AI SIGNAL{}
arXiv Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization◆arXiv Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent◆arXiv ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion◆arXiv Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning◆arXiv Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization◆arXiv Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery◆arXiv Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution◆arXiv Synthetic Benchmarks Overstate Forward-Forward Scaling: Real-Data Limits of Layer-Local Training◆arXiv Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning◆arXiv ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training◆arXiv Hub-Aware Hybrid Search: Accelerating the Locally Aligned Ant Technique◆arXiv Emergent Language as an Approach to Conscious AI◆arXiv LLM-Guided Evolution for Medical Decision Pipelines◆arXiv Combinatorial Landscape Analysis for Dominating Set and Vertex Coloring◆arXiv Sparsely gated tiny linear experts◆arXiv Representational Similarity and Model Behavior in Multi-Agent Interaction◆arXiv OpenOpt: An Open-Source SRAM Optimizer Based on Equivalent Circuit Model◆arXiv Hybrid Metaheuristic Combining the Dragonfly Algorithm and Tabu Search for the Traveling Salesman Problem◆arXiv Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration◆arXiv Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration◆arXiv Modelling Opinion Dynamics at Scale with Deep MARL◆arXiv Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems◆arXiv Cherry-pick Override: Unsafe Directional Commitment in LLM Judges under Mixed Evidence◆arXiv GRPO Does Not Close the Multi-Agent Coordination Gap◆arXiv Cost-Aware Speculative Execution for LLM-Agent Workflows: An Integrated Five-Dimension Method◆arXiv Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study◆arXiv EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation◆arXiv Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure◆arXiv Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems◆arXiv SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows◆arXiv Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization◆arXiv Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent◆arXiv ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion◆arXiv Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning◆arXiv Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization◆arXiv Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery◆arXiv Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution◆arXiv Synthetic Benchmarks Overstate Forward-Forward Scaling: Real-Data Limits of Layer-Local Training◆arXiv Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning◆arXiv ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training◆arXiv Hub-Aware Hybrid Search: Accelerating the Locally Aligned Ant Technique◆arXiv Emergent Language as an Approach to Conscious AI◆arXiv LLM-Guided Evolution for Medical Decision Pipelines◆arXiv Combinatorial Landscape Analysis for Dominating Set and Vertex Coloring◆arXiv Sparsely gated tiny linear experts◆arXiv Representational Similarity and Model Behavior in Multi-Agent Interaction◆arXiv OpenOpt: An Open-Source SRAM Optimizer Based on Equivalent Circuit Model◆arXiv Hybrid Metaheuristic Combining the Dragonfly Algorithm and Tabu Search for the Traveling Salesman Problem◆arXiv Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration◆arXiv Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration◆arXiv Modelling Opinion Dynamics at Scale with Deep MARL◆arXiv Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems◆arXiv Cherry-pick Override: Unsafe Directional Commitment in LLM Judges under Mixed Evidence◆arXiv GRPO Does Not Close the Multi-Agent Coordination Gap◆arXiv Cost-Aware Speculative Execution for LLM-Agent Workflows: An Integrated Five-Dimension Method◆arXiv Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study◆arXiv EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation◆arXiv Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure◆arXiv Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems◆arXiv SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows◆
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755 ITEMS — NOT YET WRITTEN
01.arXiv
arXiv cs.NEJun 9
Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization

Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realistic traffic simulation across…

[cs.AI][cs.CY][cs.NE]https://arxiv.org/abs/2606.03823v1
02.arXiv
arXiv cs.NEJun 9
Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent

The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing. However, for the extensively used leaky int…

[cs.NE][cs.LG][cs.NE]https://arxiv.org/abs/2606.03935v1
03.arXiv
arXiv cs.NEJun 9
ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion

Offline multi-objective optimization (Offline MOO) aims to discover novel Pareto-optimal designs based on static datasets without expensive environment interactions. While recent g…

[cs.LG][cs.AI][cs.NE]https://arxiv.org/abs/2606.04468v1
04.arXiv
arXiv cs.NEJun 9
Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurre…

