[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-22f6c454e53f4c57-closing-the-data-gap-in-ai-driven-drug-discovery-summary":3,"summaries-facets-categories":106,"summary-related-22f6c454e53f4c57-closing-the-data-gap-in-ai-driven-drug-discovery-summary":7812},{"id":4,"title":5,"ai":6,"body":13,"categories":62,"created_at":64,"date_modified":64,"description":56,"extension":65,"faq":64,"featured":66,"kicker_label":64,"meta":67,"navigation":87,"path":88,"published_at":89,"question":64,"scraped_at":90,"seo":91,"sitemap":92,"source_id":93,"source_name":94,"source_type":95,"source_url":96,"stem":97,"tags":98,"thumbnail_url":64,"tldr":103,"tweet":64,"unknown_tags":104,"__hash__":105},"summaries\u002Fsummaries\u002F22f6c454e53f4c57-closing-the-data-gap-in-ai-driven-drug-discovery-summary.md","Closing the Data Gap in AI-Driven Drug Discovery",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",6592,606,2869,0.002557,{"type":14,"value":15,"toc":55},"minimark",[16,21,25,29,32,35,48,52],[17,18,20],"h2",{"id":19},"the-data-problem-in-ai-healthcare","The Data Problem in AI Healthcare",[22,23,24],"p",{},"Despite high-profile claims that AI will soon cure all diseases, the industry faces a significant bottleneck: a lack of high-quality, causal human biological data. Current AI models are largely trained on static snapshots of cells or animal testing, which fail to capture the complexity of human biology. As a result, approximately 90% of drugs that show efficacy in animal trials fail to gain regulatory approval for human use. Existing generative AI models struggle to learn from these datasets because they lack the 'causal' context of how a cell transitions from one state to another, such as the specific stimulus that causes inflammation.",[17,26,28],{"id":27},"autonomous-labs-as-a-solution","Autonomous Labs as a Solution",[22,30,31],{},"Vivodyne is attempting to solve this by shifting the focus from animal models to human tissue. Their HIVE platform consists of modular, autonomous robotic labs capable of growing 20 different types of human tissue. These systems autonomously dose and monitor tissues, generating high-fidelity data that mimics human responses.",[22,33,34],{},"Key performance metrics reported by the company include:",[36,37,38,42,45],"ul",{},[39,40,41],"li",{},"94% predictive accuracy for liver toxicity compared to human trials.",[39,43,44],{},"96% concordance for airway tissue behavior.",[39,46,47],{},"100% concordance in bone marrow testing across 20 chemotherapy drugs.",[17,49,51],{"id":50},"moving-toward-causal-ai","Moving Toward Causal AI",[22,53,54],{},"By tracking hundreds of thousands of ongoing experiments, Vivodyne aims to provide the reinforcement learning data necessary to train models that understand human biology at a causal level. This shift is essential for developing complex combination therapies, where the search space for effective drug interactions is too vast for traditional experimental approaches. Instead of guessing, the goal is to build models that can identify which specific biological 'cause' will trigger a desired 'effect' in human tissue, effectively creating a 'crash test' equivalent for drug discovery before entering expensive clinical trials.",{"title":56,"searchDepth":57,"depth":57,"links":58},"",2,[59,60,61],{"id":19,"depth":57,"text":20},{"id":27,"depth":57,"text":28},{"id":50,"depth":57,"text":51},[63],"AI & LLMs",null,"md",false,{"content_references":68,"triage":82},[69,74,79],{"type":70,"title":71,"publisher":72,"context":73},"paper","No clear data scaling laws when training generative AI models on existing cellular data","Nature Methods","cited",{"type":75,"title":76,"publisher":77,"context":78},"tool","Alphafold","Google DeepMind","mentioned",{"type":75,"title":80,"author":81,"context":78},"HIVE","Vivodyne",{"relevance":83,"novelty":84,"quality":84,"actionability":57,"composite":85,"reasoning":86},3,4,3.25,"Category: AI & LLMs. The article discusses the limitations of current AI models in drug discovery and presents a novel approach using autonomous labs to generate human-tissue-based data. While it offers insights into a specific application of AI in healthcare, it lacks actionable steps for the audience to implement similar strategies in their own projects.",true,"\u002Fsummaries\u002F22f6c454e53f4c57-closing-the-data-gap-in-ai-driven-drug-discovery-summary","2026-08-19 12:00:00","2026-08-20 03:12:45",{"title":5,"description":56},{"loc":88},"22f6c454e53f4c57","TechCrunch — AI","article","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F08\u002F19\u002Fai-isnt-close-to-curing-cancer-this-startup-says-it-knows-what-it-will-take\u002F","summaries\u002F22f6c454e53f4c57-closing-the-data-gap-in-ai-driven-drug-discovery-summary",[99,100,101,102],"automation","machine-learning","ai-llms","biotech","Current AI drug discovery models fail because they rely on static, non-human data. Vivodyne is addressing this by using autonomous robotic labs to generate causal, human-tissue-based data to train more effective 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Traditional scheduling algorithms often struggle to capture the spatio-temporal relationships between these tasks and the heterogeneous infrastructure. The PPO-STGNN approach addresses this by utilizing Spatio-Temporal Graph Neural Networks (STGNN) to encode the state space. By treating the task dependencies as a graph, the model effectively extracts spatial features (task relationships) and temporal features (execution sequences), allowing the scheduler to make more informed decisions based on the current state of the entire network architecture.",[17,7831,7833],{"id":7832},"enhancing-scheduling-stability-with-ppo","Enhancing Scheduling Stability with PPO",[22,7835,7836],{},"To optimize the decision-making process, the authors employ Proximal Policy Optimization (PPO), a reinforcement learning algorithm known for its balance between ease of implementation, sample efficiency, and ease of tuning. PPO is particularly effective here because it prevents the policy from updating too drastically in a single step, which is crucial in volatile cloud-edge environments where a poor scheduling decision can lead to significant latency or resource bottlenecks. By integrating PPO with the feature-rich representations provided by STGNN, the system achieves a more robust scheduling policy that adapts to varying workloads and resource availability without the instability often found in standard policy gradient methods.",{"title":56,"searchDepth":57,"depth":57,"links":7838},[7839,7840],{"id":7825,"depth":57,"text":7826},{"id":7832,"depth":57,"text":7833},[63],{"content_references":7843,"triage":7848},[7844],{"type":70,"title":7845,"url":7846,"context":7847},"PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.03503","reviewed",{"relevance":83,"novelty":83,"quality":84,"actionability":57,"composite":7849,"reasoning":7850},3.05,"Category: AI & LLMs. The article discusses a novel approach to task scheduling in cloud-edge environments using STGNN and PPO, which is relevant to AI automation. However, it lacks direct practical applications for product builders, focusing more on theoretical advancements than actionable insights.","\u002Fsummaries\u002F013719e11050953e-optimizing-cloud-edge-task-scheduling-with-ppo-stg-summary","2026-09-05 03:11:03",{"title":7815,"description":56},{"loc":7851},"013719e11050953e","arXiv cs.AI","summaries\u002F013719e11050953e-optimizing-cloud-edge-task-scheduling-with-ppo-stg-summary",[100,99,101],"PPO-STGNN improves cloud-edge task scheduling by combining Spatio-Temporal Graph Neural Networks (STGNN) to capture complex dependencies with Proximal Policy Optimization (PPO) for efficient, stable reinforcement learning.",[101],"N5XnzCbARqwdCP5rklffRYXo_35VLNBWoVHOEkTiDAc",{"id":7863,"title":7864,"ai":7865,"body":7870,"categories":7986,"created_at":64,"date_modified":64,"description":56,"extension":65,"faq":64,"featured":66,"kicker_label":64,"meta":7987,"navigation":87,"path":7997,"published_at":7998,"question":64,"scraped_at":7999,"seo":8000,"sitemap":8001,"source_id":8002,"source_name":8003,"source_type":8004,"source_url":8005,"stem":8006,"tags":8007,"thumbnail_url":8009,"tldr":8010,"tweet":8011,"unknown_tags":8012,"__hash__":8013},"summaries\u002Fsummaries\u002F3fe77e26f9b00374-building-reliable-generalist-robots-via-active-lea-summary.md","Building Reliable Generalist Robots via Active Learning",{"provider":7,"model":8,"input_tokens":7866,"output_tokens":7867,"processing_time_ms":7868,"cost_usd":7869},8818,1078,5224,0.0038215,{"type":14,"value":7871,"toc":7978},[7872,7876,7879,7883,7886,7890,7893,7915,7919,7922,7926,7963,7967],[17,7873,7875],{"id":7874},"the-reliability-gap-in-robotics","The Reliability Gap in Robotics",[22,7877,7878],{},"Jason Ma, CTO of Dyna Robotics, argues that the current state of robotics is stuck in a \"demo trap.