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@andres4ny@social.ridetrans.it
2026-02-01 23:55:20

How embarrassing for us. We need to buy our bike share system. fed.brid.gy/r/https://bsky.app

A skeet from Jason Rabinowitz, saying "Good luck with that, CitiBike riders..." and showing a picture of a citibike station. The sidewalk behind the station is almost totally clear, but the bike station itself is so completely covered in snow that you can't see the back half of the bikes. The only thing visible is the saddles popping out of the snow bank, and part of the front wheel and handlebars.
@cobordism@berlin.social
2026-02-15 11:44:43

Why is society outraged at Epstein while still celebrating Michael Jackson?
Or am I misunderstanding the MJ musical, and is a JE sequel in the works?
#epstein #mj #musical

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 10:45:31

Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
Yining Hong, Huang Huang, Manling Li, Li Fei-Fei, Jiajun Wu, Yejin Choi
arxiv.org/abs/2602.21198 arxiv.org/pdf/2602.21198 arxiv.org/html/2602.21198
arXiv:2602.21198v1 Announce Type: new
Abstract: Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistakes repeat rather than accumulate into experience. Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: \textit{reflection-in-action}, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and \textit{reflection-on-action}, which uses test-time training to update both its internal reflection model and its action policy based on external reflections after execution. We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with ablative studies validating the complementary roles of reflection-in-action and reflection-on-action. Qualitative analyses, including real-robot trials, highlight behavioral correction through reflection.
toXiv_bot_toot