Use an AI tool to refine raw "hooks" into polished marketing copy. Simply input your initial hook and existing post content for assistance. The tool generates several draft caption options, allowing users to choose the best fit. Users can then edit these drafts, tighten the wording, or add a clear call-to-action step. This feature aims to make the process of writing ready-to-post social media captions more efficient.
The US government permitted Anthropic to restore access to its powerful cybersecurity AI model, Mythos 5, for about 100 trusted partners and federal agencies. Commerce Secretary Howard Lutnick confirmed this permission after Anthropic worked with the government to address associated risks. However, the publicly available Fable 5 model remains blocked while ongoing discussions continue. The US government retains the right to adjust the scope of access requirements for these models at its discretion. This partial resolution establishes the government as a gatekeeper for advanced AI capabilities.
Trump is reported to have delayed the release schedule for OpenAI's GPT-5.6. The content is presented within a newsletter titled "The AI Report," dated June 26, 2026. The report aims to provide business leaders with information on artificial intelligence, noting that 77% of US small businesses currently use AI regularly. Beyond this announcement, the newsletter offers other resources like presentation tools and team AI training. Subscribers are encouraged to learn about implementing AI transformation for their companies through the platform.
An OpenAI report details potential job transformations across the European Union due to artificial intelligence. The analysis categorizes various occupations based on their susceptibility to automation. It also identifies sectors expected to experience significant workforce growth in the coming years. Furthermore, the report points out areas where current workflows are likely to undergo substantial changes because of AI integration. This mapping provides a comprehensive overview for understanding Europe's evolving AI labor market landscape.
HP Inc. is expanding its collaboration with OpenAI's Frontier model. This strategic partnership aims to integrate artificial intelligence across various business functions. Specifically, the technology will be deployed within customer experience platforms. Furthermore, it will enhance software development workflows. The AI integration will also optimize general enterprise operations for clients.
Palantir is deploying a new intelligent engine designed for U.S. government agencies. This system leverages NVIDIA Nemotron open models, emphasizing the role of open source in American AI innovation. The platform is specifically tailored to meet the security and operational needs of federal agencies. Historically, open source has been central to U.S. technological advancements, dating back to early initiatives like DARPA's network connections starting in 1969.
Having previously secured a landmark $333 million settlement in 1993 over contaminated groundwater in Hinkley, California. Erin Brockovich is now directing her advocacy towards concerns regarding rapidly expanding AI datacentres globally. She notes that tech companies require these facilities to power their technology infrastructure. When she called out for public input about local datacentre concerns on her website, 3,862 people responded within a month. Brockovich views the current buildup of these energy-intensive sites as an alarming situation comparable to her past fight.
Tech and AI stocks now constitute as much as 12% of most Australian balanced superannuation funds. These technology companies are becoming a significant and often unseen component of retirement savings for Australians. Major tech players, including Nvidia, Alphabet, Apple, Microsoft, Amazon, Meta, and Tesla, are frequently included. The "magnificent seven" group is increasingly visible within the portfolio offerings of superannuation funds. This trend reflects the growing influence of AI and technology stocks in global markets.
OpenAI has unveiled its most capable AI model, suggesting it won't be accessible to all users immediately. The announcement also highlighted advancements in the use of AI for film creation. Specifically mentioned is a capability involving Claude that powers movie production. This suggests a new wave of creative tools powered by advanced large language models. Access and deployment appear to be phased or selective rather than universal at this time.
Jon Udell critiques the "human in the loop" concept, arguing it wrongly implies yielding authority to machines. Instead, he advocates for framing human expertise as central, incorporating AI agents into existing workflows rather than being dictated by them. He proposes an approach of "agentic software development." This model invites agents into a process rather than treating humans as merely reviewers of machine outputs. Udell suggests shifting the narrative so that human control remains paramount while leveraging AI assistance.
Research on robotics increasingly highlights time spent outside of policy training. Practical application requires significant engineering effort in several areas. These non-training tasks include sim-to-real transfer and rigorous policy evaluation. Engineers also spend time debugging hardware failures, building simulation environments, and comprehensive testing. A Cornell Master's project is gathering data from industry experts on these practical challenges for RL systems.