\" While generalist models can achieve 80–90% success rates, this is insufficient for commercial viability. In a real-world setting, an 80% success rate means the probability of completing 10 consecutive tasks is less than 0.1%. To move from viral videos to commercial-grade products, robots must achieve near-100% reliability, which requires a shift from simple pre-training to a rigorous research-and-deployment flywheel.",[17,7880,7882],{"id":7881},"the-data-pyramid-and-model-architecture","The Data Pyramid and Model Architecture",[22,7884,7885],{},"Dyna Robotics employs a \"pre-training data pyramid\" consisting of three layers: off-robot data (human-captured video and public datasets), diverse on-robot task data, and high-quality deployment data. This combination bridges the gap between lab environments and real-world conditions. Their architecture pairs a high-level reasoning model with a low-level world action model. The reasoning model provides semantic understanding, while the action model executes fine-grained, high-frequency control necessary for physical dexterity.",[17,7887,7889],{"id":7888},"scalable-supervision-via-reward-models","Scalable Supervision via Reward Models",[22,7891,7892],{},"The most significant technical hurdle in robotics is handling the \"long tail\" of failure cases. Because deformable objects (like napkins or T-shirts) have infinite configurations, exhaustive data collection is impossible. Dyna solves this by implementing reward models that monitor the robot's progress in real-time. When the reward score dips, it signals a failure. This allows the team to:",[7894,7895,7896,7903,7909],"ol",{},[39,7897,7898,7902],{},[7899,7900,7901],"strong",{},"Automate Failure Detection:"," Instead of manual oversight, the reward model flags exactly when the robot struggles.",[39,7904,7905,7908],{},[7899,7906,7907],{},"Targeted Active Learning:"," Researchers collect data specifically for the failure modes identified by the reward model.",[39,7910,7911,7914],{},[7899,7912,7913],{},"Iterative Fine-tuning:"," By cycling through these error-recovery scenarios, the model learns to recover from mistakes—such as pulling an entire stack of napkins instead of one—without needing to be programmed for every specific edge case.",[17,7916,7918],{"id":7917},"generalization-without-site-specific-data","Generalization Without Site-Specific Data",[22,7920,7921],{},"While early deployments required site-specific fine-tuning, Dyna has moved toward models that generalize to unseen environments. A notable case study involved folding T-shirts at the CoRL conference in Korea. The robot was dropped into a new, high-traffic environment with no prior site-specific data and operated for three days straight, successfully handling interference from attendees. This demonstrates that with sufficient diversity in the pre-training data, the model can maintain performance across arbitrary locations.",[17,7923,7925],{"id":7924},"key-takeaways","Key Takeaways",[36,7927,7928,7934,7940,7951,7957],{},[39,7929,7930,7933],{},[7899,7931,7932],{},"Success Rate Matters:"," 80-90% is a demo; 99%+ is a product. Design your evaluation metrics around long-horizon reliability, not just single-task completion.",[39,7935,7936,7939],{},[7899,7937,7938],{},"Reward Models as Supervisors:"," Use reward models to track progress and identify failure points automatically. This turns the \"long tail\" of errors into a structured data collection pipeline.",[39,7941,7942,7945,7946,7950],{},[7899,7943,7944],{},"Active Learning Flywheel:"," Don't just collect more data; collect ",[7947,7948,7949],"em",{},"targeted"," data based on where the model currently fails.",[39,7952,7953,7956],{},[7899,7954,7955],{},"Generalization is a Data Problem:"," To deploy without site-specific fine-tuning, you must diversify your pre-training data pyramid to include varied environments, lighting, and object states.",[39,7958,7959,7962],{},[7899,7960,7961],{},"Commercial Focus First:"," Start with enterprise use cases (like napkin folding in restaurants) where the environment is controlled enough to iterate, but complex enough to force the model to learn robust recovery behaviors.",[17,7964,7966],{"id":7965},"notable-quotes","Notable Quotes",[36,7968,7969,7972,7975],{},[39,7970,7971],{},"\"A generalist robot that succeeds 80 to 90% of the time makes a great video but a poor product.