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This technical report presents the 1st-place solution for the SeePhys Pro challenge at ICML 2026's AI4Math Workshop, using a two-stage framework with visual information extraction and multi-agent debate to answer college-level physics questions from images.
This paper introduces a framework to reduce per-sample harm in stochastic optimization, where parameter updates from batch averaging and historical states increase individual sample loss. The method uses dimensionality reduction and focuses on the last linear layer for efficiency, showing improved generalization on image classification tasks.
Nankai University and Lenovo collaborated to propose Graph of States (GoS), a neuro-symbolic framework for general abductive reasoning, which uses explicit belief states and state machines to control multi-agent collaboration, achieving significant improvements in medical diagnosis and system fault diagnosis tasks. This work was accepted at ICML 2026.
Introduces PerceptionRubrics, a rubric-based evaluation framework for multimodal AI that shifts from holistic matching to atomic auditing using 1,038 images and over 10,000 instance-specific rubrics, with gated scoring to enforce strict perceptual fidelity.
Memora is a scalable memory system for AI agents that decouples storage from retrieval, achieving state-of-the-art performance on long-horizon tasks while using up to 98% fewer tokens. The research is published at ICML 2026.
This paper introduces a categorical framework for constructing verifiable, local truth-preserving foundation models using composable foundries, implemented in the Odyssey system, and scheduled for a tutorial at ICML 2026.
Quantitative finance firms are heavily sponsoring and participating in ICML 2026 as Diamond sponsors, sparking questions about the reasons behind their increased involvement.
This paper studies off-policy evaluation (OPE) when decision subjects (agents) strategically modify their covariates in response to a policy. It proposes a method that uses local disclosure via post-hoc explanations to reveal agents' pre-strategic covariates and construct a doubly robust estimator for policy value.
A paper analyzing AI agent reliability, accepted at ICML 2026, finds that even the latest frontier models (GPT 5.5, Gemini 3.1 Pro, Claude Opus 4.7) show only marginal reliability improvements over earlier versions, with low outcome consistency and persistent issues in agent scaffolding.
This ICML 2026 paper introduces Derivative Informed XC-Loss (DI-Loss), a training approach for machine-learned exchange-correlation functionals that incorporates first and second derivative supervision on the Grassmannian of density matrices. Across four architectures, DI-Loss reduces total-energy MAE by 66% compared to energy and density supervision alone, and improves excited-state predictions in TDDFT calculations.
RT-Lynx proposes using activation sparsity instead of weight sparsity to accelerate diffusion models, achieving up to 1.55× linear-layer speedup while maintaining generation quality, and is accepted at ICML 2026.
This paper proposes a scalable supervised fine-tuning method for training language models to propose research hypotheses across disciplines. It has been accepted by ICML 2026 and the code is open source.
MOOSE-Star presents a 7B model fine-tuned from DeepSeek-R1-Distill-Qwen-7B for scientific hypothesis discovery, along with a dataset of 108K NCBI papers. The model achieves state-of-the-art inspiration retrieval accuracy, outperforming larger models like GPT-5.4 and Gemini-3 Pro.
The Fast Byte Latent Transformer (BLT-D) has been accepted to ICML 2026, introducing a text diffusion method for parallel byte-level decoding to overcome the speed limitations of traditional byte-level language models.