Tootfinder

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Panda Bear:
🎵 Praise
#NowPlaying #PandaBear
https://pandabearmusic.bandcamp.com/track/praise
https://open.spotify.com/track/08aaQA6A7UHvbPGsfPrAFe

Even by his chaotic standards,
Donald Trump has just presided over an unusually wild week in his misguided war on Iran.
The president had threatened imminent, punitive bombing of Iran’s civilian energy infrastructure.
Though Iran didn’t quail, markets did.
So a u-turn followed -- Trump said he had become aware of secret proposals for peace talks, and held off.
The Pentagon then said it would send some of the 82nd Airborne Division.
That suggests escal…

🐻 Bye bye, bear: beast living ‘rent-free’ under California home has been removed
https://www.theguardian.com/us-news/2026/jan/09/bear-under-california-home-evicted

‘Boom, he’s out’: bear living ‘rent-free’ under California home has been removed
The 550lb black bear was drawn out with paintball guns after it had resided under the home for more than a month

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Jack White:
🎵 Sixteen Saltines
#NowPlaying #JackWhite
https://pentafonica.bandcamp.com/track/jack-white-sixteen-saltines
https://open.spotify.com/track/3XBPCbTU9rSGuyuK7Xbj4B

Probing Dec-POMDP Reasoning in Cooperative MARL
Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni, David Abel, Amos Storkey
https://arxiv.org/abs/2602.20804 https://arxiv.org/pdf/2602.20804 https://arxiv.org/html/2602.20804
arXiv:2602.20804v1 Announce Type: new
Abstract: Cooperative multi-agent reinforcement learning (MARL) is typically framed as a decentralised partially observable Markov decision process (Dec-POMDP), a setting whose hardness stems from two key challenges: partial observability and decentralised coordination. Genuinely solving such tasks requires Dec-POMDP reasoning, where agents use history to infer hidden states and coordinate based on local information. Yet it remains unclear whether popular benchmarks actually demand this reasoning or permit success via simpler strategies. We introduce a diagnostic suite combining statistically grounded performance comparisons and information-theoretic probes to audit the behavioural complexity of baseline policies (IPPO and MAPPO) across 37 scenarios spanning MPE, SMAX, Overcooked, Hanabi, and MaBrax. Our diagnostics reveal that success on these benchmarks rarely requires genuine Dec-POMDP reasoning. Reactive policies match the performance of memory-based agents in over half the scenarios, and emergent coordination frequently relies on brittle, synchronous action coupling rather than robust temporal influence. These findings suggest that some widely used benchmarks may not adequately test core Dec-POMDP assumptions under current training paradigms, potentially leading to over-optimistic assessments of progress. We release our diagnostic tooling to support more rigorous environment design and evaluation in cooperative MARL.
toXiv_bot_toot

»Quantel Paintbox« hat ab Anfang der 1980er Grafik und Animation im Fernsehen digitalisiert und massive Änderungen in der bis dahin üblichen Arbeitsweise gebracht. Und trotz des Preises für Hard- und Software die Herstellungskosten von Animationen im TV vermutlich deutlich gesenkt. In einem Werbevideo von 1983 kann man einen Eindruck gewinnen, was mit der Software möglich war, enjoy: …

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Panda Bear:
🎵 Praise
#NowPlaying #PandaBear
https://pandabearmusic.bandcamp.com/track/praise
https://open.spotify.com/track/08aaQA6A7UHvbPGsfPrAFe