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2,348 notes and 295 topics.
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2,348 notes, page 24 of 98
Continual Learning in Transition
The text discusses a shift in continual learning (CL) away from just updating parameters and towards system-level adaptation. The paper explores three dimensions of this transition: When (pre-training, post-training…
Continual Learning in Transition
The authors explore the evolution of continual learning (CL) in a tri-axial framework: When, How, and Where learning occurs. They discuss how CL is transitioning from parameter-centric learning to system-level…
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
Researchers investigate whether applying super-resolution (SR) techniques to FLAIR scans for White Matter Hyperintensities (WMH) segmentation could result in the erasure or hallucination of small WMH lesions. The study…
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
The study investigates whether using super-resolution (SR) techniques on Fluid-attenuated Inversion Recovery (FLAIR) scans affects the detection of small white-matter lesions. The research finds that SR tends to erase…
ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
The paper introduces ECHO, a locally-deployable health assistant designed for long-term chronic care management. It consists of three integrated modules: an agentic chatbot, a safety layer, and a speech assessment…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Researchers have developed EnvACE, a method for training large language model agents that simulates environment interactions during training using world rehearsal instead of relying on real or synthesized executable…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
The researchers introduce EnvACE, a method for reinforcement learning that replaces external environment interactions during training with world rehearsal. This method allows the policy to internalize the relationship…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Researchers have introduced EnvACE, a method for training large language model agents using world rehearsal instead of interactions with real or simulated environments. The method involves the policy alternating…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
The paper introduces EnvACE, a method for training large language model agents for long-term tool use without relying on interactions with real or synthesized environments, which can be costly to construct and verify…
Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
The paper introduces InvestLogicBench, a new benchmark to evaluate the investment logic of large language models (LLMs). The benchmark focuses on personalized financial decision-making and uses real-world data from 151…
Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
The paper introduces InvestLogicBench, a benchmark for evaluating the investment logic of large language models (LLMs). The benchmark measures the logical plausibility and event grounding of LLMs, suggesting that…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
Researchers have created a new benchmark for self-evolving agents in financial workflows called FinEvo-Bench. The benchmark includes 120 real-life tasks across six domains, with each task having a specific professional…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
Researchers introduce a new benchmark called FinEvo-Bench for testing self-evolving agents in various financial workflows. The benchmark consists of 120 real-world tasks across six domains, with each task having a…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
The research introduces a new benchmark called FinEvo-Bench for evaluating the performance of self-evolving agents in professional financial workflows. The benchmark consists of 120 real-case-grounded tasks across six…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
The study introduces FinEvo-Bench, a benchmark for evaluating the performance of self-evolving agents in various financial tasks. The benchmark consists of 120 real-case-grounded tasks across six financial domains…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a framework called Holonic Digital Twins Networks (HDT-Nets) to improve the performance of AI in physical systems like robots and vehicles. The framework aims to deliver real-time physical AI…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a new framework called HDT-Nets to enable real-time physical AI inference for robots and vehicles operating under real-world conditions. This is achieved by creating holonic digital twins (HDTs) that…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a framework called HDT-Nets to enable real-time physical AI inference for robots and vehicles operating under real-world conditions. The framework utilizes holonic digital twins (HDTs), which are…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a new framework called HDT-Nets to improve the performance of AI in physical systems like robots and vehicles. This framework uses holonic digital twins (HDTs) which are hierarchical structures that…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered architecture for hospital AI systems aimed at addressing the challenges of isolated AI deployments in hospitals. The proposed architecture includes an Agent Orchestration Layer, a…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a new architecture for hospital AI systems aimed at addressing the issues of isolated point solutions and lack of compliance in current AI deployments in hospitals. The proposed architecture…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital systems. The architecture aims to address the challenges of siloed AI deployments in hospitals by integrating various layers…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital AI systems. This architecture aims to address the issues of duplicated effort, hidden risks, and unrealized value in current…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
Researchers propose a system to store AI agent's private keys in hardware-confined devices, improving key confidentiality and authorization. They replace software-stored keys with ones stored in HSMs, TPMs, or smart…
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