1 00:00:03,030 --> 00:00:05,840 Evan: Welcome to Daily Paper Cast. 2 00:00:05,984 --> 00:00:14,924 Evan: Today's paper comes from Hugging Face's daily paper list of September fifteenth, twenty twenty-six, with one hundred and fifty-six upvotes. 3 00:00:14,976 --> 00:00:21,616 Ashley: The paper is titled: Dream-RSI: Recursive Self-Improvement through Evolving Worlds. 4 00:00:21,672 --> 00:00:25,762 Ashley: The first two authors are Tong Zheng and Xidong Wu from Google. 5 00:00:25,762 --> 00:00:29,572 The corresponding author is Xidong Wu, also from Google. 6 00:00:29,616 --> 00:00:32,876 Evan: Alright, let's dive into the background of this paper. 7 00:00:32,876 --> 00:00:38,636 Why is recursive self-improvement becoming so vital for autonomous AI agents? 8 00:00:38,688 --> 00:00:45,788 Ashley: Recursive self-improvement, or RSI, has emerged as an ambitious goal for autonomous AI systems. 9 00:00:45,788 --> 00:00:49,058 The concept revolves around an iterative discovery loop. 10 00:00:49,058 --> 00:00:56,768 Here, agents generate candidate solutions, evaluate outcomes, incorporate feedback, and refine future iterations. 11 00:00:56,902 --> 00:01:02,472 Evan: And these discovery loops have driven progress in various fields, right? 12 00:01:02,520 --> 00:01:03,570 Ashley: Exactly. 13 00:01:03,570 --> 00:01:11,550 They’ve contributed to algorithm design, open-ended mathematical optimization, systems design, and agent self-improvement. 14 00:01:11,550 --> 00:01:15,240 These improvements often lead to more capable AI systems. 15 00:01:15,288 --> 00:01:20,168 Evan: But as these systems advance, what challenges do they encounter? 16 00:01:20,232 --> 00:01:24,942 Ashley: The key challenge is long-horizon exploration over vast search spaces. 17 00:01:24,942 --> 00:01:27,972 Effective exploration strategies are critical here. 18 00:01:27,972 --> 00:01:35,732 Poor exploration can waste substantial computation and time, severely limiting the efficiency and scalability of RSI. 19 00:01:35,784 --> 00:01:41,024 Evan: So what have been the limitations of existing approaches? 20 00:01:41,088 --> 00:01:47,178 Ashley: Current approaches generally rely on manually designed exploration strategies that remain mostly fixed. 21 00:01:47,178 --> 00:01:55,388 These fixed strategies can't improve based on accumulated discovery experience and often misallocate resources to ineffective search directions. 22 00:01:55,470 --> 00:02:00,760 Evan: And how does Dream-RSI propose to address these limitations? 23 00:02:00,846 --> 00:02:05,826 Ashley: Dream-RSI introduces a scalable and recursively self-improving framework. 24 00:02:05,826 --> 00:02:11,856 The main idea is to make exploration explicit and programmable through a lightweight orchestration layer. 25 00:02:11,856 --> 00:02:18,476 This controls branching, parallel exploration, and stopping decisions without altering the underlying coding agent. 26 00:02:18,528 --> 00:02:24,428 Evan: So the framework allows for dynamic policy changes instead of fixed strategies. 27 00:02:24,428 --> 00:02:27,988 How does it secure feedback to refine these policies? 28 00:02:28,032 --> 00:02:34,812 Ashley: The key insight is that accumulated discovery history can act as a replay simulator over the realized search space. 29 00:02:34,812 --> 00:02:44,202 Dream-RSI constructs replay simulators from historical discovery trees, enabling the system to evaluate and refine exploration policies through 'dreaming'. 30 00:02:44,202 --> 00:02:52,392 This process provides immediate, low-cost off-policy feedback, bypassing the need for repetitive, expensive online evaluations. 31 00:02:52,440 --> 00:02:55,140 Evan: That sounds very resource-efficient. 32 00:02:55,140 --> 00:02:58,820 How does the self-improvement loop work in Dream-RSI? 33 00:02:58,872 --> 00:03:11,052 Ashley: In Dream-RSI, the self-improvement loop has three core stages: One, Online Explore, where the current exploration policy guides the coding agent to expand the discovery tree and log historical traces. 34 00:03:11,052 --> 00:03:17,672 Two, Construct Replay Simulator, where the generated discovery tree is turned into a reusable replay simulator. 