A European fertility organization advocates for implementing international limits on the number of children a single sperm donor can contribute to. This concern stems from instances where individuals discover having many half-siblings due to anonymous donations. The issue highlights the impact of donor conception on individuals' sense of self and family structure. Researchers are also developing "world models" to help artificial intelligence understand physical reality, representing a new AI frontier.
Nokia shares surged nearly 10% after announcing a defense AI partnership with NestAI for NATO operational needs. The collaboration offers integrated capabilities like AI-enabled command and control on 5G networks. Additionally, Nokia secured approximately €1 billion in optical network orders related to AI and cloud infrastructure. These developments signal that the company's focus is shifting from traditional telecom hardware to AI infrastructure plays. Investors reacted positively, leading analysts to reassess Nokia as an optical networking peer rather than just a legacy vendor.
Apple filed a lawsuit against OpenAI alleging trade secret theft. The company claims OpenAI misappropriated its intellectual property to develop its own consumer hardware. These accusations follow a high-profile partnership established in 2024 when ChatGPT was integrated into the iPhone's operating system. Apple specifically alleges that former employee Tang Tan directed interviews to extract confidential information from current employees. Furthermore, Apple alleged coaching for departing staff to evade security and cited theft of an Apple laptop by former employee Chang Liu.
A man conceived using anonymous donor sperm in the Netherlands discovered his unknown sibling count due to destroyed records from 2004. Other donor-conceived individuals report finding dozens or even hundreds of siblings through genetic testing and registries. This unexpected discovery prompts calls for international limitations on how many children a single donor can contribute. The ease of tracing genetics, despite bans on anonymity in some regions, has revealed vast unknown familial connections. These revelations have raised concerns regarding the physical risks to potential offspring from highly prolific donors.
A union representing 12 nurses alleges that Montefiore Hospital violated a recently secured contract during layoffs. Marilyn Shuler, a utilization review nurse with 39 years of experience, was among those dismissed on Sunday. These nurses were reportedly replaced by AI-powered software at the facility in the Bronx. NYSNA confirmed the layoffs and indicated significant job displacement due to technological changes. The situation has raised concerns regarding the future quality of patient care.
China is rapidly adopting artificial intelligence across numerous sectors, contrasting with Western skepticism. Examples of this widespread adoption include medical avatars and intelligent robots used in factories. The technology extends to daily life through services like food delivery drones operating on the Great Wall of China. Furthermore, the Chinese state has enthusiastically embraced AI for applications such as enhanced surveillance capabilities. These diverse implementations showcase a comprehensive national rollout of artificial intelligence.
Christopher Nolan believes big-budget action films shot on location are resilient even amid artificial intelligence's growth, a technology many people reportedly "disdain." He views anxieties about AI replacing human creative talent as nonsensical. Promoting his upcoming film, an adaptation of *The Odyssey*, Nolan addressed industry concerns surrounding the technology. Separately, he dismissed rightwing criticism directed at Lupita Nyong’o regarding her portrayal of Helen of Troy as irrelevant.
Open-source Artificial Intelligence (AI) faces its most critical evaluation period currently. The ongoing situation serves as a major test regarding the practical viability of openly accessible AI models. These current circumstances directly challenge the sustainability and real-world applicability of open AI frameworks. Success in this period will be crucial for determining the future trajectory of open-source AI development.
Directly Responsible Individuals (DRI) is a term originally used at Apple to identify the person ultimately accountable for a project's outcome. The concept has recently been applied to discuss LLM-powered agents within human organizations. The author argues that an AI agent should never be designated as a DRI. This belief stems from the unique human capacity to assume accountability, which machines lack. Supporting this, historical advice suggests computers cannot be held accountable for management decisions.
Incorporates new customization options along with Gemini updates. Features enhancements for users navigating on motorcycles specifically through a new "Waze motorcycle mode." These updates aim to improve the overall user experience across various platforms. The roll-out suggests an increased focus on tailored navigation tools. Overall, Waze is expanding its functionality with both personalization and AI integration.
