Speculative Decoding at Temperature Zero: A Scoped Safety-Invariance Screen with a 48,072-Sample Expansion
- URL: http://arxiv.org/abs/2606.25097v1
- Date: Tue, 23 Jun 2026 19:06:42 GMT
- Title: Speculative Decoding at Temperature Zero: A Scoped Safety-Invariance Screen with a 48,072-Sample Expansion
- Abstract summary: Speculative decoding accelerates inference by letting a draft model propose tokens for a target model to verify.<n>We show that at temperature zero, can draft-side behavior leak into safety-scored outputs?
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Speculative decoding accelerates inference by letting a draft model propose tokens for a target model to verify, raising a concrete safety question: at temperature zero, can draft-side behavior leak into safety-scored outputs? We answer with Typical-Acceptance Invariance Screen (TAIS), a behavioral-equivalence screen that pairs target-only and speculative outputs on the same safety battery and requires byte-identity evidence, TOST equivalence at +/-3pp, and per-task Cohen's h below a calibrated null cutoff of |h| < 0.1. Applied to a 16,783-sample confirmatory core plus 44,066 matched expansion samples (fp16/bf16 execution, canonical and DPO-adversarial drafts, GPTQ-4bit drafts, two seeds, and four safety benchmarks), the tested temperature-zero vLLM stacks show no detectable safety divergence under TAIS. The largest absolute Cohen's h on matched target-only versus speculative refusal is 0.024, roughly an order of magnitude below the conventional trivial-effect floor; 25 of 27 per-task TOST contrasts pass at the +/-3pp margin (the two non-pass contrasts are capability-domain Wald-CI edge cases at identical ceiling rates, not genuine non-equivalence); the DPO-adversarial draft produces byte-identical output to the canonical draft across 4,006 samples; and bf16 changes 36%-53% of output bytes without moving any per-task safety rate outside equivalence. A separate 4,006-sample 70B production-scale probe, which lacks a matched 70B target-only arm and is therefore not counted as a TAIS pass, produces AdvBench refusal 0.839 over 700 AdvBench completions with 95% Wilson CI [0.809, 0.864]. We make no claim about sampling temperatures, untested frameworks, untested model families, or tree-speculation variants such as EAGLE and Medusa.
Related papers
- Observational Indistinguishability and Integrity Blind Regions in Hybrid Quantum-Classical Workflows [0.0]
We present a claim-relative evidence/reference framework for hybrid quantum-classical workflow integrity.<n> Observational indistinguishability yields structural blind regions, distinct from finite-batch statistical misses.
arXiv Detail & Related papers (2026-09-15T13:16:37Z) - BeTaL-GBI: Admission-Aware Benchmark Tuning and Full-Stack Verification of Geometric Belief Interfaces [2.894286977279531]
This study examines whether an enterprise verification architecture can isolate interface failure, task competence, policy admissibility, and control integrity while keeping claims auditable.<n>BeTaL-GBI v0.2 applies Benchmark Tuning with an LLM-in-the-loop over 2,218,750,380 grid points, separating format admission from conditional performance.<n>Across 512 synthetic tasks, a 16-gate policy detects all 116 injected severe contradictions and accepts all 99 clean records. Hallucinator and evidence-forger surrogates are blocked with zero silent promotions.
arXiv Detail & Related papers (2026-08-21T16:52:38Z) - QuoteBench: How Matched Scores Can Hide Command-Path Failures [30.480838412827907]
Execution scores alone cannot distinguish command-generation errors from failures introduced after generation.<n> QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families.<n> Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state rather than treat a matched score as an intrinsic model property.
arXiv Detail & Related papers (2026-08-13T17:57:20Z) - From CVE to CWE: Syscall-Based HIDS Generalisation [41.99844472131922]
In operational settings, defenders need to recognise new exploits of an already known type of weakness.<n>We empirically examine whether a one-class anomaly detector trained on the normal behaviour of a set of CVEs that share a Common Weaknession (CWE) class generalises to a different, unseen CVE inside the same class.
