pwc-archive/papers-with-abstracts huggingface dataset:
Cognition without neurons: modelling anticipation in a basal reservoir computer
Authors: Polyphony Bruna, Linnéa Gyllingberg
Date: 2025-05-04 00:00:00

How do non-neural organisms, such as the slime mould \textit{Physarum polycephalum}, anticipate periodic events in their environment? We present a minimal, biologically inspired reservoir model that demonstrates simple temporal anticipation without neurons, spikes, or trained readouts. The model consists of a spatially embedded hexagonal network in which nodes regulate their energy through local, allostatic adaptation. Input perturbations shape energy dynamics over time, allowing the system to internalize temporal regularities into its structure. After being exposed to a periodic input signal, the model spontaneously re-enacts those dynamics even in the absence of further input -- a form of unsupervised temporal pattern completion. This behaviour emerges from internal homeodynamic regulation, without supervised learning or symbolic processing. Our results show that simple homeodynamic regulation can support unsupervised prediction, suggesting a pathway to memory and anticipation in basal organisms.
Aggregating empirical evidence from data strategy studies: a case on model quantization
Authors: Santiago del Rey, Paulo Sérgio Medeiros dos Santos, Guilherme Horta Travassos, Xavier Franch, Silverio Martínez-Fernández
Date: 2025-05-01 00:00:00
Tasks: GPU, Quantization

Background: As empirical software engineering evolves, more studies adopt data strategies$-$approaches that investigate digital artifacts such as models, source code, or system logs rather than relying on human subjects. Synthesizing results from such studies introduces new methodological challenges. Aims: This study assesses the effects of model quantization on correctness and resource efficiency in deep learning (DL) systems. Additionally, it explores the methodological implications of aggregating evidence from empirical studies that adopt data strategies. Method: We conducted a research synthesis of six primary studies that empirically evaluate model quantization. We applied the Structured Synthesis Method (SSM) to aggregate the findings, which combines qualitative and quantitative evidence through diagrammatic modeling. A total of 19 evidence models were extracted and aggregated. Results: The aggregated evidence indicates that model quantization weakly negatively affects correctness metrics while consistently improving resource efficiency metrics, including storage size, inference latency, and GPU energy consumption$-$a manageable trade-off for many DL deployment contexts. Evidence across quantization techniques remains fragmented, underscoring the need for more focused empirical studies per technique. Conclusions: Model quantization offers substantial efficiency benefits with minor trade-offs in correctness, making it a suitable optimization strategy for resource-constrained environments. This study also demonstrates the feasibility of using SSM to synthesize findings from data strategy-based research.
A brain-inspired generative model for EEG-based cognitive state identification
Authors: Bin Hu, Zhi-Hong Guan
Date: 2025-05-03 00:00:00
Tasks: EEG, Network Interpretation

This article proposes a brain-inspired generative (BIG) model that merges an impulsive-attention neural network and a variational autoencoder (VAE) for identifying cognitive states based on electroencephalography (EEG) data. A hybrid learning method is presented for training the model by integrating gradient-based learning and heteroassociative memory. The BIG model is capable of achieving multi-task objectives: EEG classification, generating new EEG, and brain network interpretation, alleviating the limitations of excessive data training and high computational cost in conventional approaches. Experimental results on two public EEG datasets with different sampling rates demonstrate that the BIG model achieves a classification accuracy above 89\%, comparable with state-of-the-art methods, while reducing computational cost by nearly 11\% over the baseline EEGNet. Incorporating the generated EEG data for training, the BIG model exhibits comparative performance in a few-shot pattern. Ablation studies justify the poised brain-inspired characteristic regarding the impulsive-attention module and the hybrid learning method. Thanks to the performance advantages with interpretable outputs, this BIG model has application potential for building digital twins of the brain.
Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
Authors: Yasser Alghouass, Benjamin Doerr, Martin S. Krejca, Mohammed Lagmah
Date: 2025-05-02 00:00:00
Tasks: Evolutionary Algorithms

