Papers with reasoning backbone
Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)
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Zhongbin Guo, Zhen Yang, Yushan Li, Xinyue Zhang, Wenyu Gao, Jiacheng Wang, Chengzhi Li, Xiangrui Liu, Ping Jian
| Challenge: | Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders? |
| Approach: | They propose to evaluate the SI performance of Large Language Models without pixel-level input. |
| Outcome: | The proposed benchmark challenges large language models to perform symbolic reasoning rather than visual pattern matching. |
FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse (2026.findings-acl)
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| Challenge: | Large Language Models lack the mechanism to preserve dynamic context, forcing agents to redundantly reprocess history to maintain long-horizon autonomy. |
| Approach: | They propose a framework that distills intrinsic memory directly from transient reasoning states via computation reuse. |
| Outcome: | Experiments show that FlashMem matches heavy baselines while reducing inference latency by 5 times, effectively bridging the gap between efficiency and persistent cognition. |