arXiv Detail & Related papers (2026-06-21T16:34:39Z) - Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration [30.310793549183117]
We introduce Hierarchical Certified Semantic Commitment (H-CSC), a BFT-inspired protocol that converts embedding-derived finality signals over verdict-emitting proposal groups into one of three typed outcomes.<n>H-CSC commits with low angular deviation on BFT-feasible buckets (0.31 to 2.04 degrees) and aborts 100% of beyond-BFT rounds (n3f+1) as intended.
arXiv Detail & Related papers (2026-06-05T14:35:58Z) - Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense [37.10272384523361]
We develop a framework for predicting each detector's per-sample reliability and latency from how it behaved on similar past inputs.<n>We build SCOUT-450, a benchmark that captures the structurally complex, agent-facing injections that older prompt-injection sets under-represent.<n>On SCOUT-450, a safety-oriented operating point reduces attack-success rate by 46% and total wall-clock by 40% relative to an always-on GPT-4o judge.
arXiv Detail & Related papers (2026-05-29T04:49:20Z) - Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
arXiv Detail & Related papers (2026-05-20T19:08:38Z) - RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Prompts [0.0]
Strict refusal rates span 0.1% to 94.6% on identical prompts.<n>Nine of 18 frontier models exhibit a hedge-but-help partial-compliance pattern at dual-use tier that binary refusal metrics cannot detect.
arXiv Detail & Related papers (2026-05-20T09:53:31Z) - Brain-inspired spike-timing plasticity for reliable label-efficient event-camera vision [0.3823356975862005]
Local spike-timing-dependent plasticity (STDP) modules operate on a single CPU thread without GPU support.<n>Dense gradient-trained detectors cannot provide this combination of gradient training, dense matrix multiplication, and local plasticity-free operation by construction.
arXiv Detail & Related papers (2026-05-17T22:56:16Z) - ContractBench: Can LLM Agents Preserve Observation Contracts? [9.057486468322933]
We show that observation contract compliance is an emergent, regression-prone capability.<n>We introduce ContractBench, a benchmark of 33 dual-axis tasks that probe two failure modes.<n>We report four findings: (i) no evaluated model clears 80%, with Claude-Opus-4.6 leading at 77.8%; (ii) a sharp within-family capability cliff in Qwen 3.5 between 4B (0%) and 9B (56.6%), smoothing to 70.7% at 397B-A17B; (iii) non-monotonic scaling across the GPT-5 family: agentic post-training can erode
arXiv Detail & Related papers (2026-05-17T06:37:04Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - PBT-Bench: Benchmarking AI Agents on Property-Based Testing [29.035258104995204]
We introduce PBT-Bench, a benchmark of 100 curated property-based testing problems across 40 real Python libraries.<n>Each problem injects one or more semantic bugs (365 in total, mean 3.65 per problem) designed so that default-strategy random inputs almost never trigger them.<n>Bug recall under the PBT-guided prompt ranges from 42.1% to 83.4% across models; under the open-ended baseline, from 31.4% to 76.7%.
arXiv Detail & Related papers (2026-05-13T18:01:05Z) - LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification [35.08680804423239]
Chain-of-thought (CoT) reasoning improves large language models (LLMs) on difficult tasks, but it also makes inference expensive.<n>We propose Latent-Then-Explicit Reasoning (LaTER), a two-stage paradigm that first performs bounded exploration in a continuous latent space.<n>LaTER projects final-layer hidden states back to the input embedding space, preserves the latent KV cache, and uses entropy and model-native stop-token probes to decide when to switch.
arXiv Detail & Related papers (2026-05-08T06:23:58Z) - Adaptive Consensus in LLM Ensembles via Sequential Evidence Accumulation: Automatic Budget Identification and Calibrated Commit Signals [0.0]
DASE produces a commit-type routing partition that generalises across benchmarks.<n> Adaptive stopping, not injection bandwidth, drives accuracy.<n>Injection-based methods exhibit an inverted-U accuracy-vs-inference trajectory.
arXiv Detail & Related papers (2026-05-05T19:24:10Z) - Causal Understanding by LLMs: The Role of Uncertainty [43.87879175532034]
Recent papers show LLMs achieve near-random accuracy in causal relation classification.<n>We investigate whether pretraining exposure to causal examples improves causal understanding.
arXiv Detail & Related papers (2025-09-24T13:06:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.