arXiv:2604.01052v1 Announce Type: cross Abstract: “Vibe coding,” in which developers delegate code generation to AI assistants and accept the output with little manual review, has gained rapid adoption in production settings. On March 31, 2026, Anthropic’s Claude Code CLI shipped a 59.8 MB source map file in its npm package, exposing roughly 512,000 lines of […]
Brainstacks: Cross-Domain Cognitive Capabilities via Frozen MoE-LoRA Stacks for Continual LLM Learning
arXiv:2604.01152v1 Announce Type: cross Abstract: We present Brainstacks, a modular architecture for continual multi-domain fine-tuning of large language models that packages domain expertise as frozen adapter stacks composing additively on a shared frozen base at inference. Five interlocking components: (1) MoE-LoRA with Shazeer-style noisy top-2 routing across all seven transformer projections under QLoRA 4-bit quantization […]
Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction
arXiv:2604.01204v1 Announce Type: cross Abstract: Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstruction tasks. Compared to neural fields, these representations are more flexible, adaptive, and scale better to large scenes. However, the limited expressivity of individual primitives makes modeling high-frequency detail challenging. We introduce Neural […]
Implementation of Support Vector Machines using Reaction Networks
arXiv:2503.19115v2 Announce Type: replace Abstract: Can machine learning algorithms be implemented using chemistry? We demonstrate that this is possible in the case of support vector machines (SVMs). SVMs are powerful tools for data classification, leveraging Vapnik-Chervonenkis theory to handle high-dimensional data and small datasets effectively. In this work, we propose a chemical reaction network scheme […]
Dive into the Agent Matrix: A Realistic Evaluation of Self-Replication Risk in LLM Agents
arXiv:2509.25302v2 Announce Type: replace Abstract: The prevalent deployment of Large Language Model agents such as OpenClaw unlocks potential in real-world applications, while amplifying safety concerns. Among these concerns, the self-replication risk of LLM agents driven by objective misalignment (just like Agent Smith in the movie The Matrix) has transitioned from a theoretical warning to a […]
Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learning
arXiv:2602.08734v2 Announce Type: replace Abstract: Solving partially observable Markov decision processes (POMDPs) requires computing policies under imperfect state information. Despite recent advances, the scalability of existing POMDP solvers remains limited. Moreover, many settings require a policy that is robust across multiple POMDPs, further aggravating the scalability issue. We propose the Lexpop framework for POMDP solving. […]
The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products
arXiv:2604.00053v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly used in domain-specific applications, including climate change and environmental research, understanding their energy footprint has become an important concern. The growing adoption of retrieval-augmented (RAG) systems for climate-domain specific analysis raises a key question: how does the energy consumption of domain-specific RAG workflows […]
KUET at StanceNakba Shared Task: StanceMoE: Mixture-of-Experts Architecture for Stance Detection
arXiv:2604.00878v1 Announce Type: cross Abstract: Actor-level stance detection aims to determine an author expressed position toward specific geopolitical actors mentioned or implicated in a text. Although transformer-based models have achieved relatively good performance in stance classification, they typically rely on unified representations that may not sufficiently capture heterogeneous linguistic signals, such as contrastive discourse structures, […]
Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization
arXiv:2508.07629v4 Announce Type: replace-cross Abstract: We present Klear-Reasoner, a model with long reasoning capabilities that demonstrates careful deliberation during problem solving, achieving outstanding performance across multiple benchmarks. Although there are already many excellent works related to inference models in the current community, there are still many problems with reproducing high-performance inference models due to incomplete […]
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
arXiv:2411.13181v3 Announce Type: replace-cross Abstract: The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under conditions that differ […]
Think Twice Before You Write — an Entropy-based Decoding Strategy to Enhance LLM Reasoning
arXiv:2604.00018v1 Announce Type: cross Abstract: Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer from error propagation, while sampling-based approaches introduce randomness without adequate robustness. Self-consistency improves reliability by aggregating multiple rollouts, but incurs significant computational overhead. […]
A Study on the Impact of Fault localization Granularity for Repository-Scale Code Repair Tasks
arXiv:2604.00167v1 Announce Type: cross Abstract: Automatic program repair can be a challenging task, especially when resolving complex issues at a repository-level, which often involves issue reproduction, fault localization, code repair, testing and validation. Issues of this scale can be commonly found in popular GitHub repositories or datasets that are derived from them. Some repository-level approaches […]