Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 40/100, so the next move is to benchmark the weakest pillars before repeating the format.
broadcast-heavy conference agenda analysis
ICLR - Diffusion On Syntax Trees For Program Synthesis - Workshops - Towards Agentic AI for Science: Hypothesis Generation, Comprehension...
This broadcast-heavy conference in Technology / AI / Startup shows 40 visible agenda rows from iclr.cc and scores 40/100: a moderate design signal with incomplete evidence. The clearest public signals sit in Future-of-Work Fit and Problem Specificity; the main limits are Follow Through and Participation Architecture. Visible mechanisms include Participant work, Feedback, Network design, and Learning transfer. The public record does not show follow-up or tracking, so the score should be read as design intent... A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as a broadcast-heavy conference, with the strongest visible signal in future-of-work fit and problem specificity and the biggest open question around follow through and participation architecture. The practical test is whether the published agenda connects the room to post-event continuation and evidence. This page is an original public-evidence analysis, not a copy of the source agenda or an endorsement of the event. The score places the visible agenda in the moderate design signal band. The strongest visible pillars are Future-of-Work Fit, Problem Specificity, and Learning Transfer; the thinnest visible pillars are Follow Through, Network Design, and Participation Architecture. Visible mechanisms include Participant work, Feedback, Network design, and Learning transfer. The extracted agenda preview includes 77 visible rows. The most common formats are Unknown, Presentation, and Keynote; the most common inferred purposes are Unknown, Knowledge Transfer, and Expert Framing.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?30
Participation Architecture - 30/100. Participant work, contribution, interaction, and alternatives to passive broadcast.Missing: Turn passive airtime into participant work: practice, sensemaking, decisions, critique, or artifact creation.
Follow Through?5
Follow Through - 5/100. Owners, dates, commitments, progress checks, and accountability after the room.Missing: Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
Problem Specificity?58
Problem Specificity - 58/100. A clear costly problem, objective, decision, or performance target.
Personalization?36
Personalization - 36/100. Role, path, goal, preparation, or connection tailoring for participants.
Network Design?30
Network Design - 30/100. Structured weak ties, bridge-building, mixers, and relationship persistence.Missing: Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
Learning Transfer?55
Learning Transfer - 55/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.
Evidence Maturity?45
Evidence Maturity - 45/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?64
Future-of-Work Fit - 64/100. Value against time, hybrid reality, accessibility, AI, and meeting load.
Fix the gaps
Field-tested exercises matched to this agenda's weakest pillars, from the exercise library.
Follow Through (5/100)
Exercises that strengthen it: Mental Toughness Workshop • WorkshopBank · 15% Solutions • WorkshopBank · Network Patches
Participation Architecture (30/100)
Exercises that strengthen it: Make A World · Awestruck 3 Minutes · Spectrum Mapping
Network Design (30/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Agenda Preview
The actual agenda we captured. Every block is classified by format and purpose. Open any block to see how we read it; the colored edge shows whether it is participant work, broadcast, logistics, or a showcase.
Room vs wrapper
6 percent of the 77 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventBlog Track PostersPoster sessionShowcase+
Format · ShowcasePoster sessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisOutcome inferred from formatInferred from format
all eventWorkshopsWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventWorkshopWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
5:30 PMOpening Remarks Lifu Huang VideoOpeningOrientation+
Format · BroadcastOpeningFormat not classified from the source; treated as a broadcast block by default.
