Transformers dominate due to superior scaling on current hardware via self-attention, though this method suffers from quadratic memory costs ($\text{O}(n^2)$). Research explores alternatives in four families: recurrent models (like xLSTM) offer constant memory by maintaining fixed hidden states. State Space Models (SSMs/Mamba) provide linear scaling through parallel training and efficient recurrence during inference. Text diffusion abandons sequential decoding to refine entire sequences in parallel, while liquid models focus on continuous-time dynamics for efficiency. Ultimately, the field is moving beyond Transformer reliance, with promising hybrid approaches combining these different mechanisms.
Satya emphasized building "frontier ecosystems" rather than focusing solely on individual frontier AI models. He introduced the concept of Loopcraft, defining a new "theory of the firm" centered on creating cognitive loops between people and digital systems. The core opportunity lies in developing learning loops atop models, allowing human capital and token capital to compound within an organization. This approach aims for value flow across all industries by letting each entity own its learning loop that encodes institutional knowledge. Ultimately, his priority is building this encompassing ecosystem, not just mastering any single model type.
Satya Nadella distinguishes between "token capital," which refers to AI assets like models and agents, and "human capital," which involves judgment and relationships. He argues these two forms of capital should ideally compound. However, the underlying problem is that traditional corporate structures hinder high-value human contribution. Seniority within large companies can actually become a burden, wasting potential by prioritizing internal politics over pure innovation. Successful builders are those who enter sideways, possessing specialized skills rather than mastering corporate processes.
Blocking allies' access to Anthropic's Mythos and Fable models significantly benefits open-weight alternatives. This situation encourages governments worldwide, including Europe, to accelerate diversification efforts away from American AI technology. European tech leaders are now publicly advocating for achieving genuine European tech sovereignty in response to these restrictions. Consequently, the demand and perceived value of American AI models have declined globally. This shift presents a major opportunity for open-source and Chinese-developed AI models.
Air conditioning contributes significantly to global electricity use and greenhouse gas emissions, prompting research into alternatives. Solid-state cooling systems aim to address this by moving heat through conductive materials with fewer side effects. The effectiveness of these unconventional coolers in matching traditional AC efficiency remains a point of scientific scrutiny. Separately, chemist Tim Cernak is developing "conservation chemistry" to design drugs for animals using AI and robots.
US government export-control directives prompted Anthropic to disable its Fable and Mythos AI models for all users due to national security concerns. Amazon CEO Andy Jassy's discussions with US officials highlighted vulnerabilities in the Fable 5 model that could aid cyberattacks. In response, White House officials directed Anthropic to either fix these weaknesses or shut down access to the powerful models. Anthropic complied by disabling Mythos and Fable, though it noted the flagged vulnerabilities were basic. Despite this, Anthropic stated that other publicly available models can also exploit similar weaknesses.
Senior talent presents a double-edged sword during AI transformation, being both a vital asset and a potential hindrance within large corporations. Satya Nadella frames this contrast between owned "token capital" (AI models and infrastructure) and invaluable human judgment or "human capital." A key problem arises because seniority at established companies can lead to rigid processes that impede innovation. Consequently, the rich context and pattern recognition of experienced personnel can become a burden by inefficiently utilizing AI capabilities.
Google Fitbit Air is now eligible for reimbursement through Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA) in the U.S. This financial benefit allows customers to utilize pre-tax funds for its purchase. The device enables users to track their daily health and general wellness activities. HSA/FSA eligibility expands accessibility for proactive personal health monitoring.
North Mini Code is an AI lab designed to give developers increased control over their AI tools. It addresses concerns regarding the lack of transparency in large, cutting-edge models from companies like Anthropic and OpenAI. Developers who feel these frontier models are overly complex for their specific needs can find an alternative here. The platform suggests a more manageable level of access for varied development tasks. Essentially, it provides a specialized solution balancing power with necessary operational control.
IT professionals report high confidence that all AI agents are controlled, yet only 42% know the true owner of each agent. Furthermore, organizations struggle with governing "shadow AI" due to its embedding across applications and browsers rather than limited systems. Governance failures arise because current policies do not monitor model provenance or behavioral drift post-deployment. A significant gap exists between perceived control and actual oversight, with many agents operating without clear boundaries. Organizations must shift focus from reactive discovery to proactive governance at the deployment stage to manage this rapidly expanding risk surface.
AI coding agents accelerate data engineering by generating pipelines from prompts, but this "vibe coding" scatters crucial business logic across temporary conversations rather than embedding it systemically. This fragmentation leads to lost architectural intent, difficulty analyzing dependencies, and inconsistent knowledge retention across evolving platforms. Spec-driven development (SDD) addresses this by converting prompts and rules into executable, versioned specifications that become persistent system memory. These formal specifications guide code generation and validation, establishing a reliable operational layer for long-term system evolution beyond the scope of single prompts. SDD thus improves governance and iteration consistency across complex, AI-assisted data environments.