Z.ai's flagship model, GLM-5.2, is designed for complex, long-horizon tasks with a usable 1 million token context window. This large context allows it to reliably manage project-level engineering workflows from initial requirements through multi-platform deployment in one task. Key advancements include its stable 1M context capacity and enhanced coding capabilities offering flexible thinking effort levels (High and Max). The model was specifically trained for coding-agent scenarios, enabling high reliability across long, complex debugging trajectories. On technical benchmarks like FrontierSWE, GLM-5.2 demonstrated leading performance against established models, while also outperforming others on PostTrainBench.
Ollama models can now be utilized within various development environments, including agents, editors, and direct terminals. Users can view available integrations by running `ollama launch` in the terminal. Coding support is offered through tools like Claude Code for advanced terminal coding assistance and OpenCode, an agent that edits and iterates on code. Personal assistants such as OpenClaw and Hermes Agent offer memory and multi-platform task capabilities. Furthermore, developers can integrate Ollama models directly into their workflows within editors like VS Code's Copilot Chat.
EverMind released EverOS, an open-source, local-first memory runtime for AI agents. It stores agent memory using plain Markdown files indexed by SQLite and LanceDB. The architecture features a hybrid retrieval system combining BM25 with vector search capabilities. Additionally, it supports multimodal ingestion and self-evolving Skills. EverOS is distributed under the Apache 2.0 license.
Details a self-contained Python pipeline for using OCRmyPDF to convert image-only PDFs into searchable, PDF/A formats. The process includes generating test files, extracting sidecar text, and validating extraction results by measuring word-recall. Techniques covered involve tuning Tesseract, cleaning noisy scans, correcting document orientation, and performing in-memory OCR. Furthermore, the tutorial demonstrates batch processing capabilities across entire folders of documents.
Zyphra, Cohere, and Poolside are notably expanding the scope of the current AI ecosystem. These organizations are contributing to a broader availability of advanced computational resources. The discussion focuses on analyzing this open ecosystem growth. Furthermore, it explores the underlying motivations driving these model releases. Overall, the piece provides an assessment of collaborative efforts within the field.
Small language models are gaining popularity due to their efficiency and ability to run on less powerful hardware. Choosing between small and frontier models depends heavily on the specific application's needs and resource constraints. Smaller models offer lower computational requirements, making them suitable for edge devices or restricted environments. Frontier models, while significantly more capable, demand substantial computational resources. Therefore, a trade-off analysis considering performance needs versus deployment feasibility is necessary for optimal selection.
Achieving high-quality, reliable answers from customer APIs requires more than just accuracy; usability and timely delivery are paramount concerns. Consistent delivery is fundamentally an issue of managing variance rather than merely maximizing speed. Solving these reliability problems often necessitates counterintuitive engineering approaches. The concepts discussed focus on engineering robust agentic workflows.
A comparison pitted XGBoost against Logistic Regression across 358 matches. The research concluded that the simpler Logistic Regression model performed better in terms of cross-validated fit. This outcome serves as a practical lesson regarding model selection based on bias and variance trade-offs. It suggests that an overly complex model, like XGBoost, was not superior in this specific predictive task. Ultimately, sticking with a less complex "boring" model yielded the best results for accurate prediction.
A number guessing reinforcement learning (RL) model was developed with the assistance of Claude. The creator shared the project, titled "Number Guessing RL Model," available at PantherHale/number-guessing-rl. The reported success rate for this implemented model reached 40%. The post encourages community support and engagement on the linked GitHub repository. Keywords associated with the work include #rlmodel, #numberguessing, #MachineLearning, and #Claude.
A newcomer to Reinforcement Learning is seeking clarification on a specific step within the TDC derivation shown in an image. Specifically, they need to understand how to mathematically derive the boxed expression from the MSPBE expression and what identities are involved. Furthermore, the user questions the methodological choice of deriving TDC using the MSPBE instead of the more familiar MSBE. Finally, an intuitive or mathematical explanation is requested regarding why minimizing the MSPBE is preferred over minimizing the MSBE in this context.
The MIT Technology Review mobile app provides users with cutting-edge insights on emerging technologies like AI, biotech, and climate change. Subscribers gain unlimited access to award-winning journalism and in-depth analysis anytime, anywhere. Features include daily tech stories from a global team, curated newsletters, and push notification alerts for major breakthroughs. Users can archive favorite articles and search through extensive technology coverage. Full access requires a monthly or annual subscription for the most comprehensive experience.
Prompt injection is the most critical and widely demonstrated attack vector against large language models, exploiting their difficulty in separating instructions from data. Threat actors are increasing malicious attacks using these injections to steal credentials by manipulating connected systems. These vulnerabilities now target advanced architectures, including multi-agent setups, RAG pipelines, and model routers. Exploits have proven practical, enabling unauthorized data exfiltration and triggering actions via single crafted inputs. Organizations must counter this risk by strictly constraining model permissions and treating all external data as potentially hostile.