Stanford developed TRACE, a system designed to address recurrent failures in agentic LLMs due to missing specific capabilities. TRACE diagnoses these performance gaps by analyzing an agent's own operational trajectories. It then synthesizes one verifiable Reinforcement Learning (RL) environment for each identified capability deficiency. Subsequently, the system trains a specialized LoRA adapter within that synthesized environment. This process significantly improved $\tau^2$-Bench scores by +15.3 points and achieved 73.2% Pass@1 on SWE-bench Verified.
Prime Intellect released verifiers 0.2.0, showcasing a rewritten core in the verifiers.v1 namespace. This new structure modularizes environments into distinct components: a taskset defining the objective (what), a harness detailing execution methods (how), and a runtime specifying the environment location (where). An integrated interception server manages requests while meticulously recording training-ready traces. A key feature is the compatibility allowing any taskset to function under any compatible harness. The release offers full support for prime-rl training from its launch.
Loop engineering describes a modern pattern that moves beyond simple manual queries into AI tools. This process utilizes "autoresearch" and "Bilevel Autoresearch" to create autonomous machine learning research loops for AI agents. These concepts are exemplified using Andrej Karpathy’s autoresearch repository. The framework is based on documentation provided by @0xCodila. Such looping mechanisms transform the interaction with AI beyond simple prompt-response cycles.
Version 1.11 introduces minor improvements, primarily enhancing command option consistency. The server mechanism now waits up to 30 seconds for a target URL connection, replacing the previous one-second delay. A new `--js-file` option allows loading JavaScript from local files, standard input, or GitHub sources, offering an alternative to direct argument passing with `--javascript`. Furthermore, `shot-scraper multi` supports this functionality via a `js_file:` YAML key. Finally, both `shot-scraper javascript` and `shot-scraper html` commands now include a `--timeout` option for standardization.
Anthropic has extended access to Claude Fable 5 on all paid plans through July 19th and increased the weekly rate limits for Claude Code by 50%. Previously, Anthropic cited compute constraints as the reason for these adjustments. In contrast, OpenAI announced the temporary removal of the five-hour usage limit restriction across its Plus, Business, and Pro plans. OpenAI is also rolling out efficiency improvements to GPT-5.6 Sol, suggesting less usage going forward. An observer noted that OpenAI's recent updates are attracting users due to the uncertainty surrounding Fable access.
Version 4.1.1 of sqlite-utils primarily addresses an edge case concerning `ON DELETE` actions within transactions. Specifically, calling `table.transform()` now raises a `TransactionError` if foreign keys with destructive `ON DELETE` actions (like CASCADE) exist while a transaction is active and PRAGMA foreign\_keys is enabled. This prevents unintended data modification caused by dropping old tables during transformation. Additionally, the documentation has been updated to ensure bidirectional cross-referencing between the CLI and Python API sections.
RAG (Retrieval-Augmented Generation) and fine-tuning are distinct techniques for improving large language models, addressing different needs. Fine-tuning modifies the model's underlying weights using domain-specific data to change its style or knowledge base fundamentally. Conversely, RAG enhances responses by retrieving external, up-to-date information before generating an answer, grounding it in provided sources. The decision between them depends on whether the goal is behavioral adaptation (fine-tuning) or access to current/specific facts without retraining (RAG). They are complementary tools rather than competing solutions.
Details instructions for running over one hundred AI agents concurrently using Claude Code. The guide was originally published on the Towards Data Science platform. It addresses the technical methods required for managing such a large-scale, parallel agent system. Readers can learn how to orchestrate this complex setup with specific coding practices.
The content features a screenshot of an online forum post. The post title is "Hello everyone, rate my first own open-source library for Reinforcement Learning." It includes a direct link to the associated GitHub repository, which is named `DenisDrobyshev/reinforce`. The source indicates that a user with the handle `/u/Middle_Affective_576` submitted the content. Additionally, there are links to comment sections accompanying the main submission.
