Balancing only first-turn agent requests while keeping follow-ups cache-aware raises LLM cluster TPS 10-16% (colocation) and prefill TPS 2-34% (disaggregation) over SOTA schedulers on production agent traces.
Dualpath: Breaking the storage bandwidth bottleneck in agentic llm inference
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
Hive is a multi-agent infrastructure with a logits cache for reducing cross-path redundancy in sampling and agent-aware scheduling for better compute and KV-cache allocation, shown to deliver 1.11x-1.76x speedups and 33%-51% lower hotspot miss rates.
ForkKV uses copy-on-write disaggregated KV cache with DualRadixTree and ResidualAttention kernels to deliver up to 3x throughput over prior multi-LoRA serving systems with negligible quality loss.
KernelFlume presents a disaggregated decode architecture that separates core attention from projection/FFN paths to enable elastic scaling of attention nodes, reporting up to 61% lower cost per million tokens versus full-instance scaling on H100 hardware for Llama-3.1-8B under dynamic long-context w
A joint media selection and resource allocation algorithm (JMSRA) adaptively chooses token or KV-cache transmission and bandwidth allocation to reduce E2E latency compared to fixed baselines in wireless multi-agent systems.
PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.
citing papers explorer
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SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
Balancing only first-turn agent requests while keeping follow-ups cache-aware raises LLM cluster TPS 10-16% (colocation) and prefill TPS 2-34% (disaggregation) over SOTA schedulers on production agent traces.
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Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling
Hive is a multi-agent infrastructure with a logits cache for reducing cross-path redundancy in sampling and agent-aware scheduling for better compute and KV-cache allocation, shown to deliver 1.11x-1.76x speedups and 33%-51% lower hotspot miss rates.
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ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache
ForkKV uses copy-on-write disaggregated KV cache with DualRadixTree and ResidualAttention kernels to deliver up to 3x throughput over prior multi-LoRA serving systems with negligible quality loss.
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KernelFlume: Elastic Core-Attention Scaling for Agentic Long-Context Decoding
KernelFlume presents a disaggregated decode architecture that separates core attention from projection/FFN paths to enable elastic scaling of attention nodes, reporting up to 61% lower cost per million tokens versus full-instance scaling on H100 hardware for Llama-3.1-8B under dynamic long-context w
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A Token/KV-Cache Communication Media Selection and Resource Allocation Strategy for Multi-Agent Collaboration
A joint media selection and resource allocation algorithm (JMSRA) adaptively chooses token or KV-cache transmission and bandwidth allocation to reduce E2E latency compared to fixed baselines in wireless multi-agent systems.
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Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management
PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.