Evidence basisNo participant output visible from this rowRead from source, no work signal
5:40 PMKeynote #1: Empowering Biomedical Discovery with "AI Scientists" Marinka Zitnik VideoKeynoteExpert framing+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisNo participant output visible from this rowRead from source, no work signal
6:25 PMKeynote #2: Toward Agentic AI Systems for Interpretable Scientific Equation Discovery Chandan Reddy VideoKeynoteExpert framing+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisNo participant output visible from this rowRead from source, no work signal
7:10 PMKeynote #3: Exploitation vs. Exploration in Sequential Decision Making Jingrui He VideoKeynoteExpert framing+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisSession topic references decisions or metrics; event-level follow-through is not shownRead from source
7:55 PMCoffee BreakBreakPacing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisOutcome inferred from formatInferred from format
8:05 PMKeynote #4: Preference-Guided Multi-Objective Optimization for Scientific Discovery Sanmi Koyejo VideoKeynoteExpert framing+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisNo participant output visible from this rowRead from source, no work signal
8:50 PMKeynote #5: Physics-Aware AI: Bridging Science Through Multi-Agent Reasoning Systems Markus J. Buehler VideoKeynoteExpert framing+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisNo participant output visible from this rowRead from source, no work signal
9:35 PMLunch BreakBreakPacing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisOutcome inferred from formatInferred from format
10:30 PMNeural Nonmyopic Bayesian Optimization in Dynamic Cost Settings Sang Truong ⋅ Duc Nguyen ⋅ Willie Neiswanger ⋅ Ryan-Rhys Griffiths ⋅ Stefano Ermon ⋅ Nick Haber ⋅ Sanmi Koyejo Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
10:45 PMLarge Language Models Are Innate Crystal Structure Generators Jingru Gan ⋅ Peichen Zhong ⋅ Yuanqi Du ⋅ Yanqiao Zhu ⋅ Chenru Duan ⋅ Haorui Wang ⋅ Daniel Schwalbe-Koda ⋅ Carla Gomes ⋅ Kristin Persson ⋅ Wei Wang LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
11:00 PMLLM-Augmented Chemical Synthesis and Design Decision Programs Haorui Wang ⋅ Jeff Guo ⋅ Lingkai Kong ⋅ Rampi Ramprasad ⋅ Philippe Schwaller ⋅ Yuanqi Du ⋅ Chao Zhang Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisSession topic references decisions or metrics; event-level follow-through is not shownRead from source
11:15 PMMOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses Zonglin Yang ⋅ Wanhao Liu ⋅ Ben Gao ⋅ Tong Xie ⋅ Yuqiang Li ⋅ Wanli Ouyang ⋅ Soujanya Poria ⋅ Erik Cambria ⋅ Dongzhan Zhou Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
11:30 PMLarge Language Models powered Neural Solvers for Generalized Vehicle Routing Problems Dao Tran ⋅ Quan Nguyen-Tri ⋅ Huynh Thi Thanh Binh ⋅ Thanh Tung Hoang Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
11:45 PMAgent S: An Open Agentic Framework that Uses Computers Like a Human Saaket Agashe ⋅ Jiuzhou Han ⋅ Shuyu Gan ⋅ Jiachen Yang ⋅ Ang Li ⋅ Xin Wang Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
12:00 AMCMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models 梁学辰 ⋅ Yangfan He ⋅ Meiling Tao ⋅ Yinghui XIA ⋅ Yijin Wang ⋅ Jianhui Wang ⋅ Kun Li ⋅ Jiayi Su ⋅ TIANYU SHI ⋅ Jun Wang ⋅ Yang Jingsong Video LinkWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