Two types of learning loops are presented: human feedback and automated feedback. In a human feedback loop, direct input is provided regarding the quality of retrieval outputs like RAG or SQL. An automated example involves manufacturing alarms considering multiple action pathways. After an action is taken, an agent calculates its efficacy by monitoring metrics such as time to stabilization (TTS). This process allows the agent to create a ranked leaderboard for future recommendations.
Meta removed its Muse Image AI feature from Instagram after facing significant backlash over its default settings. The tool allowed users to generate AI photos using prompts referencing public accounts without notifying the account owners or obtaining their explicit consent. Criticism focused specifically on this lack of consent architecture, as individuals could have their photos used without warning. In response to feedback and industry critique, Meta stated the feature was withdrawn because it "missed the mark." The reversal highlights heightened user skepticism regarding AI's use with personal likenesses.
NeuroVFM is a generalist neuroimaging foundation model developed by the University of Michigan. It was trained using a volumetric extension, Vol-JEPA, applied to 5.24 million uncurated clinical MRI and CT volumes. The model learns complex brain anatomy and pathology directly from the raw imaging data. A key feature is its ability to learn without requiring specific radiology report labels for training. This represents an advancement in applying foundation models to volumetric medical imaging analysis.
Josh Fawaz's popular cover of "Like a Prayer" has become a viral sensation on Australian radio charts. Music experts and other musicians are questioning the song's origin. Concerns exist regarding whether generative artificial intelligence was used in its production. The buzz surrounds the possibility that the hit track is AI-generated rather than purely human creation. These questions have sparked discussion about the role of AI in modern music composition.
An author confronts her initial aversion to artificial intelligence and chatbots while exploring their potential influence. The piece explores themes surrounding AI's place in a "decent society." Through the context of a podcast titled 'Navigating the unknown together', personal skepticism meets technological possibility. This internal conflict suggests a journey from outright repulsion to potential fascination with advanced AI companions, personified as an "idiot AI boyfriend." The writing grapples with the emotional and social implications of increasingly sophisticated artificial intelligence interaction.
Proposes a framework to align autonomous agentic AI with overarching enterprise intent. This alignment aims to guarantee consistent behavior across various operational scenarios. The framework structures this alignment around three key dimensions: Purpose, Principles, and Practices. Establishing these three areas helps guide the development and deployment of sophisticated AI agents within organizations. Adopting this multidimensional approach ensures that autonomous agents function consistently according to predefined business goals.
Developed a new auditing technique to assess generative AI models for dangerous functionalities. This method tests the models' compliance without requiring prompts that illicitly generate prohibited content. Researchers aim for this tool to enhance safety against illegal AI-generated material. The innovation allows developers to proactively identify and mitigate potential risks in AI systems.
Tech reporting is increasingly moving from digital coverage to investigating the physical infrastructure supporting the AI revolution. Journalists now engage in extensive "shoe-leather reporting" concerning these large-scale facilities. A recent Guardian investigation revealed that an £8.2bn AI complex in rural Scotland misrepresented its plans for entirely renewable energy sourcing. Coverage highlights that building datacentres involves significant, tangible physical constraints and realities. These concrete elements are crucial factors determining the success or failure of the AI boom.
A theoretical framework is presented connecting adversarial robustness to a lattice traversal problem concerning multilayered perceptrons (MLPs). A sound certification interval $I$ ensures that perturbing an input $\mathbf{x} \in I$ does not change the MLP's output, while a complete certification guarantees prediction change when moving outside $I$. The authors develop lattice traversal operators for a refine & verify iterative scheme, validating both sound maximality and complete minimality. They observe that finding minimum solutions for complete certifications is polynomial in oracle calls, unlike sound certifications which face strong intractability. Furthermore, logarithmic algorithms are provided for optimization problems within symmetric $\ell_\infty$-spheres, validated via the ParallelepipedoNN system.
