arXiv:2603.26778v1 Announce Type: cross Abstract: Knowledge distillation is typically realized by transferring a teacher model’s knowledge into a student’s parameters through supervised or reinforcement-based optimization. While effective, such approaches require repeated parameter updates and large-scale training data, limiting their applicability in resource-constrained environments. In this work, we propose TED, a training-free, context-based distillation framework that […]
Capability Safety as Datalog: A Foundational Equivalence
arXiv:2603.26725v1 Announce Type: cross Abstract: We prove that capability safety admits an exact representation as propositional Datalog evaluation (Datalogprop: the monadic, ground, function-free fragment of first-order logic), enabling the transfer of algorithmic and structural results unavailable in the native formulation. This addresses two structural limitations of the capability hypergraph framework of Spera [2026]: the absence […]
Beyond Static Visual Tokens: Structured Sequential Visual Chain-of-Thought Reasoning
arXiv:2603.26737v1 Announce Type: cross Abstract: Current multimodal LLMs encode images as static visual prefixes and rely on text-based reasoning, lacking goal-driven and adaptive visual access. Inspired by human visual perception-where attention is selectively and sequentially shifted from the most informative regions to secondary cues-we propose Structural Sequential Visual CoT SSV-CoT. First, a question-relevant saliency map […]
LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval
arXiv:2603.26683v1 Announce Type: cross Abstract: Retrieving relevant evidence from visually rich documents such as textbooks, technical reports, and manuals is challenging due to long context, complex layouts, and weak lexical overlap between user questions and supporting pages. We propose LITTA, a query-expansion-centric retrieval framework for evidence page retrieval that improves multimodal document retrieval without retriever […]
The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop
arXiv:2603.26707v1 Announce Type: cross Abstract: This paper documents and theorises a self-reinforcing dynamic between two measurable trends: the exponential expansion of large language model (LLM) context windows and the secular contraction of human sustained-attention capacity. We term the resulting asymmetry the Cognitive Divergence. AI context windows have grown from 512 tokens in 2017 to 2,000,000 […]
Seeing with You: Perception-Reasoning Coevolution for Multimodal Reasoning
arXiv:2603.28618v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially enhanced the reasoning capabilities of multimodal large language models (MLLMs). However, existing RLVR approaches typically rely on outcome-driven optimization that updates both perception and reasoning using a shared reward based solely on the final answer. This shared reward blurs credit assignment, frequently […]
Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
arXiv:2603.26668v1 Announce Type: cross Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG. To overcome the accuracy challenge, we introduce the concept of abstract to bridge query […]
COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game
arXiv:2603.28386v1 Announce Type: new Abstract: A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and generalization beyond the training distribution. We address this with COvolve, a co-evolutionary framework that leverages large language models (LLMs) to generate both environments and agent policies, expressed […]
Tiny-ViT: A Compact Vision Transformer for Efficient and Explainable Potato Leaf Disease Classification
arXiv:2603.26761v1 Announce Type: cross Abstract: Early and precise identification of plant diseases, especially in potato crops is important to ensure the health of the crops and ensure the maximum yield . Potato leaf diseases, such as Early Blight and Late Blight, pose significant challenges to farmers, often resulting in yield losses and increased pesticide use. […]
Evaluating LLMs for Answering Student Questions in Introductory Programming Courses
arXiv:2603.28295v1 Announce Type: new Abstract: The rapid emergence of Large Language Models (LLMs) presents both opportunities and challenges for programming education. While students increasingly use generative AI tools, direct access often hinders the learning process by providing complete solutions rather than pedagogical hints. Concurrently, educators face significant workload and scalability challenges when providing timely, personalized […]
T-Norm Operators for EU AI Act Compliance Classification: An Empirical Comparison of Lukasiewicz, Product, and G”odel Semantics in a Neuro-Symbolic Reasoning System
arXiv:2603.28558v1 Announce Type: new Abstract: We present a first comparative pilot study of three t-norm operators — Lukasiewicz (T_L), Product (T_P), and G”odel (T_G) – as logical conjunction mechanisms in a neuro-symbolic reasoning system for EU AI Act compliance classification. Using the LGGT+ (Logic-Guided Graph Transformers Plus) engine and a benchmark of 1035 annotated AI […]
Exploring Cultural Variations in Moral Judgments with Large Language Models
arXiv:2506.12433v2 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance across many tasks, but their ability to capture culturally diverse moral values remains unclear. In this paper, we examine whether LLMs mirror variations in moral attitudes reported by the World Values Survey (WVS) and the Pew Research Center’s Global Attitudes Survey (PEW). […]