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SpaceFlow: Locally Controllable 3D Generation

SpaceFlow is a training-free method for 3D generation that allows local control over geometry and appearance based on text and geometric primitives. It uses primitives as proxies for object parts, assigning local control levels to specify whether regions should strictly follow input shapes or allow generative completion. The approach enforces spatial constraints during generation and conditions appearance synthesis on assigned cues to limit cross-part leakage.

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PublishedOctober 8, 2026Neil De La Fuente, Joan Lafuente, Mukhammadali Sayfiddinov
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WHY IT MAY MATTER

This method enables more precise control over 3D object generation by allowing localized adjustments to geometry and appearance, which can be useful in applications requiring specific structural and visual details.

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Tetris3D: 3D Scene Generation With Objects That Fit Together

Tetris3D is a generative framework for 3D scene reconstruction from a single image, ensuring objects are geometrically and physically coherent. It uses explicit conditioning on surrounding objects' geometry and physical relationships, along with a physics-based dataset called ComOb for training and evaluation.

2 days agoDaily Papers

From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation

The paper introduces Trace2Env, a learning-free framework that uses historical interaction traces to create a reusable environment worldbook for simulating realistic environments without rebuilding the original system. It improves next-observation fidelity and long-horizon interaction consistency compared to conventional prompt-based language world models.

4 days agoDaily Papers

CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?

CheckerBench is a benchmark for evaluating coding agents' ability to synthesize static-analysis checkers from defect specifications, including tasks derived from 300 CVEs across multiple repositories and language ecosystems. CheckerLab, an evaluation framework, measures diagnostic contrast, patch localization, and tool use, with results showing that current agents struggle to develop reliable checkers, achieving a maximum Pass@1 of 45.33%.

3 days agoDaily Papers

Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability

The paper investigates on-policy distillation (OPD) in cross-tokenizer settings, focusing on alignment coverage and supervision reliability. It finds that strict 1:1 token alignment covers most student-generated tokens despite vocabulary mismatches, and that restricting reverse KL to a top-16 subset of shared vocabulary achieves comparable accuracy to full shared-vocabulary OPD, while adding span supervision reduces accuracy.

3 days agoDaily Papers

NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesis

NAMVIS is a diffusion-free framework for multi-view image synthesis that uses geometry-conditioned next-scale autoregression, predicting discrete visual tokens through coarse-to-fine scale steps and parallel sampling across target views. It outperforms diffusion-based methods in PSNR, SSIM, and LPIPS while being over three times faster.

6 days agoDaily Papers

MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers

Sparse attention is used to reduce latency in long-sequence generation, but existing methods can degrade generation quality at high sparsity. MC-Sparse is a training-free framework that selects individual key-value tokens and organizes similar queries into groups for efficient GPU execution.

4 days ago