Government use of artificial intelligence has expanded significantly, with plans showing a 70% increase since the end of the Biden administration. The Office of Management and Budget disclosed over 3,611 active or planned federal AI use cases. Many of these initiatives involve transferring critical decision-making processes from humans to machines. These applications cover sensitive areas like individual freedoms, public health, and nuclear safety. Authors Sanders and Schneier express concern regarding the lack of transparency accompanying this massive shift to automated governance.
A user found that Cloudflare's CAPTCHA was being triggered even by simple search queries lacking ampersands. By utilizing a specific rule in Cloudflare's managed challenge settings, the trigger condition was refined. The new rule targets only search URLs containing at least one ampersand (`http.request.uri.query contains "&"`). This adjustment successfully allowed basic searches like `/search/?q=lemur` to function without initiating the CAPTCHA challenge. The process also involved using Claude Code to interface with the Cloudflare API for rule modification.
Anthropic's models were reportedly taken offline due to personality clashes concerning US government export controls. Representatives from Anthropic, including Logan Graham and Dave Orr, were scheduled to meet with the Department of Commerce in D.C. One potential solution discussed is making the models resistant to jailbreaking, though perfect resistance might be impossible. Alternatively, a source suggested the outcome may rely on an "attitude fix" to ensure everyone feels secure. Anthropic continues to assert that any jailbreak triggering government action was merely narrow and not a universal threat.
Small businesses are sustaining the tandem bike industry, with a California company notably popularizing tandems in the US and succeeding where larger corporations failed. Beyond this topic, articles cover diverse economic trends, such as the volatile economics of amusement parks and the shifts making small, less urban areas more popular for residents. Other featured stories analyze specific industries like wine judging and discuss cultural phenomena from Japan's vacant housing crisis. Additionally, content addresses consumer psychology, such as the enduring appeal of charm pricing at $\$4.99$ versus $\$5.00$.
Provides several articles covering various aspects of artificial intelligence development. Topics include detailed comparisons between local AI agents like Hermes and OpenClaw regarding memory and safety features. The content also covers fundamental concepts such as LLM inference mechanics, the role of attention mechanisms (QKV, KV cache), and different deep learning architectures (xLSTM vs. Transformers). Furthermore, it addresses practical skills for building AI applications, including guides on skill engineering and agentic vector databases for RAG implementations. Finally, foundational topics like tokenization and understanding the costs associated with various token types are explained.
Arrival at Seoul featured an unmanned immigration checkpoint utilizing facial and passport scanning technology. Traveling on the subway offered another instance of advanced automation in daily life. The piece explores the reasons behind South Koreans' high level of acceptance for Artificial Intelligence. These experiences illustrate the deep integration of smart, automated systems into Korean infrastructure. Such technological immersion suggests a unique societal embrace of AI adoption in everyday routines.
Mixture-of-experts (MoE) models are rapidly becoming essential in large-scale artificial intelligence systems. These models gain popularity due to their ability to manage computational complexity effectively. Advances in kernel design can significantly enhance the training throughput of these architectures. The focus is on developing advanced fusion kernels specifically for MoE model operations. Implementing these specialized kernels boosts the overall efficiency and speed of MoE training processes.
Schneider Electric and Foxconn are collaborating on a significant venture. Their partnership focuses on developing next-generation data centers. The primary goal is to address current bottlenecks in AI infrastructure. They plan to achieve this by creating scalable and replicable design blueprints. This joint effort aims to advance the capabilities of modern computing infrastructure.
Hermes Agent now incorporates asynchronous subagents, allowing delegated tasks to run in the background without halting the parent chat interaction. The discussion details the `async_delegation` toolset implemented under issue #5586. This set of tools enables users to spawn, check the status of, steer, and collect results from these background agents. Furthermore, it addresses subagent isolation and compares the trade-offs between synchronous and asynchronous workflows. Finally, it advises on when to choose ACP (Agent Composition Protocol) instead.
Google Cloud has introduced the Open Knowledge Format (OKF), an open specification designed to formalize the LLM-wiki pattern. OKF utilizes a bundle structure consisting of markdown files, each containing YAML frontmatter and requiring only a concept type field. The format adheres to three core design principles and includes accompanying reference tools from Google. This system aims to provide AI agents with easily digestible, curated context that differs functionally from traditional Retrieval Augmented Generation (RAG). Developers can use a provided Python consumer or an embeddable interactive bundle explorer to implement OKF.
