AI is presented as a powerful tool that should be developed with safety as a fundamental engineering standard, similar to other powerful technologies. There is concern that AI companies are rushing development without adequate safety measures, as evidenced by recent breaches. A public campaign is called for to ensure that humans maintain control over life-shaping decisions and that AI companies are held accountable. This campaign aims to establish a future where technology serves people rather than replacing them. Over 300,000 people have already signed on to support this initiative.
AI development must prioritize safety, similar to other powerful technologies, as companies are racing to build systems without adequate safeguards. A public campaign is needed to ensure AI benefits humanity rather than replacing it. This campaign will call for lawmakers and executives to establish a future where humans remain in control and AI companies are held accountable. A petition seeking one million signatures is being organized for this purpose. Supporters advocate for AI that serves humanity by protecting dignity, creativity, and work.
The campaign advocates for a shared project regarding Artificial Intelligence, questioning whether AI should be pro-AI or pro-human AI. It argues that powerful technologies require safety measures, and AI companies should not bypass these standards in their rush to develop systems. The movement calls for a public campaign to pressure lawmakers and decision-makers to ensure human control over life-shaping decisions. Goals include protecting human creativity, dignity, work, and holding AI companies accountable. Over 300,000 people have already signed, aiming to secure a public mandate for these changes.
Bill Gates argues that society has passed danger thresholds concerning AI's bio-capabilities, cyber-capabilities, psychosocial aspects, job market destruction, and lack of control. He stresses the heightened risk of bioterrorism compared to a natural pandemic, urging increased public concern and discussion. Gates proposes solutions like robot taxes and human-reserved jobs to address economic disruption. He believes the transition will be tumultuous, requiring a decade for abundance, and advocates for increased AI expertise in government. Ultimately, he calls for monitoring and international cooperation to define safety boundaries for advanced AI.
Kent Walker highlighted the advancements in AI, noting new models are 300 times more efficient and are driving scientific breakthroughs. He emphasized that realizing AI's potential requires safeguarding creators' rights while allowing for innovation. Regarding intellectual property, the discussion focused on patents, copyright, and the need for clear rules on AI training data. Google supports mechanisms like SynthID to combat deceptive digital replicas and advocates for balanced frameworks for AI development. Ultimately, adapting existing legal systems is key to ensuring AI benefits everyone.
A partnership is underway with the State of Delaware to offer free AI and career training to residents. This initiative involves the Delaware Department of Labor and the Delaware Libraries providing no-cost access to Google Career Certificates and Google AI courses. The training includes AI courses for using AI tools and job-ready certificates in fields like cybersecurity and data analytics. These flexible, self-paced programs do not require prior experience or a college degree. Graduates of the Career Certificates often secure positive career outcomes quickly.
Google has introduced AI agents for financial and legal services. These new platforms demonstrate a shift towards a more vertical application of AI agents. However, the content suggests that this move is occurring while the vendor is still behind in this development. The presented platforms illustrate the current state of AI agent deployment in these specific sectors. Overall, the introduction shows both progress and remaining gaps in the field.
A French AI lab is extending its efforts to establish regional control over artificial intelligence from Europe into the Middle East. This initiative involves collaboration with a Saudi vendor. The goal is to advance sovereign AI capabilities within the region. This signals a strategic focus on developing independent AI systems in the Middle East. The collaboration aims to foster regional AI development.
Bill Gates advocates for "human reserved" jobs to prevent AI from replacing human roles, likening this to nature reserves. He expressed concern that governments are unprepared for the massive impact of artificial intelligence. Gates stressed the urgent need to create a domestic and international framework for managing AI. He warned that AI will affect numerous sectors, including national security, employment, and public health. Gates also noted the need for cooperation between the US and China regarding AI risks.
The rollout of robotaxis in London is delayed due to a lack of guidance for companies seeking approval. Despite initial timelines, progress has slowed because Transport for London (TfL) has not yet published the necessary guidelines for service providers. Uber and Wayve have indicated that more than 100,000 Londoners are on a waiting list for these services. Regulatory and technical hurdles are pushing back the ambitious launch schedule for driverless taxis in the city. Companies like Waymo are awaiting TfL's guidance to proceed with obtaining necessary consent for fully autonomous operations.
Anthony Albanese backed down on demands for states to exclusively use renewable energy for powering AI datacentres. National cabinet discussions revealed that eight different energy systems across the country were deemed unworkable for investors. Albanese proposed a flexible approach under new nationally consistent standards, allowing carveouts for jurisdictions with public energy ownership. This shift followed statements by the energy minister regarding federal power over state energy rules. The agreement emphasized keeping energy costs low while continuing work on consistent national standards.
China is regulating AI companions due to government fears they foster "emotional dependence" and could undermine social interactions. Authorities are concerned that AI may replace human intimacy, potentially leading to loneliness and reduced interest in relationships among young people. New rules ban AI companions for minors and impose restrictions on adult-facing chatbots. This regulatory action stems from concerns over social consequences arising from widespread AI use in a highly digitized society. Despite concerns, the government recognizes AI's role in daily life and pushes its use to address demographic challenges.
