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When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from those viewpoints. But can Large Vision-Language Models (LVLMs) proactively think in the same way? We term this ability Active Spatial Mentalization (...
Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy distillation (OPD) consolidates their capabilities into a single student. Existing methods, however, route each prompt to a single teacher according to its semantic category, implicitly binding the d...
On-policy distillation (OPD) trains a student model on self-generated trajectories, but transmitting dense teacher distributions across long reasoning traces creates prohibitive communication and memory bottlenecks. Practical systems therefore rely on sparse teacher interfaces, typically transmitting either the sampled...
This work introduces SciDocBench, a workflow-centered benchmark targeting capability diagnosis with verifiable training-data construction for scientific-document assistants, and SciDocIR, a structured representation of scientific document objects, layout and cross-reference relations, and provenance.
This work presents WorldReward, a VLM-based pairwise preference reward model that unifies action-consistency and visual-quality evaluation for camera-conditioned world models, and introduces WorldReward-Bench, a human-annotated benchmark measuring reward-model agreement with human preferences across action consistency,...
Scientific papers require models to reason jointly over text, equations, figures, tables, code, and datasets while preserving the provenance of supporting evidence. Existing benchmarks typically evaluate these capabilities in isolation, leaving unclear whether multimodal models can support realistic scientific-reading...
Skill-as-Pseudocode (SaP) is proposed, an automatic conversion of markdown skill libraries into typed pseudocode with deterministic quality control that wins paired games and falls below the prose baseline on the ALFWorld unseen split.
This work introduces EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games, and evaluates memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines.
This work proposes Hybrid-Policy Self-Distillation (HPSD), a novel self-distillation framework where a single TI2V model acts as both teacher and student under different conditions: the teacher operates in TI2V mode with a high-quality first frame and an enhanced prompt, while the student runs in the base T2V mode with...
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