This work reframe knowledge editing from a manifold perspective, viewing it as a localized displacement of an edit sub-manifold within the global knowledge manifold, and proposes ManiEdit, a novel manifold-aware autoregressive editing framework consisting of two core components: Pivot Localization and Manifold-Aware Pr...
Rui Liu, Chen-Heng Zhang, Hao-Xuan Li et al.· 0 citations
The results show that group identity shapes how LLMs aggregate information across agents, independently of its correctness, and identify a manipulation surface for multi-agent AI systems.
Inspired by distributed computing frameworks like Apache Spark, DISCO partitions long context across a fleet of Worker LLMs dedicated exclusively to parallel, localized grounding, establishing a highly efficient paradigm for robust long-context inference.
The Global State Model (GSM), a causal encoder--decoder architecture that concentrates the selection and aggregation of long-range information in the encoding stage, is introduced, offering a shared-state architecture for efficient language modeling.
Yun Zheng, Bin Wen, Xiao-Jie Wang et al.· 0 citations
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SCALE (Selective Control of Adaptation via Local Entropy), an entropy-guided adaptation-strength-control method that freezes the pretrained model and the SFT delta and learns bounded token- and module-specific gates by minimizing predictive entropy alone is proposed.
Cun-Chun Li, Hao-Nan He, Yi-Fan Gao et al.· 0 citations
Context Spanning shows that external information can be injected directly into a duplex speech model, introducing a new simple and powerful mechanism for duplex systems.
Seonghyeon Go, Yongwoo Kim, Hyeonjin Cha et al.· 0 citations
Claims paired with AI-generated images are a rapidly growing form of misinformation. Existing automated fact-checking (AFC) methods mainly treat this as a provenance problem, detecting low-level synthesis artifacts to decide whether an image is AI-generated. However, such methods do not verify what human fact-checkers...
Rui-Hong Zeng, Jonathan Tonglet, Preslav Nakov et al.· 0 citations
Teacher Alignment is proposed, which directly adapts the teacher toward the student's distribution without discarding data or degrading reasoning quality, and which significantly outperforms baselines across diverse reasoning benchmarks and distillation methods.
Zhen-Yu Lei, Zi-Han Chen, Yao-Chen Zhu et al.· 0 citations
This work proposes MemSuit, a self-distillation framework in which a teacher summarizer, conditioned on observed query-answer pairs, produces utility-aware memory entries that a student learns to reproduce from the raw conversation alone, confirming the value of grounding memory in downstream utility.
Zhen-Yu Lei, Ming-Jia Shi, Xing-Bo Fu et al.· 0 citations
This work forms long-horizon OPD as hierarchical supervision allocation as well as a coarse-to-fine framework that organizes supervision through Locate, Validate, and Refine and suggests that effective long-horizon distillation requires teaching at the right depth, the right decision, and the right token.
Yu-Hao Sun, Bin-Rui Wu, Zhuo-Er Xu et al.· 0 citations
Downstream the effect is small: NER improves only on entities that contain words unseen in training, POS tagging and news classification do not change, and published Nepali tokenizers perform about as well.
It is shown that strengthening the model's ability to discriminate languages during pretraining reduces and, on some measures, closes this multilingual gap on continuous phonetic and higher-level linguistic measures, while preserving substantial cross-language sharing.
Maureen de Seyssel, Jie Chi, Zakaria Aldeneh· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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