Paper

PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning

Unreadunread

AI summary

arXiv:2605. 19382v1 Announce Type: new Abstract: Programmatic video generation through code offers geometric precision and temporal coherence beyond pixel-level diffusion models, yet rigorously evaluating whether language models can produce spatially correct animated outputs remains an open problem.

Reading status

Priority

About this paper

Authors: Qiran Zhang, Yuheng Wang, Runde Yang, Lin Wu, Jingru Fan, Shu Yao, Jie Zhang, Tianle Zhou, Huatao Li, Ruijie Shi, Yihan Li, Chen Qian
Released: 2026-05-21 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, correct, eval, rate, does, full, language. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.19382

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.