35 00:03:17,672 --> 00:03:29,112 And three, Dreaming-based Policy Improvement, where the agent 'dreams' up a pool of alternative policies, simulates them in the replay simulator, and continuously refines its strategy using the feedback. 36 00:03:29,160 --> 00:03:40,920 Evan: So Dream-RSI essentially forms a loop where each improved policy is redeployed online, driving further discoveries and expanding the simulator pool? 37 00:03:40,968 --> 00:03:41,728 Ashley: Correct. 38 00:03:41,728 --> 00:03:48,568 This recursive loop not only improves exploration quality but also significantly reduces discovery costs. 39 00:03:48,568 --> 00:03:56,948 Dream-RSI has shown competitive or improved discovery quality while cutting down on computational expenses in several sets of experiments. 40 00:03:57,000 --> 00:03:59,890 Evan: That's the end of the Introduction section. 41 00:03:59,890 --> 00:04:01,920 Let's move on to... 42 00:04:03,170 --> 00:04:07,780 Evan: Okay Ashley, let's dive into the methods proposed in this paper. 43 00:04:07,780 --> 00:04:11,290 How does Dream-RSI actually work in practice? 44 00:04:11,354 --> 00:04:17,204 Ashley: Dream-RSI operates through a recursive self-improvement loop consisting of three core stages. 45 00:04:17,204 --> 00:04:26,024 The first stage is Online Exploration, where the current exploration policy guides a coding agent to expand a discovery tree and log historical traces. 46 00:04:26,024 --> 00:04:30,274 This stage ensures that exploration is both explicit and programmable. 47 00:04:30,338 --> 00:04:34,178 Evan: You mentioned the concept of a discovery tree earlier. 48 00:04:34,178 --> 00:04:38,618 Could you explain what it is and how it functions in this context? 49 00:04:38,666 --> 00:04:39,796 Ashley: Certainly. 50 00:04:39,796 --> 00:04:44,806 A discovery tree begins with a root node, representing the initial state of the workspace. 51 00:04:44,806 --> 00:04:51,106 Each non-root node has a primary parent and records the outcome of a new generation-evaluation attempt. 52 00:04:51,106 --> 00:04:58,556 These nodes preserve inherited history, including files, generated artifacts, diagnostics, and evaluation scores. 53 00:04:58,556 --> 00:05:02,886 The tree grows as new attempts are made, logging every decision and result. 54 00:05:02,930 --> 00:05:09,670 Evan: So, it's essentially a structured log of all exploration actions and results. 55 00:05:09,670 --> 00:05:12,630 What's the next stage after the tree is constructed? 56 00:05:12,674 --> 00:05:13,384 Ashley: Right. 57 00:05:13,384 --> 00:05:16,774 The second stage is the Construction of the Replay Simulator. 58 00:05:16,774 --> 00:05:21,414 Here, the recorded discovery tree is converted into a replay simulator pool. 59 00:05:21,414 --> 00:05:24,544 This pool allows for dreaming-based policy improvements. 60 00:05:24,544 --> 00:05:33,274 The simulator makes it possible to evaluate alternative exploration policies without additional online deployments, by replaying historical discoveries. 61 00:05:33,398 --> 00:05:37,138 Evan: And how exactly does this replay simulator work? 62 00:05:37,202 --> 00:05:40,612 Ashley: Think of it like a model-based reinforcement learning setup. 63 00:05:40,612 --> 00:05:45,382 The replay simulator serves as a grounded model of the observed discovery space. 64 00:05:45,382 --> 00:05:52,042 Alternative policies can navigate this model, exploring different branches and combinations that were recorded earlier. 65 00:05:52,042 --> 00:05:58,242 Because all outcomes are already stored, evaluating a new strategy is much faster and cost-effective. 66 00:05:58,358 --> 00:06:03,108 Evan: So, it eliminates the need for expensive online evaluations. 67 00:06:03,108 --> 00:06:07,578 What happens in the final stage, the dreaming-based policy improvement? 68 00:06:07,634 --> 00:06:14,694 Ashley: In this stage, the agent uses the replay simulator to 'dream' up and evaluate a massive pool of alternative policies. 69 00:06:14,694 --> 00:06:20,804 These candidate policies are simulated, and rapid feedback is gathered to continuously refine the strategy. 