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Hypothesis Controller

Speculative Decoding with Dynamic Draft Length via Entropy Filtering

Search Depth: 4

Pruning Strictness (Δ Loss > 0.15):

GPU Compute Budget: 48 GPU-hrs

Empirical Telemetry

Speedup Factor

2.41x

Accuracy Delta

-0.12%

Optimal Branch

EXP-04-ENTROPY-TAU-0.45

Pruned Nodes

Active Autonomous Agents

🤖 Formulator Agent: Synthesizing search heuristics

⚙ Coder Agent: Emitting PyTorch CUDA kernels

📊 Evaluator Agent: Measuring MMLU & throughput

✍ LaTeX Synthesizer: Recompiling paper sections

Optimal Path Active Trial Pruned / Refuted

[Orchestrator] Initializing autonomous discovery tree...

[Formulator] Hypothesis drafted: Entropy-gated dynamic draft verification.

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Dynamic Entropy-Gated Speculative Decoding for Large Language Models

Autonomous Research Agent (alphaXiv Framework) • OpenResearch Lab

Abstract

We introduce dynamic entropy gating for speculative decoding. By dynamically truncating candidate draft tokens when target model prefix entropy exceeds a calibrated threshold $\tau$ , we eliminate catastrophic verification rollovers. Our empirical results demonstrate a 2.41x token/s speedup over the Llama-3-70B baseline with a negligible accuracy delta of -0.12% on MMLU.

Methodology: Dynamic Entropy Drafting

Given draft policy $q(x_t \mid x_{<t})$ and target distribution $p(x_t \mid x_{<t})$ , the agent modulates sequence length $K$ via Shannon entropy:

K^* = \min \left\{ k \in [1, K_{\max}] : H\big(p(\cdot \mid x_{<t+k})\big) > \tau \right\}

Empirical Evaluation & Table 1

Experiment Node	Methodology	Throughput	MMLU Δ	Status
ROOT-BASE	Vanilla Autoregressive	34 tok/s	0.00%	Baseline
EXP-04-ENTROPY-TAU-0.45	Dynamic Entropy Gating	82 tok/s (2.41x)	-0.12%	Optimal
EXP-02-FIXED-K8	Fixed Speculation (K=8)	58 tok/s	-0.84%	Pruned
EXP-03-GREEDY-DRAFT	Pure Greedy Draft	44 tok/s	-1.42%	Pruned

Ablation Analysis

Sweeping $\tau \in [0.2, 0.8]$ highlights the optimal Pareto efficiency at $\tau = 0.45$ , minimizing speculative verification latency while maintaining mathematical consistency.