This study disentangles the effects of classifier tuning vs. joint optimization in semi-supervised learning (SSL) pipelines for security classification, finding that simply tuning the classifier with Bayesian Optimization recovers 86% of the performance gain, and a simplified recipe matches the full pipeline.

Generative models used as surrogates for physical simulation often fail to enforce physics constraints. Constrained sampling can enforce constraints at inference but is computationally expensive. This paper introduces SNAP-FM, which leverages sparse GPU nonlinear optimization to accelerate constraint projection. Using block-sparse Jacobian and KKT systems with ExaModels.jl and MadNLP.jl, the method achieves faster nonlinear constraint projection on PDE benchmarks while maintaining accuracy.

This paper introduces the Manifestation Unit Protocol, a structured representation scheme for mechanistic interpretability that organizes component-level analysis outputs into queryable, reusable fields, validated across multiple models.

Seed2.0 is a model series that takes a meaningful step toward solving complex, real-world tasks. It identifies user needs, builds a reliable evaluation system, and targets long-tail knowledge and complex instruction following. It also delivers world-leading reasoning, visual understanding, and search capabilities.

A study on Lewis signaling games with LLM agents shows that memory architecture, specifically a persistent private notebook, significantly improves coordination over stateless agents, and channel capacity alone cannot predict success.

This study introduces Epistemic AI Literacy (EAIL), reframing AI literacy as a process-oriented epistemic phenomenon. Using the AIR framework, it analyzes epistemic aims and processes in GenAI-supported co-programming. Analysis of a large human-AI dialogue dataset reveals prevalent lack of EAIL: 78.8% of interactions relied on non-mastery-oriented aims and less reliable strategies, while only 11.1% showed high epistemic engagement.

A new paper introduces a contextual-bandit team game with two-sided asymmetric information, studying runtime human oversight of AI agents. It provides exact one-shot characterizations of team-optimal and myopic oversight rules, revealing a gap of avoidable harm and the price of non-credible oversight communication.

RareDxR1 is an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. It bypasses traditional pipeline-based phenotype extraction or retrieval-augmented generation by synergizing knowledge internalization with autonomous evolutionary learning, and employs Reflection-Enhanced Reasoning Sampling and dual-level curriculum reinforcement learning to improve diagnostic accuracy. Experiments show state-of-the-art results across multiple benchmarks.

A new conflict-free path planning algorithm for en-route air traffic control is proposed, leveraging solution-space displays for interpretability and flexibility. It integrates three intent-based conflict detection methods and two search variants (SSPPV and SSPPE). Empirical results using MUAC data show SSPPV with zone-based detection achieves optimal performance with average computation time of 3.69 ms.

Proposes a framework that replaces free-form LLM-generated web scraper code with typed JSON configurations, combined with a six-type collector taxonomy, template and utility-function constraints, static Airflow DAG execution, rule-based quality checking, and structured feedback correction. Experiments show zero execution-stage LLM tokens and lowest average wall-clock time on verified tasks.

This paper introduces MMM, a data model designed for knowledge documentation and interoperability across disciplines. Combining normative constraints with free-text labels, it addresses limitations of document-centric systems and formal approaches. A reference implementation demonstrates its usability.

A new paper proposes Bounded Morality, a formal framework that extends bounded rationality to moral cognition, modeling ethical theories as locally efficient strategies within a tradeoff between moral breadth and depth.

A new paper on arXiv introduces Constructive Alignment, a paradigm that reframes AI alignment as controlling how AI shapes human preferences over time, rather than satisfying static preferences.

As AI shifts from development to production inference, compute demand is growing and moving to continuously operating AI factories. NVIDIA introduces a new strategy to provide large-scale accelerated computing access to startups and enterprises through a revenue-sharing model, with initial deployments by Sharon AI and Firmus.

This paper studies domain generalization in an anti-causal setting where the outcome causes the covariates. The authors propose two methods that leverage unlabeled data from multiple environments to regularize the model's sensitivity to changes in the mean and covariance of covariates, with worst-case optimality guarantees. Empirical results are shown on a controlled physical system and a physiological signal dataset.

Reasoning Large Language Models (LLMs) benefit from test-time scaling, with accuracy improving as token budget increases, motivating adaptive reasoning. However, setting token budgets and thresholds involves a risk-accuracy trade-off. This paper re-frames budget setting as risk control, limiting error rate while minimizing compute. It introduces an upper threshold to stop when confident and a lower threshold to stop unsolvable instances. Using distribution-free risk control, the framework optimally specifies stopping mechanisms given a target risk and validation set. Experiments across diverse tasks and models demonstrate computational efficiency gains while adhering to risk targets.

Residual Context Diffusion (RCD) is a new module for diffusion large language models (dLLMs) that recycles computation from discarded tokens, improving efficiency and accuracy. RCD converts discarded token representations into contextual residuals and reinjects them into the denoising process. It uses a decoupled two-stage training pipeline and achieves 5-10 point accuracy gains across benchmarks, with up to 4-5x fewer denoising steps on challenging tasks like AIME.