70 00:06:20,804 --> 00:06:29,274 The best-performing policy is then redeployed online, which adds to the discovery history and further expands the simulator pool for the next cycle. 71 00:06:29,330 --> 00:06:30,730 Evan: Fascinating. 72 00:06:30,730 --> 00:06:35,010 It sounds like a robust mechanism for continuous self-improvement. 73 00:06:35,010 --> 00:06:38,830 How is performance measured and ensured during this process? 74 00:06:38,882 --> 00:06:46,912 Ashley: Performance is measured using a three-component replay objective: discovery quality, execution cost, and parallelism. 75 00:06:46,912 --> 00:06:55,122 The replay score combines the best solution quality attained during the replay, the number of attempted generations, and a parallelism bonus. 76 00:06:55,122 --> 00:06:59,402 This ensures a balanced focus on discovery effectiveness and efficiency. 77 00:06:59,510 --> 00:07:05,850 Evan: Can you give an example of how this works during the replay evaluation phase? 78 00:07:05,906 --> 00:07:06,706 Ashley: Sure. 79 00:07:06,706 --> 00:07:14,696 During offline evaluation, the method constructs and evaluates multiple policy versions using the fixed history of discovery trees. 80 00:07:14,696 --> 00:07:19,546 For each version, the policy's performance is assessed based on replay trajectories. 81 00:07:19,546 --> 00:07:25,986 Successful decisions and recurring failures are identified, and the feedback is utilized to revise the policy. 82 00:07:25,986 --> 00:07:32,206 This iterative process continues until the best policy version is selected for the next online deployment. 83 00:07:32,318 --> 00:07:40,378 Evan: It sounds like a lot of thought has been put into making every exploration step and policy change as efficient as possible. 84 00:07:40,378 --> 00:07:45,078 How is the decision-making structured in both online and offline phases? 85 00:07:45,122 --> 00:07:56,532 Ashley: In the online phase, the exploration policy observes the discovery tree and selects nodes to explore next, considering parallel execution where multiple nodes are processed concurrently. 86 00:07:56,532 --> 00:08:03,702 Each decision round attempts and evaluates new candidates, and when the tree reaches completion, the final state is recorded. 87 00:08:03,746 --> 00:08:09,166 Evan: And in the offline phase, how do they handle these evaluations? 88 00:08:09,218 --> 00:08:15,748 Ashley: In the offline phase, each policy version is evaluated separately across all historical trees. 89 00:08:15,748 --> 00:08:21,068 Every decision and outcome is deterministic, replaying recorded nodes as needed. 90 00:08:21,068 --> 00:08:27,478 The replay evaluates how far to pursue each opened branch, group parallel attempts, and when to stop. 91 00:08:27,478 --> 00:08:33,538 Hence, policy development is solely based on revealed nodes to ensure no new data leakage. 92 00:08:33,662 --> 00:08:36,382 Evan: Wow, that’s impressively detailed. 93 00:08:36,382 --> 00:08:47,342 So to sum it up, Dream-RSI leverages historical discovery data to create a sophisticated replay simulator for evaluating and refining exploration policies efficiently. 94 00:08:47,402 --> 00:08:48,502 Ashley: Exactly. 95 00:08:48,502 --> 00:08:57,852 By converting discovery histories into a replay simulator, Dream-RSI can consistently improve exploration strategies at a relatively low cost. 96 00:08:57,852 --> 00:09:02,102 This approach ensures that the framework is both scalable and adaptable. 97 00:09:02,162 --> 00:09:05,312 Evan: This marks the end of the Method section. 98 00:09:05,312 --> 00:09:12,862 Let's now discuss the results and experiments to see how well Dream-RSI performs in practice. 99 00:09:14,115 --> 00:09:18,035 Evan: Alright, Ashley, let’s talk about the experiments and results. 100 00:09:18,035 --> 00:09:21,115 How did the authors evaluate Dream-RSI? 101 00:09:21,171 --> 00:09:32,081 Ashley: The authors conducted a comprehensive evaluation across three scientific discovery domains: algorithm engineering, mathematical optimization, and GPU kernel engineering. 102 00:09:32,081 --> 00:09:40,911 They compared Dream-RSI with both fixed exploration strategies and strong task-specific baseline methods to measure its effectiveness and efficiency. 103 00:09:40,971 --> 00:09:42,371 Evan: Interesting. 