…AI-generated summary Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling them to complete both…
…TransplantQA, a benchmark of real patient questions, each answered by grounding generation in multiple institutional handbooks as candidate sources; HERO-QA, a hierarchical retrieval strategy that grounds and audits each answer; and…
…and click through to Etsy for additional details or purchase. This isn’t Etsy’s first experiment inside ChatGPT. Back in September, Etsy became an early partner in ChatGPT’s Instant Checkout…
…On the algorithmic side, we identify the per-trajectory normalizer 1/|τ_i| in multi-step GRPO as the root cause of trajectory-level and token-level inefficiency : because failures are systematically…
…Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels (2026) FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning (2026) FedCova: Robust Federated Covariance Learning…
…latent space, identical in architecture to its visual counterpart. The central challenge we address is obtaining high-quality continuous text representations: we find that reconstruction fidelity alone is insufficient, and that aligning…
…Kai Xu , Ellis Brown , , , , Saining Xie Abstract PaintBench presents a scalable benchmark for precise visual editing tasks, revealing low performance across models and identifying key challenges in geometric transformations and structural manipulations…
…to residual networks, transformers, and RNNs through shared mathematical structures. Generated by Qwen/Qwen2.5-Coder-32B-Instruct In this paper, training a neural network is identified, exactly, as a search through…
…For each stage, we provide systematic taxonomies and formally characterize the dependencies between adjacent stages, revealing how each stage both depends on and constrains the next. Beyond synthesizing existing work, we identify…
…architectural alignment and representational alignment. We identify Test-Time Training (TTT) as a linear-complexity architecture whose two-layer dynamic formulation is structurally aligned with Softmax attention , enabling direct inheritance of pretrained…