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
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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
These findings demonstrate that current defense paradigms optimize for single-turn refusal benchmarks while rendering multi-step agents fundamentally unreliable, necessitating new approaches that preserve tool execution competence under adversarial conditions.
A consistency boundary analysis is presented that characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps that remain and proposes InfoMamba, an attention-free hybrid architecture that consistently outperforms strong Transformer and SSM baselines.
Youjin Wang, Jiaqi Zhao, Rong Fu et al.· arXiv.org· 0 citations
Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.
Hyeonbeom Choi, Daechul Ahn, Youhan Lee et al.· arXiv.org· 3 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