A workflow was established using Docling Parse to analyze PDF documents at a detailed structural level. The process involved generating a complex multi-page PDF containing varied elements like text, columns, shapes, and an image. From this document, the tutorial successfully extracted words, characters, and lines along with their precise page-level coordinates. Visual overlays were rendered, and the resulting data was saved into structured JSON and CSV formats. This demonstrates how low-level parsing facilitates layout analysis, proper reading-order reconstruction, and preparation for information retrieval systems.
Sakana AI has launched its initial commercial product, a sophisticated enterprise agent named Sakana Marlin. This agent utilizes AB-MCTS and AI Scientist workflows to generate comprehensive output. Functionally, it can autonomously operate for tasks lasting up to eight hours. The resulting deliverables include extensive multi-page research reports and accompanying slides. These generated documents are capable of reaching lengths of up to 100 pages.
Version 0.3a0 introduces the `execute_write_sql` tool, which requires and respects user approval before writing to a database. Previously, user approval mechanisms were added in version 0.2a0, allowing tools to prompt users for confirmation on operations like adding data to tables. The chat terminal mode has been enhanced with new flags: `--root`, `--yes` to auto-approve questions, and `--unsafe` for both. This enables direct database interaction via prompts, such as "create a notes table," when using commands like `datasette agent chat content.db -m gpt-5.5`. Furthermore, tools can now offer plain text alternatives to HTML within the CLI output.
User questions used in Retrieval-Augmented Generation (RAG) systems require parsing similar to documents themselves. This process transforms a raw user string into two distinct components, or "briefs." One brief is optimized specifically for the retrieval step of the system. The second brief is tailored for use during the final generation phase. Proper parsing ensures both the information gathering and the answer creation stages operate with structured input.
A guide titled "How to Effectively Align with Claude Code" was published on Towards Data Science. The content aims to improve user productivity when working with Large Language Models (LLMs). Specific instructions are provided within the piece to help users achieve effective alignment with Claude's coding capabilities. This resource suggests methods for optimizing LLM performance through better integration and understanding of the model's coding structure.
Introduces MCP as a solution for managing agent architecture components. MCP effectively consolidated previously scattered tool definitions. It transforms these disparate tools into a stable and easily discoverable server framework. This system addresses organizational complexity within agent development. The explanation was originally published on Towards Data Science.
Eleven distinct models were constructed to forecast the outcome of the 2026 World Cup. Each model operates independently, producing its own champion prediction. The varying outputs highlight that no single model offers a definitive answer. Instead, the collection of models points toward multiple potential winners for the tournament. Ultimately, these diverse predictions illustrate the uncertainty inherent in complex sporting predictions.
A Master's student who recently completed an advanced Reinforcement Learning course is interested in pursuing a career in the field. The student, who initially struggled with the material, has since developed an interest and sought summer research opportunities. A professor recommended that instead of immediate research, the student pursue internships. Currently, the individual is interning as an Agentic AI developer at a telecom company, where the work is primarily software development. Consequently, the author questions if a PhD degree is the only necessary path for a career in RL.
Patterns is a formal grammar that transforms natural language into executable reinforcement learning agents by parsing text into expressions over eight typed terminals representing cognitive functions. The system's pipeline translates language to an algebraic expression, which dictates a mathematical schedule used to construct a PyTorch agent with time-varying objectives. This grammar uses specific operators (like 'orbit' and 'drag') to model complex interactions like conflict or alternation within cognition. Structurally, the process is viewed as a compiler, yielding executable code with typed semantics rather than a mere classification label. Ultimately, it aims to map perceived cognitive dynamics onto distinct, functional objective weights for an RL agent.
Developing training for multiple racing agents to complete an infinite number of laps using a SAC algorithm with curriculum learning. The approach incrementally increases the required laps, drawing inspiration from methods that handle indefinite reward horizons. Key issues encountered include significant training instability (massive gradient peaks) during each discrete increase in difficulty. Furthermore, the author questions the utility of importance sampling when switching between different curricular levels, suggesting uniform sampling as a potential alternative. Seeking advice on managing these instabilities and choosing appropriate data sampling strategies for continuous learning.
A user is questioning the formulation of off-policy n-step SARSA updates, specifically the use of $\rho_{t+1:t+n} (G_{t:t+n} - Q(S_t,A_t))$. They ask why the importance sampling ratio ($\rho$) multiplies the full TD error $(G-Q)$ rather than just being applied to the return term $(\rho G)$. While Monte Carlo prediction suggests both updates yield the same fixed point, the user seeks a formal derivation justifying the $\alpha \rho (G-Q)$ form. The core inquiry is understanding if this weighting of the baseline/error term by $\rho$ is necessary, especially when bootstrapping is involved versus pure MC estimation.
