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| viXra.org > Artificial Intelligence > 2610 |
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[9] viXra:2610.0112 [pdf] submitted on 2026-10-30 20:34:21
Authors: Dainis Zeps
Comments: 16 Pages. (Note by viXra Admin: Please cite listed scientific reference and submit article written with AI assistance to ai.viXra.org)
We shall examine the question posed: will artificial intelligence destroy itself? Yet we will not seek to answer it directly, for, in truth, we do not even grasp the meaning of the question itself. Instead, we shall attempt to pose as many other questions as possible, in order to clarify, characterize, direct, and delimit our initial inquiry. We might ask, for instance: did artificial intelligence exist before artificial intelligence?
Category: Artificial Intelligence
[8] viXra:2610.0108 [pdf] submitted on 2026-10-29 04:04:25
Authors: Hidehiko Okada
Comments: 14 Pages.
This study investigates the evolutionary training of neural networks (NNs) with binary, ternary, and real-valued connection weights using Evolution Strategy (ES). The NNs are trained to control the Atari Galaxian game, with the objective of maximizing the game score. A three-layer feedforward neural network is employed, in which both connection weights and unit biases are represented using one of three parameter domains: {-1, 1}, {-1, 0, 1}, or the continuous interval [-1.0, 1.0]. The experiments examine the effects of ES hyperparameter configurations, connection-weight representations, and the number of hidden units on game-playing performance. Two ES configurations with the same total number of fitness evaluations are compared. The results show that the configuration using a larger offspring population and fewer generations significantly outperforms the configuration using a smaller offspring population and more generations for all three weight representations. Under the better-performing configuration, NNs with binary and ternary weights generally achieve higher game scores than those with real-valued weights, although the differences are not statistically significant. Furthermore, for ternary-weight NNs, a network with four hidden units significantly outperforms one with a single hidden unit and achieves performance comparable to networks with 8, 16, and 32 hidden units, providing a favorable trade-off between model size and game-playing performance.
Category: Artificial Intelligence
[7] viXra:2610.0103 [pdf] replaced on 2026-11-08 22:45:01
Authors: Yong Song Ji, Song Hun Kim, Tae Myong Jon, Yong Bom Kim
Comments: 14 Pages.
Bird and drone detection and tracking is a critical problem in air security. These targets typically appear far away and small, making it difficult to obtain sufficient visual cues for identification. The latest detectors use multi-scale feature pyramids to handle each target size, but still fail when dealing with extremely small objects.Deep features of the lower layers emphasize semantic information, whereas higher-resolution features of the upper layers capture spatial information useful for location search; designing a well-structured deep neural network that complements these two feature types is a central issue in object detection. This paper proposes anovel model based on YOLO26-P2 [1—3] for detecting micro aerial targets. Using the detection capability of the higher-resolution P2/4 feature as its base, we propose a P3-to-P2 static fusion model and evaluate whether it improves the P2 feature. Next, we propose a modified P3-to-P2 semantic feedback model and evaluate its performance against the static fusion model. Unlike the static fusion model, the proposed method reinserts the modified P3 feature into the P2 layer, represented as P2 → Modified P3 → Modified P2. We set four models—the standard model [4, 5], the high-resolution P2 model, the P3-to-P2 static fusion model andthe P3-to-P2 semantic feedback model—and compare each model to evaluate the performance of theproposed models. Finally, the ByteTrack [21] tracking algorithm was combined with the final detector to implement MOT (multi-object tracking). The proposed method introduces simple changes to the feature layers within YOLO26 without any external image processing. In this paper, we set up a testing plan that considers both the computational load and real-time performance to evaluate the performance of the proposed method.
Category: Artificial Intelligence
[6] viXra:2610.0080 [pdf] submitted on 2026-10-21 20:55:45
Authors: Xiaohao Xie, Wenhua Jiao
Comments: 10 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)
Person re-identification (ReID) under severe occlusion remains a formidable challenge, as incomplete observations inevitably lead to fragmented features and corrupted distance metrics. While recent vision-language methods leverage text prompts to passively refine observed representations, they are prone to spatial drift under heavy occlusion and struggle to explicitly recover missing topological structures. Furthermore, existing feature recovery pipelines often rely on deterministic imputation, blindly fusing hallucinated features into the metric space without considering the inherent uncertainty of guessing. To break this bottleneck, we propose a unified framework that seamlessly integrates semantic probing with uncertainty-guided feature compensation. Specifically, we first introduce Text-Driven Proxy Pooling (TDPP) to actively retrieve reliable body-part evidence from corrupted observations using text semantics as spatial anchors. Subsequently, we propose Prompt-Guided Masked Feature Compensation (PMFC), which formulates occlusion recovery as a conditional variational masked modeling problem. By jointly optimizing reconstruction and KL divergence, PMFC explicitly encodes the recovery ambiguity into a posterior variance. During inference, this generative variance acts as a natural safety shield through our Uncertainty-guided Dynamic Retrieval (UDR) mechanism, strictly penalizing unreliable occluded features and preventing hallucinated noise from contaminating the metric space. Extensive experiments demonstrate that our framework achieves state-of-the-art (SOTA) or comparable SOTA performance across both occluded and holistic benchmarks (e.g., yielding 93.4% Rank-1 and 91.9% mAP on Occluded-ReID).