104 00:09:42,371 --> 00:09:45,411 Let’s start with algorithm engineering. 105 00:09:45,411 --> 00:09:48,091 What specific tasks did they test? 106 00:09:48,147 --> 00:09:53,647 Ashley: In algorithm engineering, they focused on the Lasso Regularization Path discovery task. 107 00:09:53,647 --> 00:10:00,247 This task is crucial in high-dimensional statistics, often used for model selection and cross-validation. 108 00:10:00,247 --> 00:10:08,587 They used a benchmark setting involving 17 synthetic instances to explore the efficiency and correctness of the discovered implementations. 109 00:10:08,703 --> 00:10:12,903 Evan: And how did Dream-RSI perform on these tasks? 110 00:10:12,963 --> 00:10:14,603 Ashley: The results were impressive. 111 00:10:14,603 --> 00:10:33,923 For instance, Dream-RSI using the Gemini-3.1 Pro backend reduced runtime across six held-out datasets from 3587 milliseconds to 2931 milliseconds while significantly lowering the number of discovery-agent calls from 550 to 317. 112 00:10:33,923 --> 00:10:48,963 Similarly, with the Gemini-3.7-Flash backend, it improved average runtime from 2517 milliseconds to 2351 milliseconds while reducing calls from 3200 to 1879. 113 00:10:49,011 --> 00:10:53,701 Evan: That’s a substantial improvement in both runtime and computation cost. 114 00:10:53,701 --> 00:10:56,271 How did it compare to other standard solvers? 115 00:10:56,331 --> 00:11:03,361 Ashley: Compared to standard solvers like sklearn and glmnet, Dream-RSI outperformed them in all held-out datasets. 116 00:11:03,361 --> 00:11:13,591 It also achieved lower average downstream runtime compared to SimpleTES, which used a significantly larger computational budget of 51,200 generations. 117 00:11:13,635 --> 00:11:15,135 Evan: Fantastic. 118 00:11:15,135 --> 00:11:18,215 Let’s move on to mathematical optimization. 119 00:11:18,215 --> 00:11:20,775 What kind of tasks were involved here? 120 00:11:20,835 --> 00:11:28,895 Ashley: They evaluated Dream-RSI on three tasks: the Sum–Difference Problem, Circle Packing, and Autocorrelation Inequalities. 121 00:11:28,895 --> 00:11:35,175 These tasks are aimed at discovering high-quality solutions under task-specific mathematical constraints. 122 00:11:35,235 --> 00:11:41,115 Evan: And how did Dream-RSI fare in these mathematical optimization tasks? 123 00:11:41,163 --> 00:11:44,943 Ashley: In these tasks, Dream-RSI showed notable improvements. 124 00:11:44,943 --> 00:11:52,023 For the Sum–Difference Problem, it achieved a better score than SimpleTES with a score of 1.145427. 125 00:11:52,023 --> 00:11:56,603 In Circle Packing, it reached an optimal score matching other leading methods. 126 00:11:56,603 --> 00:12:07,483 And for Autocorrelation Inequalities, while SimpleTES required 51,200 generations, Dream-RSI achieved competitive results with far fewer generations. 127 00:12:07,599 --> 00:12:14,469 Evan: It seems Dream-RSI managed to maintain high performance even with fewer generations. 128 00:12:14,469 --> 00:12:18,259 How about the GPU kernel engineering experiments? 129 00:12:18,315 --> 00:12:26,725 Ashley: In the third domain, they focused on tasks from KernelBench like VGG16, LayerNorm, ConvDiv, and ConvMax. 130 00:12:26,725 --> 00:12:35,535 These tasks required optimization of execution performance measured as inverse runtime or other performance scores subject to correctness checks. 131 00:12:35,595 --> 00:12:39,335 Evan: And what were the key findings in this domain? 132 00:12:39,387 --> 00:12:53,787 Ashley: On VGG16 and LayerNorm tasks, Dream-RSI achieved comparable performance using significantly fewer generations—2.43 times fewer for VGG16 and 1.79 times fewer for LayerNorm. 133 00:12:53,787 --> 00:13:05,747 Similarly, for ConvDiv and ConvMax tasks, Dream-RSI achieved 2.09 times and 1.44 times higher performance, respectively, with comparable discovery budgets. 134 00:13:05,881 --> 00:13:14,221 Evan: So overall, Dream-RSI consistently improved performance across diverse tasks with reduced computational expense. 135 00:13:14,221 --> 00:13:17,171 Any final thoughts on the experimental validation? 