CogniConsole proposes that reliable large language model (LLM) interaction depends significantly on explicit inference-time control rather than solely on inherent model capability. This architecture externalizes control into a structured interface blending programmatic coordination with bounded prompt reasoning. By testing the system across $N=489$ probes in an interactive environment, researchers found that increasing structural scaffolding systematically reduced output variance and failure rates regardless of the underlying fixed model. The findings suggest many LLM failures stem from poor control specification, not insufficient ability. Consequently, this work establishes inference-time control as a primary abstraction for designing and evaluating reliable LLM systems.
GATS, a new planning framework, addresses the high computational cost and stochasticity of existing LLM-based agents like LATS and ReAct. It combines systematic UCB1-based tree search with a novel three-layer world model to eliminate runtime LLM calls during inference. This layered model integrates exact symbolic matching (L1), statistical analysis (L2), and LLM prediction for unknown actions (L3). On synthetic tasks, GATS achieved a 100% success rate compared to LATS's 92% and ReAct's 64%. Critically, GATS requires zero LLM calls per task while maintaining superior performance on complex benchmarks.
Long-Horizon-Terminal-Bench addresses the limitation of current benchmarks by evaluating agents on 46 complex, multi-stage tasks across nine categories, including software engineering and scientific computing. Unlike previous setups that only reward final outcomes, this benchmark decomposes tasks into fine-grained subtasks to provide dense intermediate rewards and partial credit. Tasks require extensive computational time, often spanning minutes to hours, demanding robust long-horizon planning and iterative debugging capabilities. Evaluating 15 frontier models revealed that even the best performers scored relatively low pass rates (e.g., 15.2% at a partial reward threshold of 0.95). The benchmark suggests significant room for improvement in training advanced agents for extended, complex workflows.
A novel approach formalizes deriving the Vlasov equation's well-posedness using an AI-assisted process structured as a "formalization game." In this setup, a mathematician directs an AI assistant to convert LaTeX mathematics into verifiable Lean 4 code. The success criteria include successful compilation, absence of placeholders, and reliance solely on foundational axioms for proof validity. A key outcome was the formalization of the Vlasov equation's existence, uniqueness, stability estimate, and weak solution properties. Furthermore, a significant portion of the resulting optimal-transport machinery proved self-contained and reusable within the broader Mathlib library.
ARCANA is introduced as a collaborative, multi-agent framework designed to solve ARC AGI 2 tasks within strict time and hardware limitations. The system breaks down each task into four iterative steps: perception, hypothesis generation, symbolic execution, and reflective refinement. Specific agents handle object-centric scene graph construction from raw grids, propose diverse DSL programs via a latent policy, and verify candidates using a symbolic executor. A crucial reflective agent generates failure-driven feedback for subsequent attempts. These agents coordinate their actions through a shared differentiable blackboard, guided by a learned meta controller to enhance reasoning efficiency.
Traditional rule-based monitoring fails against modern cyberattacks on operational technology, and LLMs pose safety risks for closed-loop control. A novel neuro-agentic control framework is proposed, merging an LLM planner (like Gemini 2.5 Flash-Lite) with a pre-trained Time-Series Foundation Model (TimesFM). This system employs "Counterfactual Physics Injection" to simulate potential interventions in the model's latent space before real-world action, rejecting unsafe proposals. Testing on industrial datasets, the framework outperformed LSTM and TCN baselines by preventing five breaches under stochastic attack scenarios. These results confirm foundation models can act as deterministic safeguards for agentic AI in critical infrastructure.
