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Crosslisted article(s) found for q-bio.PE. https://arxiv.org/list/q-bio.PE/new
[1/1]:
- Braess's paradox in tandem-running ants: When shortest path is not the quickest
Joy Das Bairagya, Udipta Chakraborti, Sumana Annagiri, Sagar Chakraborty
https://arxiv.org/abs/2603.26226 https://mastoxiv.page/@arXiv_physicsbioph_bot/116317046208603894
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🇺🇦 #NowPlaying on BBCRadio3's #ClassicalMixtape
Andrea Casarrubios, Andrea Casarrubios & Chicago Symphony Orchestra:
🎵 Lullaby (Piano quintet, 2nd mvt)
#AndreaCasarrubios #ChicagoSymphonyOrchestra

I got a fence down the ends for that sweet CDM.
https://www.bbc.co.uk/news/articles/ce3gqr7p0lqo

Chocolate kept in anti-theft boxes as retailers warn it's being stolen to order
Retailers and police forces tell the BBC that thieves are targeting chocolate and selling it on.

Technological dependence on American software and cloud services : an assessment of the economic consequences in Europe - Cigref
https://www.cigref.fr/technological-dependence-on-american-software-and-…

Technological dependence on American software and cloud services : an assessment of the economic consequences in Europe - Cigref
‘Technological dependence on American software and cloud services: an estimate of the economic consequences in Europe’ is a new study on Europe's

Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training
Anas Barakat, Souradip Chakraborty, Khushbu Pahwa, Amrit Singh Bedi
https://arxiv.org/abs/2602.21189 https://arxiv.org/pdf/2602.21189 https://arxiv.org/html/2602.21189
arXiv:2602.21189v1 Announce Type: new
Abstract: Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning. It defines success if any of $k$ independently sampled solutions passes a verifier. This multi-sample inference metric has motivated inference-aware fine-tuning methods that directly optimize pass@$k$. However, prior work reports a recurring trade-off: pass@k improves while pass@1 degrades under such methods. This trade-off is practically important because pass@1 often remains a hard operational constraint due to latency and cost budgets, imperfect verifier coverage, and the need for a reliable single-shot fallback. We study the origin of this trade-off and provide a theoretical characterization of when pass@k policy optimization can reduce pass@1 through gradient conflict induced by prompt interference. We show that pass@$k$ policy gradients can conflict with pass@1 gradients because pass@$k$ optimization implicitly reweights prompts toward low-success prompts; when these prompts are what we term negatively interfering, their upweighting can rotate the pass@k update direction away from the pass@1 direction. We illustrate our theoretical findings with large language model experiments on verifiable mathematical reasoning tasks.
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