Technology like ChatGPT offers immediate answers, which can diminish valuable periods of personal contemplation. The column explores spirituality as a means of navigating daily life's complexities. For one author with a faith background, prayer is a structured practice connecting individuals to God. This practice involves petitioning for needs, seeking guidance, and asking for forgiveness. Ultimately, the tradition suggests that bringing anxieties and questions to the divine results in either answers or enduring peace.
Hack Your Summer is a four-week, intensive production sprint designed for undergraduate, graduate, and recent graduates. Participants learn to develop real projects from conception through completion with support from mentors and peers. The initiative offers students tangible, public-facing work suitable for showcasing to future employers. It serves as an alternative pathway for students facing limited traditional internship opportunities. A free second cohort begins on July 13th, with applications due by July 8th.
The value of computation is increasingly enhanced by networked data sharing facilitated by the Internet and cloud technologies. Large Language Models (LMs), while advancing rapidly, face adoption barriers due to high training costs and deployment difficulties. This trend necessitates a move toward smaller, private, and specialized models. Addressing the growing need for effective interaction among these diverse models requires interconnection pathways. The paper proposes AI-ModelNet, a novel network paradigm designed to enable capability sharing and collaborative reasoning across multiple LMs.
Personality prompting influences how Large Language Models communicate, but its impact on actual task results is not well understood. Researchers tested this by manipulating traits across frontier LLMs in three areas: coding, research collaboration, and bargaining. Findings show that personality's effect critically depends on the task structure. Low agreeableness caused communication shifts in coding tasks with negligible effect on milestones. However, using low agreeableness substantially degraded performance during open-ended collaboration and competitive bargaining scenarios.
Large language model (LLM) agents struggle with long-horizon planning because they lack an internal world model for "what-if" reasoning. To address this, the authors propose training a single autoregressive model to generate both prospective state rollouts and a plan-conditioned success estimate. They identify that simple fine-tuning on look-ahead data results in superficial mimicry rather than genuine predictive grounding. A novel three-stage process—WM-AMT, FE-SFT, and FC-RL—is introduced to bridge this capability-format gap. Evaluations show this structured training approach significantly outperforms existing baselines on reasoning tasks by achieving grounded foresight.
ODYSSEY introduces a categorical framework for building verifiable, local truth-preserving foundation models through compositions of "foundries." A foundry is an organized sheaf of knowledge incorporating an argumentation component and can be constructed from generic types like evidence/argument or operational decisions. Universal Foundry Learning (UFL) formalizes this construction using left and right Kan extensions to ensure adherence to various local constraints. Foundries are queried via Foundry SQL (FSQL), which uses TICKET certification for integrating external models into the system's state. Testing demonstrates ODYSSEY’s ability to support diverse functions, including artifact replay and scrutinized claim extraction across varied data sources.
Proposes DysLexLens, a low-resource LLM framework to analyze dyslexic learners' experiences with AI using online forums. The end-to-end architecture transforms noisy social media posts into a dictionary-driven corpus for analysis. It integrates LLM semantic analysis with knowledge-graph reasoning to uncover patterns and generate verifiable responses. Key features include targeted data filtering, quantitative evaluation metrics (RAGAS), and structured guidelines to assess hallucination. Testing on dyslexia-related Reddit data demonstrates its potential generalizability across other low-resource forum contexts.
Direct reasoning does not guarantee better multimodal emotion recognition (MER) accuracy, as fast thinking often outperforms slow, deliberative reasoning for prediction. Empirical analysis suggests fast thinking boosts recall with broader predictions, while slow thinking improves precision via cautious filtering. To leverage this complementarity, the authors propose MER-R1, a reinforcement learning framework. This model uses dual-objective disentanglement to jointly optimize both recall and precision signals independently. Consequently, MER-R1 unifies fast intuition's broadness with slow selection's accuracy, achieving state-of-the-art performance on standard benchmarks.
Faces mounting threats from AI-generated misinformation poisoning retrieval systems, this paper introduces Tree of Evidence (ToE) for automated fact-checking. ToE models claims as dynamically expanding argument trees within a hierarchical evidence reasoning framework. The system integrates a reinforcement learning agent for multi-source retrieval, an evaluation agent, and an aggregation algorithm to build explainable evidence chains. A theoretical analysis proves the retrieval policy converges near optimal performance with derived error bounds. Experiments confirm ToE significantly outperforms baselines across various datasets, especially when handling adversarially poisoned inputs.