Together with the NSGA-II and SMS-EMOA, the strength Pareto evolutionary algorithm 2 (SPEA2) is one of the most prominent dominance-based multi-objective evolutionary algorithms (MOEAs). Different from the NSGA-II, it does not employ the crowding distance (essentially the distance to neighboring solutions) to compare pairwise non-dominating solutions but a complex system of $\sigma$-distances that builds on the distances to all other solutions. In this work, we give a first mathematical proof showing that this more complex system of distances can be superior. More specifically, we prove that a simple steady-state SPEA2 can compute optimal approximations of the Pareto front of the OneMinMax benchmark in polynomial time. The best proven guarantee for a comparable variant of the NSGA-II only assures approximation ratios of roughly a factor of two, and both mathematical analyses and experiments indicate that optimal approximations are not found efficiently.
A Framework for Testing and Adapting REST APIs as LLM Tools
Authors: Jayachandu Bandlamudi, Ritwik Chaudhuri, Neelamadhav Gantayat, Kushal Mukherjee, Prerna Agarwal, Renuka Sindhgatta, Sameep Mehta
Date: 2025-04-22 00:00:00

Large Language Models (LLMs) are enabling autonomous agents to perform complex workflows using external tools or functions, often provided via REST APIs in enterprise systems. However, directly utilizing these APIs as tools poses challenges due to their complex input schemas, elaborate responses, and often ambiguous documentation. Current benchmarks for tool testing do not adequately address these complexities, leading to a critical gap in evaluating API readiness for agent-driven automation. In this work, we present a novel testing framework aimed at evaluating and enhancing the readiness of REST APIs to function as tools for LLM-based agents. Our framework transforms apis as tools, generates comprehensive test cases for the APIs, translates tests cases into natural language instructions suitable for agents, enriches tool definitions and evaluates the agent's ability t correctly invoke the API and process its inputs and responses. To provide actionable insights, we analyze the outcomes of 750 test cases, presenting a detailed taxonomy of errors, including input misinterpretation, output handling inconsistencies, and schema mismatches. Additionally, we classify these test cases to streamline debugging and refinement of tool integrations. This work offers a foundational step toward enabling enterprise APIs as tools, improving their usability in agent-based applications.
ABCO: Adaptive Bacterial Colony Optimisation
Authors: Barisi Kogam, Yevgeniya Kovalchuk, Mohamed Medhat Gaber
Date: 2025-05-02 00:00:00

This paper introduces a new optimisation algorithm, called Adaptive Bacterial Colony Optimisation (ABCO), modelled after the foraging behaviour of E. coli bacteria. The algorithm follows three stages--explore, exploit and reproduce--and is adaptable to meet the requirements of its applications. The performance of the proposed ABCO algorithm is compared to that of established optimisation algorithms--particle swarm optimisation (PSO) and ant colony optimisation (ACO)--on a set of benchmark functions. Experimental results demonstrate the benefits of the adaptive nature of the proposed algorithm: ABCO runs much faster than PSO and ACO while producing competitive results and outperforms PSO and ACO in a scenario where the running time is not crucial.
Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm
Authors: Benjamin Doerr, Martin Krejca, Andre Opris
Date: 2025-05-02 00:00:00

The global simple evolutionary multi-objective optimizer (GSEMO) is a simple, yet often effective multi-objective evolutionary algorithm (MOEA). By only maintaining non-dominated solutions, it has a variable population size that automatically adjusts to the needs of the optimization process. The downside of the dynamic population size is that the population dynamics of this algorithm are harder to understand, resulting, e.g., in the fact that only sporadic tight runtime analyses exist. In this work, we significantly enhance our understanding of the dynamics of the GSEMO, in particular, for the classic CountingOnesCountingZeros (COCZ) benchmark. From this, we prove a lower bound of order $\Omega(n^2 \log n)$, for the first time matching the seminal upper bounds known for over twenty years. We also show that the GSEMO finds any constant fraction of the Pareto front in time $O(n^2)$, improving over the previous estimate of $O(n^2 \log n)$ for the time to find the first Pareto optimum. Our methods extend to other classic benchmarks and yield, e.g., the first $\Omega(n^{k+1})$ lower bound for the OJZJ benchmark in the case that the gap parameter is $k \in \{2,3\}$. We are therefore optimistic that our new methods will be useful in future mathematical analyses of MOEAs.
Thinking Outside the Template with Modular GP-GOMEA
Authors: Joe Harrison, Peter A. N. Bosman, Tanja Alderliesten
Date: 2025-05-02 00:00:00
Tasks: regression, Symbolic Regression

