arXiv AI
Jul 24
Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing
arXiv:2607. 20433v1 Announce Type: cross Abstract: While language models remain frozen at their training state, the world evolves continuously.
Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing
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performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.
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arXiv Computation and Language
Aug 27
Addressing the Reasoning Gap: Mechanistic Circuit-Based Knowledge Editing in Large Language Models
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 AI
Sep 25
ALOE: Semantically Addressed Low-Rank Operators for Knowledge Editing
The paper introduces ALOE, a method for knowledge editing that learns semantic addresses from paraphrases and hard negatives, aligns them with autoregressive hidden states, and embeds a gated low‑rank operator within a single MLP layer. This design allows the edited model to run in one forward pass without external retrievers or routers. Experiments on CounterFact, ZSRE, and KnowEdit show high efficacy (0.955–0.999) and locality (0.981–1.000) across 7–8B model families, with analyses indicating effective separation of edits and suppression of out‑of‑scope activation.
arXiv AI
Jun 10
Benchmarking Knowledge Editing using Logical Rules
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 AI
Jun 30
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).
arXiv AI
4d ago
Generalizable Lifelong Model Editing via Preference Optimization
The paper introduces GLIME, a lifelong model editing framework that integrates knowledge editing with preference optimization to handle continual updates in large language models. GLIME employs replay-based editing and a gradient constraint to prevent overfitting to target prompts and preserve previously edited knowledge. Experiments demonstrate that GLIME enhances knowledge generalization while maintaining editing performance and overall model capabilities.
arXiv AI
Jun 2
Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
arXiv:2606. 00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted significant attention.
arXiv AI
Jul 3
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
arXiv:2607. 01978v1 Announce Type: new Abstract: Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) with bounded overhead and minimal disruption to unrelated behaviors.
arXiv AI
4d ago
Train Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models
arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level super...
arXiv AI
Jul 31
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
arXiv:2607. 26455v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood.
arXiv AI
Jun 24
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs
arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.
Hugging Face Trending Papers
Jul 29
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood. Existing evaluation paradigms primarily focus on single-step reasoning or static knowledge editing, which fail to capture the temporal dynamics of knowledge retention and degradation during continual model modification.