01arXiv cs.AI· Just in
Researchers introduce Cura 1T, a healthcare-specialized LLM trained via a human-gated self-evolution loop
Cura 1T is a healthcare-specialized LLM introduced in an arXiv preprint (arXiv:2607.15314).
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01 02 02arXiv preprint· Just inGraphDx framework improves LLM-based clinical diagnosis accuracy and reduces test costs in study
GraphDx introduces a multi-agent framework with Perception, Reasoning, and Decision agents to balance diagnostic accuracy and resource costs.
Trust79HypeLow hype03 03arXiv cs.AI· Just inResearchers propose Causal-Audit, a framework for explicit and auditable graph-based causal reasoning in LLMs
Causal-Audit is a new framework for explicit, auditable causal reasoning in large language models (LLMs) designed for context-free intervention-based question answering.
Trust79HypeLow hype04 04AI research· This weekAnthropic reports discovery of an internal reasoning space in its Claude models
Anthropic says it has identified a previously undetected internal space in its Claude models that influences reasoning but does not appear in outputs.
Trust72HypeSome hype05 05Apple ML Research· This weekApple researchers propose interactive proof systems to verify distribution property claims with sublinear overhead
Apple ML Research published a paper introducing interactive proof systems for verifying general distribution properties with bounded-depth circuits.
Trust79HypeLow hype06 06Apple ML Research· This weekApple researchers propose doubly sub-linear interactive proofs for verifying large inputs
Apple ML Research published a paper introducing doubly sub-linear interactive proofs of proximity (dsIPPs).
Trust79HypeLow hype07 07Apple ML Research· This weekApple researchers show separation between testing and verification for location-invariant properties
A new Apple Machine Learning Research paper distinguishes testing and verification complexity for location-invariant properties of functions.
Trust84HypeLow hype08 08Apple ML Research· This weekApple proposes unlearning method that cuts computational cost by up to 50% by focusing on low-influence data points
Apple’s ML Research team proposes an unlearning framework that reduces computational costs by up to 50% by focusing on low-influence data points.
Trust79HypeLow hype09 09Apple ML Research· This weekApple proposes new benchmark to test vision-language models’ ability to infer visual concepts from image sets
Apple’s ML Research team introduces Visual Concept Inference from Sets (VICIS), a new task to evaluate vision-language models’ (VLMs) ability to infer shared visual concepts from small sets of example images.
Trust84HypeLow hype10 10arXiv cs.AI· This weekResearchers propose three-level learning architecture for autonomous UAV swarms in search and rescue
Proposes a three-level hierarchical learning architecture for autonomous UAV swarms in search and rescue, integrating reflex-level neuroplasticity, skill-level MARL with GNNs, and strategy-level meta learning with BDI reasoning.
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