Fashion

[2506.24000] The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models

[Submitted on 30 Jun 2025 (v1), last revised 13 Oct 2025 (this version, v2)] View a PDF of the paper titled The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models, by Lijun Sheng and 4 other authors View PDF HTML (experimental) Abstract:Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models …

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[2509.18180] Large Language Models and Operations Research: A Structured Survey

[Submitted on 18 Sep 2025 (v1), last revised 13 Oct 2025 (this version, v2)] View a PDF of the paper titled Large Language Models and Operations Research: A Structured Survey, by Yang Wang and 1 other authors View PDF HTML (experimental) Abstract:Operations research (OR) provides fundamental methodologies for complex system decision-making, with established applications in transportation, supply chain management, and …

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Dreaming in Blocks — MineWorld, the Minecraft World Model

Mineworld gameplay, taken from the GitHub repository [4], licensed under the MIT License. games growing up was definitely Minecraft. To this day, I still remember meeting up with a couple of friends after school and figuring out what new, odd red-stone contraption we would build next. That’s why, when Oasis, an automatically generated open AI world model, was released in …

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Is vibe coding ruining a generation of engineers?

AI tools are revolutionizing software development by automating repetitive tasks, refactoring bloated code, and identifying bugs in real-time. Developers can now generate well-structured code from plain language prompts, saving hours of manual effort. These tools learn from vast codebases, offering context-aware recommendations that enhance productivity and reduce errors. Rather than starting from scratch, engineers can prototype quickly, iterate faster and …

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10 Data + AI Observations for Fall 2025

the final quarter of 2025, it’s time to step back and examine the trends that will shape data and AI in 2026.  While the headlines might focus on the latest model releases and benchmark wars, they’re far from the most transformative developments on the ground. The real change is playing out in the trenches — where data scientists, data + …

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Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving

[Submitted on 5 Feb 2025 (v1), last revised 9 Oct 2025 (this version, v3)] View a PDF of the paper titled BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving, by Ran Xin and 8 other authors View PDF HTML (experimental) Abstract:Recent advancements in large language models (LLMs) have spurred growing interest in automatic theorem proving using Lean4, where …

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[2509.04128] Who Pays for Fairness? Rethinking Recourse under Social Burden

[Submitted on 4 Sep 2025 (v1), last revised 8 Oct 2025 (this version, v2)] View a PDF of the paper titled Who Pays for Fairness? Rethinking Recourse under Social Burden, by Ainhize Barrainkua and 3 other authors View PDF HTML (experimental) Abstract:Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led …

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[2509.22284] Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models

[Submitted on 26 Sep 2025 (v1), last revised 7 Oct 2025 (this version, v2)] View a PDF of the paper titled Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models, by Aleksandar Terzi\’c and 4 other authors View PDF HTML (experimental) Abstract:Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the …

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SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder

arXiv:2510.05081v1 Announce Type: cross Abstract: Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two properties are especially desirable: disentanglement, where changing one attribute does not unintentionally alter others, and continuous control, where the strength of an edit can be smoothly adjusted. We introduce …

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[2405.14715] Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models

[Submitted on 23 May 2024 (v1), last revised 6 Oct 2025 (this version, v3)] View a PDF of the paper titled Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models, by Young Kyun Jang and 1 other authors View PDF HTML (experimental) Abstract:Modern retrieval systems often struggle with upgrading to new and more powerful models due to the incompatibility of embeddings …

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