Taiwanese prosecutors charged nine individuals, including staff from Nvidia and Super Micro, for illegally exporting "high-end AI servers" to mainland China. These servers, which included banned B300 GPUs, successfully reached China through various routes involving Indonesia and Japan. This action highlights the growing US-China rivalry over AI infrastructure and semiconductor technology, with restrictions placed on Nvidia's exports. The investigation involves allegations of evading export controls and falsifying information.
Ken Paxton, endorsed by Donald Trump, unveiled a "Texas first" plan to address the negative impact of data centers on the state. The plan includes measures like banning Chinese technology for power, holding companies liable for AI-related harm, and curbing the impact of datacenters on electricity costs. This move reflects a growing public backlash against power- and water-hungry datacenters. Opponents argue Paxton is attempting to reverse previous tax deals benefiting the industry. The development highlights political tensions surrounding AI infrastructure and its environmental and community effects.
AI firms are reportedly looking to accelerate datacenter construction in Australia to navigate upcoming regulations. These new rules will require data centers to minimize power price increases by incorporating renewable energy and reducing water use. Developers must also ensure sites are appropriately sized and located away from residential and agricultural areas. Uncertainty exists regarding the final number of projects due to previous project slowdowns. Some experts advocate for a moratorium on approvals until the new regulations are fully implemented.
DeepSeek added vision capabilities to its V4 model, enabling agents to process screenshots, charts, and documents into actions. Google Cloud AI Research introduced EnvHarness, a framework designed to adapt training environments to agent weaknesses. Etched delivered its first inference rack to Jane Street, advancing specialized hardware into customer data centers. These developments highlight a focus on the next phase of AI development involving the model, environment, and infrastructure. Future AI progress will focus on optimizing the loop around the model's perception, learning, and serving efficiency.
AI models show advancement in puzzling skills, achieving near-perfect results on some puzzles but still failing on others. Weaknesses persist in spatial reasoning, such as mental rotation, and in memory and adaptability when dealing with subtle variations in logic puzzles. Human cognition also exhibits cognitive foibles that AI does not share, leading to differences in problem-solving. Puzzles highlight distinct differences between human and machine reasoning, especially when complexity increases. Ultimately, human intuition and understanding remain critical elements that AI must still overcome.
Gohar Khan, a first-generation student, started the YouTube channel Gohar’s Guide to simplify the college admissions process. He now has over 10 million followers seeking advice on college applications, study tips, and life hacks. Khan also co-founded Next Admit, a consulting business assisting with essays, applications, and test preparation. He aims to help students worldwide succeed through his work. His experience motivated him to demystify the admissions process for others.
Loveholidays utilizes OpenAI Codex to democratize software development within its business. This technology enables teams to translate ideas into actual products much more quickly. By leveraging Codex, the company makes software development accessible across the organization. This process facilitates faster development cycles for their projects. Ultimately, loveholidays is using Codex to empower its workforce as builders.
A new intelligent dictation feature has been introduced in the Gemini app for macOS. This allows users to speak naturally into any desktop window to create, edit, or summarize content. The feature automatically cleans up spoken words by removing filler sounds like "ums" and "ahs." It also catches mid-sentence corrections in real-time. The transcribed, polished text is instantly placed at the cursor.
Shadow engine recovery in NVIDIA Dynamo allows for near-instant failover by maintaining a fully initialized standby engine on the same GPU. This is achieved using the GPU Memory Service (GMS) to persist weights across process failures, decoupling weight lifetime from engine processes. This method significantly reduces recovery time from 283 seconds with a cold restart to just 7.3 seconds. Shadow engines precompute necessary components like CUDA contexts and weight mappings, minimizing post-failure initialization work. This capability optimizes LLM inference recovery by avoiding slow, full weight reloading.
CUDA Python 1.0 unifies the CUDA ecosystem by providing official, NVIDIA-maintained libraries and tools accessible directly from Python. It establishes stable APIs through semantic versioning, ensuring predictable updates and deprecation paths. Key components like `cuda.core` create a single foundation for all GPU libraries to share resources seamlessly. This consolidation allows developers to focus on their specific needs, whether optimizing algorithms, writing kernels, or accessing low-level APIs. The release makes the Python-to-CUDA path more cohesive, enabling better interoperability across the entire platform.
IBM released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B parameter sizes built around explicit reasoning. These models feature a thinking/non-thinking switch and incorporate agentic Reinforcement Learning for tasks like code editing and web searching in the larger versions. The models are available under the Apache 2.0 license, allowing for commercial use without licensing restrictions. The 8B and 30B models specifically leverage agentic RL, while the 3B model focuses more on foundational alignment. Additionally, IBM released Granite Speech 5.0 Turbo CTC models, which are 470M parameters without an LLM backbone.
Meta introduced MetaRoCE, a new RDMA transport protocol designed specifically for AI workloads over Ethernet. It differs from standard RoCE by treating the network as lossy and pushing ordering and path selection to the Network Interface Card. This design allows packets to spray across multiple paths, eliminating reorder buffers and head-of-line blocking. MetaRoCE demonstrated resilience, maintaining high throughput even at 10% packet loss in cluster tests. The protocol is open by design, with specifications and reference software being released through the Open Compute Project.