12:25 AMPanel Discussion Sanmi Koyejo ⋅ Marinka Zitnik ⋅ Yujun Yan ⋅ Beatrice Soh ⋅ Yarin Gal VideoPanelDiscussion+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:15 AMAstroAgents: A Multi-Agent AI for Hypothesis Generation from Mass Spectrometry Data Daniel Saeedi ⋅ Denise Buckner ⋅ Jose Aponte ⋅ Amirali Aghazadeh Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:30 AMML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code Xiangru Tang ⋅ Yuliang Liu ⋅ Zefan Cai ⋅ Daniel Shao ⋅ Junjie Lu ⋅ Yichi Zhang ⋅ Zexuan Deng ⋅ Helan Hu ⋅ Kaikai An ⋅ Ruijun Huang ⋅ Shuzheng Si ⋅ Chen...PresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMOrchestrating Tool Ecosystem of Drug Discovery with Intention-Aware LLM Agents Mingyu Derek Ma ⋅ Karina Zadorozhny ⋅ Jesse Swanson ⋅ Nathan Frey ⋅ Keunwoo Choi ⋅ Maksim Eremeev ⋅ Sabrina Mielke ⋅ Wenmo Sun ⋅ Melody Liu ⋅ Jonathan Wickes ⋅ Vladimir...PresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMA Simplified a priori Theory of Meaning; Nature Based AI 'First Principles' Marcus Abundis LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMENHANCING DIVERSITY AND NOVELTY IN TEXT GENERATION VIA MULTI-VIEW EMBEDDINGS Arash Lagzian ⋅ Srinivas Anumasa ⋅ Dianbo Liu Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMEvaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots Damir Nurtdinov ⋅ Aliaksei Korshuk ⋅ Alexei Kornaev ⋅ Alexander Maloletov Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMEmerging Multi-AI Agent Framework for Autonomous Agentic AI Solution Optimization Kamer Yuksel ⋅ Hassan Sawaf LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMHEP-JEPA: A foundation model for collider physics Jai Bardhan ⋅ Radhikesh Agrawal ⋅ Abhiram Tilak ⋅ Cyrin Neeraj ⋅ Subhadip Mitra LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMAgenticHypothesis: A Survey on Hypothesis Generation Using LLM Systems Adib Bazgir ⋅ Rama Madugula ⋅ Yuwen Zhang LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMAgentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions Mourad Gridach ⋅ Jay Nanavati ⋅ Christina Mack ⋅ Khaldoun Abidine ⋅ Lenon Mendes LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMAutomated Machine Learning Research via Agentic Exploration with Human Oversight Shervin Ardeshir ⋅ Navid Azizan Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMLLM AGENTS FOR LITERATURE TO CODE CONVERSION:CASE STUDY OF HEAT EXCHANGER DESIGN Sandeep Mishra ⋅ Vishal Jadhav ⋅ Shirish Karande ⋅ Venkataramana Runkana Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMMDCROW: AUTOMATING MOLECULAR DYNAMICS WORKFLOWS WITH LARGE LANGUAGE MODELS Sam Cox ⋅ Quintina Campbell ⋅ Jorge Medina ⋅ Brittany Watterson ⋅ Andrew White LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMEvolving RL: Discovering New Activation Functions using LLMs Kalyan V Nadimpalli ⋅ Shashank Reddy Chirra ⋅ Pradeep Varakantham ⋅ Stefan Bauer LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMProteinHypothesis: A Physics-Aware Chain of Multi-Agent RAG LLM for Hypothesis Generation in Protein Science Adib Bazgir ⋅ Rama Madugula ⋅ Yuwen Zhang LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMDynamic Knowledge Integration in Multi-Agent Systems for Content Inference Atsushi Yamamoto ⋅ Takumi Iida ⋅ Taito Naruki ⋅ Akihiko Katagiri ⋅ Yudai Koike ⋅ Ryuta Shimogauchi ⋅ Kota Shimomura ⋅ Eri Onami ⋅ Koki Inoue ⋅ Osamu Ito Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
1:45 AMAPPA : Agentic Preformulation Pathway Assistant Julius Lange ⋅ Leonid Komissarov ⋅ Nicole Wyttenbach ⋅ Andrea Anelli LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
2:45 AMClosing Remarks with Awards Yaoqing Yang ⋅ Yujun Yan ⋅ Beatrice SohClosingOrientation+
Format · BroadcastClosingFormat not classified from the source; treated as a broadcast block by default.
Evidence basisNo participant output visible from this rowRead from source, no work signal
all eventMain ConferenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventBlog Track PostersPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventWorkshopsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventCommunityUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventWorkshopWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventTimezone: America/LosAngelesUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
5:30 PMOpening Remarks Lifu Huang VideoOpening RemarksOrientation+
Format · BroadcastOpening RemarksFraming or welcome from the stage. Orients the room, not participatory.
