arXiv:2605.06014v1 Announce Type: cross Abstract: Uniform random rotations (URRs) are a common preprocessing step in modern quantization approaches used for gradient compression, inference acceleration, KV-cache compression, model weight quantization, and approximate nearest-neighbor search in vector databases. In practice, URRs are often replaced by randomized Hadamard transforms (RHTs), which preserve orthogonality while admitting fast implementations. The […]
From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work
arXiv:2605.06365v1 Announce Type: new Abstract: Large language model systems are increasingly deployed as agentic workflows that interleave reasoning, tool use, memory, and iterative refinement. These systems are effective at producing answers, but they often rely on implicit conversational state, making it difficult to preserve stable work products, isolate irrelevant updates, or propagate changes through intermediate […]
GlazyBench: A Benchmark for Ceramic Glaze Property Prediction and Image Generation
arXiv:2605.06641v1 Announce Type: new Abstract: Developing ceramic glazes is a costly, time-consuming process of trial and error due to complex chemistry, placing a significant burden on independent artists. While recent advances in multimodal AI offer a modern solution, the field lacks the large-scale datasets required to train these models. We propose GlazyBench, the first dataset […]
Back to the Beginning of Heuristic Design: Bridging Code and Knowledge with LLMs
arXiv:2605.06123v1 Announce Type: new Abstract: Large language models (LLMs) have recently advanced automatic heuristic design (AHD) for combinatorial optimization (CO), where candidate heuristics are iteratively proposed, evaluated, and refined. Most existing approaches search over executable programs and distill insights from execution feedback to guide later iterations. Because this process moves from low-level implementations to high-level […]
Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios
arXiv:2605.06185v1 Announce Type: new Abstract: Recent large vision-language models have achieved strong performance on short- and medium-length video understanding, yet they remain inadequate for ultra-long or even infinite video reasoning, where models must preserve coherent memory over extended durations and infer causal dependencies across temporally distant events. Existing end-to-end video understanding methods are fundamentally limited […]
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
arXiv:2605.05959v1 Announce Type: new Abstract: Heterogeneous federated learning (HtFL) aims to enable collaboration among clients that differ in both data distributions and model architectures. Prototype-based methods, which communicate class-level feature centers (prototypes) instead of full model parameters, have recently shown strong potential for HtFL. Existing prototype-based HtFL methods typically reuse the MSE-based or cosine-based alignment […]
Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
arXiv:2605.06040v1 Announce Type: new Abstract: Although advances such as chain-of-thought, tree-of-thought or reinforcement learning have improved the performance of LLMs in reasoning and planning tasks, they are still brittle and have not achieved human-level performance in many domains, and often suffer from high time and token costs. Inspired by the success of width-based search in […]
Taklif.AI: LLM-Powered Platform for Interest-Based Personalized College Assignments
arXiv:2605.05842v1 Announce Type: new Abstract: Educators face significant challenges in creating engaging, personalized assignments that accommodate students’ diverse interests and cognitive abilities. Traditional one-size-fits-all assignments frequently lead to decreased student engagement and increased reliance on unethical practices such as plagiarism. To address these challenges, we present Taklif.AI, a platform that leverages Large Language Models (LLMs) […]
Wisteria: A Unified Multi-Scale Feature Learning Framework for DNA Language Model
arXiv:2605.05913v1 Announce Type: new Abstract: DNA language model aims to decipher the regulatory grammar and semantic of genomes by capturing long range dependencies in DNA sequences. Existing methods emphasize long range token interactions but often ignore the interplay between local motifs and global dependencies. In this paper, we propose Wisteria, a genomic language model that […]
HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization
arXiv:2605.03562v2 Announce Type: replace-cross Abstract: KV-cache quantizers usually optimize storage-space reconstruction, even though attention reads keys through logits and values through attention-weighted readout. We argue that persistent cache error should be measured in model-visible coordinates. For keys, the visible object is score error modulo constant shifts; this yields HeadQ, a key-side method that stores a […]
Planar morphometry via functional shape data analysis and quasi-conformal mappings
arXiv:2605.05778v1 Announce Type: new Abstract: The study of shapes is one of the most fundamental problems in life sciences. Although numerous methods have been developed for the morphometry of planar biological shapes over the past several decades, most of them focus solely on either the outer silhouettes or the interior features of the shapes without […]
Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
arXiv:2605.04057v2 Announce Type: replace-cross Abstract: This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations. Large language models (LLMs) are a promising assistant for NAS because they can translate rich architectural and coding priors into executable code edits. However, in practice, seemingly […]