timebill-inference

Guarantee LLM inference completes within time budgets via response length prediction, execution time estimation, and adaptive KV cache eviction. Three-stage pipeline predicts response length, estimates end-to-end time with 1.22% accuracy, adjusts cache eviction ratio—enabling time-critical deployment in robotics and autonomous systems.

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Repositório: ADu2021/skillXiv
Última atividade na origem: 24 de março de 2026 às 19:42
Idioma detectado do SKILL.md: inglês
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name	timebill-inference
title	TimeBill: Time-Budgeted Inference for LLMs
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.21859
keywords	["inference","efficiency","time-budget","adaptive","latency"]
description	Guarantee LLM inference completes within time budgets via response length prediction, execution time estimation, and adaptive KV cache eviction. Three-stage pipeline predicts response length, estimates end-to-end time with 1.22% accuracy, adjusts cache eviction ratio—enabling time-critical deployment in robotics and autonomous systems.

Overview

TimeBill solves the problem of unpredictable LLM inference latency in time-critical systems.

Core Technique

Three-Stage Pipeline:

# Stage 1: Response Length Prediction
length_predictor = fine_grained_response_length_model()
predicted_length = length_predictor.predict(input)

# Stage 2: Execution Time Estimation
ete = execution_time_estimator()
worst_case_time = ete.estimate(predicted_length)

# Stage 3: Adaptive KV Cache Eviction
cache_eviction_ratio = calculate_optimal_eviction(worst_case_time, time_budget)

When to Use

Use when: Robotics, autonomous driving, time-critical inference.

References

Response length prediction
FLOPs-based execution time modeling
Adaptive KV cache management

timebill-inference

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Overview

Core Technique

When to Use

References

Overview

Core Technique

When to Use

References