these optimisations within isolated open-source EDA tools and often lack the flexibility to handle multiple domains, such as analogue, digital, and radio-frequency design. In contrast, modern systems require to interface with commercial EDA environments, adhere to tool-specific operation rules, and incorporate feedback from design outcomes while supporting diverse design flows. We propose a versatile framework that uses LLMs to generate files compatible with commercial EDA tools and optimise designs using power-performance-area reports. This is accomplished by guiding the LLMs with tool constraints and feedback from design outputs to meet tool requirements and user specifications. Case studies on operational transconductance amplifiers, microstrip patch antennas, and FPGA circuits show that the framework is effective as an EDA-aware assistant, handling diverse design challenges reliably.
Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place-and-route. Existing methods primarily focus these optimisations within isolated open-source EDA tools and often lack the flexibility to handle multiple domains, such as analogue, digital, and radio-frequency design. In contrast, modern systems require to interface with commercial EDA environments, adhere to tool-specific operation rules, and incorporate feedback from design outcomes while supporting diverse design flows. We propose a versatile framework that uses LLMs to generate files compatible with commercial EDA tools and optimise designs using power-performance-area reports. This is accomplished by guiding the LLMs with tool constraints and feedback from design outputs to meet tool requirements and user specifications. Case studies on operational transconductance amplifiers, microstrip patch antennas, and FPGA circuits show that the framework is effective as an EDA-aware assistant, handling diverse design challenges reliably.
Why we are recommending this paper?
Due to your Interest in Programming Language Design
This paper explores the application of large language models in electronic design automation, a field often reliant on sophisticated programming techniques and design patterns. Itβs a modern approach to automating design processes, which could be of interest given your interest in programming paradigms.
Due to your Interest in Programming Language Design
This paper explores the application of large language models in electronic design automation, a field often reliant on sophisticated programming techniques and design patterns. Itβs a modern approach to automating design processes, which could be of interest given your interest in programming paradigms.
Universidad Andres Bello
AI Insights
- A corpus-operational framework for making canonical structural transformation testable in cultural analytics. (ML: 0.98)
- The framework bridges structural anthropology and cultural analytics by shifting comparison from resemblance to lawful transformation, while making context sensitivity explicit through structured operator choice. (ML: 0.98)
- The present corpus is a disciplined testbed for comparison, not a claim about global narrative space. (ML: 0.97)
- The framework is a scaffold that keeps two functions distinct: material/social reorganization (X) and the systems that authorize, forbid, punish, or recognize that reorganization (Y). (ML: 0.96)
- Some elements can play both roles across episodes. (ML: 0.94)
- Narratives are represented as typed rewrites over a two-register configuration (X, Y), and canonical transformation is treated as coherence data linking two update policies by a natural transformation Ξ·: UβV. (ML: 0.83)
- Canonical structural transformation Coherence data Structured operator choice A replication package will be made publicly available upon acceptance. (ML: 0.76)
Abstract
Structural approaches to myth and narrative are compelling in close reading but hard to compare across traditions, media, and scale. We propose a formal framework that renders LΓ©vi-Straussian transformation as mathematics while remaining readable as narrative analysis. Variants, superhero continuities, and franchise arcs are modeled as typed rewrite programs on a coupled two-register state $(X,Y)$, abstracting an everyday/social channel and a symbolic/legitimation channel. The canonical formula becomes coherence data: a natural transformation $Ξ·:U\Rightarrow V$ between update endofunctors, where $U$ updates each register in place and $V$ performs a swap+inversion. Context is internalized by operator choice, turning naturality into a corpus-facing type check: failures diagnose mis-specified oppositions or illegal transport; successes witness coherent structural models. Order effects are summarized by a five-value invariant (Key). We apply the method to 80 narratives (20 folktales, 20 religious myths, 20 superheroes, 20 franchises), each encoded as $(a,b,x,y)$ with a Key. 59/80 (74\%) explicitly name a normative constraint in $y$ (law, taboo, contract, prophecy), supporting the two-register abstraction. The result is a testable bridge between structural anthropology and cultural analytics: stories remain interpretable yet become transportable objects for computation, comparison, and falsifiable constraints on transformation.
