All Updates
Page 741 of 752
February 12, 2026
Google Gemini 3.1 Pro Launch Imminent
Google appears set to release Gemini 3.1 Pro soon, with model references already spotted in related arenas. This follows recent launches like Zhipu's open-source GLM-5 and DeepSeek's upgraded model with larger context window.
Gemini Blocks Disney Content Post-IP Claim
Google Gemini and related tools now refuse Disney character generation requests after Disney's IP infringement notice. The update rolled out about two months after Disney's December cease-and-desist letter.
Wavelet Flows Speed Universe Reconstruction
Cosmo3DFlow uses 3D wavelet transform and flow matching for efficient cosmological inference from N-body simulations. Addresses sparsity via spectral compression, enabling 50x faster sampling than diffusion models. Samples initial conditions in seconds at 128^3 resolution.
VulReaD: KG-Guided Vulnerability Reasoning
VulReaD uses a security knowledge graph and teacher LLM for CWE-consistent vulnerability detection beyond binary classification. Student models are fine-tuned with ORPO for taxonomy-aligned reasoning. Boosts F1 scores significantly on real datasets.
VLM-Enhanced RL for Autonomous Driving
Found-RL integrates foundation models into RL for end-to-end driving via async batch inference to cut latency. Distills VLM guidance using VMR, AWAG; CLIP rewards shaped by conditional alignment. Lightweight policy matches VLM perf at 500 FPS.
Visual Jailbreaks Hit Image Editors
Vision-Centric Jailbreak Attack (VJA) uses visual inputs to bypass safety in image editing models. IESBench benchmark tests vulnerabilities with up to 80.9% success rates. A training-free defense via multimodal reasoning mitigates risks effectively.
VESPO Stabilizes Off-Policy LLM Training
VESPO introduces variational sequence-level soft policy optimization to tackle training instability in RL for LLMs caused by policy staleness and async execution. It derives a closed-form reshaping kernel for importance weights without length normalization. Experiments demonstrate stable training up to 64x staleness on math benchmarks.
Versor Revolutionizes Geometric Sequences
Versor uses Conformal Geometric Algebra (CGA) for sequence modeling with SE(3)-equivariance. Outperforms Transformers on N-body dynamics, topology, and benchmarks with fewer parameters. Offers linear complexity and interpretability via rotors.
V-STAR: Value-Guided RecSys Sampling
V-STAR addresses probability-reward mismatch in generative recsys via value-guided decoding and sibling-relative RL. VED efficiently explores high-potential prefixes; Sibling-GRPO focuses on decisive branches. Outperforms baselines in accuracy and diversity.
Universal Multimodal Immune System Model
EVA is a cross-species, multimodal foundation model harmonizing transcriptomics and histology for immunology. It shows scaling laws and SOTA on 39 tasks from discovery to clinical trials. Open version released for transcriptomics research.
Unified Theory for Sketching Influence Functions
Develops theory for random projections in computing influence functions, covering unregularized, regularized, and factorized cases. Shows exact preservation conditions and handles out-of-range gradients via leakage term. Guides sketch size selection for scalable computation.
TwiFF Enables Dynamic Visual CoT
TwiFF-2.7M dataset and model advance VCoT for videos via future frame generation. TwiFF-Bench evaluates reasoning trajectories. Outperforms baselines on dynamic VQA.
Transformers Collapse to Low-Dim Manifolds
Transformer training on modular arithmetic tasks collapses high-dimensional parameters to 3-4D execution manifolds. This structure explains attention concentration, SGD integrability, and sparse autoencoder limits. Core computation occurs in reduced subspaces amid overparameterization.
Transformer for Experimental NMR Structure Elucidation
NMRTrans uses set transformers on experimental NMR spectra for molecular structure elucidation, trained on NMRSpec corpus from literature. It models spectra as unordered peak sets aligning with NMR physics. Achieves SOTA Top-10 accuracy of 61.15% on benchmarks.
Topology Meets NNs Under Uncertainty
Integrates neural networks, topological data analysis, and Bayesian methods for AI in military domains. Covers image, time-series, graph applications like fraud detection. Emphasizes robustness and interpretability.
Tokens Enable Emergent Resource Rationality
Inference-time scaling in language models leads to adaptive resource rationality without explicit cost rewards. Models shift from brute-force to analytic strategies as task complexity rises. LRMs show robustness on challenging functions like XOR/XNOR unlike IT models.
TokaMark Launches Fusion Plasma Benchmark
TokaMark standardizes AI evaluation on MAST tokamak data with unified multi-modal access and 14 tasks. Harmonizes formats, metadata, and protocols for reproducible comparisons. Includes baseline model; fully open-sourced for community use.
Text Boosts Multimodal Anomaly Detection
Text-guided framework enhances weakly supervised multimodal video anomaly detection. Employs in-context learning for anomaly text augmentation and multi-scale bottleneck Transformer for fusion. Achieves state-of-the-art on UCF-Crime and XD-Violence benchmarks.
ฮด_TCB Measures LLM Prediction Stability
Introduces ฮด_TCB metric to quantify LLM internal state robustness against perturbations, beyond traditional accuracy. Linked to output embedding geometry, it reveals prediction instabilities missed by perplexity. Correlates with prompt engineering in in-context learning.
Synthetic Underspecification for Agents
LHAW generates controllable underspecified long-horizon tasks by removing info across goals, constraints, inputs, context. Validates via agent trials, classifying ambiguity impacts. Releases 285 variants from benchmarks.