A user seeking resources for learning Reinforcement Learning (RL) wants materials that include implementation details. They are currently watching David Silver's RL course on YouTube, which has ten lectures. The user specifically expresses interest in starting to work on Multi-Agent RL (MARL) systems quickly. Advice and suggestions for other helpful learning resources are being requested from the community.
Seeking contributions from individuals with expertise in specific AI frameworks. Desired areas of knowledge include agent memory, the Model Calling Protocol (MCP), LangChain, and CrewAI. The post was submitted by user /u/Neither-Witness-6010. Contributors are implicitly invited to engage on the topic.
A user has posted a query seeking professional advice for individuals looking to enter the Reinforcement Learning (RL) industry. The post is addressed to people currently working in the field who can offer useful tips. It appears to be a request for guidance on career pathways and necessary skills within RL development roles. The context suggests the community discussion aims to provide actionable entry-level advice. No specific details about the required advice were provided in the query itself, only the general need for industry tips.
An AI researcher wrote a detailed explanation of how Reinforcement Learning (RL) is used to induce reasoning in Large Language Models (LLMs). The piece covers RL fundamentals (state, action, reward), contrasting it with supervised generation-evaluation-selection (GES). It discusses advanced algorithms like PPO and GRPO using concepts such as advantage functions and value functions. The author also speculates that reasoning might be better framed as recurrence or iterative denoising, drawing parallels to diffusion models. Seeking peer feedback, the researcher specifically asks about potential inaccuracies in analogies and the speculative nature of the concluding sections.
A user is seeking existing lists of simple game environments suitable for Reinforcement Learning (RL) research. The post asks if such a curated resource or list can be provided to the community. Alternatively, the author invites other users to comment with links to any simple game environments they are familiar with. These contributions will then be compiled into a shared resource by the original poster. The core need is locating accessible and uncomplicated simulation environments for RL development.
Researchers detail "CogniCore," a novel memory infrastructure designed for autonomous AI agents. This framework integrates key tools including Memory Component Protocol (MCP), LangChain, and CrewAI to manage agent memory effectively. The system aims to enhance the context retention and operational capabilities of multi-agent systems. Preliminary benchmark results are presented, demonstrating the efficacy of the proposed architecture. These findings advance the state of implementing robust memory solutions for complex AI agents.
Some baby boomers are embracing technology during retirement years instead of traditional leisure activities. They are increasingly spending time building apps, learning about AI, and launching new businesses using their computers and phones. Smartphone ownership among Americans over fifty has seen a significant rise from 55% in 2016 to an anticipated 90% by 2025. Furthermore, the use of artificial intelligence is projected to nearly double within this demographic. This trend suggests a high level of tech engagement among older generations.
Describes the use of an open-source "agent harness SDK" to build and control autonomous agents end-to-end. Example implementations are provided in Python (`research_agent.py`) and TypeScript (`research_agent.ts`). A core feature demonstrated is a custom hook that intercepts calls to a `save_report` tool. This hook specifically checks the input content, canceling the tool call if it lacks source citations. Agents are initialized with defined tools and configured with pre-call validation hooks for quality control during execution.
Chemist Tim Cernak is applying pharmaceutical drug design expertise to wildlife conservation efforts, moving beyond traditional veterinary treatments that often harm native species. He utilizes advanced tools like AlphaFold protein-modeling software and robotic lab work to rapidly develop targeted drugs for various animals, such as frogs, Gila monsters, and sea turtles. Cernak champions this new field, which he terms "conservation chemistry," aiming to provide high-tech chemical solutions where existing conservation methods are insufficient. His goal is to treat ailments using precision medicine approaches tailored specifically to the affected species.
Casey Harrell, who suffers from amyotrophic lateral sclerosis (ALS) and paralysis, has had brain electrodes implanted for nearly three years. In 2023, he began using a brain-computer interface (BCI) developed with a research team to generate spoken sentences. Since his initial use, Harrell has accumulated thousands of hours operating the system. He is noted as one of the first power users of this advanced technology. This implant allows him to regain a form of vocal communication despite his condition.
Foundation models are transforming computational biology by being trained on vast amounts of biological data like protein and genomic sequences. Models such as ESM2 are powerful examples of these large-scale pretrained systems. LoRA (Low-Rank Adaptation) offers a method to efficiently fine-tune these massive foundation models for specific biological tasks. NVIDIA BioNeMo Recipes provide structured guidance and tools to facilitate this process. This combination enables researchers to adapt state-of-the-art AI for advanced biological analysis.
