The transformer reduces multiplication to addition in discrete-log space, implementing a "Discrete-Log Clock" algorithm analogous to Nanda et al.'s Clock algorithm for addition, which generalizes: matching the analysis basis to the algebraic structure of the task reveals interpretable structure where standard tools see noise.
TokenPilot is presented, a dual-granularity context management framework that reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems.
Buqiang Xu, Z. Xue, Dian Chen et al.· arXiv.org· 1 citation
LongDS is introduced, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states, suggesting that the key bottleneck is maintaining a correct analytical state rather than increasing interaction budget.
This work presents a systematic study of scale vectors in LLMs from the perspectives of expressivity, optimization, and architectural structure, and proposes three lightweight and complementary improvements to scale vectors: branch-specific heterogeneity, improved placement around linear mappings, and magnitude-direction reparameterization.
This work proposes Mixture of Activations (MoA), a token-adaptive FFN design that mixes a dictionary of activation functions using lightweight input-dependent gates while sharing the same linear projections, suggesting that token-adaptive activation mixing is a simple and effective mechanism for improving FFN expressivity in LLMs.
This work presents SkillSafetyBench, a runnable benchmark for evaluating skill-facing safety failures, and suggests that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments.
This work proposes ABC: Any-Subset Autoregressive Models via Non-Markovian Diffusion Bridges in Continuous Time and Space, and derives SDE dynamics via changes-of-measure on path space, yielding another advantage: path-dependent conditioning on arbitrary subsets of the state history and/or future.
Gabriel Guo, Thanawat Sornwanee, L. Hao et al.· arXiv.org· 0 citations
G-Loss is presented, a graph-guided loss function that incorporates semi-supervised label propagation to use structural relationships within the embedding manifold to build a document-similarity graph that captures global semantic relationships.
This work introduces CodeRQ-Bench, the first benchmark for evaluating LLM reasoning quality across three coding task categories: generation, summarization, and classification, and proposes VERA, a two-stage evaluator that combines evidence-grounded verification with ambiguity-aware score correction.
Yuangang Li, Justin Tian Jin Chen, Ethan Yu et al.· arXiv.org· 1 citation
PolicyLong is proposed, shifting data construction towards a dynamic on-policy paradigm, by iteratively re-executing data screening (entropy computation, retrieval, and verification) using the current model, which ensures the training distribution tracks evolving capabilities, yielding an emergent self-curriculum.
This paper proposes a camera-agnostic, one-shot, post-training pruning method for 3D Gaussian splats that relies solely on attribute-derived neighbourhood descriptors, and introduces a hybrid descriptor framework that captures structural and appearance consistency directly from the splat representation.
Peter O. Fasogbon, Ugurcan Budak, P. R. Alface et al.· arXiv.org· 0 citations
The Variational JEPA (Var-JEPA), which makes the latent generative structure explicit by optimizing a single Evidence Lower Bound (ELBO) and yields meaningful representations without ad-hoc anti-collapse regularizers and allows principled uncertainty quantification in the latent space.
Moritz Gögl, Christopher Yau· arXiv.org· 3 citations· ⚡1
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