[cs.LG][cs.NE][q-fin.ST]https://arxiv.org/abs/2606.04574v1
05.arXiv
arXiv cs.NEJun 9
Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization

The radial basis function neural network (RBFN) trained with a gradient descending algorithm provides an effective fully connected structure in both shallow and deep networks. The …

[cs.NE][cs.AI][cs.NE]https://arxiv.org/abs/2606.05150v1
06.arXiv
arXiv cs.NEJun 9
Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery

Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms. In this paper, we propose Seq…

[cs.NE][cs.AI][cs.NE]https://arxiv.org/abs/2606.07664v1
07.arXiv
arXiv cs.NEJun 9
Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution

When an LLM repeatedly mutates a program, does it explore new forms or circle back to the same ones? We study this question by analyzing LLM-driven mutation chains in the absence o…

[cs.AI][cs.NE][cs.AI]https://arxiv.org/abs/2606.05408v1
08.arXiv
arXiv cs.NEJun 9
Synthetic Benchmarks Overstate Forward-Forward Scaling: Real-Data Limits of Layer-Local Training

Forward-Forward (FF) learning [Hinton, 2022] replaces backpropagation with strictly layer-local goodness updates. Recent FF-CNN work has narrowed the gap to BP on 32x32 benchmarks,…

[cs.CV][cs.AI][cs.LG]https://arxiv.org/abs/2606.06539v1
09.arXiv
arXiv cs.NEJun 9
Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorit…

[cs.RO][cs.AI][cs.NE]https://arxiv.org/abs/2606.06041v1
10.arXiv
arXiv cs.NEJun 9
ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training

Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across a wide range of applications. …

[cs.AR][cs.AI][cs.NE]https://arxiv.org/abs/2606.06159v1
11.arXiv
arXiv cs.NEJun 9
Hub-Aware Hybrid Search: Accelerating the Locally Aligned Ant Technique

Finding manifold structures in noisy and high-dimensional point clouds is a challenging but important problem. In astronomical observation survey and simulation data the detection …

[cs.NE][astro-ph.CO][astro-ph.GA]https://arxiv.org/abs/2606.06198v1
12.arXiv
arXiv cs.NEJun 9
Emergent Language as an Approach to Conscious AI

The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-derived checklists (discriminat…

[cs.CL][cs.AI][cs.MA]https://arxiv.org/abs/2606.06380v1
13.arXiv
arXiv cs.NEJun 9
LLM-Guided Evolution for Medical Decision Pipelines

Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-guided MAP-Elites evolution as…

[cs.CL][cs.NE][cs.CL]https://arxiv.org/abs/2606.07342v1
14.arXiv
arXiv cs.NEJun 9
Combinatorial Landscape Analysis for Dominating Set and Vertex Coloring

We analyze the two combinatorial problems of Dominating Set and Vertex Coloring regarding what kind of local optima are present for various instances. For a variety of graph classe…

[cs.NE][cs.DM][cs.NE]https://arxiv.org/abs/2606.07361v1
15.arXiv
arXiv cs.NEJun 9
Sparsely gated tiny linear experts

Sparsity allows scaling model parameters without proportionally increasing computational cost. While mixture of experts (MoE) models are made increasingly sparse, individual expert…

[cs.LG][cs.NE][cs.LG]https://arxiv.org/abs/2606.07414v1
16.arXiv
arXiv cs.NEJun 9
Representational Similarity and Model Behavior in Multi-Agent Interaction

Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar in…

[cs.CL][cs.NE][cs.CL]https://arxiv.org/abs/2606.07818v1
17.arXiv
arXiv cs.NEJun 9
OpenOpt: An Open-Source SRAM Optimizer Based on Equivalent Circuit Model

This paper proposes a co-optimization framework that jointly optimizes SRAM architecture and transistor sizing using equivalent circuit models. The framework simplifies inactive SR…