Introduces the Legal Multi-Agent Debate (L-MAD) framework to systematically evaluate debate structures for Legal Textual Entailment. L-MAD utilizes distinct expert personas across multiple agents, showing performance improvements up to 8\% over single-agent baselines. Analysis reveals that increasing the agent population reduces inconsistency and enhances accuracy in legal reasoning tasks. Conversely, extending discussion rounds leads to "over-deliberation drift," causing agents to reinforce errors. The findings establish practical boundaries for deploying collaborative multi-agent systems in high-stakes legal settings.
Introducing MedRealMM, a large-scale benchmark for multimodal online medical consultation derived from de-identified Chinese patient-doctor interactions. It employs a Multimodal Clinical Challenge Point (MCCP) framework to create standardized next-response tasks while maintaining the text-image context of real consultations. Each instance is graded by physicians using rubrics that reward beneficial behaviors and penalize unsafe or contradictory responses. The initial release comprises 5,620 multimodal cases across 64 clinical departments, evaluating various LLMs. Results indicate that image information is crucial for reliable performance, though current frontier models struggle with safety-sensitive error avoidance compared to human physicians.
Process Reward Models (PRMs) enhance multi-agent systems via test-time scaling but face computational bottlenecks due to expensive re-encoding of long trajectories ($O(L^2)$ cost). KV-PRM addresses this by directly utilizing the existing Key-Value (KV) cache generated during LLM decoding instead of reprocessing text. This approach reduces the scoring complexity from quadratic to linear ($O(L)$), significantly improving efficiency for long contexts. Benchmarking on MATH, GSM8K, and AIME demonstrates that KV-PRM matches or exceeds traditional text-based PRMs across methods like Beam Search and MCTS. Quantitatively, it achieves dramatic reductions, including up to a 5,000x decrease in scoring FLOPs and a 34x reduction in memory footprint.
Existing knowledge distillation (KD) methods for Large Language Models (LLMs) lack a clear understanding of their underlying mechanism. This paper proposes analyzing various KD techniques by decomposing LLM output scores into constituent interactions. The core finding is that the common mechanism involves the sparsification of these interactions, where student models zero out certain relationships during inference. Performance variance among KD methods relates to how effectively they handle complex interactions; higher sparsity of complex interactions generally boosts performance. To enforce this, the authors introduce a Complex Interaction Penalty (CIP) loss function, which improves diverse KD methods across various benchmarks.
Alzheimer's Disease (AD) conversion prediction during prodromal stages requires advanced risk models, but existing survival predictors lack interpretability and natural language reasoning. Introducing iLENS, a framework utilizes an interpretable Large Language Model (LLM) guided Mixture-of-Experts (MoE) approach for AD survival analysis. This method leverages the LLM to synthesize both structured neuroimaging measurements and unstructured clinical information. iLENS guides expert routing while demonstrating competitive predictive performance and robust patient subtyping capability. Crucially, it offers transparent, biologically grounded rationales, improving clinical decision support in survival analysis.
Standard symmetric quantizers often suffer quantization errors for positive outliers due to their strictly positive scale assignment when using signed integers. While asymmetric quantization solves this with a zero point, it incurs runtime overhead. Signed symmetric quantization offers a third approach by placing the extra negative representable value on the dominant outlier tail via a lightweight sign selection rule, keeping the zero point at zero. Theoretical analysis proves this signed absmax grid is conditionally bound-optimal for $\ell_2$ error in most LLM weight groups. Empirical tests confirm this method improves perplexity and accuracy compared to standard unsigned quantizers with no extra inference cost.
StickyMoE introduces a differentiable routing consistency loss designed to improve memory efficiency in Mixture-of-Experts (MoE) models during inference. This loss penalizes abrupt expert switching between adjacent tokens, encouraging stable expert assignment across semantically related spans. Unlike previous system or post-hoc methods, StickyMoE integrates this constraint directly into the training process without architectural changes. Experiments demonstrated that applying StickyMoE significantly reduces the expert switch rate by up to 60%. This method achieves such gains while maintaining performance comparable to state-of-the-art techniques with minimal perplexity degradation.
