RealMath-Eval benchmark shows LLM judges have an evaluation gap, performing worse on diverse real human math reasoning than on synthetic solutions due to greater error diversity and higher surprisal.
arXiv preprint arXiv:2505.16988 , year=
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RIFT-Bench is a graph representation-driven methodology for dynamic red-teaming that enables unified evaluations across diverse agentic AI architectures, demonstrated on 45 systems.
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
MAS-Lab proposes a specification-driven framework with Spec, MAS-OS, and Labs layers to enable intent-based validation and reliable evolution of multi-agent systems.
SparseX adds segment-level KV cache reuse with Sparse-Q guided recomputation and layer-wise hybrid attention to handle interleaved serving patterns beyond standard prefix caching.
citing papers explorer
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RealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning
RealMath-Eval benchmark shows LLM judges have an evaluation gap, performing worse on diverse real human math reasoning than on synthetic solutions due to greater error diversity and higher surprisal.
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RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
RIFT-Bench is a graph representation-driven methodology for dynamic red-teaming that enables unified evaluations across diverse agentic AI architectures, demonstrated on 45 systems.
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Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
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MAS-Lab: A Specification-Driven Validation Framework for Reliable Multi-Agent Systems
MAS-Lab proposes a specification-driven framework with Spec, MAS-OS, and Labs layers to enable intent-based validation and reliable evolution of multi-agent systems.
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SparseX: Efficient Segment-Level KV Cache Sharing for Interleaved LLM Serving
SparseX adds segment-level KV cache reuse with Sparse-Q guided recomputation and layer-wise hybrid attention to handle interleaved serving patterns beyond standard prefix caching.