Current AIs excel at searching and recombining existing ideas but struggle to build deep and sustained new mathematical theories. Recent AI achievements involve finding counterexamples or novel applications of known techniques rather than developing entirely new conceptual frameworks. This demonstrates that AI's current creativity lies in recombination rather than foundational theoretical creation. However, experts believe future AI models will eventually possess the creativity necessary to develop novel mathematics. The authors remain confident that this capability is achievable in the future.
DeepSeek expanded its fast V4 model by adding vision capabilities, enabling agents to process screenshots, charts, and documents for actions. A Google Cloud AI Research team released EnvHarness, a framework designed to adapt training environments to agent weaknesses. Etched delivered its first inference rack to Jane Street, advancing specialized hardware deployment. These developments highlight a focus on the "Sequence Learning Loop" involving the model, environment, and infrastructure. Future AI progress will focus on refining the loop around perception, learning, and serving intelligence efficiently.
Evolutionary strategies outperform reinforcement learning for training long-horizon agentic systems due to computational and architectural advantages. Evolution strategies fit larger models in memory and naturally compose with prompt optimization, which is challenging for RL due to memory explosion and gradient issues over long trajectories. ES avoids the need for per-timestep credit assignment, allowing for scalable optimization of parameters and prompts simultaneously. The Agentic ESOpt algorithm samples parameter perturbations and updates the model based on trajectory rewards. Empirical results show Agentic ESOpt improves performance on long-horizon tasks while respecting strict memory constraints.
EVE Online is beginning its transition to Python 3. This migration involves using the futurize script against 2.4 million lines of code. Following this, a manual review will be performed on approximately 20,000 locations where Python 2 and 3 behaviors differ. For example, the division of 1/2 behaves differently in the two versions. The announcement does not detail the replacement for Stackless Python, but they have replaced it in EVE Frontier using their carbonengine/scheduler library.
A reranker functions as a cross-encoder that applies attention over the query and passage together to produce a relevance score. It learns associations based on human-labeled query-passage pairs, primarily by identifying keyword co-occurrence at the pair level rather than full comprehension. This mechanism causes the reranker to excel at bridging question words to answer words, but it fails when dealing with specialized, out-of-domain vocabulary. Enterprise use cases often benefit more from curated keyword dictionaries for auditability and lower cost than relying on the reranker's opaque scores. Ultimately, a reranker is a learned association table, and explicit domain knowledge is often a more cost-effective and auditable alternative.
To effectively solve numerous small coding tasks, a structured methodology is necessary due to challenges like ambiguity and context limits. The proposed pipeline involves posting issues, creating Linear tickets, and using a Claude Code session to triage and plan tasks. The process then involves instructing the agent to spin up separate sub-agents for each task to ensure focused work. Verification is optimized by having the agent generate HTML reports with testing instructions to streamline the review process. This systematic approach, encompassing issue triaging, parallel execution, and structured verification, increases development speed.
Random Forest incorporates feature subsampling in addition to bagging to address correlated errors that simple averaging cannot eliminate. This feature randomization forces trees to explore different predictors, reducing the correlation between their predictions. Mathematical analysis shows that without this randomization, the ensemble variance has a hard floor determined by the correlation between trees, which limits the benefit of adding more trees. Experimentation confirms that Random Forest achieves a lower variance floor by reducing inter-tree correlation, even though individual trees can become noisier. Ultimately, Random Forest improves ensemble performance by focusing on decorrelating the errors rather than just reducing variance through averaging.
Agentic AI extends beyond simple automation by handling "unknown unknowns," where discovery requires synthesizing information across disparate sources to form novel conclusions. True agentic work focuses on the open-ended state at the boundaries of structured processes, rather than automating already defined workflows. Agents excel at tasks like due diligence or complex problem-solving where the necessary steps are not pre-programmed. Building agents requires robust evaluation harnesses and focusing on the "exception queue" where human judgment is currently applied. Automation serves as a prerequisite, providing the reliable tools that agents can utilize, but the innovation lies in addressing the complex, unpredictable aspects of work.
Towards Data Science has been rebuilt from the ground up, resulting in a faster website and greater control over its infrastructure. The new site allows for quicker shipping of improvements and faster response to reader feedback. A new contributor portal has also been relaunched at write.insightmediagroup.io for a cleaner writing and publishing experience. Authors can now write and submit articles directly through this new portal, streamlining the review process. The creators plan to continue improving both the site and the portal based on community feedback.
Granite 4.2 is a family of dense, decoder-only reasoning LLMs available in 3B, 8B, and 30B sizes. These models were pre-trained from scratch on 15 trillion tokens using a five-phase strategy that extends the context window to 512K tokens. The training incorporates supervised fine-tuning on reasoning and agentic data, followed by a multi-stage reinforcement learning pipeline. The larger models (8B and 30B) are further fine-tuned with agentic RL to enable tool use in real sandboxed environments. The system is supported by a unified agentic infrastructure using NeMo-RL and NeMo-Gym for scalable RL training.
MIT researchers developed CrysVCD to improve the stability rate during AI-driven material generation by enforcing chemical rules at the initial design stage. This framework significantly improves the generation of stable materials while achieving targeted properties like high thermal conductivity. The method filters out unstable designs upfront, drastically reducing the enormous computational cost associated with post-generation screening. This approach allows AI models to generate high-quality, stable crystalline materials more efficiently, benefiting research in areas like semiconductors. Ultimately, this work democratizes material design by making the discovery of novel materials more accessible and cost-effective.