Reward Transport reinterprets the coupling in flow matching as an alignment interface by pairing noise vectors with data based on a target molecular property. This method utilizes optimal transport at training time to align a scalar noise-space coordinate with specific molecular rewards. During inference, changing this single coordinate steers the generated distribution without needing external models or gradient calculations. Practically, sweeping this coordinate allows for continuous control over properties like logP and QED across different chemical datasets. The technique demonstrates transferable control, exhibiting opposite structural effects (e.g., growing vs. shrinking molecules) depending on the targeted property.
A new distributed MoE serving system named Director improves efficiency by proactively managing expert placement across GPUs. It addresses limitations in current methods by employing an online prediction approach suited for diverse request patterns. Director utilizes a lightweight predictor or quantized replica to forecast incoming request expert activations. An online migration module minimizes downtime by performing necessary changes during computation-heavy phases. Experiments show that using this relaxation-based optimization leads to significant latency reductions of $11\sim55\%$ compared to existing techniques on popular MoE models.
Manifold-valued data requires specialized normalization techniques, known as Riemannian normalization, for Deep Neural Networks (DNNs). Existing methods are often limited to specific geometries or fail to normalize sample distributions effectively. This paper introduces LieBN, a framework providing Riemannian Batch Normalization specifically for Lie groups. LieBN utilizes theoretically convenient left- and right-invariant metrics inherent to all Lie groups, offering theoretical control over the Riemannian mean and variance. The proposed method is demonstrated across nine distinct manifold geometries, including novel metrics on the SPD manifold.
Federated continual learning (FCL) evaluation suffers due to inconsistent variable changes across existing benchmarks. Introducing HERO, a new library, addresses this by separating task split, client data split, and client task sequence into manageable components. HERO offers a core benchmark ($\alpha$ for data skew, $\rho$ for order mismatch) tested on CIFAR-100 and TinyImageNet using multiple metrics. Evaluation confirms that average accuracy masks poor performance among individual clients and that different settings favor distinct learning strategies. The library provides reproducible streams and scripts to enable setting-aware comparison beyond image datasets.
Data marketplaces facilitate increasing data transactions, yet accurately pricing unique data products is a major challenge. Traditional economic pricing methods prove inadequate due to issues like near-zero marginal cost or unpredictable revenue streams. To solve this gap, researchers introduce \texttt{DaDaDa}, the first dataset for data product pricing. This dataset contains metadata for 16,147 data products sourced from nine global marketplaces. $\texttt{DaDaDa}$ allows for training robust pricing models and supports other market tasks like classification and retrieval.
High LLM inference costs necessitate pruning weights into sparse matrices, yet current GPU kernels struggle to outperform dense operations with moderate sparsity levels ($\sim 50\%$). This research introduces an efficient GPU inference method employing a novel three-layer matrix storage format. This format includes Sparse-TC for tensor core acceleration, a Slot-Filling layer for compression, and a Residual Layer for computation integrity. A new SpMM kernel jointly utilizes sparse tensor cores and CUDA cores to create an overlapping execution pipeline. Evaluations demonstrate up to 1.64x speedup over prior state-of-the-art methods like SpInfer on modern GPUs.
Sepsis management lacks clear optimal treatment protocols, and current reinforcement learning (RL) methods use fixed strategies. EHRMPC addresses this by decoupling patient dynamic learning from treatment optimization using a generative electronic health record (EHR) model as a digital twin. This digital twin predicts patient trajectories under potential interventions. Model predictive control (MPC) then utilizes these predictions to plan optimal treatments during inference time via simulation. Evaluations on an 8-hospital ICU sepsis cohort showed EHR-MPC matched off-policy performance while improving simulated results compared to RL baselines.
