Papers with general-purpose framework
PEIRCE: Unifying Material and Formal Reasoning via LLM-Driven Neuro-Symbolic Refinement (2025.acl-demo)
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| Challenge: | Large Language Models (LLMs) are capable of material inference but lack formal rigour and verifiability. |
| Approach: | They propose a framework to unify material and formal inference through an iterative conjecture–criticism process. |
| Outcome: | The proposed framework unifies material and formal inference through an iterative conjecture–criticism process. |
Automatic Rule Induction for Efficient Semi-Supervised Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
| Approach: | They propose a framework for automatic discovery and integration of symbolic rules into pretrained transformer models by using an attention mechanism. |
| Outcome: | The proposed framework can improve state-of-the-art methods with no manual effort and minimal computational overhead. |
Deep Equilibrium Non-Autoregressive Sequence Learning (2023.findings-acl)
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| Challenge: | et al., 2017) is the most prevailing neural architecture for sequence-to-sequence learning. |
| Approach: | They propose to solve for the equilibrium state of NAR models with black-box root-finding solvers and back-propagate through the equilibrium point via implicit differentiation with constant memory. |
| Outcome: | The proposed framework can converge to a more accurate prediction on four WMT benchmarks. |
InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents (2026.findings-acl)
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| Challenge: | Existing LLMs break down on long-horizon tasks due to unbounded context growth and accumulated errors. |
| Approach: | They propose a framework that externalizes persistent state into a file-centric state abstraction and keeps the agent’s reasoning context strictly bounded regardless of task duration. |
| Outcome: | Experiments on DeepResearch and an 80-paper literature review show that the proposed framework maintains higher long-horizon coverage than baseline models without task-specific fine-tuning. |