A continual test-time adaptation framework that updates only a small subset of model parameters that consistently outperforms existing TTA and OVSS adaptation baselines across multiple datasets and corruptions, while maintaining stable performance over extended and cross-corruption continual streams without introducing additional trainable modules.
Fen Luo, Sen Li, Zhanqiang Huo· Journal of King Saud Univers...· 0 citations
The findings indicate a dissociation between performance quality and short-term recall in LLM-supported study, which aligns with cognitive-psychology evidence that elaboration improves comprehension while retrieval practice consolidates retention.
V. Tsiligkiris· International Journal of Edu...· 0 citations
PhysMLLMs is a training-stage prior injection architecture that injects physics-inspired spatial continuity priors into Video MLLMs, demonstrating that the injected spatial prior improves video consistency without compromising image-level grounding or general multimodal capability.
Siyao Yan, Bo Han, Jisheng Dang et al.· 0 citations
Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems, and the 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form.
Apodex Team B. An, B. Li, B. Wang et al.· 1 citation
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MGQL is presented, the first mechanized, small-step operational semantics for a substantial read-only fragment of GQL that is grounded in the ISO/IEC 39075 standard, and it is proved that the type system is sound, ensuring an end-to-end guarantee of well-formed queries yielding results that conform to their declared schemas.
The role of a frozen off-the-shelf instruct model as the teacher in on-policy distillation is investigated, and a key insight is revealed: the teacher reshapes the student's policy distribution so that subsequent RL converges to a superior solution that RL alone cannot reach.
Qi Ye, Zhiyuan Gu, Jingjie Xia et al.· 0 citations
A two-dimensional framework, specifically tailored for analyzing multilingual embedding benchmarks under dataset scarcity, is proposed and applied on the Slavic-language subset of the MTEB benchmark, revealing severe benchmark sparsity.
Ana Gjorgjevikj, B. Seljak, T. Eftimov· 0 citations
Ternary multiplicative adaptation is proposed, which represents discrete updates of ternary weights such as sign flips or zeroing through a low-rank Kronecker factorization into two small ternary matrices applied element-wise to ternary weights.
Alexandru-Dragos Manolache, Yun-qiang Li, Jan van Gemert· 0 citations
A supervised ensembling framework that trains a classifier over heterogeneous UQ-based scorer outputs on a small, domain-specific dataset of labeled LLM responses, then applies it to out-of-sample hallucination classification without retrieval, tools, or reference documents is studied.
Parason is introduced, which reveals and learns both forms of parallelism in LLM reasoning, and identifies Trial Parallelism as the majority of parallelizable reasoning computation, and it becomes increasingly dominant on hard problems.
Zhengyang Zhang, Zijian Zhang, Jiaxuan Gao et al.· 0 citations
FPGAgent is the first task-specification-to-executable HLS generation framework experimentally validated on a well-established benchmark, and the value of end-to-end validation is demonstrated.
Tianyu Wang, Wenjie Wang, Jianguo Yao et al.· 0 citations
A multilevel conceptual pathway in wearable reflectance PPG is supported, in which mechanical conditions at the sensor-skin interface are associated with changes in PPG signal characteristics, derived features, and, in a smaller body of studies, downstream physiological estimation.
Chenxi Yang, Jiahang Xie, Zifei He et al.· JMIR mHealth and uHealth· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.