136 00:13:17,235 --> 00:13:26,975 Ashley: The experiments demonstrate that by leveraging historical discovery data, Dream-RSI can make exploration both efficient and effective across various domains. 137 00:13:26,975 --> 00:13:37,355 This approach not only improves discovery quality but also substantially cuts down on discovery costs, proving its robustness and adaptability in different scientific fields. 138 00:13:37,419 --> 00:13:40,039 Evan: That’s the end of the Experiment section. 139 00:13:40,039 --> 00:13:45,199 Next, we will dive into the related work and the discussion of this paper. 140 00:13:46,445 --> 00:13:49,405 Evan: Let's move on to the Related Work section. 141 00:13:49,469 --> 00:13:56,649 Ashley: The paper situates Dream-RSI within the broader context of AI-driven scientific and algorithmic discovery methods. 142 00:13:56,649 --> 00:14:07,569 Many modern discovery systems rely on large language models, or LLMs, to iteratively generate, evaluate, and refine candidate solutions using prior artifacts and feedback. 143 00:14:07,569 --> 00:14:16,569 Examples include systems like AlphaEvolve, OpenEvolve, CodeEvolve, ShinkaEvolve, PACEvolve, DeltaEvolve, and MLEvolve. 144 00:14:16,613 --> 00:14:20,063 Evan: These systems sound quite similar in their approach. 145 00:14:20,063 --> 00:14:22,933 What sets Dream-RSI apart from them? 146 00:14:22,997 --> 00:14:33,927 Ashley: While all these systems continuously improve their solutions, Dream-RSI takes it a step further by emphasizing exploration itself as a meta-level optimization problem. 147 00:14:33,927 --> 00:14:43,797 Unlike other systems that treat exploration as static or fixed, Dream-RSI dynamically updates exploration policies using historical discovery data. 148 00:14:43,913 --> 00:14:53,383 Evan: So Dream-RSI changes the focus from just optimizing solutions to also optimizing the exploration strategies. 149 00:14:53,383 --> 00:14:57,493 Are there other systems that emphasize this kind of exploration? 150 00:14:57,557 --> 00:15:05,527 Ashley: Yes, there's a growing interest in adaptive discovery infrastructures, as seen with SkyDiscover, SwarmResearch, and EvoX. 151 00:15:05,527 --> 00:15:13,027 These systems dynamically orchestrate multiple search branches and optimize search strategies rather than merely candidate solutions. 152 00:15:13,027 --> 00:15:24,717 However, Dream-RSI distinguishes itself by converting completed discovery histories into reusable replay simulators, allowing policy improvement to occur offline and at low cost. 153 00:15:24,773 --> 00:15:26,193 Evan: Interesting. 154 00:15:26,193 --> 00:15:31,213 How does this method for utilizing historical data compare to other approaches? 155 00:15:31,277 --> 00:15:39,617 Ashley: Historically, agent experiences have been reused in various ways, like context memory, reusable skills, or training signals. 156 00:15:39,617 --> 00:15:49,967 For instance, DeltaEvolve structures evolutionary history through semantic deltas, while SwarmResearch and MLEvolve use cross-branch information to guide subsequent searches. 157 00:15:49,967 --> 00:16:00,297 Dream-RSI, however, offers a distinct advantage by organizing exploration history into replay simulators where many exploration controllers can be evaluated cheaply. 158 00:16:00,341 --> 00:16:08,861 Evan: So, instead of just using historical data for context or memory, Dream-RSI leverages it as a dynamic simulator. 159 00:16:08,861 --> 00:16:13,241 How does this impact training signals for improving exploration strategies? 160 00:16:13,301 --> 00:16:23,741 Ashley: This transformation of discovery experiences into reusable feedback addresses the high cost and scarcity of training signals typically associated with exploration strategies. 161 00:16:23,741 --> 00:16:31,641 It allows for efficient evaluation and iterative improvement of exploration policies without the need for extensive new data collection. 162 00:16:31,685 --> 00:16:35,185 Evan: That's an innovative approach to handling training signals. 163 00:16:35,185 --> 00:16:39,665 How does Dream-RSI fit within the research on self-evolving agents? 