reconstruction

The paper proposes δ-Vision, a method to reduce computational overhead in multimodal language models by using lightweight low-rank adapters to reconstruct visual states efficiently without discarding visual tokens.

FuseReg is a regularization method for Representation Autoencoders that mitigates the reconstruction-generation gap by using random layer-subset sampling during training, improving generation performance and decoder robustness across different layer inputs.

A machine learning framework is proposed to accurately reconstruct optical properties in bilayered media from single-distance time-resolved reflectance measurements, showing improved accuracy and speed over conventional model-based methods.

A user shares excitement about using AI tools like Hyper3D Rodin MCP and GPT-6 Astra to rebuild the classic game Road Rash, highlighting advancements in 3D generation.

ReconSpan introduces an adaptive latent tokenization method that divides text into reconstructible chunks using a backward decoder, enabling variable-length latent tokens and post-training control of granularity.

MemHarness proposes changing Agent memory from simple replay to reconstruction based on the current state, trained end-to-end with GRPO, significantly improving success rates on ALFWorld and WebShop.

MemHarness is a framework that enables LLM agents to reconstruct past experiences conditioned on the current context instead of replaying them verbatim, improving performance on ALFWorld and WebShop while reducing negative transfer.

This is an open-source 3D model generation project that can generate detailed 3D models from images, such as CS2 butterfly knife and Sony camera, with realistic results.

This paper demonstrates that reconstruction-based tests for activation explanations can be gamed, producing high scores while specific claims remain false. It proposes RECAP, which trains linear heads alongside the target model to keep designated internal content reliably decodable and independently verifiable against probes, improving safety auditing.

Introduces a new open-source toolkit, img2threejs, that converts photos into Three.js models and automatically compares the rendering result with the original image to calibrate reconstruction quality, especially suitable for hard-surface objects.

This paper introduces Lorentz Encoding (LE), a physics-informed framework that uses implicit neural representations and physical constraints to reconstruct high-resolution CEST MRI from sparsely sampled data, achieving superior performance over existing methods.

The author strongly criticizes the concept of an infinite canvas, arguing that the essence of AI video is horizontally arranged film strips, and announces a complete refactoring from first principles before release.

This paper investigates the impact of inference stride (disjoint vs. overlapping windows) on reconstruction-based time series anomaly detection, finding that overlapping windows consistently improve performance by up to 28% across multiple model architectures on the TSB-AD and UCR benchmarks.

MRAgent introduces a novel graph-based memory framework that dynamically reconstructs memory during reasoning, achieving up to 23% improvement on long-horizon benchmarks while reducing computational costs.

This paper presents a comprehensive taxonomy of 3D vision research, covering geometric representations, datasets, learning paradigms, and applications in reconstruction, generation, and video modeling.

This paper identifies and explains a spectral bias in reconstruction-based EEG foundation models, where embeddings over-represent aperiodic and low-frequency components while under-representing oscillatory components, especially at higher frequencies, leading to poor performance in low-resource settings.

This paper proposes CoAD, a novel framework that unifies Outlier Exposure (classification) and Masked Autoencoder (reconstruction) paradigms for time series anomaly detection, addressing their respective limitations. Extensive experiments show that CoAD significantly outperforms state-of-the-art methods while being lightweight and fast.

ShapeCodeBench is a synthetic benchmark for perception-to-program reconstruction where models generate executable drawing programs from raster images, evaluated on metrics like exact match and pixel accuracy. The benchmark is designed to be renewable via seeded RNG, and current models still achieve low exact match rates, indicating room for improvement.