← 研踪 PaperTrailarXiv:2610.36804 · v1
DFIP: Decision-Focused Inference Path for System One Models
首次提交 2026-10-27 16:05
版本更新 2026-10-27 16:05
雷达观测 2026-10-29 00:35
摘要
System One (decision) models return small structured answers, but can retain host dispatch, GPU launch overhead and full-vocabulary projection. We present DFIP (Decision-Focused Inference Path), an empirical systems study of established inference techniques in PPLX-Decider, Liquid d1-3B, Clef-Flash and GLiNER2.5-Decide. Selective CUDA graph replay, candidate-token projection and schema-compatible length-aware batching are applied at compatible model boundaries. We compare resident requests with stronger native baselines, audit exposed answers and measure changing arrivals. With optional native Qwen kernels installed, short Boolean resident speedups over strengthened native are 2.47x for Decider across five containers and 1.45x for Clef across four of five planned containers; Clef's fifth fails in native preflight. Liquid is effectively tied with released compilation; replay supplies most of its eager-baseline gain, while projection alone adds little full-request benefit. In a one-worker GLiNER follow-up, length-aware batching is 1.40x faster than compiled native FIFO batching on a diverse resident trace, but has worse arrival p95. Decider and Clef audits cover 3,024 decisions per model and preserve all 1,112 returned choices, while Clef changes scores, probabilities and serving confidence thresholds. Diverse external arrivals reverse fixed-shape replay gains and expose overload losses and a replay-path CUDA error. The results identify workload-specific acceleration and its limits, rather than a universal optimizer or production readiness.
作者备注：11 pages, 1 figure. Code and reproducible results: https://github.com/superagent-ai/dfip
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v1 · 2026-10-27 16:05 · 最新观测版本
DFIP: Decision-Focused Inference Path for System One Models
System One (decision) models return small structured answers, but can retain host dispatch, GPU launch overhead and full-vocabulary projection. We present DFIP (Decision-Focused Inference Path), an empirical systems study of established inference techniques in PPLX-Decider, Liquid d1-3B, Clef-Flash and GLiNER2.5-Decide. Selective CUDA graph replay, candidate-token projection and schema-compatible length-aware batching are applied at compatible model boundaries. We compare resident requests with stronger native baselines, audit exposed answers and measure changing arrivals. With optional native Qwen kernels installed, short Boolean resident speedups over strengthened native are 2.47x for Decider across five containers and 1.45x for Clef across four of five planned containers; Clef's fifth fails in native preflight. Liquid is effectively tied with released compilation; replay supplies most of its eager-baseline gain, while projection alone adds little full-request benefit. In a one-worker GLiNER follow-up, length-aware batching is 1.40x faster than compiled native FIFO batching on a diverse resident trace, but has worse arrival p95. Decider and Clef audits cover 3,024 decisions per model and preserve all 1,112 returned choices, while Clef changes scores, probabilities and serving confidence thresholds. Diverse external arrivals reverse fixed-shape replay gains and expose overload losses and a replay-path CUDA error. The results identify workload-specific acceleration and its limits, rather than a universal optimizer or production readiness.
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