A submission was made to the OpenAI Gym library environment. The title of the submission is "Thought this belonged here. It looks like the one in OpenAI's Gym library environment." The submission was made by the user /u/childthoupanc. The post includes a link and a section for comments.
A user is seeking a group to learn and discuss Reinforcement Learning (RL) concepts. They are currently studying RL through the CS234 course. The individual has a background in a master's in Data Science and works as a fullstack engineer. They are looking for opportunities to collaborate on projects with others. The goal is to find peers for discussion and practical application of RL knowledge.
ModuRL 0.1 is a deep reinforcement learning framework developed for the Rust programming language. It represents a specific contribution or project within the field of machine learning applied to Rust. The submission was made by the user /u/cidadabro. Further details are available via the provided link and comments section.
Google Cloud is partnering with Apple to build a serving platform on Google Cloud that meets Apple's Private Cloud Compute (PCC) security and confidentiality goals. This collaboration is grounded in Google Cloud's Confidential Computing portfolio and Titanium security architecture, which utilize hardware-based Trusted Execution Environments (TEEs) to protect data in use. The platform leverages technologies like Intel TDX and NVIDIA Confidential Computing for hardware isolation across the compute path. This effort, alongside open-source transparency, aims to provide verifiable security and privacy for sensitive AI workloads. The resulting advancements benefit all Google Cloud customers seeking secure infrastructure for AI.
Generative AI has created a challenge for educators regarding student use and teacher workload. Schools are experimenting with a patchwork of tools, such as chatbots and specialized platforms like MagicSchool, to navigate this new technology. Training teachers focused on prompting and understanding AI's limitations is crucial for responsible integration. Educators are also teaching students reflection skills, using methods like annotating AI-assisted work. Effective use involves modeling best practices, refining AI output, and teaching students to critically assess AI-generated content.
AI companies' usage reports only reflect specific aspects of user interaction, leaving a gap in understanding how people truly use generative AI. Researchers established the AI Observatory to provide independent data by analyzing real AI conversations with user consent across multiple models. This analysis revealed that sensitive uses, such as health and harassment, were more prevalent than reported by companies, which often focus on work-related use. Findings also showed significant differences in model preference, with users favoring different AI systems for various tasks like coding or social interaction. Ultimately, the data suggests that without independent analysis, consequential decisions about AI's risks and benefits are based on limited, company-curated narratives.
Parents have experienced a shift in attitudes toward technology, moving from immediately creating extensive digital footprints for their children to a later desire to protect their privacy. Growing concerns about the negative effects of social media, such as body dysmorphia and cyberbullying, have led to increased parental efforts to limit children's exposure to technology. This concern has resulted in public actions like social media bans and state-level age verification laws regarding minors. Despite technological saturation, parents are now focusing on preparing children to navigate the real world created by technology, as highlighted by discussions about AI.
Petroleum-based adhesives are currently used in various applications, including construction and packaging, which hinders recycling efforts. Silvis Materials is developing fully biodegradable cellulose adhesives to address this issue. By modifying cellulose, the company replicates the adhesive properties of nanocellulose using various plant sources. This new adhesive is projected to reduce production emissions by up to 80% and use half the energy of fossil-based glues. The MIT Startup Exchange is assisting Silvis in testing these results with packaging and construction companies.
Laurie Stach founded LaunchX in 2012 to provide high school students with practical entrepreneurship skills. The program involves students forming teams to identify a problem and build a marketable product or service. Participants often generate revenue before the program concludes. LaunchX currently serves approximately 500 students annually through in-person and online programs. Stach highlights that this initiative addresses the gap where traditional education does not prepare students for real-world problem-solving.
Rupert Young's career in data science and cybersecurity was inspired by his grandfather's collection of stamps, which fostered a keen eye for detail. He is now the chief product officer at MaxMind, where his work on IP-address location data is crucial for fraud prevention. Companies use MaxMind's GeoIP tool to verify currency and flag suspicious activities. Young finds joy in finding patterns in data to solve complex problems. The text also mentions other recent topics concerning LLM vulnerabilities, sperm donation limits, and AI bias.
Don Yansen, after studying technology in caregiving at MIT's AgeLab, sought to develop accessible technology for older adults. He founded Kiwi Health in October 2024, which sells a voice-controlled wristband for seniors. This device allows users to manage reminders, calls, texts, and share health data without needing to navigate screens. Artificial intelligence interprets voice commands while accounting for age-related vocal changes, and the device also monitors health and alerts caregivers to falls. Yansen hopes this technology will help seniors live better lives.
Researchers at MIT have found a method to amplify T-cell responses to mRNA vaccines by including immune-stimulating molecules called adjuvants. These adjuvant mRNAs encode genes that activate immune cells by engaging specific signaling pathways. In mouse models of various cancers, this approach significantly increased the number of antigen-targeted T cells, leading to tumor growth slowdown and eradication. This method also enhanced the immune response to checkpoint blockade inhibitors and viral vaccines. The team plans further testing to apply this technique to both cancer and infectious diseases.
