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Related Articles from SNS
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
arXiv:2605.00369v4 Announce Type: replace Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based evolutionary search, such as AlphaEvolve, which demonstrates strong performance on static and highly structured problems such as mathematical discovery, but is not directly suited to dynamic inventory settings with online updates. We propose InvEvolve, an...
Hearn makes Aspinall plea and Benn promise to White
Boxing promoter Eddie Hearn calls on UFC president Dana White to release Tom Aspinall from his contract and says the heavyweight champion is not being paid his worth.
Hearn makes Aspinall plea and Benn promise to White
Boxing promoter Eddie Hearn calls on UFC president Dana White to release Tom Aspinall from his contract and says the heavyweight champion is not being paid his worth.
Hearn makes Aspinall plea and Benn promise to White
Boxing promoter Eddie Hearn calls on UFC president Dana White to release Tom Aspinall from his contract and says the heavyweight champion is not being paid his worth.
Hearn makes Aspinall plea and Benn promise to White
Boxing promoter Eddie Hearn calls on UFC president Dana White to release Tom Aspinall from his contract and says the heavyweight champion is not being paid his worth.
Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes
Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability. Existing testing methods, whether black-box or white-box, primarily use global mutation or coverage-guided strategies, both of which struggle to efficiently uncover diverse model failures while remaining proximate to the original data distribution and semantics. We propose BayesWarp, a testing framework that addresses this...
Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?
Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpretability, and white-box text-based reasoning cannot restore lost pixel-level details. This work investigates a fundamental research question: Can MLLMs recover...
'Big things coming! - what next for Zuffa Boxing after 'incredible' UK debut?
What next for Zuffa Boxing after UK debut? Dana White celebrates Bournemouth show and teases 'big announcements' ahead Dana White promised ‘a lot of big announcements’ coming up after Zuffa Boxing’s debut UK show in Bournemouth, where Chris Billam-Smith's victory over Ryan Rozicki and Cheavon Clarke’s comeback win over Jack Massey delivered two enthralling co-main events Sunday 7 June 2026 13:05, UK Even before Chris Billam-Smith’s brutal battle with Ryan Rozicki and gruelling hometown...
Token-Efficient Change Detection in LLM APIs
Announce Type: replace Abstract: Remote change detection in LLMs is a difficult problem. Existing methods are either too expensive for deployment at scale, or require initial white-box access to model weights or grey-box access to log probabilities. We aim to achieve both low cost and strict black-box operation, observing only output tokens.
Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing
Announce Type: replace Abstract: Narrowly finetuned language models memorize implanted content verbatim, but auditing what a deployed model has been taught, without access to its weights or training data, remains an open challenge. Recent work shows that activation differences between base and finetuned models carry readable traces of the finetuning domain; the state-of-the-art Activation Difference Lens (ADL) recovers a vague domain-level description but requires full "white-box" access to...