Paper

Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions

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arXiv:2605. 22438v1 Announce Type: new Abstract: Shilling is the use of artificial bids to make competition appear stronger and push prices upward.

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Authors: Luigi Foscari, Matilde Tullii, Vianney Perchet
Released: 2026-05-22 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions 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: strong, rate, without, factor, suffix. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.22438

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