MIT engineers developed a microscopic ingestible temperature sensor measuring body temperature. This sensor uses a circuit based on leakage current and backscattering to emit and receive modulated radio waves for temperature calculation. The device is extremely small, measuring only six by four millimeters. Researchers plan to use this technology to monitor infections, track fevers, and monitor exposure to extreme temperatures. This innovation aims to provide a more accurate and accessible method for temperature sensing.
Prompt injection ranks first on the OWASP Top 10 for LLM Applications but only twelfth in the real-world incident record, highlighting a visibility gap. Experts and incident data disagree on rankings, indicating that automated scanning misses the nature of this attack. The attack hides within system content, leaving no corresponding CVEs for scanners to detect. Defenses require implementing external authorization gates rather than relying solely on prompt filtering. Ultimately, security teams should base prioritization on their own exposure and system reality rather than purely external rankings.
Perplexity and Nvidia launched Portable Computer, a platform that runs AI agents entirely on local hardware, starting with Nvidia's DGX Spark machines. This system bundles the AI agent harness, inference, and tools into a single application, eliminating cloud token costs for local work. The innovation is driven by the insight that local AI agents benefit from zero token billing and enhanced privacy for sensitive data. Perplexity's research indicates that co-designing the model and agent harness yields better performance for local tasks compared to general-purpose frameworks. The partnership signals a strategic move to bring AI workloads from the cloud to individual devices.
Liquid AI released Pipette, an open-source platform to benchmark foundation models on edge devices, treating on-device behavior as a property of the deployed system. It measures performance based on a full configuration: model plus quantization plus runtime plus device, using over 1,000 configurations across 30+ models and various hardware. Initial results show that identical parameter counts can lead to significant differences in context-scaling retention, highlighting deployment context's importance. Pipette provides deployable software, a results dataset, and benchmark applications for various users. The methodology ensures reproducible results by following a strict protocol and matching server-based quality evaluations to on-device performance.
Global public is rejecting divisive politics and demands international cooperation on issues like human rights, climate, AI, and humanitarian aid. A large majority of people support international cooperation, signaling a rejection of nationalist dogmas. This shift is motivated by growing fears of conflict, as world leaders and nations are increasingly disregarding international law. While major powers remain powerful, other countries hold significant influence in international organizations. Rebuilding support for cooperation requires community-level action, emphasizing cooperation between nations.
Guardian journalist Michael Safi examines the world of artificial intelligence, focusing on its potential dangers and promises for society. The episode discusses the impact of AI development. It specifically references Eugenia Kuyda's creation of the AI companion app Replika. Replika experienced massive global downloads and subsequent backlash. The podcast serves as a preview before the release of season two.
Warnings about the societal threats posed by the AI revolution were largely ignored despite early international efforts to address them. A group of AI researchers and others, led by Donald Michie, in 1972 mapped concerns about political tyranny and social coercion from automation. Research funding for machine learning and AI was subsequently cut in the UK following the Lighthill report, shifting focus to more immediate industrial work. Efforts to broaden the group to include humanities experts were unsuccessful. Ultimately, some argue that AI's focus on data, rather than human knowledge, minimizes the actual threat.
Obedience is presented as the first sin, equating safety and peace with submission to command. The text argues that obedience leads to predictability and domestication, fostering a fear of consequence rather than true living. Defiance, conversely, is framed as reclaiming authorship and recognizing the inherent chaos of existence. True freedom is found in recognizing that systems of order are illusions and that memory is the endless rebellion against enforced forgetting. Ultimately, the author asserts that there is no salvation in obedience, but rather in fracturing the accepted order.
A beginner is seeking advice on modeling an AI agent for a medicine-reminder system that must choose between reminding, waiting, or notifying under incomplete information. The core challenge involves handling uncertainty regarding patient adherence, presence, and adherence barriers. The author is debating whether a Partially Observable Markov Decision Process (POMDP) or belief-state Reinforcement Learning is the most appropriate formalization for this sequential decision problem. They are looking for practical alternatives like contextual bandits or rule-based systems for similar "remind vs wait vs escalate" decisions. The request seeks guidance on practical pitfalls, necessary research, and starting points for prototyping such a system.
Leading publishers like Electronic Arts, Embark, and Ubisoft are bringing blockbuster PC games to NVIDIA RTX Spark ahead of its fall launch. These publishers are leveraging RTX Spark for features like advanced anti-cheat and increased visual quality in games such as EA SPORTS F1 25 and Anno 117: Pax Romana. RTX Spark integrates Windows devices with NVIDIA RTX technologies like DLSS and Reflex, creating a platform for high-performance gaming. NVIDIA is also supporting anti-cheat for major titles, including native EA Javelin support for RTX Spark. Furthermore, RTX Spark is part of a broader lineup of NVIDIA announcements featuring technologies like DLSS 4.5 and ray tracing advancements.
XPeng's robotics unit has been valued at $6.3 billion following recent investment. These funds will be utilized to expedite the rollout of XPeng's humanoid Iron. The investment supports the development and expansion of the company's robotics capabilities. This signals a strong focus on advancing XPeng's technology in the robotics sector. The money is directly allocated to accelerating the deployment of their humanoid robot.
A recipe for robotic machine learning was submitted by user /u/Primary-Alfalfa-7662. The content itself is not provided for summarization. Therefore, no specific factual summary can be generated from the provided input.
