Hidden in Plain Sight: Probing Implicit Reasoning in Multimodal Language
  Models
Multimodal large language models (MLLMs) are increasingly deployed in
open-ended, real-world environments where inputs are messy, underspecified, and
not always trustworthy. Unlike curated benchmarks, these settings frequently
involve instructions that refer to missing objects or contradictory facts, rely
on ambiguous references, or request infeasible actions. In such cases, success
hinges not on task execution alone, but on a model's ability to detect when
something is silently wrong. This paper presents a systematic analysis of how
current MLLMs handle such implicit reasoning scenarios: cases where the flaw is
not explicitly stated but must be inferred from context. Using a curated
diagnostic suite spanning four categories of real-world failure modes, we
evaluate six MLLMs, including o3 and GPT-4o, and find that models frequently
fail to surface hidden issues, even when they possess the necessary perceptual
and reasoning skills. Explicit prompting reveals that the underlying
capabilities exist but are often suppressed in favor of user compliance. We
further show that simple inference-time interventions, such as cautious persona
prompting and, in particular, requiring a clarifying question, can dramatically
recover performance. Our findings highlight a persistent gap between reasoning
competence and behavioral compliance in current MLLMs and suggest practical
strategies for making these models more trustworthy in underconstrained
environments.
683e722795d42e04112536fa
Qianqi Yan
683e722795d42e04112536fb
Hongquan Li
683e722795d42e04112536fc
Shan Jiang
683e722795d42e04112536fd
Yang Zhao
683e722795d42e04112536fe
Xinze Guan
683e722795d42e04112536ff
Ching-Chen Kuo
683e722795d42e0411253700
Xin Eric Wang
claimed_verified
2025-07-24T09:13:52.624Z
64679a226192d39142245e5e
/avatars/05abee0b6317f100923936ca2099e9eb.svg
Xin Eric Wang
user
xw-eric
Hidden in Plain Sight: Probing Implicit Reasoning in Multimodal Language
  Models
Multimodal large language models (MLLMs) are increasingly deployed in
open-ended, real-world environments where inputs are messy, underspecified, and
not always trustworthy. Unlike curated benchmarks, these settings frequently
involve instructions that refer to missing objects or contradictory facts, rely
on ambiguous references, or request infeasible actions. In such cases, success
hinges not on task execution alone, but on a model's ability to detect when
something is silently wrong. This paper presents a systematic analysis of how
current MLLMs handle such implicit reasoning scenarios: cases where the flaw is
not explicitly stated but must be inferred from context. Using a curated
diagnostic suite spanning four categories of real-world failure modes, we
evaluate six MLLMs, including o3 and GPT-4o, and find that models frequently
fail to surface hidden issues, even when they possess the necessary perceptual
and reasoning skills. Explicit prompting reveals that the underlying
capabilities exist but are often suppressed in favor of user compliance. We
further show that simple inference-time interventions, such as cautious persona
prompting and, in particular, requiring a clarifying question, can dramatically
recover performance. Our findings highlight a persistent gap between reasoning
competence and behavioral compliance in current MLLMs and suggest practical
strategies for making these models more trustworthy in underconstrained
environments.
Current multimodal large language models often fail to detect implicit reasoning flaws in messy, real-world inputs, but performance can be improved with cautious prompting and clarifying questions.
multimodal large language models MLLMs implicit reasoning scenarios perceptual and reasoning skills cautious persona prompting clarifying questions