[cs.NE][cs.AR][cs.NE]https://arxiv.org/abs/2606.09129v1
18.arXiv
arXiv cs.NEJun 9
Hybrid Metaheuristic Combining the Dragonfly Algorithm and Tabu Search for the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a classical NP-hard combinatorial optimization problem that aims to find the shortest Hamiltonian cycle visiting each city exactly once and …

[cs.NE][cs.NE]https://arxiv.org/abs/2606.09529v1
19.arXiv
arXiv cs.NEJun 9
Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration

This study addresses the challenges composers and sound designers face in creating and refining tools to achieve their musical goals. Using evolutionary processes to promote divers…

[cs.SD][cs.NE][cs.SD]https://arxiv.org/abs/2606.09780v1
20.arXiv
arXiv cs.MAJun 9
Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration

Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-language proposals, the protocol mus…

[cs.MA][cs.AI][cs.DC]https://arxiv.org/abs/2606.07316v1
21.arXiv
arXiv cs.MAJun 9
Modelling Opinion Dynamics at Scale with Deep MARL

Modelling opinion dynamics typically relies on hand-crafted local interaction rules to study emergent macroscopic phenomena such as consensus and polarisation. In contrast, multi-a…

[cs.MA][cs.GT][cs.SI]https://arxiv.org/abs/2606.07487v1
22.arXiv
arXiv cs.MAJun 9
Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems

The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational risks. Most current evaluati…

[cs.AI][cs.MA][cs.AI]https://arxiv.org/abs/2606.07805v1
23.arXiv
arXiv cs.MAJun 9
Cherry-pick Override: Unsafe Directional Commitment in LLM Judges under Mixed Evidence

LLM judges increasingly turn verdicts into system commitments. Under mixed evidence (claims with both supporting and refuting sources) this is unsafe: when the schema exposes CONFL…

[cs.SE][cs.AI][cs.CL]https://arxiv.org/abs/2606.07834v1
24.arXiv
arXiv cs.MAJun 9
GRPO Does Not Close the Multi-Agent Coordination Gap

We measure how well current large language models coordinate as multiple agents sharing a common resource, using the dining philosophers problem as a clean test bed. Across 630 epi…

[cs.MA][cs.LG][cs.MA]https://arxiv.org/abs/2606.07845v1
25.arXiv
arXiv cs.MAJun 9
Cost-Aware Speculative Execution for LLM-Agent Workflows: An Integrated Five-Dimension Method

LLM-agent workflows chain model calls and tool invocations, and spend most of their wall-clock time waiting on upstream operations before downstream ones can start. Speculative exe…

[cs.DC][cs.AI][cs.MA]https://arxiv.org/abs/2606.07846v1
26.arXiv
arXiv cs.MAJun 9
Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study

Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined regulator and applicant labor. …

[cs.AI][cs.MA][cs.AI]https://arxiv.org/abs/2606.07866v1
27.arXiv
arXiv cs.MAJun 9
EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation

Understanding how educational social dynamics evolve is critical for informing effective educational policies and counterfactual interventions. However, traditional methods face a …

[cs.MA][cs.CY][cs.MA]https://arxiv.org/abs/2606.07948v1
28.arXiv
arXiv cs.MAJun 9
Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure

As large language model (LLM) agents are integrated into autonomous cloud operations, distributed systems face a semantic reliability problem: proposer agents can generate producti…

[cs.LG][cs.AI][cs.MA]https://arxiv.org/abs/2606.08021v1
29.arXiv
arXiv cs.MAJun 9
Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems

Agentic tutoring systems introduce a coordination challenge: multiple agents may propose different but reasonable interventions, yet only one response can be delivered to the learn…

[cs.MA][cs.AI][cs.MA]https://arxiv.org/abs/2606.08030v1
30.arXiv
arXiv cs.MAJun 9
SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows

AI agents increasingly turn past experience into reusable artifacts such as code, workflows, and procedural memories. Reuse can improve efficiency, but it also creates a lifecycle …

[cs.AI][cs.MA][cs.AI]https://arxiv.org/abs/2606.08049v1
…