Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 46/100, so the next move is to benchmark the weakest pillars before repeating the format.
Atlas/Events/ICML - WCA4Z Platform - Accelerating Legacy Code Modernization Using AI Agents, Deep Program Analysis, and - 2026 - Workshops
applied learning or working session agenda analysis
ICML - WCA4Z Platform - Accelerating Legacy Code Modernization Using AI Agents, Deep Program Analysis, and - 2026 - Workshops
This applied learning or working session in Academic / Research / Science shows 80 visible agenda rows from icml.cc and scores 46/100: a moderate design signal with incomplete evidence. The clearest public signals sit in Participation Architecture and Learning Transfer; the main limits are Follow Through and Network Design. 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... A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as an applied learning or working session, with the strongest visible signal in participation architecture and learning transfer and the biggest open question around follow through and network design. 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 Participation Architecture, Learning Transfer, and Problem Specificity; the thinnest visible pillars are Follow Through, Network Design, and Evidence Maturity. Visible mechanisms include Participant work, Feedback, Network design, Learning transfer, and Personalization. The extracted agenda preview includes 152 visible rows. The most common formats are Workshop, Unknown, and Presentation; the most common inferred purposes are Unknown, Knowledge Transfer, and Co Creation.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?70
Participation Architecture - 70/100. Participant work, contribution, interaction, and alternatives to passive broadcast.
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?54
Problem Specificity - 54/100. A clear costly problem, objective, decision, or performance target.
Personalization?52
Personalization - 52/100. Role, path, goal, preparation, or connection tailoring for participants.
Network Design?38
Network Design - 38/100. 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?69
Learning Transfer - 69/100. 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.Missing: Add baseline measurement, comparison logic, tracking, or post-event impact reporting so effectiveness is not inferred only from format.
Future-of-Work Fit?52
Future-of-Work Fit - 52/100. Value against time, hybrid reality, accessibility, AI, and meeting load.
Fix the gaps
Exercises matched to this agenda's weakest pillars, from the exercise library. Check each run sheet for its source and intended setting.
Follow Through (5/100)
Exercises that strengthen it: The Comfort Confession · 15% Solutions · Network Patches
Network Design (38/100)
Exercises that strengthen it: Izzat You? · Levitation · Sticks
Personalization (52/100)
Exercises that strengthen it: Journey Mapping · Paradigm Shift Clock · Photo Storytelling
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
42 percent of the 152 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventTutorialsTrainingSkill building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisParticipant work is implied by the formatInferred from format
all eventSpotlight 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 eventPosition Paper 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 eventJournal 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 eventExpo Talks, Demos, and WorkshopsWorkshopParticipant 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
all eventFoundations of Deep Generative Models: Understanding Memorization, Generalization, and ReasoningPresentationKnowledge 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
8:00 AM - 5:00 PMRecently, diffusion models, flow-based models, and autoregressive language models have emerged as a powerful class of deep generative models (DGMs) with remarkable generation capabilities across a wide range of applications, including image synthesis, video...WorkshopParticipant 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 eventThe 2nd Workshop of AI4NextG: AI and ML for Next-Generation WirelessWorkshopParticipant 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
8:00 AM - 5:00 PMJul 10, ASEM Ballroom 203PresentationKnowledge 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
all eventFailure Modes in Agentic AI: Reproducible Triggers, Trace Diagnostics, and Verified FixesPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, grand ballroom 104-105PresentationKnowledge 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
all eventTrustworthy AI for Good WorkshopWorkshopParticipant 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
8:00 AM - 5:00 PMJul 10, grand ballroom 103PresentationKnowledge 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
all event4th Structured Probabilistic Inference & Generative ModelingPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, HALL D1PresentationKnowledge 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
all eventLearning to Listen: ICML 2026 Workshop on Machine Learning for AudioWorkshopParticipant 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
8:00 AM - 5:00 PMJul 10, ASEM Ballroom 202PresentationKnowledge 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
all eventCulture x AI: Evaluating AI as a Cultural TechnologyPresentationKnowledge 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
8:00 AM - 5:00 PMGenerative AI is increasingly recognised as a social and cultural technology. These systems process an enormous amount of social data to produce novel cultural artefacts, such as text, images, and videos. While much progress has been made in evaluating...WorkshopParticipant 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 eventSecond Workshop on Technical AI Governance ResearchWorkshopParticipant 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
8:00 AM - 5:00 PMWe propose an ICML workshop on Technical AI Governance Research (TAIGR). TAIGR encompasses technical analysis and tools that support the effective governance of AI, such as evaluations, safeguards, and access controls. Despite increasing interest in TAIGR,...WorkshopParticipant 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 eventWorkshop on Weight-Space Symmetries: from Foundations to Practical ApplicationsWorkshopParticipant 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 eventContinual Adaptation at Scale: Towards Sustainable AIPresentationKnowledge 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
