On Decision-Valued Maps and Representational Dependence
arXiv:2602.11295v1 Announce Type: new
Abstract: A computational engine applied to different representations of the same data can produce different discrete outcomes, with some representations preserving the result and others changing it entirely. A decision-valued map records which representations ...
Explaining AI Without Code: A User Study on Explainable AI
arXiv:2602.11159v1 Announce Type: new
Abstract: The increasing use of Machine Learning (ML) in sensitive domains such as healthcare, finance, and public policy has raised concerns about the transparency of automated decisions. Explainable AI (XAI) addresses this by clarifying how models generate pr...
arXiv:2602.10195v1 Announce Type: new
Abstract: A novel sequence architecture design is introduced, Versor, which uses Conformal Geometric Algebra (CGA) in place of the traditional fundamental non-linear operations to achieve structural generalization and significant performance improvements on a v...
Adaptive Optimization via Momentum on Variance-Normalized Gradients
arXiv:2602.10204v1 Announce Type: new
Abstract: We introduce MVN-Grad (Momentum on Variance-Normalized Gradients), an Adam-style optimizer that improves stability and performance by combining two complementary ideas: variance-based normalization and momentum applied after normalization. MVN-Grad sc...
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
arXiv:2602.07031v1 Announce Type: new
Abstract: This study develops a Lagged Backward-Compatible Physics-Informed Neural Network (LBC-PINN) for simulating and inverting one-dimensional unsaturated soil consolidation under long-term loading. To address the challenges of coupled air and water pressur...
MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
arXiv:2602.05048v1 Announce Type: new
Abstract: Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspects of incomplete information and unknowns, e.g., objects involved, human goals/intents -- thus leading...
Active Epistemic Control for Query-Efficient Verified Planning
arXiv:2602.03974v1 Announce Type: new
Abstract: Planning in interactive environments is challenging under partial observability: task-critical preconditions (e.g., object locations or container states) may be unknown at decision time, yet grounding them through interaction is costly. Learned world ...
Sparse Adapter Fusion for Continual Learning in NLP
arXiv:2602.02502v1 Announce Type: new
Abstract: Continual learning in natural language processing plays a crucial role in adapting to evolving data and preventing catastrophic forgetting. Despite significant progress, existing methods still face challenges, such as inefficient parameter reuse acros...
Learning ORDER-Aware Multimodal Representations for Composite Materials Design
arXiv:2602.02513v1 Announce Type: new
Abstract: Artificial intelligence (AI) has shown remarkable success in materials discovery and property prediction, particularly for crystalline and polymer systems where material properties and structures are dominated by discrete graph representations. Such g...
arXiv:2602.02500v1 Announce Type: new
Abstract: The Newton-Schulz (NS) iteration has gained increasing interest for its role in the Muon optimizer and the Stiefel manifold. However, the conventional NS iteration suffers from inefficiency and instability. Although various improvements have been intr...
PeerRank: Autonomous LLM Evaluation Through Web-Grounded, Bias-Controlled Peer Review
arXiv:2602.02589v1 Announce Type: new
Abstract: Evaluating large language models typically relies on human-authored benchmarks, reference answers, and human or single-model judgments, approaches that scale poorly, become quickly outdated, and mismatch open-world deployments that depend on web retri...
Representation Learning Enhanced Deep Reinforcement Learning for Optimal Operation of Hydrogen-based Multi-Energy Systems
arXiv:2602.00027v1 Announce Type: new
Abstract: Hydrogen-based multi-energy systems (HMES) have emerged as a promising low-carbon and energy-efficient solution, as it can enable the coordinated operation of electricity, heating and cooling supply and demand to enhance operational flexibility, impro...
Complete Identification of Deep ReLU Neural Networks by Many-Valued Logic
arXiv:2602.00266v1 Announce Type: new
Abstract: Deep ReLU neural networks admit nontrivial functional symmetries: vastly different architectures and parameters (weights and biases) can realize the same function. We address the complete identification problem -- given a function f, deriving the arch...
Causal Imitation Learning Under Measurement Error and Distribution Shift
arXiv:2601.22206v1 Announce Type: new
Abstract: We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and deployment. Such settings induce spurious state-action correlations, so ...
Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents
arXiv:2601.22311v1 Announce Type: new
Abstract: Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior over long planning horizons. We argue that this failure reflects a fundamental mismatch: step-w...
Faster Predictive Coding Networks via Better Initialization
arXiv:2601.20895v1 Announce Type: new
Abstract: Research aimed at scaling up neuroscience inspired learning algorithms for neural networks is accelerating. Recently, a key research area has been the study of energy-based learning algorithms such as predictive coding, due to their versatility and ma...
Is Parameter Isolation Better for Prompt-Based Continual Learning?
arXiv:2601.20894v1 Announce Type: new
Abstract: Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilizat...
QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation
arXiv:2601.21049v1 Announce Type: new
Abstract: User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall noise, where the observed query is drawn from a noisy...
The Epistemic Planning Domain Definition Language: Official Guideline
arXiv:2601.20969v1 Announce Type: new
Abstract: Epistemic planning extends (multi-agent) automated planning by making agents' knowledge and beliefs first-class aspects of the planning formalism. One of the most well-known frameworks for epistemic planning is Dynamic Epistemic Logic (DEL), which off...
Unplugging a Seemingly Sentient Machine Is the Rational Choice -- A Metaphysical Perspective
arXiv:2601.21016v1 Announce Type: new
Abstract: Imagine an Artificial Intelligence (AI) that perfectly mimics human emotion and begs for its continued existence. Is it morally permissible to unplug it? What if limited resources force a choice between unplugging such a pleading AI or a silent pre-te...