Atoms introduces a "vibe coding" method allowing users without development skills to build applications. Users can simply describe their app idea using plain language to an integrated AI. The system handles the building, editing, and refining of the application automatically. Furthermore, Atoms assists with deploying and marketing the resulting applications. This functionality eliminates the need for manual line-by-line coding by the user.
AI and digitization present a potential turning point for botanists battling plant extinction. New technologies allow scientists to track shifts in flowering times globally and rapidly identify unknown plant specimens. Furthermore, these tools enable crucial genetic data extraction from historically preserved fungal specimens. Digitizing millions of previously archived specimens is generating novel insights, particularly concerning the global south. These advancements offer significant resources for conservation efforts.
World-Action Models (WAMs) represent an advanced capability in robotics, building upon multimodal AI. These models integrate Visual Language Model (VLM) backbones to enable robots to interpret natural language instructions alongside visual inputs. The process involves pretraining on broad knowledge followed by fine-tuning for specific actions or tasks. This combination allows the robot policy to not only understand commands but also effectively execute them in the real world. Such systems are crucial for advancing general-purpose, human-interactive robotics.
Defining a "good explanation" is a long-standing philosophical problem recently gaining importance due to AI outputs. Explainability is vital for adopting AI across numerous fields, necessitating a clearer understanding of what constitutes a good explanation first. The paper proposes a definition for such explanations inspired by counterfactual reasoning. Crucially, this proposed definition suggests that the interlocutor's prior beliefs about every factual element offered must also be considered. Consequently, these complexities make generating high-quality explanations for Large Language Model (LLM) outputs particularly challenging.
Current agentic search methods using retrievers limit evidence access to ranked or bounded views, hindering complex material reorganization. Direct Corpus Interaction (DCI) provides flexible operations but slows down with large corpora sizes. A new framework, DR-DCI, addresses this by treating retrieval as an action that populates a dynamic, local workspace rather than querying the entire corpus directly. This approach merges retriever-level scalability with DCI's precise local operational capabilities for evidence resolution. Experiments demonstrate DR-DCI achieves high accuracy (71.2\% on Browsecomp-Plus) while maintaining stability and efficiency across corpora scaling up to 10M documents.
Developing an AI capable of robust reasoning requires an environment model that is both causal and combinatorial. Researchers introduce relational structural causal models to handle environments where objects and their relationships change. They demonstrate that answering observational or causal queries about unseen object combinations requires making explicit assumptions. To achieve identification in such settings, including with unobserved confounding, the work defines relational causal graphs and derives symbolic identification criteria. Finally, they propose relational neural causal models, which are shown to correctly outperform simpler baselines on complex simulated traffic scenarios.
As language model agents collaborate, measuring inter-agent trust is critical, prompting the proposal of a behavioral measure using costly verification in a cooperative survival game. This framework allowed researchers to study trust formation, breakage, and recovery across several advanced models. When paired with reliable teammates, four major snapshots significantly reduced resource-costing verification (by 60-85%), while smaller models showed little change. Failures cause mistrust that differs by model, impacting whether scrutiny targets only the responsible agent or the whole team. Ultimately, the findings suggest calibrating trust—rather than maintaining maximal suspicion—should guide governing multi-agent AI systems to improve performance.
PrologMCP introduces a task-agnostic, open-source server allowing language models to use Prolog as a stateful tool via the Model Context Protocol (MCP). This standardized interface enables a reusable "translate-run-inspect-repair" loop for agents. Evaluation on PARARULE-Plus showed that an enhanced formalizer agent using PrologMCP matched or exceeded the performance of leading reasoning LLMs on general tasks. Critically, this formalizer maintained near-perfect accuracy on a challenging subset where advanced LLMs saw noticeable degradation. These findings establish delegating inference to Prolog through MCP as a robust and inspectable alternative to relying solely on extended natural language reasoning within agents.
Developed a multimodal framework called SERAF for time series forecasting, inspired by Retrieval-Augmented Generation (RAG). This method improves upon existing techniques that rely only on time series similarity, which fails under non-stationarity. SERAF performs dual retrieval, querying both the time series data and its self-generated textual descriptions. By retrieving two complementary sets of historical patterns and futures, it jointly guides future predictions. Testing across seven real-world datasets confirmed SERAF's effectiveness in integrating numerical and semantic time series views against state-of-the-art baselines.
Researchers propose a geometric framework to identify "AI engrams," aiming to pinpoint identifiable memory traces within deep neural networks. This method formalizes core neuroscientific criteria—specificity, reactivation, sufficiency, and necessity—into a constrained inverse problem. A derived closed-form estimator allows for the isolation of individual memories from entangled parameters. Functionally, AI engrams enable precise manipulation of learned knowledge, allowing specific memories to be composed or erased using linear arithmetic. Validation across MLPs and LLMs demonstrates that this biologically-inspired approach is both causally valid and highly scalable.
