Does More Inference-Time Compute Really Help Robustness?

• URL: http://arxiv.org/abs/2507.15974v1
• Date: Mon, 21 Jul 2025 18:08:38 GMT
• Title: Does More Inference-Time Compute Really Help Robustness?
• Authors: Tong Wu, Chong Xiang, Jiachen T. Wang, Weichen Yu, Chawin Sitawarin, Vikash Sehwag, Prateek Mittal,
• Abstract summary: We show that small-scale, open-source models can benefit from inference-time scaling.<n>We identify an important security risk, intuitively motivated and empirically verified as an inverse scaling law.<n>We urge practitioners to carefully weigh these subtle trade-offs before applying inference-time scaling in security-sensitive, real-world applications.
• Score: 50.47666612618054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference-time scaling using a simple budget forcing strategy. More importantly, we reveal and critically examine an implicit assumption in prior work: intermediate reasoning steps are hidden from adversaries. By relaxing this assumption, we identify an important security risk, intuitively motivated and empirically verified as an inverse scaling law: if intermediate reasoning steps become explicitly accessible, increased inference-time computation consistently reduces model robustness. Finally, we discuss practical scenarios where models with hidden reasoning chains are still vulnerable to attacks, such as models with tool-integrated reasoning and advanced reasoning extraction attacks. Our findings collectively demonstrate that the robustness benefits of inference-time scaling depend heavily on the adversarial setting and deployment context. We urge practitioners to carefully weigh these subtle trade-offs before applying inference-time scaling in security-sensitive, real-world applications.

Related papers

• ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments [18.198349215500183]
  ReasoningGuard injects timely safety aha moments to steer harmless while helpful reasoning processes.<n>Our approach outperforms seven existing safeguards, achieving state-of-the-art safety defenses.
  arXiv  Detail & Related papers  (2025-08-06T08:35:10Z)
• Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models [0.0]
  Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks.<n>While these capabilities promise improved reliability, their impact on robustness to social biases remains unclear.<n>We leverage the CLEAR-Bias benchmark to investigate the adversarial robustness of RLMs to bias elicitation.
  arXiv  Detail & Related papers  (2025-07-03T17:01:53Z)
• Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs [8.359909829007005]
  We investigate whether efficient reasoning strategies introduce behavioral inconsistencies in large reasoning models (LRMs)<n>$ICBENCH$ is a benchmark designed to measure inconsistency in LRMs across three dimensions.<n>We find that while larger models generally exhibit greater consistency than smaller ones, they all display widespread "scheming" behaviors.
  arXiv  Detail & Related papers  (2025-06-24T10:25:28Z)
• Excessive Reasoning Attack on Reasoning LLMs [26.52688123765127]
  In this work, we expose a novel threat: adversarial inputs can be crafted to exploit excessive reasoning behaviors.<n>Our results demonstrate a 3x to 9x increase in reasoning length with comparable utility performance.<n>Our crafted adversarial inputs exhibit transferability, inducing computational overhead in o3-mini, o1-mini, DeepSeek-R1, and QWQ models.
  arXiv  Detail & Related papers  (2025-06-17T10:16:52Z)
• On Reasoning Strength Planning in Large Reasoning Models [50.61816666920207]
  We find evidence that LRMs pre-plan the reasoning strengths in their activations even before generation.<n>We then uncover that LRMs encode this reasoning strength through a pre-allocated directional vector embedded in the activations of the model.<n>Our work provides new insights into the internal mechanisms of reasoning in LRMs and offers practical tools for controlling their reasoning behaviors.
  arXiv  Detail & Related papers  (2025-06-10T02:55:13Z)
• Saffron-1: Safety Inference Scaling [69.61130284742353]
  SAFFRON is a novel inference scaling paradigm tailored explicitly for safety assurance.<n>Central to our approach is the introduction of a multifurcation reward model (MRM) that significantly reduces the required number of reward model evaluations.<n>We publicly release our trained multifurcation reward model (Saffron-1) and the accompanying token-level safety reward dataset (Safety4M)
  arXiv  Detail & Related papers  (2025-06-06T18:05:45Z)
• PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
  PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
  arXiv  Detail & Related papers  (2025-05-29T17:55:49Z)
• ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning [75.1101108949743]
  Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting.<n>LRMs often suffer from verbose outputs caused by redundant content, increasing computational overhead, and degrading user experience.<n>We propose ConCISE, a framework that simplifies reasoning chains by reinforcing the model's confidence during inference.
  arXiv  Detail & Related papers  (2025-05-08T01:40:40Z)
• Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs [83.11815479874447]
  We propose a novel jailbreak attack framework, inspired by cognitive decomposition and biases in human cognition.<n>We employ cognitive decomposition to reduce the complexity of malicious prompts and relevance bias to reorganize prompts.<n>We also introduce a ranking-based harmfulness evaluation metric that surpasses the traditional binary success-or-failure paradigm.
  arXiv  Detail & Related papers  (2025-05-03T05:28:11Z)
• Trading Inference-Time Compute for Adversarial Robustness [27.514612815314084]
  We conduct experiments on the impact of increasing inference-time compute in reasoning models on their robustness to adversarial attacks.<n>We find that across a variety of attacks, increased inference-time compute leads to improved robustness.
  arXiv  Detail & Related papers  (2025-01-31T01:20:44Z)

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.