8:00 AM - 5:00 PMTraining Foundation Models (FMs) is currently so costly that only few can afford it. The immense data, compute, and energy demands are increasingly unsustainable. Continual adaptation offers a viable alternative, where AI models can learn quickly and...TrainingParticipant work+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisParticipant work is implied by the formatInferred from format
all eventScale: scalable learning and optimization for efficient multimodal AI agentsWorkshopParticipant 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
8:00 AM - 5:00 PMJul 10, ASEM Ballroom 201PresentationKnowledge 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
all eventHigh Dimensional Learning Dynamics: the Science of ScalingPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, ROOM E1 - E4PresentationKnowledge 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
all eventThe 2nd Workshop on Epistemic Intelligence in Machine Learning: Learning under Unknown Unknowns for Real-world ImpactWorkshopParticipant 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
8:00 AM - 5:00 PMJul 10, ROOM E5 - E6PresentationKnowledge 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
all eventDeep Learning for Code: Towards Human-Centered Coding AgentsPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, HALL B2PresentationKnowledge 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
all eventGenerative and Agentic AI for BiologyPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, HALL D2PresentationKnowledge 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
all eventWorkshop on Mechanistic InterpretabilityWorkshopParticipant 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
8:00 AM - 5:00 PMJul 10, HALL CPresentationKnowledge 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
all eventRLxF: RL from World FeedbackPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, grand ballroom 101-102PresentationKnowledge 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
all eventAI for Law WorkshopWorkshopParticipant 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
8:00 AM - 5:00 PMRecent advances in machine learning have substantially improved general-purpose reasoning, multimodal understanding, and test-time scaling. Yet law remains a uniquely demanding and high-stakes domain that exposes the limits of generic AI progress. Many legal...WorkshopParticipant 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 eventCombining Theory and Benchmarks: Towards A Virtuous Cycle to Understand and Guarantee Foundation Model PerformancePresentationKnowledge 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
8:00 AM - 5:00 PMBenchmarks such as HELM [1] and Big-Bench [2] have significantly advanced quantitative model evaluation. However, current practice remains largely empirical and while measuring performance, does not provide guarantees on what capabilities a model has, when...WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisSession topic references decisions or metrics; event-level follow-through is not shownRead from source
all eventFrom Frames to Stories (F2S): Toward Reliable, Controllable and Trustworthy Long-Horizon Video GenerationWorkshopParticipant 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
8:00 AM - 5:00 PMVideo generation has advanced rapidly for short clips, but minutes-long, multi-shot generation remains unreliable due to compounding errors, identity drift, and weak long-range coherence. Long-horizon video therefore provides a demanding testbed for...PanelDiscussion+
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
all eventGraph Foundation Models: A New Era for Graph Machine LearningPresentationKnowledge 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
8:00 AM - 5:00 PMGraph-structured data are ubiquitous across science and industry, yet today’s graph machine learning (GML) pipelines remain largely task- and dataset-specific, limiting robustness and transferability. This workshop brings together researchers and...WorkshopExpert framing+
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 eventAI as a Tool for Mathematics, Computer Science, and Machine LearningPresentationKnowledge 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
8:00 AM - 5:00 PMJul 10, auditoriumPresentationKnowledge 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
all eventPlanning in The Era of Language Models (LM4Plan)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
8:00 AM - 5:00 PMJul 11, grand ballroom 101-102PresentationKnowledge 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
all eventPhilosophy Meets Machine Learning: What Counts as Trustworthy?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
8:00 AM - 5:00 PMPhilosophers have long thought deeply about many concepts that are used colloquially in the machine learning (ML) community such as epistemology, counterfactuals, explainability, reliability, uncertainty and causality. As ML systems are now embedded in...WorkshopParticipant 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 eventAdaptFM: Resource-Adaptive Foundation Model InferencePresentationKnowledge 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
8:00 AM - 5:00 PMJul 11, ASEM Ballroom 202PresentationKnowledge 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
all eventAI for Science: AI Scientists -- Tools, Co-authors, or Founders?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
8:00 AM - 5:00 PMJul 11, HALL CPresentationKnowledge 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
all event2nd Workshop on Compositional Learning: Safety, Interpretability, and AgentsWorkshopParticipant 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
8:00 AM - 5:00 PMJul 11, auditoriumPresentationKnowledge 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
all eventWorkshop on Human-AI Co-Creativity: Advances, Opportunities, and ChallengesWorkshopParticipant 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
8:00 AM - 5:00 PMThis workshop will bring together researchers and practitioners interested in topics of generative AI, creativity, and human-AI co-creation. On the one hand, we will explore opportunities in how recent advances in generative AI can support people in...WorkshopParticipant 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 eventGame Theory in Nature: From Optimality to EquilibriumPresentationKnowledge 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