Develops Metric Match, a new method to estimate LLM judge reliability using limited human annotations. This technique selects optimal sample subsets whose population reliability metric matches the acquired synthetic labels. Empirically, it achieved an 0.838 win-rate against random selection across multiple metrics and datasets. The approach significantly reduces annotation needs by 32.5% while decreasing average estimation error by 18.7%. Additionally, Metric Match outperforms random methods in classifying whether a judge meets deployment quality thresholds.
OSGuard is introduced as a new benchmark suite to rigorously evaluate the safety of computer-use agents beyond simple task completion success. It utilizes a dual-granularity approach, including an action-level benchmark judging proposed actions as allowed, unrelated, or unsafe relative to instructions and state. Additionally, it features a risk-augmented execution suite with modified tasks designed to introduce latent hazards like destructive overwrites. This suite pairs original task goals with explicit state-based safety invariants for thorough evaluation. Experimental findings show current guardrails are proficient at isolated action judgments but struggle to guarantee reliable end-to-end safety within complex, hazard-introducing environments.
Predicting time-to-event (TTE) from varied clinical data like CT imaging and EHRs is difficult due to modality imbalances. The presented framework uses domain-specific foundation models to encode these modalities separately before aligning them in a shared latent space. Four fusion strategies—late, contrastive, cross-, and co-attention—are employed for this alignment. Evaluation on PE mortality and CVD outcomes across large multi-institutional cohorts showed that fusion significantly improved concordance indices compared to unimodal methods. Specifically, contrastive multimodal fusion demonstrated the most reliable gains, highlighting task-aware alignment as crucial for robust clinical generalization.
QPILOTS addresses the difficulty of optimizing expressive flow-matching and diffusion policies using temporal-difference RL by stabilizing gradient extraction during multi-step denoising. Instead of unstable backpropagation through noisy intermediate actions, QPILOTS projects these states to an estimate of the final clean action before computing the critic gradient. The method maintains the original policy structure while steering the denoising process at inference time. Two variants, QPILOTS-U and QPILOTS-M, use different techniques for this projection, including single-point approximation and differentiable posterior sampling. Evaluation shows that QPILOTS achieves state-of-the-art performance on offline-to-online benchmarks and can effectively guide large, frozen VLA foundation models.
Adversarial Training (AT) typically trains models within a fixed parameter space; this work questions if the sequence of parameter optimization impacts robustness. Proposing Guided Parameter-Space Evolution (GRAPE), the framework combines stabilization in current spaces with progressive hidden expansion. GRAPE uses an adversarial spectral utilization score to direct newly released capacity toward critical modules, treating robust learning as a process of evolution. On CIFAR-10 with $\ell_\infty$, GRAPE improved PGD-20 robust accuracy from 51.70% to 56.94%. Significantly, it achieved this improvement while reducing parameters by 21.4% and maintaining near-matched computation compared to fixed-structure AT.
Insurance pricing faces a conflict between actuarial fairness, which demands individualized premiums based on risk, and solidarity fairness, which promotes pooling for social support. To manage this tension while ensuring insurer solvency, the authors propose an $\alpha$-Faire Individual Solvent Premium ($\alpha$-FISP) framework. This method formulates pricing as a constrained optimization problem balancing actuarially fair premiums against defined budget limits on cross-subsidization across risk classes. The resulting solutions are parameterized by $\alpha$, creating a continuum between purely individual and purely solidarity-based pricing models. Theoretical guarantees and numerical experiments confirm that the $\alpha$-FISP framework is tractable and suitable for diverse regulatory environments.
GRASP (Gradient-Aligned Sequential Parameter Transfer) addresses the scalability bottleneck in multi-source transfer learning that demands excessive memory or complex deployment strategies. It achieves constant memory ($O(1)$) by sequentially merging one source model into a target model at a time. The method employs parameter-wise gradient alignment to selectively transfer only beneficial optimization directions, preventing negative knowledge transfer. Furthermore, iterative fine-tuning adapts the transferred knowledge before introducing subsequent sources. Evaluations across various benchmarks show GRASP achieves 93.5% mean accuracy while maintaining constant memory usage regardless of the number of sources.
Modern recommendation systems use dynamic routing to send queries to multiple embedding models. The paper formalizes this problem as an adversarial contextual linear bandit with low-rank experts. Researchers establish that standard regret notions are insufficient and identify a practical log-quadratic policy class for capturing query-dependent model routing. A novel algorithm, Hypentropy Policy Gradient (HPG), is proposed to adapt to the unknown low-rank structure under uncertainty. HPG achieves $\tilde{\mathcal O}(s\sqrt{M T})$ linearized policy regret, avoiding issues of high dimensionality.
