The paper introduces MCircKE, a mechanistic circuit-based knowledge editing framework for large language models. MCircKE identifies the causal circuits involved in a specific reasoning task and surgically updates parameters only within those circuits, thereby addressing the reasoning gap where edited facts are not used in multi-step reasoning. Experiments on the MQuAKE-series benchmarks show that this approach improves multi-hop reasoning performance after knowledge editing.
arXiv:2602. 02028v2 Announce Type: replace Abstract: Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a central challenge.
arXiv:2608. 11660v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.
arXiv:2609.39346v1 Announce Type: new Abstract: Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (S...
arXiv:2607. 14049v1 Announce Type: new Abstract: The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks.
EngramEdit introduces a method for decoupled knowledge updates in large language models using conditional memory architectures. By computing target memory representations that align with updated facts across multiple expressions, it jointly adjusts shared n‑gram embeddings while penalizing frequent ones to preserve unrelated knowledge. Experiments demonstrate near‑perfect editing success, improved multi‑hop reasoning, and strong performance under chain‑of‑thought prompting, all while maintaining general capabilities.
arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).
The paper introduces collaborative learning for large language models, where a single large model is replaced by smaller, heterogeneous models—called model slices—under a comparable parameter budget. By enabling prior reasoning to be reassessed across different model parameterizations and employing pyramid supervision that blends protocol distillation with step‑level supervision, the authors refine reasoning and interaction decisions during collaboration. Experiments on multi‑hop question answering, mathematical reasoning, code generation, and open‑ended constrained generation show that the trained heterogeneous models match or surpass the performance of the original large model and established multi‑agent baselines, while also achieving higher inference efficiency and exhibiting task‑specific collaboration patterns.
arXiv:2608.22753v1 Announce Type: new Abstract: Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided proced...
arXiv:2606. 10554v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge.
arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...
The paper introduces FOVEATED, a plug‑and‑play framework that improves atomic‑fact recall in unstructured knowledge editing (UKE) for large language models. By randomly shifting Rotary Position Embedding (RoPE) positions during editing, FOVEATED creates focused views of each sentence, counteracting the context‑reliance problem where edited LLMs reproduce passages but fail to recall individual facts. Experiments show consistent gains across five editors, two LLM backbones, and three benchmarks.
arXiv:2602.21061v2 Announce Type: replace Abstract: Many current paths to more advanced AI depend on the assumption that large language models (LLMs) can generalize learned relationships to solve com...