SmoothAgent introduces lookahead context engineering to eliminate transformation overhead in LLM agents, reducing TTFT by up to 11.9x through proactive KV cache preparation.
Kvflow: Efficient prefix caching for accelerating llm-based multi-agent workflows, 2025 b
11 Pith papers cite this work. Polarity classification is still indexing.
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SAECache uses a multi-queue semantic-aware eviction policy with fully adaptive online learning to improve TTFT by 1.4x-2.7x over LRU-style baselines in LLM prefix caching.
Streaming an agent's reasoning steps to the next agent as they are produced cuts latency and, when early steps are more reliable than late ones, also improves final accuracy.
VeriCache turns lossy KV cache compression into lossless LLM inference by drafting with compressed cache and verifying drafts with full cache, achieving up to 4x throughput with identical outputs.
PRISM reduces P99 TTFT by 23.3-37.1% and raises exact-prefix KV-cache hit rates by 5.9-12.2 points versus the strongest baseline on 4B and 13B models by jointly optimizing scheduling and memory.
AMMA is a memory-centric multi-chiplet architecture using HBM-PNM cubes, custom logic dies, hybrid parallelism, and reordered collectives that delivers 15.5X lower attention latency and 6.9X lower energy than NVIDIA H100 for 1M context serving.
A single shared asymmetrically compressed KV cache pool enables up to 15 concurrent LLM agents with 2.91x compression, 97.7% memory reduction, and only +0.57% perplexity increase on Llama-3-8B.
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.
VikingMem implements the Memory Base paradigm via event-centric extraction and entity updates on VikingDB with temporal compression, claiming up to 30% better retrieval effectiveness on long-term memory benchmarks.
Resident KV claims define a portable contract for managing future-reuse KV-cache state when active and resident allocations compete for limited memory in systems like vLLM.
citing papers explorer
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SmoothAgent: Efficient Long-Horizon LLM-Based Agent Serving with Lookahead Context Engineering
SmoothAgent introduces lookahead context engineering to eliminate transformation overhead in LLM agents, reducing TTFT by up to 11.9x through proactive KV cache preparation.
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Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches
SAECache uses a multi-queue semantic-aware eviction policy with fully adaptive online learning to improve TTFT by 1.4x-2.7x over LRU-style baselines in LLM prefix caching.
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Streaming Communication in Multi-Agent Reasoning
Streaming an agent's reasoning steps to the next agent as they are produced cuts latency and, when early steps are more reliable than late ones, also improves final accuracy.
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VeriCache: Turning Lossy KV Cache into Lossless LLM Inference
VeriCache turns lossy KV cache compression into lossless LLM inference by drafting with compressed cache and verifying drafts with full cache, achieving up to 4x throughput with identical outputs.
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PRISM: Fast Online LLM Serving via Scheduling-Memory Co-design
PRISM reduces P99 TTFT by 23.3-37.1% and raises exact-prefix KV-cache hit rates by 5.9-12.2 points versus the strongest baseline on 4B and 13B models by jointly optimizing scheduling and memory.
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AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving
AMMA is a memory-centric multi-chiplet architecture using HBM-PNM cubes, custom logic dies, hybrid parallelism, and reordered collectives that delivers 15.5X lower attention latency and 6.9X lower energy than NVIDIA H100 for 1M context serving.
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PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference
A single shared asymmetrically compressed KV cache pool enables up to 15 concurrent LLM agents with 2.91x compression, 97.7% memory reduction, and only +0.57% perplexity increase on Llama-3-8B.
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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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VikingMem: A Memory Base Management System for Stateful LLM-based Applications
VikingMem implements the Memory Base paradigm via event-centric extraction and entity updates on VikingDB with temporal compression, claiming up to 30% better retrieval effectiveness on long-term memory benchmarks.
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Resident KV Claims: A Conformance Contract for Future Reuse under Active KV Pressure
Resident KV claims define a portable contract for managing future-reuse KV-cache state when active and resident allocations compete for limited memory in systems like vLLM.