8:00 AM - 5:00 PMThis workshop explores nature as a massive distributed learning system to bridge the gap between biological adaptation and multi-agent machine learning. While modern AI often relies on centralized optimization and global objectives, natural systems like...WorkshopParticipant 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 eventICML 2026 Hypothesis Testing WorkshopWorkshopParticipant 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
8:00 AM - 5:00 PMHypothesis testing, while much-maligned, remains a key component of scientific practice. Machine learning contributes to helping develop testing methodology, with many key advances in testing coming from the ML community, from widely used nonparametric tests...WorkshopParticipant 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 eventForecasting as a New Frontier of IntelligencePresentationKnowledge 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
8:00 AM - 5:00 PMJul 11, grand ballroom 104-105PresentationKnowledge 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
all eventAI4Physics: An ICML 2026 Workshop on AI for PhysicsWorkshopParticipant 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
8:00 AM - 5:00 PMAI is rapidly reshaping physics research, but progress is often fragmented across subfields and stages of the scientific workflow. This workshop, AI4Physics at ICML 2026, will bring together machine learning researchers and physicists to develop and evaluate...WorkshopParticipant 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 event3rd AI for Math Workshop: Toward Self-Evolving Scientific AgentsWorkshopParticipant 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
8:00 AM - 5:00 PMJul 11, HALL D1PresentationKnowledge 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
all eventTutorialsTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventMain ConferenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventSpotlight PostersPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventPosition Paper PostersPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventJournal 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 eventExpo Talks, Demos, and WorkshopsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead 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 eventFoundations of Deep Generative Models: Understanding Memorization, Generalization, and ReasoningUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMRecently, diffusion models, flow-based models, and autoregressive language models have emerged as a powerful class of deep generative models (DGMs) with remarkable generation capabilities across a wide range of applications, including image synthesis, video generation, natural language generation, and scientific discovery. Despite these successes, they still face significant challenges, particularly in understanding memorization, generalization, and reasoning, which limit their reliability, interpretability, and broader adoption in many scientific disciplines. This workshop will bring together researchers from both theoretical and applied communities to address these challenges, providing a focused forum for exchanging ideas, identifying key open problems, and fostering new collaborations in this rapidly evolving area.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventShow moreUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventView full detailsUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventThe 2nd Workshop of AI4NextG: AI and ML for Next-Generation WirelessWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 10, ASEM Ballroom 203UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventFailure Modes in Agentic AI: Reproducible Triggers, Trace Diagnostics, and Verified FixesUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, GRAND BALLROOM 104-105UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventTrustworthy AI for Good WorkshopWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 10, GRAND BALLROOM 103UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all event4th Structured Probabilistic Inference & Generative ModelingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, HALL D1UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventLearning to Listen: ICML 2026 Workshop on Machine Learning for AudioWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 10, ASEM Ballroom 202UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventCulture x AI: Evaluating AI as a Cultural TechnologyUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMGenerative AI is increasingly recognised as a social and cultural technology. These systems process an enormous amount of social data to produce novel cultural artefacts, such as text, images, and videos. While much progress has been made in evaluating cultural aspects of AI, it has tended to focus on harm mitigation: identifying and preventing moral violations, the spread of bias and misinformation, and deviation from human values. But a more positive or constructive notion of culture in AI remains underdeveloped. How can we evaluate cultural aspects of AI technology in a way that not only seeks to avoid failure, but gives a more robust definition of success? This workshop covers current approaches for evaluating cultural aspects of generative AI. Our primary focus is on work that aims to bring ideas and techniques from the humanities, arts, and qualitative social sciences upstream in AI development. We'll bring together a range of work at the intersection of culture and AI, with the goal of not just studying the effects of AI after deployment but also in actively shaping the design of the technology itself. The workshop will give special focus to research that seeks to articulate a positive vision for cultural AI.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventSecond Workshop on Technical AI Governance ResearchWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMWe propose an ICML workshop on Technical AI Governance Research (TAIGR). TAIGR encompasses technical analysis and tools that support the effective governance of AI, such as evaluations, safeguards, and access controls. Despite increasing interest in TAIGR, the field lacks a consistent, dedicated venue for sharing research. This will be the second edition of the workshop, building on the inaugural technical AI governance workshop held at ICML 2025.