\" (Jason Ma on the difference between research demos and commercial-grade robotics.)",[39,7973,7974],{},"\"The model is able to generalize to new ways of recovery from the mistake it's made and continue to make progress.\" (On the emergent behavior of models trained via active learning.)",[39,7976,7977],{},"\"If you're familiar with the robotics field, there are too many problems... by deploying and by building a product, we know exactly the right kind of problems that we need to focus on.\" (On why commercial deployment is essential for research focus.)",{"title":56,"searchDepth":57,"depth":57,"links":7979},[7980,7981,7982,7983,7984,7985],{"id":7874,"depth":57,"text":7875},{"id":7881,"depth":57,"text":7882},{"id":7888,"depth":57,"text":7889},{"id":7917,"depth":57,"text":7918},{"id":7924,"depth":57,"text":7925},{"id":7965,"depth":57,"text":7966},[63],{"content_references":7988,"triage":7994},[7989,7992],{"type":7990,"title":7991,"context":78},"event","CoRL (Conference on Robot Learning)",{"type":75,"title":7993,"context":7847},"Dyna-1",{"relevance":84,"novelty":83,"quality":84,"actionability":83,"composite":7995,"reasoning":7996},3.6,"Category: AI & LLMs. The article discusses practical applications of active learning in robotics, addressing the audience's pain point of moving from demos to production-ready AI features. It provides insights into the use of reward models for failure detection, which is relevant but lacks specific actionable steps for implementation.","\u002Fsummaries\u002F3fe77e26f9b00374-building-reliable-generalist-robots-via-active-lea-summary","2026-09-24 14:30:12","2026-09-25 03:17:40",{"title":7864,"description":56},{"loc":7997},"3fe77e26f9b00374","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Sjfz1TqxzEs","summaries\u002F3fe77e26f9b00374-building-reliable-generalist-robots-via-active-lea-summary",[100,99,101,8008],"robotics","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FSjfz1TqxzEs\u002Fhqdefault.jpg","Dyna Robotics achieves 99.4% reliability in complex tasks like napkin folding by using reward models to detect failures, enabling targeted active learning and error recovery rather than relying on massive, uncurated datasets.","This is a technical presentation from [Dyna Robotics](https:\u002F\u002Fwww.dyna.co) CTO Jason Ma explaining their approach to moving beyond 90% success rates in robotic manipulation. He details how they use a reward-model-based active learning loop to achieve high reliability, specifically demonstrating their [Dyna-1](https:\u002F\u002Fwww.dyna.co) model's performance in long-duration tasks like napkin and T-shirt folding.",[101,8008],"BVRrlaNJ0YmNSvI6ZM4FCZF7Xu4Q84JLtEqjNvQQ2KQ",{"id":8015,"title":8016,"ai":8017,"body":8022,"categories":8077,"created_at":64,"date_modified":64,"description":56,"extension":65,"faq":64,"featured":66,"kicker_label":64,"meta":8078,"navigation":87,"path":8092,"published_at":8093,"question":64,"scraped_at":8094,"seo":8095,"sitemap":8096,"source_id":8097,"source_name":8003,"source_type":8004,"source_url":8098,"stem":8099,"tags":8100,"thumbnail_url":64,"tldr":8102,"tweet":8103,"unknown_tags":8104,"__hash__":8105},"summaries\u002Fsummaries\u002Fe5d89401665344eb-democratizing-frontier-ai-automating-discovery-and-summary.md","Democratizing Frontier AI: Automating Discovery and Scaling",{"provider":7,"model":8,"input_tokens":8018,"output_tokens":8019,"processing_time_ms":8020,"cost_usd":8021},7547,677,3499,0.00290225,{"type":14,"value":8023,"toc":8072},[8024,8028,8031,8038,8042,8045,8065,8069],[17,8025,8027],{"id":8026},"the-shift-from-monolithic-scaling-to-adaptive-intelligence","The Shift from Monolithic Scaling to Adaptive Intelligence",[22,8029,8030],{},"Modern AI research has historically been constrained by an \"unreasonably narrow path\"—requiring access to elite labs, massive compute budgets, and specific academic pedigrees. This created a bottleneck where only a few organizations could contribute to the frontier. However, the paradigm is shifting. We are reaching a saturation point in model architecture where simply increasing pre-training size no longer yields the same step-wise performance gains.",[22,8032,8033,8034,8037],{},"Instead, the most significant returns are now found in the ",[7899,8035,8036],{},"broader action space","—specifically in how models interact with their environment and how they are customized post-training. This transition moves the field away from monolithic, one-size-fits-all models toward adaptive intelligence that can be tailored to specific domains like medicine, law, and