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OpenAI announces limited preview of GPT‑5.6 series including Sol, Terra, and Luna models, with new pricing and caching features.
TabPFN-CFM is a causal foundation model that predicts both causal structure and outcomes from observational data, supporting all three levels of Pearl's Causal Hierarchy and achieving improved performance over baselines.
This paper proposes a two-stage adapter that embeds foundation model predictions into a multinomial logit model, preserving economic properties like cost monotonicity and interpretable willingness-to-pay while improving accuracy by up to 12.8 percentage points.
Liquid AI releases LFM2.5-230M, a lightweight foundation model that runs on devices from cloud GPUs to CPUs and Raspberry Pi, with strong performance on tool use and data extraction tasks.
Wan-Streamer is a unified end-to-end multimodal model for real-time audio-visual interaction using causal attention and integrated processing of visual, audio, and text modalities, achieving sub-second latency.
ABACUS is a unified vision-language model that handles multiple counting tasks and count-faithful image generation without benchmark-specific training, achieving state-of-the-art results across seven benchmarks.
Apertus is a fully open foundation model for sovereign AI, developed by the Swiss AI Initiative. It is open weights, open data, open science, compliant with EU AI Act, and competitive with top open models at 8B and 70B parameters, supporting over 1000 languages.
Introduces DeXposure-Claw, a forecast-grounded agentic system for DeFi risk supervision that uses a graph time-series foundation model to forecast exposure networks, with deterministic monitors and confidence gates to constrain LLM-generated supervisory tickets. Also presents DeXposure-Bench, a six-axis evaluation harness for regulator-aligned assessment.
BioMatrix is a multimodal foundation model that unifies molecular sequences, structures, and natural language in a single decoder-only architecture, achieving state-of-the-art performance on 77 out of 80 biological tasks.
General Intuition, a startup building a foundation model for training AI agents in spatial-temporal reasoning using video game data, is in talks to raise $300 million at a $2 billion valuation, with backing from Jeff Bezos and Eric Schmidt.
This paper presents the first foundation model-orchestrated workflow for crash safety design, enabling surrogate-assisted exploration for pedestrian protection that reduces evaluation time from hours to seconds, demonstrated in an automotive front-bumper case study.
Geometric Action Model repurposes a geometric foundation model for robot policy learning, achieving 85.5% on LIBERO-Plus with 6.9 ms inference, 55× faster than baselines.
Qwen-Robot Suite is a foundation model suite designed for physical world intelligence, enabling robots to understand and interact with the real world effectively.
This paper introduces Neuro-JEPA, a foundation model that uses a latent predictive objective and Mixture-of-Experts architecture to encode brain MRI scans across T1w, T2w, and FLAIR sequences, pretrained on a large dataset of 1.55 million scans.
Forgis Labs presents a family of foundation models for time series sensor data in industrial settings, with five papers accepted to ICML 2026 workshops, enabling event prediction and natural language explanation from raw sensor streams.
OdysSim presents a systematic investigation into behavioral foundation models for simulating human behavior, introducing the Soul taxonomy, a corpus of 21.4M interactions, and a training recipe that achieves state-of-the-art on 8 of 23 benchmark tasks while producing more human-like outputs.
LOGOS is a scientific generative language model that encodes diverse scientific objects and spatial interactions as token sequences, enabling a unified autoregressive framework for tasks across natural sciences. Models at 1B, 3B, and 8B parameters show consistent performance scaling and are released to facilitate research.
OmniLoc is a geometry-aware foundation model for anchor-free user equipment localization across diverse indoor environments, using a unified tokenization module, a geometry-aware Transformer, and geometric embeddings to significantly outperform existing methods.
This paper introduces GLACIER, a multimodal student-teacher foundation model that integrates molecular graphs, SMILES strings, and physicochemical descriptors to predict molecular properties efficiently. It leverages Finsler geometry-aware fusion and knowledge distillation from larger teacher models (MiniMol, MolFormer) to achieve high performance with a lightweight architecture.
LakeFM is a foundation model for aquatic systems, pre-trained on large-scale ecological datasets to forecast lake dynamics using irregular multivariate multi-depth time series data, achieving competitive performance compared to existing models.