The goal in Symbolic Regression (SR) is to discover expressions that accurately map input to output data. Because often the intent is to understand these expressions, there is a trade-off between accuracy and the interpretability of expressions. GP-GOMEA excels at producing small SR expressions (increasing the potential for interpretability) with high accuracy, but requires a fixed tree template, which limits the types of expressions that can be evolved. This paper presents a modular representation for GP-GOMEA that allows multiple trees to be evolved simultaneously that can be used as (functional) subexpressions. While each tree individually is constrained to a (small) fixed tree template, the final expression, if expanded, can exhibit a much larger structure. Furthermore, the use of subexpressions decomposes the original regression problem and opens the possibility for enhanced interpretability through the piece-wise understanding of small subexpressions. We compare the performance of GP-GOMEA with and without modular templates on a variety of datasets. We find that our proposed approach generally outperforms single-template GP-GOMEA and can moreover uncover ground-truth expressions underlying synthetic datasets with modular subexpressions at a faster rate than GP-GOMEA without modular subexpressions.
A First Runtime Analysis of NSGA-III on a Many-Objective Multimodal Problem: Provable Exponential Speedup via Stochastic Population Update
Authors: Andre Opris
Date: 2025-05-02 00:00:00

The NSGA-III is a prominent algorithm in evolutionary many-objective optimization. It is well-suited for optimizing functions with more than three objectives, setting it apart from the classic NSGA-II. However, theoretical insights about NSGA-III of when and why it performs well are still in its early development. This paper addresses this point and conducts a rigorous runtime analysis of NSGA-III on the many-objective $\OJZJfull$ benchmark ($\OJZJ$ for short), providing runtime bounds where the number of objectives is constant. We show that NSGA-III finds the Pareto front of $\OJZJ$ in time $O(n^{k+d/2}+ \mu n \ln(n))$ where $n$ is the problem size, $d$ is the number of objectives, $k$ is the gap size, a problem specific parameter, if its population size $\mu \in 2^{O(n)}$ is at least $(2n/d+1)^{d/2}$. Notably, NSGA-III is faster than NSGA-II by a factor of $\mu/n^{d/2}$ for some $\mu \in \omega(n^{d/2})$. We also show that a stochastic population update, proposed by~\citet{UpBian}, provably guarantees a speedup of order $\Theta((k/b)^{k-1})$ in the runtime where $b>0$ is a constant. Besides~\cite{DoerrNearTight}, this is the first rigorous runtime analysis of NSGA-III on \OJZJ. Proving these bounds requires a much deeper understanding of the population dynamics of NSGA-III than previous papers achieved.
Quantitative Attractor Analysis of High-Capacity Kernel Logistic Regression Hopfield Networks
Authors: Akira Tamamori
Date: 2025-05-02 00:00:00

Traditional Hopfield networks, using Hebbian learning, face severe storage capacity limits ($\approx 0.14$ P/N) and spurious attractors. Kernel Logistic Regression (KLR) offers a non-linear approach, mapping patterns to high-dimensional feature spaces for improved separability. Our previous work showed KLR dramatically improves capacity and noise robustness over conventional methods. This paper quantitatively analyzes the attractor structures in KLR-trained networks via extensive simulations. We evaluated recall from diverse initial states across wide storage loads (up to 4.0 P/N) and noise levels. We quantified convergence rates and speed. Our analysis confirms KLR's superior performance: high capacity (up to 4.0 P/N) and robustness. The attractor landscape is remarkably "clean," with near-zero spurious fixed points. Recall failures under high load/noise are primarily due to convergence to other learned patterns, not spurious ones. Dynamics are exceptionally fast (typically 1-2 steps for high-similarity states). This characterization reveals how KLR reshapes dynamics for high-capacity associative memory, highlighting its effectiveness and contributing to AM understanding.
Multi-start Optimization Method via Scalarization based on Target Point-based Tchebycheff Distance for Multi-objective Optimization
Authors: Kota Nagakane, Masahiro Nomura, Isao Ono
Date: 2025-05-01 00:00:00
Tasks: Computational Efficiency