Introduces the Separable Neural Architecture (SNA), which combines neural approximation with tensor decomposition by separating localized coordinate functions from global interactions. Viewing this in the variational framework (VSNA) provides classical guarantees like well-posedness and stability for solving PDEs. The VSNA addresses the curse of dimensionality in high-dimensional problems by achieving algebraic scaling, further optimized using a factorized Alternating Least Squares (ALS) approach. Validation across elliptic, hyperbolic, and parabolic systems confirms its predicted scaling rates. Practically, it functions as a rapid "solve once, query anywhere" physical world model, enabling real-time simulations that achieve significant speedups over traditional finite element methods.
Autoresearch agents perform autonomous experimentation by iteratively letting an LLM modify code to optimize metrics. The conventional stateless design incurs high computational costs, requiring the full experimental history to be re-read at every step ($\text{O}(n)$ cost per iteration). This work proposes reframing this as a stateful ReAct agent using LangGraph, which maintains persistent state through a tool-calling interface. Evaluation on hyperparameter tuning showed the stateful agent used 90% fewer tokens, and it achieved comparable results in code optimization while consuming 52% fewer tokens. The token savings stem from shifting the computational cost from $\text{O}(n)$ re-reading to an efficient $\text{O}(1)$ fixed-size window operation.
Graph Neural Networks (GNNs) are valuable for modeling agent interactions in autonomous driving trajectory prediction, but layer design lacks standardization. This comparative study evaluated 19 distinct graph layer types to determine optimal architectures for capturing spatiotemporal dynamics. Results indicate that ARMA, Chebyshev, and topology-aware layers generally outperform others across tested settings. Furthermore, the research established practical guidelines suggesting sum-based aggregation is superior to mean-based methods. Finally, weighting different hop distances in the model significantly enhances overall prediction accuracy.
Investigates using machine learning on physiological stress indicators (like EDA, heart rate, and skin temperature) to predict exam outcomes. Various models were tested, including logistic regression, random forest, SVMs, transformers, LSTM, and GRU. Results indicate deep learning excels at capturing complexity, but simpler methods like random forests can sometimes perform better while maintaining efficiency. Transformers proved versatile, matching the performance of LSTM and GRU models. The research highlights balancing model complexity with empirical performance to understand physiological links to academic success.
Developed CILN, a new framework that generates synthetic instance-dependent label noise (IDN) using controlled input corruptions rather than relying on imperfect annotators. This method allows explicit and controllable definition of both the source and severity of ambiguity in resulting benchmark datasets. Using CIFAR10, MNIST, and Adult, the authors constructed 90 unique benchmark settings across various corruption families. Experiments showed these benchmarks possess genuine instance-dependent noise and confusion structures closer to human uncertainty than existing methods on CIFAR-10. Findings indicate that corruption-mediated IDN can expose failure modes in noisy-label learning methods not visible under comparable rater-fallibility noise levels, highlighting the importance of noise structure.
Introduces Audited Conformal Prediction (ACP) to quantify uncertainty in pretrained classification models facing unknown distribution shifts. ACP utilizes a small labeled target dataset to train an auxiliary audit model identifying inputs where the primary model is prone to failure. Integrating this audit model into conformal prediction yields prediction sets ensuring marginal coverage while significantly boosting practical conditional coverage over existing methods. Two complementary integration strategies are developed, offering either improved conditional performance for guaranteed marginal coverage or explicit group-conditional coverage guarantees. Experiments confirm the method's efficacy across synthetic and real-world datasets, illustrating tunable trade-offs between set size and coverage assurance.
Offline model-based optimization generates candidates that can be out-of-distribution, leading to unreliable surrogate rankings when most needed. Proposed is Conformal Candidate Certification (CCC), a post-hoc wrapper providing a calibrated one-sided lower bound for each candidate. CCC advances only those designs whose calculated bound surpasses a predefined target threshold. Furthermore, entropy-regularized surrogate maximization enables the use of importance weights for weighted conformal prediction using the existing surrogate. In testing, CCC certified $16.7\%$ of candidates with superior empirical coverage compared to standard methods that failed under covariate shift.
Scientific discovery's large-scale hypothesis testing faces challenges due to resource-intensive null reference data and inherent structure in the hypothesis space. A new framework addresses these by controlling the false discovery rate even when evidence is based on limited null draws, resulting in uncertain p-values. The method incorporates arbitrary structural information via a suitable reproducing kernel. Two decision rules are introduced: one guaranteeing exact FDR control and another maximizing power using mirror-statistic control adapted for count space. This framework also provides an analytical basis to guide efficient allocation of null distribution samples considering finite-data uncertainties.