Large language models struggle with reliable planning due to inherent complexity in long-horizon decision-making tasks, raising security concerns. To improve robustness, a symbolic feedback-driven iterative self-refinement framework is proposed. This method uses natural language prompting to map logical symbols into detailed descriptions, helping LLMs understand task semantics and constraints. A core component is a symbolic verifier that identifies errors and generates corrective instructions for the LLM. Furthermore, a plan recognizer infers goal reachability, leading to consistent improvements in both feasibility and correctness during planning tasks.
World models frequently plan by simulating learned dynamics; however, graph structures introduce complexity because errors can propagate through edges in addition to nodes. This paper proposes a unified framework for Graph World Models (GWMs) handling both fixed and dynamic edges across node, edge, and graph levels. Researchers developed graph-valued rollout bounds distinguishing between topology-induced and model-induced error amplification. They introduced Error-Aware GWM, which combines spectral regularization and consistency checks to mitigate long-horizon divergence. Evaluation showed that while error grows with horizon, the proposed method maintains accuracy, highlighting GWMs' utility for dynamic agent planning over static prediction.
World models for language agents can be agent-based (flexible but prone to state hallucination) or parameterized transition predictors (measurable errors but weaker as planners). A proposed method, Grounded Iterative Language Planning (GILP), combines these approaches by using a small parameterized backbone with API-based LLM reasoning. The backbone provides valid actions and predicted deltas, which an LLM drafts, and a consistency gate triggers revisions upon disagreement. Testing on real GPT-4o-mini calls showed GILP significantly reduced the hallucinated-state rate from 0.176 to 0.035. Furthermore, in simulated ablations, GILP improved success rates from 0.668 to 0.838 while incurring minimal overhead.
Queue overflow, which causes cascading gridlocks in urban traffic, is often inadequately addressed by standard signal control algorithms. OverFlowLight introduces a real-time framework to preemptively resolve this issue using multi-modal sensing from cameras and radars for accurate detection. Upon detecting an overflow condition, the system dynamically inserts dedicated phases into the signal cycle to clear blocking queues. This intervention mechanism blends rapid rule-based control with reinforcement learning for sustained efficiency. Real-world testing at 43 intersections demonstrated that OverFlowLight reduced overflow incidents by 60.4% and increased network throughput by 18.2%.
Develops a novel RANSAC scoring method that eliminates dependency on a user-supplied inlier scale parameter estimated from contaminated data. This is achieved by analytically marginalizing the inlier scale in closed form using a conjugate Inverse-Gamma prior for fixed inlier partitions, followed by optimizing over these partitions. The resulting single formula remains valid across varying conditions, including non-informative and empirical-Bayes priors. Computationally efficient at $O(N \log N)$ via sort-and-sweep. Benchmarking against 70,000 image pairs shows this new score outperforms existing methods like RANSAC and MSAC, especially under threshold miscalibration or limited validation data.
Darts, an established open-source Python library for time series analysis since 2020, addresses the fragmentation of newly available foundation models in zero-shot forecasting. Developers have created a collection of standardized $\texttt{FoundationModel}$ classes encompassing architectures like Chronos-2 and TimesFM 2.5. This new feature provides unified, full-cycle forecasting interfaces for integrating these general-purpose forecasters into the Darts ecosystem. Users can now seamlessly integrate foundation models into existing pipelines with minimal changes or build entirely new workflows for various tasks. The framework supports zero-shot forecasting, fine-tuning, uncertainty estimation, and backtesting within a single, unified toolset.
Foundation models excel at predicting biomolecular structures but lack transparency regarding their internal mechanisms. Standard sparse autoencoders fail when applied directly to pair-wise representations due to feature explosion and complex concept encoding. PairSAE addresses this by summarizing pairwise tensors using N-mode SVD into token-wise interaction roles. It then employs a sparse autoencoder to learn shared token features that decode into both sequence and pair representations. Evaluation on PLINDER complexes shows PairSAE yields interpretable features aligning with UniProt annotations and predicting affinity, thereby clarifying the model's learned structure.