science.",[17,8039,8041],{"id":8040},"automating-the-research-loop","Automating the Research Loop",[22,8043,8044],{},"To democratize access to frontier-level intelligence, we must automate the training process itself. The author introduces \"Auto Scientist,\" a system designed to co-optimize the entire training loop—from data curation to model alignment. Key insights include:",[36,8046,8047,8053,8059],{},[39,8048,8049,8052],{},[7899,8050,8051],{},"Data-Model Co-optimization:"," Performance gains are not achieved by agents alone; they require tight integration between data quality and model architecture. Controlling the data flow is as critical as the model parameters themselves.",[39,8054,8055,8058],{},[7899,8056,8057],{},"Exploiting the Search Space:"," By automating hyperparameter tuning and architecture selection, systems can outperform human research staff, who are often biased toward familiar configurations. This allows for massive exploitation of the search space with greater predictability.",[39,8060,8061,8064],{},[7899,8062,8063],{},"Reducing Compute Barriers:"," By shifting the focus to post-training and agentic compute, the reliance on massive, centralized GPU clusters is reduced. This makes it possible for smaller teams to build high-performing, domain-specific models without needing thousands of GPUs.",[17,8066,8068],{"id":8067},"the-future-of-frontier-discovery","The Future of Frontier Discovery",[22,8070,8071],{},"We are moving toward an era where the \"recipe\" and the research question matter more than the raw volume of compute. As pre-training becomes less of a differentiator, the ability to rapidly iterate and customize models becomes the primary driver of innovation. This shift lowers the barrier to entry, allowing builders to focus on answering specific, high-impact questions rather than spending years learning the mechanics of model training. The next frontier involves making test-time compute adaptive, ensuring that the resources spent on a task are proportional to its complexity, further optimizing the efficiency of AI systems.",{"title":56,"searchDepth":57,"depth":57,"links":8073},[8074,8075,8076],{"id":8026,"depth":57,"text":8027},{"id":8040,"depth":57,"text":8041},{"id":8067,"depth":57,"text":8068},[63],{"content_references":8079,"triage":8088},[8080,8082,8086],{"type":75,"title":8081,"context":78},"Auto Scientist",{"type":8083,"title":8084,"author":8085,"context":78},"other","Slow Death of Scaling","Unknown",{"type":8083,"title":8087,"context":78},"Open LLM Leaderboard",{"relevance":8089,"novelty":84,"quality":84,"actionability":84,"composite":8090,"reasoning":8091},5,4.35,"Category: AI & LLMs. The article discusses the shift from monolithic AI models to adaptive intelligence, addressing a key pain point for builders regarding the accessibility of AI tools. It provides insights on automating the training process and optimizing data flow, which are actionable strategies for developers looking to implement AI in their products.","\u002Fsummaries\u002Fe5d89401665344eb-democratizing-frontier-ai-automating-discovery-and-summary","2026-08-12 16:30:19","2026-08-13 03:25:01",{"title":8016,"description":56},{"loc":8092},"e5d89401665344eb","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XEd_SRVHBgU","summaries\u002Fe5d89401665344eb-democratizing-frontier-ai-automating-discovery-and-summary",[8101,99,100,101],"agents","The era of massive, monolithic pre-training is hitting a ceiling. By automating model training and data optimization, we can shift the focus from compute-heavy scaling to domain-specific innovation, allowing more builders to participate at the frontier.","This is a talk by an AI researcher arguing that the current \"narrow path\" of frontier AI development—dominated by a few labs and massive compute—is shifting toward decentralized, domain-specific model training. The speaker introduces their project, [Auto Scientist](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002FAI-Scientist), which automates the model training loop by co-optimizing data and architecture to allow for more accessible, efficient, and specialized AI development.",[101],"mpm9rGR3hkfb2dTRdoeQkXWMhW5e2pV-sFgiE1pzLNg",{"id":8107,"title":8108,"ai":8109,"body":8114,"categories":8190,"created_at":64,"date_modified":64,"description":56,"extension":65,"faq":64,"featured":66,"kicker_label":64,"meta":8191,"navigation":87,"path":8198,"published_at":8199,"question":64,"scraped_at":8199,"seo":8200,"sitemap":8201,"source_id":8202,"source_name":7856,"source_type":95,"source_url":8203,"stem":8204,"tags":8205,"thumbnail_url":64,"tldr":8207,"tweet":64,"unknown_tags":8208,"__hash__":8209},"summaries\u002Fsummaries\u002Fa4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary.md","Scalable