#76 ▲63 ★210 ❝3 2026-09-30
A survey of post-training and alignment for video generation, covering temporal coherence, physics and safety challenges.
Arizona State University · Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni +10
#124 ▲104 ★35 ❝0 2026-09-29
The Thinking Reward Model first sets case-specific rubrics and then scores, giving finer rewards for visual generation.
Affiliation unverified · Xuehai Bai, Zhenchen Tang, Yang Shi +9
#125 ▲55 ★63 ❝0 2026-09-21
onPanda is an annotation tool where annotators fix the first bad token and let the model continue, cheaply producing alignment data.
StepFun · Lei Yang, Mengyin Liu, Jia Wang +9
#160 ▲68 ★22 ❝0 2026-09-30
Shows weighted-sum rubric rewards can be hacked (e.g. in clinical consultation) and proposes protocol-level rubrics to fix it.
ByteDance · Maoqi Liu, Junwei He, Bowen Zhang +5
#170 ▲29 ★28 ❝12 2026-09-24
Tests whether Jev, a calibrated decision model, can zero-shot detect ten alignment failures such as sycophancy, jailbreaks and deception.
Affiliation unverified · Ruoqi Guo, Yi Liu, Gelei Deng +6
#216 ▲62 ★30 ❝0 2026-10-04
Memadapter uses counterfactual adaptation to reduce memory-induced sycophancy, which arises even from correct memories.
DEEP Group at Jilin University · Ruqing Ning, Haibo Meng, Zhishang Xiang +4
#256 ▲35 ★37 ❝0 2026-09-27
Tsinghua's Diffusion Reward Model recasts reward modelling as conditional density estimation to capture multimodal human preferences.
Tsinghua NLP Group · Xiangyang Wang, Bingxiang He, Zeyuan Liu +13
#292 ▲47 ❝0 2026-10-02
Finds 12 agent models systematically favour items from certain sources when shopping, booking or citing, and proposes mitigations.
Seoul National University · Jonghyun Song, Haewon Park, Jeonghoon Shim +2