Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncertainty, and the procedure should account for the inherent structure in the hypothesis space, when such structure exists. Here, we present a framework for controlling the false discovery rate both when each hypothesis is evidenced only by a finite count of null draws, leaving its p-value uncertain, and when the hypothesis space carries arbitrary structure, requiring only that the structure be represented through a suitable reproducing kernel. We present two decision rules that are both robust to structural mis-specification, yet offer a distinct trade-off between exact FDR control and statistical power. The first rule guarantees exact FDR control; the second maximizes power by adapting mirror-statistic control into count space, utilizing an analytical framework to assess FDR control when exact mirror symmetry is relaxed. Furthermore, the tractability gained by the RKHS framework allows us to directly investigate finite-data uncertainties, which we leverage to suggest a policy for the efficient allocation of null distribution samples.

Neural Information Processing Systems http://nips.cc/

Equivariant neural networks are designed to respect symmetries through their architecture, boosting generalization and sample efficiency when those symmetries are present in the data distribution. Real-world data, however, often departs from perfect symmetry because of noise, structural variation, measurement bias, or other symmetry-breaking effects. Strictly equivariant models may struggle to fit the data, while unconstrained models lack a principled way to leverage partial symmetries. Even when the data is fully symmetric, enforcing equivariance can hurt training by limiting the model to a restricted region of the parameter space. Guided by homotopy principles, where an optimization problem is solved by gradually transforming a simpler problem into a complex one, we introduce Adaptive Constrained Equivariance (ACE), a constrained optimization approach that starts with a flexible, non-equivariant model and gradually reduces its deviation from equivariance. This gradual tightening smooths training early on and settles the model at a data-driven equilibrium, balancing between equivariance and non-equivariance. Across multiple architectures and tasks, our method consistently improves performance metrics, sample efficiency, and robustness to input perturbations compared with strictly equivariant models and heuristic equivariance relaxations.

GPU prices could follow RAM's big rise Slack's CEO is joining OpenAI to find the money to pay for all those data centers Slack CEO Denise Dresser is OpenAI's new Chief Revenue Officer. OpenAI has announced that Denise Dresser, the current CEO of Slack, will be the company's new Chief Revenue Officer. Dresser will oversee the company's revenue strategy across enterprise and customer success, according to OpenAI's announcement, and will presumably play a key role in leading the company towards profitability now that it's reorganized as a public benefit corporation . We're on a path to put AI tools into the hands of millions of workers, across every industry, Fidji Simo, OpenAI's CEO of Products said in the announcement. Denise has led that kind of shift before, and her experience will help us make AI useful, reliable, and accessible for businesses everywhere.

A memo obtained by WIRED confirms Denise Dresser's departure from Slack. She is now headed to OpenAI. Slack CEO Denise Dresser is leaving the company and joining OpenAI as the company's chief revenue officer, multiple sources tell WIRED. Marc Benioff, the chief executive of Salesforce, which owns Slack, shared news of Dresser's departure in a message to staff on Monday evening. At OpenAI, Dresser will manage the company's enterprise unit, which has been growing rapidly this year.

Language agents are rapidly expanding from single-user assistants to multi-user collaborators in shared workspaces and groups. However, today's language models lack a mechanism for isolating user interactions and concurrent tasks, creating a new attack vector inherent to this new setting: cross-user poisoning (CUP). In a CUP attack, an adversary injects ordinary-looking messages that poison the persistent, shared state, which later triggers the agent to execute unintended, attacker-specified actions on behalf of benign users. We validate CUP on real systems, successfully attacking popular multi-user agents. To study the phenomenon systematically, we present MURMUR, a framework that composes single-user tasks into concurrent, group-based scenarios using an LLM to generate realistic, history-aware user interactions. We observe that CUP attacks succeed at high rates and their effects persist across multiple tasks, thus posing fundamental risks to multi-user LLM deployments. Finally, we introduce a first-step defense with task-based clustering to mitigate this new class of vulnerability

A message on OpenAI's internal Slack claimed the activist in question had expressed interest in "causing physical harm to OpenAI employees." OpenAI employees in San Francisco were told to stay inside the office on Friday afternoon after the company purportedly received a threat from an individual who was previously associated with the Stop AI activist group. "Our information indicates that [name] from StopAI has expressed interest in causing physical harm to OpenAI employees," a member of the internal communications team wrote on Slack. "He has previously been on site at our San Francisco facilities." Just before 11 am, San Francisco police received a 911 call about a man allegedly making threats and intending to harm others at 550 Terry Francois Boulevard, which is near OpenAI's offices in the Mission Bay neighborhood, according to data tracked by the crime app Citizen.

We consider stochastic settings for clustering, and develop provably-good (approximation) algorithms for a number of these notions.

We introduce the General Incentives-based Framework for Fairness (GIFF), a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard value functions. In resource-constrained settings, agents optimizing for efficiency often create inequitable outcomes. Our approach leverages the action-value (Q-)function to balance efficiency and fairness without requiring additional training. Specifically, our method computes a local fairness gain for each action and introduces a counterfactual advantage correction term to discourage over-allocation to already well-off agents. This approach is formalized within a centralized control setting, where an arbitrator uses the GIFF-modified Q-values to solve an allocation problem. Empirical evaluations across diverse domains, including dynamic ridesharing, homelessness prevention, and a complex job allocation task-demonstrate that our framework consistently outperforms strong baselines and can discover far-sighted, equitable policies. The framework's effectiveness is supported by a theoretical foundation; we prove its fairness surrogate is a principled lower bound on the true fairness improvement and that its trade-off parameter offers monotonic tuning. Our findings establish GIFF as a robust and principled framework for leveraging standard reinforcement learning components to achieve more equitable outcomes in complex multi-agent systems.