Skip to content
Preprint

UNISON: A Co-Designed Near-Memory Scheduler of Session KV Residency for LLM Agents

Sep 2026 · 0 citations · 35 references
Computer Science

TL;DR

This work presents Unified Native Inter-turn Session Orchestration Nexus (UNISON), an event-driven near-memory scheduler in which Survival-Penalty Eviction for Agent Return-gap and Tiering in Idle-window DMA Events share one live ranking.

Abstract

Large language models are increasingly composed into agent loops that plan, call tools, and resume the same task after each action. These loops press a shared memory hierarchy harder than conventional multi-turn chat, because they hold a growing key-value (KV) prefix across tool waits and place many sessions on one SRAM/HBM pool, so that eviction and hierarchical placement become a session-level efficiency problem orthogonal to compute-mode optimization. Existing proxies based on recency, timeout, or identity miss the mechanism information of the loop and therefore treat a live wait as a cold, discardable unit. We present Unified Native Inter-turn Session Orchestration Nexus (UNISON), an event-driven near-memory scheduler in which Survival-Penalty Eviction for Agent Return-gap (SPEAR) and Tiering in Idle-window DMA Events (TIDE) share one live ranking. SPEAR selects who leaves from a gap average and a turn-indexed hazard, while TIDE spends the observed wait as a DMA budget for who sits in the fast tier. On coding and general-mission benchmarks with three model families, totaling 1,415 sessions and 33,596 turns, the joint policy is the best non-oracle entry on every trace, raising hit rate by 0.3% to 23.1%, reducing AMAT by 22% to 51%, and lowering TTFT by 58% to 89% on long-horizon traces. A structural necessity analysis shows that the unified near-memory design cannot be decomposed into independent IPs or realized in software without re-introducing documented failure modes. The 28-nm CMOS scheduling core occupies 0.169 mm^2 at 13.6 mW and 150 MHz, a negligible overhead relative to the KV hierarchy it manages, reproducing the floating-point ranking at Kendall tau exceeding 0.998.

View source

Similar papers

#machine learning Preprint Oct 2026

JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs

LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct weights. This shift breaks a hidden assumption of today's serving systems: that the working set of models fits in GPU memory. On the memory-constrained GPUs typical of privat...

Dai-Jin Hu · 0 citations
#natural language process... Preprint Sep 2026

TomasuLLM: Out-of-Order Speculative Execution for LLM Agents

Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. This observation stall presents the same tension that drove out-of-order processors -- asequential interface hides work that can be predicted and started early, but a spec...

Jiang-Nan Yu, Ce-Yu Xu, Meng-Ming Li et al. · 0 citations
Book Open access Sep 2026

Pinning Isn't Enough: Gang-Scheduling Memory-Model Litmus Tests with sched_ext

Litmus tests are the empirical workhorse for characterizing hardware memory models: small concurrent programs whose “weak” outcomes reveal how a processor reorders memory accesses. Their effectiveness hinges on a property the test cannot control on its own: its threads must execute simultaneously on different cores, wi...

Ali Siddiqui, Xiao-Guang Wang · 0 citations
Preprint Sep 2026

AKTS: Sub-Microsecond Kernel Policy Switching for Language-Model Agents

AKTS, which verifies a policy library once, at load time, and reduces the agent's runtime action to writing an integer index into an in-kernel array of preverified policies, and an in-kernel tail call resolves that index.

Mohammadali Khodabandehlou, Mahdi Alizadeh · 0 citations
#small language model Open access Jul 2026

Hot–Cold Tiering of HBM and High Bandwidth Flash for Agentic LLM Serving

This work places the hot set in HBM and the cold pool in HBF, forming a hot-cold KV hierarchy within the GPU memory tier, while flash-establishing HBF as a cold-tier complement to HBM rather than its replacement.

Jongjin Baek, Won Ji, Seungjae Yoo et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.