The Department of Education announces a final supplemental priority and related definitions for use in discretionary education grant programs, augmenting prior Secretary’s Supplemental Priorities. The priority is intended to advance artificial intelligence in education.
The Department of Commerce (ITA) invites proposals for full-stack American AI export packages from industry-led pre-set consortia for designation under the American AI Exports Program. Designation may enable priority government advocacy and related support, subject to applicable law.
The Department of Education announces a final priority and definitions for use in currently authorized discretionary grant programs (and potentially future programs). The priority augments prior Secretary’s Supplemental Priorities published as final priorities.
The U.S. Commission on Civil Rights announces public meetings of the Maryland Advisory Committee to begin briefing planning on a topic involving artificial intelligence and its application in voting administration.
Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and s
Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (PP) reduces this cost, but its effectiveness hinges on partitioning granularity. Batch-level PP employing sequence packing exhibits high memory consumption in long-context scenarios, whereas token-level PP splitting sequences
Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting for complete context (poor time-to-first-token) and proceeding without it (reduced quality). Streaming context incrementally--overlapping retrieval with inference--can mitigate this latency, but doing so with concurrent requests introduces new challenges: requ
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation and policy optimization dynamics: (i) uniform rollout allocation ignores gradient variance heterogeneity across problems, and (ii) the softmax policy structure causes gradient attenuation for high-confidence correct acti
Purpose: This study introduces the first adaptation of RETFound for joint optic disc (OD) and optic cup (OC) segmentation. RETFound is a well-known foundation model developed for fundus camera and optical coherence tomography images, which has shown promising performance in disease diagnosis. Methods: We propose FunduSegmenter, a model integrating a series of novel modules with RETFound, including
Arena rankings changed at the top, with three systems clustered within a narrow band. The movement matters because it shows the frontier remains tightly packed on preference-based evaluation rather than opening a clear gap.
Arena rankings changed at the top, with three systems clustered within a narrow band. The movement matters because it shows the frontier remains tightly packed on preference-based evaluation rather than opening a clear gap.
Arena rankings changed at the top, with three systems clustered within a narrow band. The movement matters because it shows the frontier remains tightly packed on preference-based evaluation rather than opening a clear gap.