Structural approaches to myth and narrative are compelling in close reading but hard to compare across traditions, media, and scale. We propose a formal framework that renders LΓ©vi-Straussian transformation as mathematics while remaining readable as narrative analysis. Variants, superhero continuities, and franchise arcs are modeled as typed rewrite programs on a coupled two-register state $(X,Y)$, abstracting an everyday/social channel and a symbolic/legitimation channel. The canonical formula becomes coherence data: a natural transformation $Ξ·:U\Rightarrow V$ between update endofunctors, where $U$ updates each register in place and $V$ performs a swap+inversion. Context is internalized by operator choice, turning naturality into a corpus-facing type check: failures diagnose mis-specified oppositions or illegal transport; successes witness coherent structural models. Order effects are summarized by a five-value invariant (Key). We apply the method to 80 narratives (20 folktales, 20 religious myths, 20 superheroes, 20 franchises), each encoded as $(a,b,x,y)$ with a Key. 59/80 (74\%) explicitly name a normative constraint in $y$ (law, taboo, contract, prophecy), supporting the two-register abstraction. The result is a testable bridge between structural anthropology and cultural analytics: stories remain interpretable yet become transportable objects for computation, comparison, and falsifiable constraints on transformation.
Why we are recommending this paper?
Due to your Interest in Programming Paradigms
Due to your Interest in Programming Paradigms
Universitt DuisburgEssen
AI Insights
- The generalizations include the possibility of having learning rates converging to 0 or not converge at all, the possibility of varying parameters across dimensions, thus enabling chaotic iteration. (ML: 0.90)
- The paper builds on the work of [4] and generalizes its results to include chaotic iteration and learning rates converging to 0 or not converge at all. (ML: 0.88)
- The paper also draws inspiration from the theory of stochastic approximation [7,9,15] and the work of [17] on stochastic approximation with error terms. (ML: 0.87)
- Mann iteration dampened Mann iteration Banach space Hilbert space The paper generalizes the results of [4] on dampened Mann iteration for approximating fixpoints of functions arising from quantitative models like MDPs and SSGs. (ML: 0.83)
- The paper assumes that the functions are monotone non-expansive, which is not always the case in practice. (ML: 0.82)
- The paper provides some numerical results showing that chaotic iteration yields almost identical results in terms of convergence of standard dampened Mann iteration while giving more flexibility. (ML: 0.62)
Abstract
The problem of determining the (least) fixpoint of (higher-dimensional) functions over the non-negative reals frequently occurs when dealing with systems endowed with a quantitative semantics. We focus on the situation in which the functions of interest are not known precisely but can only be approximated. As a first contribution we generalize an iteration scheme called dampened Mann iteration, recently introduced in the literature. The improved scheme relaxes previous constraints on parameter sequences, allowing learning rates to converge to zero or not converge at all. While seemingly minor, this flexibility is essential to enable the implementation of chaotic iterations, where only a subset of components is updated in each step, allowing to tackle higher-dimensional problems. Additionally, by allowing learning rates to converge to zero, we can relax conditions on the convergence speed of function approximations, making the method more adaptable to various scenarios. We also show that dampened Mann iteration applies immediately to compute the expected payoff in various probabilistic models, including simple stochastic games, not covered by previous work.
The problem of determining the (least) fixpoint of (higher-dimensional) functions over the non-negative reals frequently occurs when dealing with systems endowed with a quantitative semantics. We focus on the situation in which the functions of interest are not known precisely but can only be approximated. As a first contribution we generalize an iteration scheme called dampened Mann iteration, recently introduced in the literature. The improved scheme relaxes previous constraints on parameter sequences, allowing learning rates to converge to zero or not converge at all. While seemingly minor, this flexibility is essential to enable the implementation of chaotic iterations, where only a subset of components is updated in each step, allowing to tackle higher-dimensional problems. Additionally, by allowing learning rates to converge to zero, we can relax conditions on the convergence speed of function approximations, making the method more adaptable to various scenarios. We also show that dampened Mann iteration applies immediately to compute the expected payoff in various probabilistic models, including simple stochastic games, not covered by previous work.
Why we are recommending this paper?
Due to your Interest in Functional Programming
Due to your Interest in Functional Programming
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