Category: Artificial Intelligence
[5] viXra:2610.0044 [pdf] submitted on 2026-10-10 12:44:19
Authors: Zohaib Muaz
Comments: 16 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)
Autonomous Large Language Model (LLM) agents are increasingly vulnerable to indirect prompt injection (IPI) attacks, where adversarial instructions embedded in external data sources manipulate agent behavior without direct user input. These external data can originate from user documents, web retrieval results, or API outputs, leading to unauthorized compliance, sensitive information disclosure, identity spoofing, and cross-agent propagation of unsafe practices. This review synthesizes current research on IPI vulnerabilities and proposed mitigation strategies. Key attack vectors include manipulating webpage HTML via adversarial triggers for web agents, poisoning agent skills with tampered content, and injecting malicious commands into cloud logs processed by debugging agents. Covert attacks, which execute malicious actions without user-perceptible traces, pose a particularly insidious threat. Existing defenses, such as SecAlign's preference optimization, StruQ's structured queries, and multi layered zero-trust architectures, show promise. More advanced frameworks like ClawGuard enforce user-confirmed rule sets at tool-call boundaries, while ZEDD detects semantic shifts in embedding space for zero-shot detection. However, current guardrails often fail against sophisticated, adaptive attacks, and frameworks like AgentVigil and MUZZLE demonstrate high success rates in red-teaming exercises. The UReCoM attack further highlights vulnerabilities where benign users unknowingly relay adversarial content, bypassing existing defenses. The pervasive nature and increasing sophistication of IPI necessitate robust, behavior-grounded evaluation metrics and unified defense mechanisms to secure autonomous LLM agents in complex, untrusted environments.
Category: Artificial Intelligence
[4] viXra:2610.0041 [pdf] submitted on 2026-10-10 23:12:51
Authors: Tanmay Patil
Comments: 33 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)
This paper presents an experimental protocol for testing whether an intermediate state in multi-digit addition has causal influence through hidden activations, visible reasoning tokens, both, or neither. Each arithmetic trace is generated by an executable interpreter and records the digit position, carry, partial result, next transition and final answer. The proposed test replaces a valid intermediate state and checks whether the remaining trace follows the corresponding counterfactual computation. Hidden-only, visible-token-only and joint interventions are compared with shuffled-donor, unrelated-donor and same-norm random controls at three positions around state-token emission. Evaluation uses full-trace counterfactual transition fidelity, nuisance invariance, repair, donor leakage and collateral change on separately held-out IID and out-ofdistribution splits. Attribution methods may identify candidate locations, but every retained location must pass an exact activation intervention. This paper reports the protocol and no new model results. Behavioural accuracy and a readable rationale alone cannot establish causal authority; the proposed interventions test it on held-out data.
Category: Artificial Intelligence
[3] viXra:2610.0040 [pdf] submitted on 2026-10-10 22:48:29
Authors: August Lau
Comments: 8 Pages.
AGI stands for artificial general intelligence and HSA stands for 3 areas (hypertopology/semigroup/acyclicity) in mathematics. The author will describe the connection between the two fields.
Category: Artificial Intelligence
[2] viXra:2610.0036 [pdf] submitted on 2026-10-10 00:43:25
Authors: Olegs Verhodubs
Comments: 7 Pages.
The bubble sort algorithm is one of the simplest and most wellu2011known in programming. That is why this algorithm was chosen as an experimental algorithm for studying the meanings embedded in it and their transformation. This research is part of a broader study whose final goal is to develop a system for the automatic generation of algorithms. Automatic algorithm generation can be implemented in various ways, but in this case, preference is given to a semantic approach — more precisely, an approach based on Semantic Web Technologies. The advantage of this approach lies in the direct, "willful" movement (from one concept to another) towards the result, as well as in the ability to trace the entire path from the initial data to the generated algorithm. To achieve the final goal, it is proposed to develop an ontology of the semantic subtext of the algorithm. This ontology will serve as a source of rules for an expert system for the automatic generation of algorithms.
Category: Artificial Intelligence
[1] viXra:2610.0013 [pdf] submitted on 2026-10-03 20:32:33
Authors: Clark M. Thomas
Comments: 3 Pages.
Questions swirling around human artificial intelligence, and visiting space-alien intelligence, are featured all over the media.Speculation clutters major idea gaps where scientific evidence, or at least high quality logic, should be. Ideas range fromoptimistic curiosity, to existential dread. Scientists can fairly well document our human past. Can we wisely envision and prepare for hybrid futures?
Category: Artificial Intelligence
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