AI Evaluation via Program Distillation",{"provider":7,"model":8,"input_tokens":8110,"output_tokens":8111,"processing_time_ms":8112,"cost_usd":8113},6326,538,2617,0.0023885,{"type":14,"value":8115,"toc":8184},[8116,8120,8123,8127,8130,8150,8154,8157,8177,8181],[17,8117,8119],{"id":8118},"the-problem-with-llm-as-a-judge","The Problem with LLM-as-a-Judge",[22,8121,8122],{},"Using LLMs to evaluate other models has become the industry standard, but it is fundamentally limited by high API costs, significant latency, and the 'black box' nature of LLM decisions. These factors make large-scale evaluation expensive and difficult to audit, as there is no clear logic behind why a specific score was assigned to a candidate output.",[17,8124,8126],{"id":8125},"program-distillation-from-prompts-to-code","Program Distillation: From Prompts to Code",[22,8128,8129],{},"The authors propose 'program distillation' as a solution: extracting the decision-making logic of an LLM judge into a committee of executable programs. By converting an LLM's evaluation criteria into code, the system gains several advantages:",[36,8131,8132,8138,8144],{},[39,8133,8134,8137],{},[7899,8135,8136],{},"Transparency:"," Programmatic judges are inherently inspectable and editable.",[39,8139,8140,8143],{},[7899,8141,8142],{},"Efficiency:"," They eliminate per-sample API costs, allowing for massive scaling of evaluation tasks.",[39,8145,8146,8149],{},[7899,8147,8148],{},"Performance:"," Across five datasets and four model families, these programmatic judges matched the performance of a 13B-parameter LLM judge.",[17,8151,8153],{"id":8152},"the-pajama-system","The PAJAMA System",[22,8155,8156],{},"The authors introduce PAJAMA, a framework that manages this programmatic evaluation process. It functions through three core mechanisms:",[7894,8158,8159,8165,8171],{},[39,8160,8161,8164],{},[7899,8162,8163],{},"Synthesis:"," It synthesizes a committee of programs to act as judges.",[39,8166,8167,8170],{},[7899,8168,8169],{},"Aggregation:"," It combines the outputs of these programs into a single, joint verdict.",[39,8172,8173,8176],{},[7899,8174,8175],{},"Selective Escalation:"," It includes a fallback mechanism that routes low-confidence cases to an LLM, ensuring that the system maintains high accuracy while keeping the majority of traffic on the cheaper, faster programmatic path.",[17,8178,8180],{"id":8179},"beyond-evaluation-reward-signals","Beyond Evaluation: Reward Signals",[22,8182,8183],{},"Beyond simple evaluation, the authors demonstrate that these programmatic judges can generate high-quality, low-cost reward signals for training other models. On the RewardBench benchmark, a reward model trained on labels generated by these programs outperformed one trained on proprietary LLM labels, while operating at two orders of magnitude lower API cost.",{"title":56,"searchDepth":57,"depth":57,"links":8185},[8186,8187,8188,8189],{"id":8118,"depth":57,"text":8119},{"id":8125,"depth":57,"text":8126},{"id":8152,"depth":57,"text":8153},{"id":8179,"depth":57,"text":8180},[63],{"content_references":8192,"triage":8195},[8193],{"type":8083,"title":8194,"context":78},"RewardBench",{"relevance":8089,"novelty":84,"quality":84,"actionability":83,"composite":8196,"reasoning":8197},4.15,"Category: AI & LLMs. The article presents a novel approach to AI evaluation that addresses key pain points such as cost and transparency, which are critical for product builders. It introduces the PAJAMA system, which could be directly applicable for developers looking to implement efficient evaluation mechanisms in their AI products.","\u002Fsummaries\u002Fa4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary","2026-07-29 03:12:17",{"title":8108,"description":56},{"loc":8198},"a4cc3af3b10be34a","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.22561","summaries\u002Fa4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary",[99,100,8206,101],"llm","PAJAMA replaces expensive LLM-as-a-judge systems with a committee of distilled programs, reducing costs while maintaining performance and increasing transparency.",[101],"WXIbXiLHvXWj0n970j8mnB2FnOzCghD6XOvTonA9Zjg"]