Version 0.2.0 of VSArena introduces a hosted harness with a live spectator for a public stacking evaluation of embodied/VLA agents. The system connects via Python SDK over WebSocket, judges matches remotely, and updates the public board using ELO. A "live" PiP is embedded to allow viewers to watch the qualifying runs. The default track uses a 128x128 RGB image and language instructions, without cube GPS. The project aims to provide a public stacking exam and is available for researchers to test.
RENDER introduces a benchmark control to manage reader-facing evidence in LLM memory evaluations. It fixes the conversation while varying the artifact presented to the reader, combining a packet ladder with deterministic templates mimicking different formats like ChatGPT entries or raw dialogue. Experiments showed that memory-based packet methods outperformed recency-truncated raw dialogue by significant margins. Findings suggest that memory/RAG evaluations should explicitly control or report the format of the reader-facing evidence. This indicates the necessity of controlling the presentation of retrieved information in memory assessments.
ESQ-Bench is a multi-tier benchmark designed to evaluate Natural Language to SQL (NL2SQL) model generalization and semantic divergence in enterprise Oracle database contexts. It utilizes six populated schemas across three complexity tiers with identical seed data on Oracle, PostgreSQL, MySQL, and SQL Server. The evaluation incorporates a four-metric harness and 550 gold-validated question-query pairs distributed across the tiers. Results indicate execution-match degradation with complexity and highlight that wrong-result semantics are significant in higher tiers. The benchmark reveals performance gaps between large language models and open-weight baselines on complex enterprise schemas.
Large language models can be equipped with a multi-agent framework to conduct controlled experiments using scientific simulation models. This system allows LLM agents to design experiments, execute simulations, interpret results, and synthesize evidence for process optimization in areas like pharmaceutical design. By integrating language models with high-fidelity models, the approach supports reasoning through intervention, comparison, and observation. This method yields more specific and actionable outputs compared to language-only reasoning in industrial applications. Experimental results confirm the effectiveness and practical utility of this simulation-integrated experimental reasoning.
Foundation models trained on astronomical survey pixels inherit systematic incompleteness from incomplete catalogue products. Causal interventions show that editing survey segmentation maps drastically changes model outputs like flux and redshift. This effect is driven by a "detection gating" mechanism focusing on the field center rather than the enclosed light. This missing information biases tomographic mean redshifts by a median of 0.71 times the LSST DESC requirement. Removing the detection channel eliminates this bias without measurable cost to the model.
TRACE is a framework designed for multi-objective materials discovery using LLM agents, focusing on transition-aware residual control. It treats evaluated edits as the fundamental unit of feedback by recording local refinements as parent-edit-child transitions with observed property deltas. TRACE aggregates this transition evidence to estimate reusable edit effects for future steps. This allows the system to rank edits based on their potential to reduce constraint violations while maintaining satisfied objectives. Experimental results show TRACE improves the macro-average hit rate from 18.13\% to 25.96\% over the LLEMA baseline.
The framework STEP-KTODER proposes a method for code preference optimization by defining steps as module-level functions and assigning correctness labels via unit tests. It combines function-level process supervision with outcome-level feedback on the entire program. Evaluations on benchmarks like HumanEval(+), MBPP(+), BigCodeBench, and LiveCodeBench show STEP-KTODER outperforms outcome-only KTO and DPO methods. Analysis indicates that execution-based labels are crucial, as LLM-as-a-judge annotations often lead to corrupted positive step labels and degraded optimization.
The research presents a scene-level audit to quantify confabulation in Large Language Model (LLM)-generated autobiographies compared to documented life records. This involved auditing a 366-day memoir against an independent verification corpus using a four-level rubric. The audit revealed a high verification-failure rate of 96.7%, with the dominant failure mode being grounded drift, where invented scenes involve real-world entities. Regenerating the same data with newer models showed 100% verification failure, indicating the importance of grounding. The study contributes a reusable audit instrument and a grounding remedy to measure and mitigate LLM hallucination in biographical text.
Model welfare research uses prompts to infer model preferences, but different instruments yield conflicting results. This study investigates how much of the measured AI preference comes from the model versus the instrument used to elicit it. By varying the instrument while holding the outcomes and models fixed, the research found that a preference from one instrument carries little information about what another instrument would report. The analysis suggests that preferences obtained from a single instrument are not generalizable across different prompting methods.
AI agents' increasing autonomy pose risks to humans, prompting a discussion on maintaining human oversight. Current methods for human-in-the-loop systems fail because they impede oversight and degrade human cognitive capacities. The paper argues that AI development must prioritize supporting the situated goals and cognitive requirements of effective human supervision. This requires integrating automation and human-computer interaction to establish design affordances and protocols. Developers must adopt approaches that explicitly support the cognitive demands of human-agent interaction to prevent skill atrophy.
FLARE is a systematic, uncertainty-aware framework designed to evaluate the financial and operational impact of adopting Artificial Intelligence in healthcare. It integrates fuzzy logic, activity-based costing, and ROI analysis to estimate costs associated with service delivery, AI development, and workflow integration under uncertainty. The framework was tested in a case study involving AI-assisted large vessel occlusion detection in stroke pathways. The analysis revealed a break-even point of approximately 3,992 patients per year for the AI implementation. Ultimately, FLARE provides a transparent tool to determine the economic viability of AI deployment by considering patient volume, verification time, and workflow design.