Evidence basisMediumRead from source
5:40 PMKeynote #1: Empowering Biomedical Discovery with "AI Scientists" Marinka Zitnik VideoKeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
6:25 PMKeynote #2: Toward Agentic AI Systems for Interpretable Scientific Equation Discovery Chandan Reddy VideoKeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
7:10 PMKeynote #3: Exploitation vs. Exploration in Sequential Decision Making Jingrui He VideoKeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
7:55 PMCoffee BreakBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
8:05 PMKeynote #4: Preference-Guided Multi-Objective Optimization for Scientific Discovery Sanmi Koyejo VideoKeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
8:50 PMKeynote #5: Physics-Aware AI: Bridging Science Through Multi-Agent Reasoning Systems Markus J. Buehler VideoKeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
9:35 PMLunch BreakMealWellbeing+
Format · LogisticsMealA pacing block. Can carry unstructured networking, not scored as participant work.
Evidence basisMediumRead from source
10:30 PMNeural Nonmyopic Bayesian Optimization in Dynamic Cost Settings Sang Truong ⋅ Duc Nguyen ⋅ Willie Neiswanger ⋅ Ryan-Rhys Griffiths ⋅ Stefano Ermon ⋅ Nick Haber ⋅ Sanmi Koyejo Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
10:45 PMLarge Language Models Are Innate Crystal Structure Generators Jingru Gan ⋅ Peichen Zhong ⋅ Yuanqi Du ⋅ Yanqiao Zhu ⋅ Chenru Duan ⋅ Haorui Wang ⋅ Daniel Schwalbe-Koda ⋅ Carla Gomes ⋅ Kristin Persson ⋅ Wei Wang LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:00 PMLLM-Augmented Chemical Synthesis and Design Decision Programs Haorui Wang ⋅ Jeff Guo ⋅ Lingkai Kong ⋅ Rampi Ramprasad ⋅ Philippe Schwaller ⋅ Yuanqi Du ⋅ Chao Zhang Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:15 PMMOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses Zonglin Yang ⋅ Wanhao Liu ⋅ Ben Gao ⋅ Tong Xie ⋅ Yuqiang Li ⋅ Wanli Ouyang ⋅ Soujanya Poria ⋅ Erik Cambria ⋅ Dongzhan Zhou Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:30 PMLarge Language Models powered Neural Solvers for Generalized Vehicle Routing Problems Dao Tran ⋅ Quan Nguyen-Tri ⋅ Huynh Thi Thanh Binh ⋅ Thanh Tung Hoang Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
11:45 PMAgent S: An Open Agentic Framework that Uses Computers Like a Human Saaket Agashe ⋅ Jiuzhou Han ⋅ Shuyu Gan ⋅ Jiachen Yang ⋅ Ang Li ⋅ Xin Wang Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:00 AMCMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models 梁学辰 ⋅ Yangfan He ⋅ Meiling Tao ⋅ Yinghui XIA ⋅ Yijin Wang ⋅ Jianhui Wang ⋅ Kun Li ⋅ Jiayi Su ⋅ TIANYU SHI ⋅ Jun Wang ⋅ Yang Jingsong Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:25 AMPanel Discussion Sanmi Koyejo ⋅ Marinka Zitnik ⋅ Yujun Yan ⋅ Beatrice Soh ⋅ Yarin Gal VideoPanelDeliberation+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisMediumRead from source
1:15 AMAstroAgents: A Multi-Agent AI for Hypothesis Generation from Mass Spectrometry Data Daniel Saeedi ⋅ Denise Buckner ⋅ Jose Aponte ⋅ Amirali Aghazadeh Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:30 AMML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code Xiangru Tang ⋅ Yuliang Liu ⋅ Zefan Cai ⋅ Daniel Shao ⋅ Junjie Lu ⋅ Yichi Zhang ⋅ Zexuan Deng ⋅ Helan Hu ⋅ Kaikai An ⋅ Ruijun Huang ⋅ Shuzheng Si ⋅ Chen Sheng ⋅ Haozhe Zhao ⋅ Liang Chen ⋅ Tianyu Liu ⋅ Yujia Qin ⋅ Wangchunshu Zhou ⋅ Yilun Zhao ⋅ Zhiwei Jiang ⋅ Baobao Chang ⋅ Arman Cohan ⋅ Mark Gerstein Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMOrchestrating Tool Ecosystem of Drug Discovery with Intention-Aware LLM Agents Mingyu Derek Ma ⋅ Karina