164 00:16:39,725 --> 00:16:42,985 Ashley: Self-evolving agents are another relevant area. 165 00:16:42,985 --> 00:16:50,215 These agents improve their components during interaction, such as model weights, harnesses, contexts, skills, and rubrics. 166 00:16:50,215 --> 00:16:58,765 Dream-RSI contributes to this field by focusing on optimizing meta-level mechanisms, including search strategies and self-improvement procedures. 167 00:16:58,765 --> 00:17:10,985 The recursive nature of Dream-RSI enables continuous off-policy evaluation and refinement, ensuring that exploration controllers are consistently upgraded without rerunning the underlying discovery process. 168 00:17:11,115 --> 00:17:20,735 Evan: So Dream-RSI not only advances exploration strategies but also fits well into the broader context of self-evolving agents. 169 00:17:20,735 --> 00:17:26,825 What are the implications for the future of AI-driven discovery, as suggested by the related work? 170 00:17:26,885 --> 00:17:28,975 Ashley: The implications are significant. 171 00:17:28,975 --> 00:17:41,885 By reducing the costs associated with exploration policy improvement and making such improvements more adaptive and scalable, Dream-RSI paves the way for more robust AI-driven discovery systems. 172 00:17:41,885 --> 00:17:50,925 As the field continues to evolve, methods like Dream-RSI that leverage historical data efficiently will likely become integral to future advancements. 173 00:17:50,981 --> 00:17:54,671 Evan: This concludes the Related Work section. 174 00:17:54,671 --> 00:18:01,361 Let's now move to the discussion to see what insights and conclusions the authors have drawn from their research. 175 00:18:02,622 --> 00:18:08,992 Evan: We've gone through the introduction, methods, experiments, and related work sections of this paper. 176 00:18:08,992 --> 00:18:13,182 Now, let’s summarize the key contributions and takeaways. 177 00:18:13,260 --> 00:18:19,960 Ashley: Dream-RSI brings forward an innovative approach to recursive self-improvement for autonomous AI systems. 178 00:18:19,960 --> 00:18:30,130 Its primary contribution is converting completed discovery histories into replay simulators, allowing for efficient meta-exploration policy evaluation and improvement. 179 00:18:30,174 --> 00:18:38,574 Evan: And this replay simulator concept significantly shifts the dynamics of exploration in AI systems, doesn't it? 180 00:18:38,622 --> 00:18:39,732 Ashley: Exactly. 181 00:18:39,732 --> 00:18:51,012 By using historical discovery data as a replay simulator, Dream-RSI can conduct offline evaluation and refinement of exploration strategies at a reduced cost. 182 00:18:51,012 --> 00:18:58,402 This transformation from static context to an active simulator is a game-changer for long-horizon exploration. 183 00:18:58,446 --> 00:19:11,666 Evan: We’ve also seen how well this approach works in practice, with substantial improvements across various domains like algorithm engineering, mathematical optimization, and GPU kernel engineering. 184 00:19:11,718 --> 00:19:20,808 Ashley: Yes, Dream-RSI not only improves the quality of discoveries but also cuts down on computational costs. 185 00:19:20,808 --> 00:19:29,418 It consistently outperformed fixed exploration strategies and other strong baselines while using fewer resources. 186 00:19:29,538 --> 00:19:43,318 Evan: Another unique aspect is Dream-RSI's ability to refine exploration strategies dynamically, turning the static replay data into actionable feedback, which is then used to improve policies continuously. 187 00:19:43,374 --> 00:19:44,074 Ashley: Right. 188 00:19:44,074 --> 00:19:55,194 This continuous loop of exploration, dreaming, and policy improvement makes Dream-RSI a robust tool for advancing AI capabilities in scientific discovery and beyond. 189 00:19:55,254 --> 00:20:00,754 Evan: This brings us to the end of our discussion on Dream-RSI. 190 00:20:00,798 --> 00:20:05,158 Ashley: We hope you found today's episode insightful and thought-provoking. 191 00:20:05,214 --> 00:20:13,494 Evan: Make sure to tune in for our next episode, where we will delve into another exciting paper from the world of AI research. 192 00:20:13,542 --> 00:20:18,482 Ashley: Thanks for listening, and see you next time on Daily Paper Cast!