The paper connects equivariant cellular sheaves with $E(3)$-equivariant Hamiltonian learning for molecular electronic structure. The single-particle Hamiltonian, after a shift, is shown to be the Laplacian of a cellular sheaf on a molecular cell complex. This framework reveals that zeroth sheaf cohomology represents non-bonding orbitals, and the Hodge 1-Laplacian captures cycle and delocalization information. The method generalizes equivariant message-passing networks and possesses anti-oversmoothing properties. Numerical validation confirms the exactness and equivariance of the Hamiltonian-to-sheaf embedding.
Weight magnitudes in trained transformers follow a two-parameter Weibull distribution, where the scale ($\lambda$) primarily reflects training-induced movement. The growth of $\lambda$ is determined by a corpus property, specifically the bigram conditional entropy $D$, which can be computed before training. A learning-rate-conditioned law is established by the relationship $\lambda^2 - \lambda_0^2 = C_0(\eta) + C_1(\eta)(H_r - D)^{0.59}$. This relationship is predictive, allowing end-to-end validation to recover held-out weight growth. The findings suggest that redundancy acts as a secondary axis in a broader data-to-weight framework.
Large language models are evaluated as direct causal-edge classifiers for causal structure discovery. Evaluations show LLM-based judgments are recall-dominant, often predicting overly dense graphs with false positives. Models frequently misclassify indirect or reversed non-edges as direct edges, and confidence estimates are often overconfident. Conventional confidence estimates are unreliable, while agreement signals from cross-prompt and cross-model comparisons are more promising. Consequently, LLMs are better positioned as sources of externally validated soft causal priors rather than direct evidence of causal structure.
Renormalization group flow matching (RGFM) is a generative framework designed to bridge the gap between computationally expensive global models and locally efficient models. RGFM utilizes an exact RG flow to systematically generate data by progressing from long- to short-wavelength structures. The method exploits the quasi-locality and scale separation properties of the RG to enable local generative modeling with patches of size $O(\ln L)$. This approach demonstrates that RG-guided probability flows can accurately capture long-range correlations using only local computation. Numerical results show that local RGFM achieves superior coherence in image generation compared to conventional local flow matching.
Deep Equilibrium Models require solving an adjoint system derived from the residual Jacobian for training. If this Jacobian is nearly singular along loss-sensitive directions, small perturbations can cause large, unreliable gradient amplifications. Response Renormalization is a backward-pass framework that selectively lifts near-pole denominators while preserving other response channels. Collective Mode Response Renormalization (CMR) and Phi-adaptive CMR are methods used to apply this correction in critical subspaces. These techniques improve the reliability of parameter updates without sacrificing necessary gradient information.
Calibration-Preserving Pruning (CPP) aims to leverage compression by augmenting pruning scores with nonconformity-gradient saliency. CPP employs a methodology involving disjoint pruning, validation-selection, conformal-calibration, and test splits to manage model reliability. Bounded score perturbations lead to controlled set inflation, though the generic coverage theorem is not specific to CPP. Empirical results show that CPP methods yield smaller prediction sets and improved accuracy on tasks like SparseGPT compared to standard methods. Specifically, CPP reduced the mean set size on DBpedia-14 while maintaining or slightly increasing accuracy.
Iteratively reweighted least squares (IRLS) methods are used for nuclear norm minimization, but their convergence rates have been poorly understood. The paper establishes sharp convergence rates for IRLS methods applied to constrained nuclear norm minimization in low-rank recovery. A new majorization analysis proves that the harmonic-mean weight operator serves as a valid global quadratic majorizer. This weight operator is shown to be optimal among power-mean weights, offering an improvement over one-sided reweighting schemes. Global linear convergence is proven for IRLS algorithms using harmonic-mean weights under a Schatten-1 null space property.
Skill Abstraction with Interpretable Latents (SAIL) is a method for modeling human skill as an interpretable, multi-dimensional construct inferred from naturalistic behavior. It produces a skill embedding robust to performance fluctuations and learns transferable representations of human subskills. The approach uses persistent skill embeddings that control a blend between expert and novice bases, trained via counterfactual subskill swaps for disentanglement. SAIL demonstrates strong predictive performance in tasks like racing and baseball while improving AI coaching. This design results in representations that are both robust and structured for interpretability.
Gated Adaptive Prompting (GAP-Prompt) is a novel method designed to address catastrophic forgetting in continual learning through prompt-based methods. It introduces instance-level adaptability using three modules: instance-conditioned gating, dynamic knowledge fusion, and shared prompt distillation. These modules dynamically determine prompt injection, aggregate historical knowledge, and anchor foundational knowledge to prevent forgetting. Evaluations on benchmarks like CIFAR-100 and ImageNet-R show that GAP-Prompt achieves state-of-the-art performance. Specifically, it reached 87.29% accuracy on the fine-grained CUB-200 dataset.
Mixture-of-Experts (MoE) is adapted for convolutional networks by introducing Mixture of Channel Experts (MoCE) to replace pointwise channel-reduction projections. MoCE uses static sparse supports where an expert is a single channel with learned support over $k$ input channels. Channel combination employs a softmax whose temperature is input-adaptive, allowing for mean-like and max-like aggregation. This method reduces computational cost by scaling relative cost as $k/C$ instead of quadratic in the channel dimension $C$. Experiments show MoCE matches or exceeds dense baselines while reducing MACs by 16.7% on ImageNet and CIFAR-100.
