ThinkMorph: Emergent Properties in Multimodal Interleaved
  Chain-of-Thought Reasoning
Multimodal reasoning requires iterative coordination between language and
vision, yet it remains unclear what constitutes a meaningful interleaved chain
of thought. We posit that text and image thoughts should function as
complementary, rather than isomorphic, modalities that mutually advance
reasoning. Guided by this principle, we build ThinkMorph, a unified model
fine-tuned on 24K high-quality interleaved reasoning traces spanning tasks with
varying visual engagement. ThinkMorph learns to generate progressive text-image
reasoning steps that concretely manipulate visual content while maintaining
coherent verbal logic. It delivers large gains on vision-centric benchmarks
(averaging 34.7% over the base model) and generalizes to out-of-domain tasks,
matching or surpassing larger and proprietary VLMs. Beyond performance,
ThinkMorph exhibits emergent multimodal intelligence, including unseen visual
manipulation skills, adaptive switching between reasoning modes, and better
test-time scaling through diversified multimodal thoughts.These findings
suggest promising directions for characterizing the emergent capabilities of
unified models for multimodal reasoning.
690813a7812eca10f9cc5e01
Jiawei Gu
claimed_verified
2025-11-03T20:52:44.478Z
645b4819f9d4ec91fdd54852
/avatars/e12efb8e030688a0afcc72176b453fb3.svg
Jiawei Gu
kuvvi
user
kuvvi
690813a7812eca10f9cc5e02
Yunzhuo Hao
claimed_verified
2025-11-03T20:52:42.043Z
653dd16277c2f09452ad37cd
/avatars/a95f9527722845a5414d86180c8e945d.svg
Yunzhuo Hao
luckychao
user
luckychao
690813a7812eca10f9cc5e03
Huichen Will Wang
claimed_verified
2025-11-03T20:52:37.981Z
6715a16f60dd5f7f395b34cb
/avatars/f92240519132c693de30c133ca784322.svg
Will Wang
wangwill
user
wangwill
690813a7812eca10f9cc5e04
Linjie Li
690813a7812eca10f9cc5e05
Michael Qizhe Shieh
690813a7812eca10f9cc5e06
Yejin Choi
690813a7812eca10f9cc5e07
Ranjay Krishna
690813a7812eca10f9cc5e08
Yu Cheng
ThinkMorph: Emergent Properties in Multimodal Interleaved
  Chain-of-Thought Reasoning
Multimodal reasoning requires iterative coordination between language and
vision, yet it remains unclear what constitutes a meaningful interleaved chain
of thought. We posit that text and image thoughts should function as
complementary, rather than isomorphic, modalities that mutually advance
reasoning. Guided by this principle, we build ThinkMorph, a unified model
fine-tuned on 24K high-quality interleaved reasoning traces spanning tasks with
varying visual engagement. ThinkMorph learns to generate progressive text-image
reasoning steps that concretely manipulate visual content while maintaining
coherent verbal logic. It delivers large gains on vision-centric benchmarks
(averaging 34.7% over the base model) and generalizes to out-of-domain tasks,
matching or surpassing larger and proprietary VLMs. Beyond performance,
ThinkMorph exhibits emergent multimodal intelligence, including unseen visual
manipulation skills, adaptive switching between reasoning modes, and better
test-time scaling through diversified multimodal thoughts.These findings
suggest promising directions for characterizing the emergent capabilities of
unified models for multimodal reasoning.
ThinkMorph, a unified model fine-tuned on interleaved reasoning traces, enhances multimodal reasoning by generating coherent text-image steps, achieving significant performance gains and demonstrating emergent capabilities.
multimodal reasoning ThinkMorph interleaved reasoning traces text-image reasoning visual manipulation reasoning modes multimodal intelligence