DART is a modular runtime that certifies semantically recoverable boundaries for failed tool-agent instances and selects admissible restore points that preserve downstream commitments or blocks recovery.
PALADIN: Self-correcting language model agents to cure tool-failure cases
5 Pith papers cite this work. Polarity classification is still indexing.
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NoisyAgent trains LLM agents with controlled user and tool noise to improve robustness in stochastic environments while also boosting clean-benchmark performance.
Bounded autonomy using typed action contracts and consumer-side execution lets LLMs safely operate enterprise systems, achieving 23 of 25 tasks with zero unsafe executions versus 17 for unconstrained AI across 25 trials.
The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.
The paper proposes a unified MDP-based research agenda for addressing sim-to-real gaps in foundation model agents and advocates adopting classical solutions such as domain randomization.
citing papers explorer
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DART: Semantic Recoverability for Structured Tool Agents
DART is a modular runtime that certifies semantically recoverable boundaries for failed tool-agent instances and selects admissible restore points that preserve downstream commitments or blocks recovery.
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Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
NoisyAgent trains LLM agents with controlled user and tool noise to improve robustness in stochastic environments while also boosting clean-benchmark performance.
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The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.
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The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
The paper proposes a unified MDP-based research agenda for addressing sim-to-real gaps in foundation model agents and advocates adopting classical solutions such as domain randomization.