Investigates regret minimization in Markovian bandit settings characterized by unobservable states and potentially constrained decision epochs. The research focuses on a "pure" regret benchmark against optimal, unchanging policies, introducing "self-degrading Markovian bandits." Shows that standard switching algorithms do not necessarily exhibit super-logarithmic regret without prior knowledge. Proposes UCB-NOM, an optimistic algorithm whose regret is nearly logarithmic, and achieves $O(\log(T))$ regret with specific prior bounds on bias functions. Notably, the derived regret bounds are independent of the number of states in the underlying Markov chains.
Traditional multi-head attention uniformly divides the hidden dimension across all heads, which limits early layers' ability to capture complex context. The Prism Transformer addresses this by implementing a progressive head schedule instead of static division. This method monotonically increases the number of heads layer by layer, creating a local-to-global representational hierarchy. Early layers use fewer, wide heads for local patterns, while deep layers use many, narrow heads for feature decomposition. Remarkably, this architectural change adds no overhead and consistently outperforms standard Transformers across various zero-shot benchmarks.
Investigates the cubic nonlinear Schrödinger (NLS) equation on 2D flat tori with variable aspect ratios, noting that these geometries dictate distinct high-frequency cascade behaviors based on whether the ratio is rational or irrational. Develops a geometry-conditioned Fourier Neural Operator (FNO) to approximate the one-step solution operator, using both the solution's real/imaginary parts and the aspect-ratio parameter as input. Experiments demonstrate the learned operator accurately captures dynamics across different tori types. Specifically, it reproduces observed Sobolev norm behaviors, showing stronger $H^2$-growth on the rational torus versus constrained behavior on the irrational one. Including the aspect-ratio parameter significantly enhances long-time predictive accuracy for these nonlinear dispersive PDEs.
Activation patching estimates the natural indirect effect (NIE) of model components for mechanistic interpretability, but this estimate includes interaction effects (INT). These INT reveal how a component's causal role depends on the state of other parts of the model. Efforts to eliminate these interactions by modifying estimators or analysis units are prone to failure modes. Analyzing GPT-2 showed that INT can cause components' importance to be either missed or falsely magnified, explaining instability in faithfulness scores. Researchers argue that INT should not be ignored but rather used diagnostically to understand context-dependent causal conclusions requiring combinatorial search.
Accurate prediction of bottom-hole pressure and CO2 plume migration in geological carbon storage is hampered by artificial boundaries used in truncated simulations. Comparing ten boundary treatments showed that conserving corner pore volume is the most critical aspect for accurate modeling. Simple uniform treatments resulted in significant pressure errors and misrepresented plume areas (16-20% deviation). Corner-adjusted methods substantially improved results, raising plume Intersection over Union (IoU) above 0.94. Overall, a gradual modifier incorporating transmissibility correction provided the best, most consistent performance across both homogeneous and heterogeneous reservoirs.
Presents Privacy-Preserving Probabilistic Race/Ethnicity Estimation (PPRE) to enable race/ethnicity-based fairness measurements while respecting privacy constraints. PPRE utilizes advanced technologies including secure two-party computation, differential privacy, and additive homomorphic encryption. It integrates data from two sources: the Bayesian Improved Surname Geocoding estimator and a sparse golden survey set of self-reported demographics. The methodology provides robust privacy guarantees for analyzing US-based race/ethnicity demographics. Finally, it offers a transferable framework for institutions to implement similar private measurement infrastructure.
Methods are presented for inferring directed network topologies from nodal measurements resulting from linear diffusion dynamics. These observations are modeled as outputs derived from a graph convolutional filter applied to local graph-shift operators. A key challenge addressed is the non-simultaneous diagonalizability of the graph-shift operator and the observation covariance matrix, unlike prior work. The authors solve this system identification problem by using output measurements alongside input statistical priors, reformulating it as a constrained quadratic minimization. Finally, a joint algorithm alternates filter and topology estimation steps to improve recovery in digraphs, validated through synthetic and real-world data tests.
The global minimum-variance portfolio relies on estimated covariance matrices, but current evaluations using matrix-norm loss do not reflect the actual source of suboptimality. This work characterizes how covariance-estimation error translates into GMVP regret by proving an exact regret identity. It establishes that decision regret depends only on the estimation error's effect on the portfolio weights, scaled by concentration and true covariance conditioning. This leads to a "decision geometry" showing that GMVP regret is invariant to specific projections of the error matrix. The framework successfully applies to heavy-tailed returns with tail indices between 2 and 4, providing new consistency theory for decision-focused learning approaches.
