Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains. An established approach to interpretability is generating visual attribution maps that highlight regions of the input most relevant to the model’s prediction. However, existing methods face a three-way trade-off. Propagation-based approaches are efficient, but they can be biased and architecture-specific. Meanwhile, perturbation-based methods are causally grounded, yet they are expensive and for vision transformers often yield coarse, patch-level explanations. […]

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arXiv:2601.19901v1 Announce Type: new Abstract: Rendering for light field displays (LFDs) requires rendering of dozens or hundreds of views, which must then be combined into a single image on the display, making real-time LFD rendering extremely difficult. We introduce light field display point rendering (LFDPR), which meets these challenges by improving eye-based point rendering [Gavane and Watson 2023] with texture-based splatting, which avoids oversampling of triangles mapped to only a few texels; and with LFD-biased sampling, which adjusts […]

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Hi everyone, I’m currently working on my thesis and I’m specifically looking for research papers or resources on solving emergency transportation or emergency dispatch problems (such as ambulance routing, dynamic fleet management, or emergency logistics) using Quantum Multi-Agent Reinforcement Learning (QMARL). My focus is on integrating quantum computing techniques (e.g., variational quantum circuits, quantum-enhanced policy/value functions, hybrid quantum-classical models) within a multi-agent RL framework to handle dynamic, stochastic, and decentralized decision-making settings. Despite extensive searching, I haven’t found […]

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We verify that persistent observers in causally invariant hypergraph substrates satisfy the conditions of the Conant-Ashby Good Regulator Theorem. Building on Wolfram’s hypergraph physics and Vanchurin’s neural network cosmology, we formalize persistent observers as entities that minimize prediction error at their boundary with the environment. Applying a modern reformulation of the Conant-Ashby theorem, we demonstrate that hypergraph observers satisfy Good Regulator conditions, requiring them to maintain internal models. Once an internal model with loss function exists, the emergence […]

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arXiv:2601.08893v1 Announce Type: new Abstract: We introduce Spectral Generative Flow Models (SGFMs), a physics-inspired alternative to transformer-based large language models. Instead of representing text or video as sequences of discrete tokens processed by attention, SGFMs treat generation as the evolution of a continuous field governed by constrained stochastic dynamics in a multiscale wavelet basis. This formulation replaces global attention with local operators, spectral projections, and Navier–Stokes-like transport, yielding a generative mechanism grounded in continuity, geometry, and physical structure. […]

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arXiv:2604.02353v1 Announce Type: new Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents’ decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with different algorithms. PRISM clusters each agent’s encoder features into $K$ concepts via K-means. Causal intervention establishes that these concepts directly drive – not merely correlate with – agent behavior: overriding concept assignments changes the selected action in 69.4% […]

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arXiv:2602.15874v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities but remain limited by their reliance on static training data. Retrieval-Augmented Generation (RAG) addresses this constraint by retrieving external knowledge during inference, though it still depends heavily on knowledge base quality. To explore potential improvements, we evaluated three RAG variants-Standard RAG, DA-RAG, and our proposed Prompt-Enhanced Parametric RAG (P-RAG), a hybrid architecture that integrates parametric knowledge within the LLM and retrieved evidence, guided by Chain-of-Thought (CoT) […]

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Learning similarity metrics for glyphs and writing systems faces a fundamental challenge: while individual graphemes within invented alphabets can be reliably labeled, the historical relationships between different scripts remain uncertain and contested. We propose a two-stage framework that addresses this epistemological constraint. First, we train an encoder with contrastive loss on labeled invented alphabets, establishing a teacher model with robust discriminative features. Second, we extend to historically attested scripts through teacher-student distillation, where the student learns unsupervised representations […]

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As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and use a theory-of-mind, or ToM) becomes increasingly critical for safe interaction with potentially adversarial partners. We propose a novel privacy-themed ToM challenge, ToM for Steering Beliefs (ToM-SB), in which a defender must act as a Double Agent to steer the beliefs of an attacker with partial prior knowledge within a shared […]

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