aiqbench analysis
The paper tests whether a two-tier LLM cascade can decide when to escalate from a small model to a large one, using a reliability signal and a load-dependent GPU cost. Its pre-registered reliability claim failed (AUROC 0.571), and the headline result is a null for the 7B-to-32B pair. The post hoc semantic evidence judge performed better, with large API savings for GPT-3.5 to GPT-4, but it underperformed random routing for open-model pairs with 2-3x price ratios, so the gains are not general. Readers should treat the work as a useful account of what did and did not hold on one setup, not as evidence that the routing approach generalizes.
TRIAGE: Reliability- and Load-Conditioned Escalation for Two-Tier LLM Serving with a Semantic Evidence Judge
arXiv:2611.01174v1 Announce Type: cross Abstract: Two-tier LLM serving escalates selected requests from a small model to a large one, but prompt-based routers cannot see whether retrieved evidence supports an answer, and they treat the GPU cost of an escalation as constant. We study TRIAGE, which conditions escalation on a post-generation reliability state and on a load-dependent GPU price, and we report both what held and what did not on one A40 serving Qwen2.5-7B and 32B (AWQ) under VLLM. Escalation cost rose 21.9x from 1 to 10 requests/s but showed no monotone dependence on KV-cache headroom, and a pre-registered claim that the reliability state detects corrupted evidence (AUROC>=0.70) was falsified at 0.571, although any prompt-only router scores exactly 0.500 by construction. Post hoc, a semantic evidence judge raised detection to 0.846 (0.926 on never-served items) and predicted wrong small-model answers at AUROC 0.90-0.96 on RGB and three RouterBench model pairs; the resulting cascade stayed within about 3 pp of GPT-4 at 79.5% lower API cost for GPT-3.5->GPT-4, yet fell below random routing for open-model pairs with 2-3x price ratios. End to end, the 7B->32B pair showed only a +2.88 pp held-out gap, so the allocator rightly declined to escalate and the headline is a null that a small pre-launch gate failed to expose.
Abstract:Two-tier LLM serving escalates selected requests from a small model to a large one, but prompt-based routers cannot see whether retrieved evidence supports an answer, and they treat the GPU cost of an escalation as constant. We study TRIAGE, which conditions escalation on a post-generation reliability state and on a load-dependent GPU price, and we report both what held and what did not on one A40 serving Qwen2.5-7B and 32B (AWQ) under VLLM. Escalation cost rose 21.9x from 1 to 10 requests/s but showed no monotone dependence on KV-cache headroom, and a pre-registered claim that the reliability state detects corrupted evidence (AUROC>=0.70) was falsified at 0.571, although any prompt-only router scores exactly 0.500 by construction. Post hoc, a semantic evidence judge raised detection to 0.846 (0.926 on never-served items) and predicted wrong small-model answers at AUROC 0.90-0.96 on RGB and three RouterBench model pairs; the resulting cascade stayed within about 3 pp of GPT-4 at 79.5% lower API cost for GPT-3.5->GPT-4, yet fell below random routing for open-model pairs with 2-3x price ratios. End to end, the 7B->32B pair showed only a +2.88 pp held-out gap, so the allocator rightly declined to escalate and the headline is a null that a small pre-launch gate failed to expose.
Machine Learning (cs.LG); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
arXiv:2611.01174 [cs.LG]
(or arXiv:2611.01174v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.01174 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 311 words