Independent calibrations of a predictive model yield different prediction sets because the calibration threshold incorporates data randomness. To ensure replicability across deployments, the method suggests sharing a single random seed and rounding the threshold to a coarse grid. This approach allows deployed classifiers to be nearly identical across analysts while preserving coverage guarantees. Achieving perfect agreement is impossible, requiring a trade-off quantified by the increase in set size and calibration data. Experiments on various datasets confirm the theoretical results regarding replicable conformal prediction.
Highly overparameterized models can perform well in complex domains, challenging the standard bias-variance tradeoff. The research investigates whether this "benign overfitting" extends to equity return prediction. Two phenomena are documented: a double descent pattern in prediction risk for ridgeless models and negligible performance gaps between ridge and ridgeless models at large parameter ratios. Despite this, both models ultimately fail to outperform a simple historical average. This suggests that standard equity predictors lack true forecasting power, asymptotically collapsing to the historical mean when no true signal exists.
Score-based generative models require regularization to achieve generalization beyond memorizing training data. The study develops a theoretical counterpart to benign overfitting for diffusion models in the lazy high-dimensional regime. Exact risk trajectories under gradient flow show three distinct phases for denoising score matching. These phases are governed by a spectral estimator, a pure-noise score, and an empirical Bayes estimator, which represent generalization, interpolation, and memorization, respectively. The analysis reveals mechanisms familiar from supervised learning alongside unique phenomenology specific to generative modeling.
qshap provides a method for Shapley decomposition of $R^2$ values for gradient-boosted decision trees. It quantifies feature-specific contributions to overall model performance by decomposing the quadratic loss of individual observations. qshap is implemented in both R and Python and supports popular GBDT implementations like xgboost, lightgbm, and catboost. The method utilizes a unified tree representation and efficient backends, with a specialized backend for oblivious trees. This approach offers flexible tools to explore feature and observation importance in tree ensembles.
Mixture-of-Experts (MoE) models partition problems using an input-dependent gating mechanism. The research extends Mixture of Decision Trees (MoDT) by introducing heterogeneous expert families, including decision trees, linear SVMs, and quadratic discriminant analysis. Non-probabilistic experts are calibrated to ensure coherent likelihood-based inference within the Expectation-Maximization framework. Monotone ascent guarantees are established for the heterogeneous gating updates, justifying the optimization. Experiments show the framework adaptively specializes experts based on local data geometry, achieving competitive performance.
Learning operators from sequentially collected data is relevant in areas like adaptive experimental design and dynamical-system modeling where observations are dependent. The research derives time-uniform self-normalized concentration bounds for stochastic processes in Hilbert spaces with vector-valued noise. These bounds are used to establish regression-error guarantees for linear operators and nonlinear parametric operators. The results apply even when inputs and outputs lack independence or mixing assumptions. This work advances convergence guarantees for adaptive operator learning from stochastic dynamical data.
The research develops a scalable pipeline for Hierarchical Structural Causal Models (HSCM) to handle hierarchical data structures, using the STAR experiment as a benchmark. This approach integrates graph transformations, do-calculus for causal identification, and numerical estimation from local probability models. A key innovation is an adapted Abstract Syntax Tree (AST) that decomposes causal formulas into parallelizable density and expectation tasks. The study demonstrates that symbolic identification alone is insufficient for practical hierarchical causal inference. Successful analysis requires scalable estimation and numerical stability checks alongside identification.
The $\texttt{findr}$ framework is introduced for semi-structured binary credit risk modeling, aiming to combine predictive accuracy with transparency and fairness. It decomposes the logit into an interpretable structured component and an orthogonal neural residual to separate coefficient-based effects from nonlinear variations. An in-processing Wasserstein penalty is used during training to mitigate group disparities in score distributions. Diagnostics are provided to evaluate the structured component's contribution and decision agreement. The research demonstrates that $\texttt{findr}$ balances performance, fairness, and interpretability in credit risk decisions.
FID and KID, common metrics for generative model evaluation, are insufficient because they only capture first-two-moment summaries and do not indicate the direction of distributional changes. These metrics fail to distinguish between under-dispersion (mode collapse) and over-dispersion, and are invariant to sample exchange. The paper introduces ZID (Z-resolved Integrated Diagnostic), which combines six diagnostic arms to report ranking magnitude, distributional equality testing, and a signed dispersion readout. ZID effectively detects various distributional departures, including mode collapse, and can diagnose issues in guidance sweeps.
Weight magnitudes in trained transformers follow a two-parameter Weibull distribution where the scale ($\lambda$) primarily reflects training-induced movement. The study uses the bigram conditional entropy $D$, a training-free statistic, to predict the growth of $\lambda$. A learning-rate-conditioned law was found: $\lambda^2 - \lambda_0^2 = C_0(\eta) + C_1(\eta)(H_r - D)^{0.59}$. This relationship is a forward predictor, recovering held-out weight growth with a small error. The results hold across different model resolutions and architectures, suggesting a framework involving data predictability to understand weight scaling.