Leave-one-out influence traditionally measures how removing a single training point affects model accuracy. Understanding this influence is difficult in high dimensions because sample effects become complexly dependent on all other data points. For convex M-estimation under Gaussian design in the high-dimensional limit where $n$ scales with $d$, the distribution of influences converges to a precisely characterized limiting measure. Furthermore, research shows that highly influential samples tend to cluster near the model's decision boundary. This observation provides support for utilizing such patterns within active learning data selection methods.
Develop a nonlinear spectral deconfounding framework specifically for gradient boosting methods to address sensitivity to unobserved confounders. The technique replaces standard squared-error loss with a spectral loss, which dampens learning in directions correlated with latent confounder information. Effective deconfounding requires the combined effect of spectral shrinkage and regularization, particularly through early stopping mechanisms. Authors propose an empirical-Bayes procedure for tuning this new spectral loss function. Experimental results demonstrate that spectrally deconfounded boosting substantially improves target function estimation compared to existing nonlinear baselines under hidden confounding.
Models representing real-world processes often involve function compositions structured as Directed Acyclic Graphs (DAGs). These models face challenges due to partially observed, noisy, and heterogeneously sampled measurements across the DAG structure. The paper introduces Deep Gaussian Processes over DAGs by placing priors on these underlying functions. Researchers theoretically investigate prior-collapse behavior, determining how graph topology and intermediate observations affect information retention. A structured variational approximation is proposed that maintains graph dependencies and captures collider effects, validated on tasks like protein signaling networks.
Bayesian and multiplicative-weights updates reweight experts or actions based on sequential feedback. These updates obey an exact information-accounting identity for their regret. On any given round, excess loss equals an immediate payment for exposed uncertainty plus a reduction in distance from chosen comparators. Summing these one-step balances reveals two exact adaptive decompositions of cumulative regret. This pathwise accounting framework applies universally across various learning paradigms, including Hedge and online convex optimization.
Addresses the challenge of quantifying the benefit of personalized interventions versus using a single best option across fields like medicine and marketing. Introduces a statistical hypothesis test designed to evaluate if a personalized policy outperforms the best non-personalized approach using historical data. The test maintains strict control over type-I error while achieving asymptotic normality with minimal variance under stated conditions. Demonstrated versatility and superior performance when tested on various datasets, including those from job training and depression treatment. Provides decision-makers in intervention sciences with a simple, powerful measure of personalization's potential added benefit.
Investigates nonasymptotic convergence for primal-dual methods applied to nonconvex optimization problems with convex-composite structures. The method addresses difficulties from constraint violation and unbounded multipliers by restricting the dual variable to an auxiliary compact set. A nonsmooth nonconvex-concave minimax reformulation leads to a finite-time mechanism converting stationarity of the truncated problem into a KKT certificate. Establishing explicit convergence rates, the approach achieves $O(K^{-1/3})$ with dual regularization or a sharper $O(K^{-1/2})$ rate under specific unregularized assumptions. This demonstrates robust analysis for nonconvex functional inequality constraints.
Investigates partially observed stochastic linear bandits, where agents only receive random subsets of action coordinates. Since full observability makes sublinear regret impossible, the work focuses on cases with low intrinsic action dimension. A proposed algorithm, TOFU-POV, estimates the latent subspace from masked actions and runs OFUL in these lower dimensions. Theory proves TOFU-POV achieves $\sqrt{T}$ regret scaled by the subspace dimension, not the full ambient dimension. The research also introduces a rank-adaptive method and provides a novel lower bound demonstrating performance guarantees in this partial observation setting.
Investigates active learning for identifying the top-$k$ items using noisy pairwise comparisons until a specified error probability $\delta$ is met. The goal is to minimize the expected number of necessary comparisons (sample complexity). While asymptotic optimality exists for some pure exploration problems, an efficient algorithm remains unestablished for top-$k$ identification from these comparisons under latent utility models. Researchers address this by characterizing the lower bound as a saddle-point problem. This structure allows them to develop an online, primal-dual procedure and construct an adaptive algorithm proven to be asymptotically optimal.