Multi-objective optimization is crucial in scientific and industrial applications where solutions must balance trade-offs among conflicting objectives. State-of-the-art methods, such as NSGA-III and MOEA/D, can handle many objectives but struggle with coverage issues, particularly in cases involving inverted triangular Pareto fronts or strong nonlinearity. Moreover, NSGA-III often relies on simulated binary crossover, which deteriorates in problems with variable dependencies. In this study, we propose a novel multi-start optimization method that addresses these challenges. Our approach introduces a newly introduced scalarization technique, the Target Point-based Tchebycheff Distance (TPTD) method, which significantly improves coverage on problems with inverted triangular Pareto fronts. For efficient multi-start optimization, TPTD leverages a target point defined in the objective space, which plays a critical role in shaping the scalarized function. The position of the target point is adaptively determined according to the shape of the Pareto front, ensuring improvement in coverage. Furthermore, the flexibility of this scalarization allows seamless integration with powerful single-objective optimization methods, such as natural evolution strategies, to efficiently handle variable dependencies. Experimental results on benchmark problems, including those with inverted triangular Pareto fronts, demonstrate that our method outperforms NSGA-II, NSGA-III, and MOEA/D-DE in terms of the Hypervolume indicator. Notably, our approach achieves computational efficiency improvements of up to 474 times over these baselines.
To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX
Authors: Jonathan Heins, Darrell Whitley, Pascal Kerschke
Date: 2025-05-01 00:00:00
Tasks: Computational Efficiency

The Edge Assembly Crossover (EAX) algorithm is the state-of-the-art heuristic for solving the Traveling Salesperson Problem (TSP). It regularly outperforms other methods, such as the Lin-Kernighan-Helsgaun heuristic (LKH), across diverse sets of TSP instances. Essentially, EAX employs a two-stage mechanism that focuses on improving the current solutions, first, at the local and, subsequently, at the global level. Although the second phase of the algorithm has been thoroughly studied, configured, and refined in the past, in particular, its first stage has hardly been examined. In this paper, we thus focus on the first stage of EAX and introduce a novel method that quickly verifies whether the AB-cycles, generated during its internal optimization procedure, yield valid tours -- or whether they need to be repaired. Knowledge of the latter is also particularly relevant before applying other powerful crossover operators such as the Generalized Partition Crossover (GPX). Based on our insights, we propose and evaluate several improved versions of EAX. According to our benchmark study across 10 000 different TSP instances, the most promising of our proposed EAX variants demonstrates improved computational efficiency and solution quality on previously rather difficult instances compared to the current state-of-the-art EAX algorithm.
Assessing LLM code generation quality through path planning tasks
Authors: Wanyi Chen, Meng-Wen Su, Mary L. Cummings
Date: 2025-04-30 00:00:00
Tasks: Code Generation

As LLM-generated code grows in popularity, more evaluation is needed to assess the risks of using such tools, especially for safety-critical applications such as path planning. Existing coding benchmarks are insufficient as they do not reflect the context and complexity of safety-critical applications. To this end, we assessed six LLMs' abilities to generate the code for three different path-planning algorithms and tested them on three maps of various difficulties. Our results suggest that LLM-generated code presents serious hazards for path planning applications and should not be applied in safety-critical contexts without rigorous testing.
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search
Authors: Fei Liu, Qingfu Zhang, Xialiang Tong, Kun Mao, Mingxuan Yuan
Date: 2025-04-28 00:00:00
Tasks: Combinatorial Optimization, Language Modeling, Language Modelling, Large Language Model, Symbolic Regression