Zadorozhny ⋅ Jesse Swanson ⋅ Nathan Frey ⋅ Keunwoo Choi ⋅ Maksim Eremeev ⋅ Sabrina Mielke ⋅ Wenmo Sun ⋅ Melody Liu ⋅ Jonathan Wickes ⋅ Vladimir Gligorijevic ⋅ Richard Bonneau ⋅ Henri Dwyer ⋅ Kyunghyun Cho ⋅ Stephen Ra LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMA Simplified a priori Theory of Meaning; Nature Based AI 'First Principles' Marcus Abundis LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMENHANCING DIVERSITY AND NOVELTY IN TEXT GENERATION VIA MULTI-VIEW EMBEDDINGS Arash Lagzian ⋅ Srinivas Anumasa ⋅ Dianbo Liu Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMEvaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots Damir Nurtdinov ⋅ Aliaksei Korshuk ⋅ Alexei Kornaev ⋅ Alexander Maloletov Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMEmerging Multi-AI Agent Framework for Autonomous Agentic AI Solution Optimization Kamer Yuksel ⋅ Hassan Sawaf LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMHEP-JEPA: A foundation model for collider physics Jai Bardhan ⋅ Radhikesh Agrawal ⋅ Abhiram Tilak ⋅ Cyrin Neeraj ⋅ Subhadip Mitra LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMAgenticHypothesis: A Survey on Hypothesis Generation Using LLM Systems Adib Bazgir ⋅ Rama Madugula ⋅ Yuwen Zhang LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMAgentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions Mourad Gridach ⋅ Jay Nanavati ⋅ Christina Mack ⋅ Khaldoun Abidine ⋅ Lenon Mendes LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMAutomated Machine Learning Research via Agentic Exploration with Human Oversight Shervin Ardeshir ⋅ Navid Azizan Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMLLM AGENTS FOR LITERATURE TO CODE CONVERSION:CASE STUDY OF HEAT EXCHANGER DESIGN Sandeep Mishra ⋅ Vishal Jadhav ⋅ Shirish Karande ⋅ Venkataramana Runkana Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMMDCROW: AUTOMATING MOLECULAR DYNAMICS WORKFLOWS WITH LARGE LANGUAGE MODELS Sam Cox ⋅ Quintina Campbell ⋅ Jorge Medina ⋅ Brittany Watterson ⋅ Andrew White LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMEvolving RL: Discovering New Activation Functions using LLMs Kalyan V Nadimpalli ⋅ Shashank Reddy Chirra ⋅ Pradeep Varakantham ⋅ Stefan Bauer LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMProteinHypothesis: A Physics-Aware Chain of Multi-Agent RAG LLM for Hypothesis Generation in Protein Science Adib Bazgir ⋅ Rama Madugula ⋅ Yuwen Zhang LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMDynamic Knowledge Integration in Multi-Agent Systems for Content Inference Atsushi Yamamoto ⋅ Takumi Iida ⋅ Taito Naruki ⋅ Akihiko Katagiri ⋅ Yudai Koike ⋅ Ryuta Shimogauchi ⋅ Kota Shimomura ⋅ Eri Onami ⋅ Koki Inoue ⋅ Osamu Ito Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
1:45 AMAPPA : Agentic Preformulation Pathway Assistant Julius Lange ⋅ Leonid Komissarov ⋅ Nicole Wyttenbach ⋅ Andrea Anelli LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
2:45 AMClosing Remarks with Awards Yaoqing Yang ⋅ Yujun Yan ⋅ Beatrice SohClosing RemarksOrientation+
Format · BroadcastClosing RemarksClosing framing from the stage. Wraps the event, not participatory.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 40/100 and verified 40/100, then capped for agenda is mostly passive without visible outcome mechanics.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as a broadcast-heavy conference, with the strongest visible signal in future-of-work fit and problem specificity and the biggest open question around follow through and participation architecture. The practical test is whether the published agenda connects the room to post-event continuation and evidence.
