Papers with structural reasoning
StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (2026.findings-acl)
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| Challenge: | Prior work focused on typographic and pixel-level perturbations, leaving the study of SCO unexplored. |
| Approach: | They propose a framework that exploits MLLMs' diagrammatic reasoning capabilities to bypass safety guardrails. |
| Outcome: | The proposed framework exploits the model's reasoning capabilities to bypass safety guardrails. |
TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal knowledge graphs (TKTs). |
| Approach: | They propose a Time-aware retrieve-rewrite-retrieve-rerank framework to integrate temporal knowledge from TKGs into Large Language Models (LLMs) to reduce temporal hallucination, they propose rewrite module to rew questions using background knowledge stored in TKG's, then implement a retrieve-rank module to retrieve semantically and temporally relevant facts from Tkgs and rerank them according to temporal constraints. |
| Outcome: | The proposed approach achieves relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs. |
SciFlow-Bench: Evaluating Structure-Aware Scientific Diagram Generation via Inverse Parsing (2026.acl-long)
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| Challenge: | Existing benchmarks for scientific diagram generation rely on image-centric metrics or evaluation of intermediate symbolic representations rather than final rendered images. |
| Approach: | They propose a structure-first benchmark for evaluating scientific diagram generation from pixel-level outputs. |
| Outcome: | The proposed benchmark evaluates scientific diagram generation directly from pixel-level outputs. |
Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning (2026.acl-long)
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| Challenge: | Recent work on LLMs has focused on fine-grained skill decomposition and consistency probing at the propositional level. |
| Approach: | They propose a benchmark evaluating immediate inference that evaluates elemental operations over categorical propositions and proposes a model that uses immediate inferential reasoning. |
| Outcome: | The proposed benchmark demonstrates that models lack robust operator grounding, oscillating between structural reasoning and surface pattern matching, inconsistent handling of quantifiers and negation. |