Large Language Models (LLMs) have demonstrated significant potential in algorithm design. However, when integrated into search frameworks for iterative algorithm search, the underlying fitness landscape--critical for understanding search behaviou--remains underexplored. In this paper, we illustrate and analyze the fitness landscape of LLM-assisted Algorithm Search (LAS) using a graph-based approach, where nodes represent algorithms and edges denote transitions between them. We conduct extensive evaluations across six algorithm design tasks and six commonly used LLMs. Our findings reveal that LAS landscapes are highly multimodal and rugged, particularly in combinatorial optimization tasks, with distinct structural variations across tasks and LLMs. For instance, heuristic design tasks exhibit dense clusters of high-performing algorithms, while symbolic regression tasks show sparse, scattered distributions. Additionally, we demonstrate how population size influences exploration-exploitation trade-offs and the evolving trajectory of elite algorithms. These insights not only advance our understanding of LAS landscapes but also provide practical guidance for designing more effective LAS methods.
LLMs for Engineering: Teaching Models to Design High Powered Rockets
Authors: Toby Simonds
Date: 2025-04-27 00:00:00
Tasks: Reinforcement Learning (RL)

Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results and ultimately plateau below human performance levels. However, when enhanced with reinforcement learning (RL), we show that a 7B parameter model outperforms both SoTA foundation models and human experts. This research demonstrates that RL-trained LLMs can serve as effective tools for complex engineering optimization, potentially transforming engineering domains beyond software development.
TNStream: Applying Tightest Neighbors to Micro-Clusters to Define Multi-Density Clusters in Streaming Data
Authors: Qifen Zeng, Haomin Bao, Yuanzhuo Hu, Zirui Zhang, Yuheng Zheng, Luosheng Wen
Date: 2025-05-01 00:00:00
Tasks: Clustering

In data stream clustering, systematic theory of stream clustering algorithms remains relatively scarce. Recently, density-based methods have gained attention. However, existing algorithms struggle to simultaneously handle arbitrarily shaped, multi-density, high-dimensional data while maintaining strong outlier resistance. Clustering quality significantly deteriorates when data density varies complexly. This paper proposes a clustering algorithm based on the novel concept of Tightest Neighbors and introduces a data stream clustering theory based on the Skeleton Set. Based on these theories, this paper develops a new method, TNStream, a fully online algorithm. The algorithm adaptively determines the clustering radius based on local similarity, summarizing the evolution of multi-density data streams in micro-clusters. It then applies a Tightest Neighbors-based clustering algorithm to form final clusters. To improve efficiency in high-dimensional cases, Locality-Sensitive Hashing (LSH) is employed to structure micro-clusters, addressing the challenge of storing k-nearest neighbors. TNStream is evaluated on various synthetic and real-world datasets using different clustering metrics. Experimental results demonstrate its effectiveness in improving clustering quality for multi-density data and validate the proposed data stream clustering theory.
Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges
Authors: Yunseo Lee, John Youngeun Song, Dongsun Kim, Jindae Kim, Mijung Kim, Jaechang Nam
Date: 2025-04-29 00:00:00
Tasks: Code Generation, Hallucination, Survey

Recent technical breakthroughs in large language models (LLMs) have enabled them to fluently generate source code. Software developers often leverage both general-purpose and code-specialized LLMs to revise existing code or even generate a whole function from scratch. These capabilities are also beneficial in no-code or low-code contexts, in which one can write programs without a technical background. However, due to their internal design, LLMs are prone to generating hallucinations, which are incorrect, nonsensical, and not justifiable information but difficult to identify its presence. This problem also occurs when generating source code. Once hallucinated code is produced, it is often challenging for users to identify and fix it, especially when such hallucinations can be identified under specific execution paths. As a result, the hallucinated code may remain unnoticed within the codebase. This survey investigates recent studies and techniques relevant to hallucinations generated by CodeLLMs. We categorize the types of hallucinations in the code generated by CodeLLMs, review existing benchmarks and mitigation strategies, and identify open challenges. Based on these findings, this survey outlines further research directions in the detection and removal of hallucinations produced by CodeLLMs.