Strongest signals: Future-of-Work Fit, Problem Specificity, and Learning Transfer. Weakest signals: Follow Through, Network Design, and Participation Architecture.
How This Agenda Could Improve +
- Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
- Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
- Turn passive airtime into participant work: practice, sensemaking, decisions, critique, or artifact creation.
Fastest next move: Add named owners, dated next steps, and a visible continuation path before treating the event as outcome-ready.
Role-Specific Reading +
Sponsor lens
Look beyond exposure. Strong sponsor value would show qualified interaction, problem work, buyer learning, customer evidence, or follow-up. The practical sponsor move is to look for structured introductions, buyer-seller fit, and relationship persistence.
Designer lens
The agenda is useful as a pattern sample from iclr.cc. Redesign attention should go first to the lowest-scoring pillars; in practice, turn the thinnest agenda blocks into participant work.
Executive lens
Treat the visible agenda as an operating plan. The executive move is to require owners, dates, and evidence before treating the event as strategic. If owners, proof, and follow-through are not visible, the public record does not yet prove strategic movement.
Aggregator lens
Treat the source URL as evidence, not decoration. The data-product move is to label the source boundary clearly before ranking the record before ranking or syndicating the record.
What GES Means Here +
The Gathering Effectiveness Score is a strict 0-100 public-evidence reading of the agenda across eight pillars. It rewards visible participant work, follow-through, transfer, network design, and proof mechanisms more than polish, speaker fame, attendance, or satisfaction.
Visible mechanisms: Participant work, Feedback, Network design, Learning transfer.
Evidence boundary: Scores reflect visible agenda/source evidence and should not be read as proof of causal event impact.
Limitations, Score Caps, and Review Flags +
Limitations
- No visible follow-up, progress monitoring, or longitudinal tracking.
- Passive stage formats dominate the visible agenda.
- No baseline measurement is visible.
Score caps
- 34: Agenda is mostly passive without visible outcome mechanics.
Review flags
- Fourth-loop score cap: Agenda is mostly passive without visible outcome mechanics.
- No source-backed follow-up, validation, baseline, tracking, or impact evidence.
Is this proof the event worked? +
No. This is a strict public-evidence reading of the agenda. Proof would require baseline, comparison, follow-up, attribution, and impact evidence beyond the listing.
What should a reader inspect first? +
Start with the source URL, then compare the eight pillar scores against the agenda rows. The biggest opportunities usually sit in follow-through, evidence maturity, and participant work.
Why publish weak records? +
Weak records are part of the map. They show where public agendas still describe sessions and speakers more often than outcomes, commitments, transfer, or proof.
How should I use the rows? +
Read the agenda rows as the visible design trace: formats, purposes, and evidence labels show what the public source made inspectable, not everything that happened in the room. This is a source-grounded interpretation of the public agenda record, not a copy of the source, and not an endorsement of the event.
Embed the verified badge +
This record is in the hand-verified gold set. Copy the snippet below to embed the verified badge on your own site.
<a href="https://unitedwetransform.com/events/evt_2025_iclr_diffusion_on_syntax_trees_for_program_synthesis_workshops_towards_agen"><img src="https://unitedwetransform.com/badge/ges-verified.svg" alt="GES verified by United We Transform" height="40"></a>Where To Go Next
Compare this agenda against other Technology / AI / Startup events scored on the same eight pillars.