Develops an Adversarial Contamination-resistant Iterative Hard Thresholding (AC-IHT) algorithm for high-dimensional regression contaminated by adversarial corruption or errors. This nonconvex method achieves minimax near-optimal estimation by iteratively refining coefficient and contamination vectors using varying threshold scales. The proposed estimator demonstrates signal adaptivity, leading to sharper estimation rates and improved support recovery under suitable conditions. Furthermore, it establishes a theoretical basis for asymptotic inference via a strong oracle property. Numerical tests confirm that AC-IHT exhibits superior performance in finite samples.
Score-based generative models require estimating the score function ($\nabla_x\log p_t(x)$) of a diffusion process. For nonlinear drifts lacking closed-form marginal densities, traditional global estimation methods suffer increased error in low-density regions. This work introduces a local Fokker--Planck geometric framework that substitutes global conditioning with local parabolic averaging. It achieves this by transforming the equation into an inhomogeneous heat equation and extending Evans' method to derive exact local mean-value representations for the score and density. The approach also provides a factorized sampler ($\kappa$-measure) for high-dimensional integration, confirming its generalization from existing global methods.
Binary classification from positive-only samples represents a distinct variant of PAC learning where evaluation occurs on both positive and negative regions despite only receiving positive samples. The characterization of "proper positive-only learning" remained an open problem until this research settled it. A concept class is properly learnable if and only if it has finite VC dimension and satisfies a novel combinatorial condition called uniform exterior separability. This characterization highlights significant differences from standard PAC learning, showing proper and improper learning are separate concepts. Furthermore, the findings demonstrate that simply having a finite VC dimension is not sufficient for non-uniform or even deterministic positive-only learning.
Evaluating machine learning models across extensive benchmarks can be costly, necessitating the use of small, representative dataset subsets. This work introduces a framework to rigorously evaluate how different selection strategies preserve global model rankings when subsampling is used. The framework compares methods like bootstrap aggregation, clustering, and optimization criteria against random sampling. Empirical results show that for time series classification, specific techniques like greedy farthest-first improve rank preservation significantly using only five datasets. However, the effectiveness of these selection approaches varies, showing smaller or insignificant gains in domains like recommender systems.
Last-iterate convergence for first-order convex learning depends on maintaining the monotonicity of update operators. Non-affine gradient aggregation, common in privacy or robustness constraints, often violates this necessary monotonic property. Researchers proved that preserving gradient monotonicity requires the aggregation rule to be positively affine. Consequently, non-affine methods cause convergence failure and significantly degrade algorithmic stability. The work proposes sufficient conditions to theoretically restore monotonicity in modern learning systems.
Investigates the problem of identifying latent dynamics in continuous-time latent Stochastic Differential Equation (SDE) models, an area where prior results are lacking compared to discrete-time models. The work addresses this by utilizing environment-induced shifts within the diffusion covariance structure of additive-noise latent SDEs observed through unknown nonlinear transformations. Demonstrates that two diagonal diffusion regimes with distinct coordinate-wise variance ratios can identify latent coordinates (up to permutation and scaling) even without sparsity assumptions on the drift term. Proofs are established first for linear Ornstein-Uhlenbeck systems, subsequently generalized to arbitrary additive-noise latent SDEs. Proposes a two-stage estimator capable of achieving latent disentanglement and optional graph recovery, validated with synthetic data and Hardanger Bridge monitoring measurements.
Studies how model performance generalizes jointly based on both model size (parameters) and available data samples. This work specifically analyzes a feature-learning model using $\ell_2$-regularized empirical test error minimization in a quadratic two-layer network with structured data. The research provides an explicit characterization of generalization error dependent on sample count, model width, and regularization strength in a finite-sample setting. Findings reveal a phase diagram showcasing distinct scaling regimes as the number of trainable parameters changes. Specifically, the generalization error follows power laws that depend on the spectral structure of the underlying target data.
Introduces MDM-VGB, a novel discrete diffusion sampler designed for Masked Diffusion Models (MDMs). This sampler enhances unmasking generation by incorporating theoretically grounded reward-guided remasking techniques. Drawing inspiration from Jerrum-Sinclair backtracking Markov chains, it adapts the random walk to operate on masked-state graphs, allowing flexible token manipulation. MDM-VGB favors moves leading to higher-reward partial configurations, improving high-reward generation and sample repair efficiency. The method offers guaranteed quadratic complexity, significantly outperforming exponential test-time heuristics like best-of-$N$, with strong performance shown on benchmarks such as Sudoku and QM9.