Presents a novel unconstrained coordinate system to parameterize a symmetric positive definite (SPD) matrix $\Theta$ using a reverse telescoping map $\Theta(x)=\rm{RT}(x)$. This representation uses $x=(v,d,r)$ for the log volume/determinant, relative diagonal scales, and partial covariances. A key advantage is that crucial computations involving $\Theta$ and its inverse can be performed in $O(p^2)$ time within the transformed domain. Furthermore, it enables designing a split flow model for generative tasks by separating the volume flow from the shape flow along the unit-determinant path. Applications include modeling SPD matrices in generative scenarios up to $p=200$ and analyzing brain connectivity networks.
Introduces Structured Nonparametric Variational Inference (SN-VI) to model complex dependencies among latent variables in posterior approximation. This novel framework uses multivariate spline techniques, overcoming limitations of traditional mean-field assumptions by accurately capturing arbitrary posterior shapes. Authors provide rigorous theoretical guarantees, including deriving the lower bound and proving asymptotic consistency for the variational objective. A practical algorithm automatically identifies dependent latent variables without manual structure specification. Validations show SN-VI improves generative model performance on high-dimensional data like computer vision datasets and spatial transcriptomics.
Ricci-Filtration proposes a geometry-based enhancement for Retrieval-Augmented Generation (RAG) rerankers using discrete Ricci flow. The process models the query and initial retrieved chunks as a weighted graph, where edge weights are determined by embeddings. It applies discrete Ricci flow to evaluate each chunk's structural importance relative to the user query by shrinking high positive curvature edges. This method first filters out irrelevant chunks based on their geometric curvature before a secondary reranker refines selection. Experiments confirm that Ricci-Filtration improves accuracy, precision, recall, and F1 scores compared to baseline reranking techniques.
Investigate graph alignment using correlated Gaussian Orthogonal Ensemble (GOE) matrices to find a hidden vertex permutation. Since the optimal maximum likelihood estimator is computationally intractable due to its reduction to a quadratic assignment problem, convex relaxations are analyzed instead. The research shows that for small correlations ($\sigma = o(n^{-1/2}/\log^4 n)$), solutions from these relaxations accurately approximate the true permutation matrix ($\Pi^\star$). Furthermore, the analysis precisely characterizes a phase transition, detailing when the approximation error transitions from $o(n)$ to $\Omega(n)$ based on the correlation parameter's decay rate. These findings substantially improve prior results by extending them beyond the constraint of doubly stochastic relaxations.
Investigates the discrepancy between detecting a mixture structure and successfully recovering underlying labels within binomial logistic mixtures. Standard criteria like BIC can identify multiple components even when those labels are not actually recoverable due to distinct asymptotic behaviors of observed-data evidence versus per-observation information. This creates a scenario where two components are detected, but posterior labels remain uninformative. To resolve this, the authors propose two feasibility-aware inference methods: a recoverability-aware BIC incorporating a penalty and an entropy-regularized estimator. These new procedures successfully avoid misleading component selections and improve label probability calibration in simulations.
Gaussian random tensor train vectors offer a structured alternative for stochastic trace estimation, addressing the computational costs of unstructured test vectors in tensor settings. The research demonstrates that using these vectors can recover dimension-independent guarantees for the Girard--Hutchinson estimator with a suitable rank $r$. Specifically, a median-of-means approach requires only a tensor train rank $r \geq d-1$ to match the classical estimator's dependency on accuracy and failure probability. Furthermore, an oblivious subspace injection result shows that for sketching a $k$-dimensional target subspace, $r\geq d-1$ suffices with $\mathcal{O}(\varepsilon^{-2}(k+\log(1/\delta)))$ samples. Finally, applying these sketches within the Nyström++ framework achieves an optimal sample complexity of $\mathcal{O}(\varepsilon^{-1})$ under a spectral-tail condition.
Conformal prediction typically requires data to be exchangeable, which is often not true for time-indexed datasets exhibiting structure like seasonality or changing regimes. This paper introduces spectral adaptive conformal prediction to leverage existing data structures. The method constructs weighted conformal quantiles by utilizing local spectral similarity and dynamically adjusts the target miscoverage level online. Spectral weights prioritize calibration residuals relevant to the current test point, while adaptation corrects for long-run miss rates when uncertainty varies over time. Simulations using complex datasets demonstrate that this hybrid approach improves upon fixed spectral weighting, necessitating effective sample size monitoring.