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventWorkshop on Weight-Space Symmetries: from Foundations to Practical ApplicationsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventContinual Adaptation at Scale: Towards Sustainable AIUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMTraining Foundation Models (FMs) is currently so costly that only few can afford it. The immense data, compute, and energy demands are increasingly unsustainable. Continual adaptation offers a viable alternative, where AI models can learn quickly and continually through every day interactions, just like humans and animals. Unfortunately, FMs lack this rapid adaptability: new behavior in FMs can be induced by prompting or fine-tuning, but there are no easy ways to quickly shape the behavior, for instance, to permanently add, remove, or modify their skill set in a sustainable way. This workshop aims to discuss new research directions that will enable fast continual adaptation at scale to drive more sustainable AI.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventSCALE: SCALABLE LEARNING AND OPTIMIZATION FOR EFFICIENT MULTIMODAL AI AGENTSUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, ASEM Ballroom 201UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventHigh Dimensional Learning Dynamics: the Science of ScalingUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, ROOM E1 - E4UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventScaling laws -- precise power-law relationships between model performance and resources (parameters, data, compute) -- have become the central organizing principle of modern large-model training. Yet the theoretical foundations of scaling remain incomplete: despite rapid recent progress, the community still lacks a unified mathematical framework connecting optimizer dynamics, architecture choice, and data structure to the observed power-law exponents that govern training at scale. This year’s HiLD workshop focuses on building a rigorous science of scaling by bringing together theoreticians and practitioners who build and train frontier models.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventThe 2nd Workshop on Epistemic Intelligence in Machine Learning: Learning under Unknown Unknowns for Real-world ImpactWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 10, ROOM E5 - E6UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventDeep Learning for Code: Towards Human-Centered Coding AgentsUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, HALL B2UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventGenerative and Agentic AI for BiologyUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, HALL D2UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventWorkshop on Mechanistic InterpretabilityWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 10, HALL CUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventWe propose a third Workshop on Mechanistic Interpretability - the study of how neural networks function - following highly successful workshops at ICML 2024 and NeurIPS 2025, the latter which attracted over 600 attendees. Mechanistic Interpretability is a cross-cutting area with relevance to multiple topics at ICML: anyone who has trained or interacted with neural networks has likely wondered how they work, and our current lack of understanding causes significant issues for safety and scientific understanding. We have designed our program to foster debate around the arising debate between pragmatic and ambitious approaches in the field, in addition to showcasing and sharing knowledge on emerging methodologiesWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventRLxF: RL from World FeedbackUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, GRAND BALLROOM 101-102UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventAI for Law WorkshopWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMRecent advances in machine learning have substantially improved general-purpose reasoning, multimodal understanding, and test-time scaling. Yet law remains a uniquely demanding and high-stakes domain that exposes the limits of generic AI progress. Many legal tasks require structured, long-form deductive and inductive reasoning grounded in doctrine, sensitivity to jurisdictional and linguistic variation, and robustness in settings where errors carry serious real-world consequences. They also raise fundamental questions about evaluation, fairness, and access to justice. Legal reasoning thus complements established AI reasoning domains, such as mathematics and coding, by emphasizing context-sensitive, norm-governed inference embedded in real-world institutions. This workshop centers on a core question: What does it mean for an AI system to be competent in law, and how can such competence be built, evaluated, and validated across jurisdictions and languages while enabling equitable access to justice? We structure the discussion around three interconnected themes: - AI for Legal Reasoning, focusing on domain-specific supervision, doctrinal grounding, and task design for robust legal inference; - AI Evaluation for Law, addressing reliable, risk-aware, and jurisdiction-sensitive evaluation paradigms; and - AI for Access to Justice, examining the technical and institutional conditions under which AI systems improve, or risk undermining, equitable legal access. To operationalize these themes, we will host a multilingual shared task on long-form legal reasoning across jurisdictions and languages, emphasizing doctrinal and jurisdictional grounding, reasoning quality, and cross-lingual robustness. By bridging machine learning and legal scholarship, the workshop aims to articulate a research agenda for AI systems that are not only more capable, but also more legally grounded and socially responsible.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventCombining Theory and Benchmarks: Towards A Virtuous Cycle to Understand and Guarantee Foundation Model PerformanceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMBenchmarks such as HELM [1] and Big-Bench [2] have significantly advanced quantitative model evaluation. However, current practice remains largely empirical and while measuring performance, does not provide guarantees on what capabilities a model has, when those capabilities will or will not manifest, and why. Debates over emergent abilities at scale illustrate this lack of predictive understanding [3,4]. In parallel, a substantial body of theoretical work addresses scaling laws for pre-training [5,6], generalization [7,8,9], and benchmark predictability [10]. Yet these theoretical advances are often disconnected from real-world benchmarking, and as a result, theoretical insights rarely inform benchmark design. This structural disconnect limits our ability to make reliable claims about model behavior, constrains trustworthy deployment, and slows the development of foundation models. This workshop will focus on advancing a predictive science of foundation model performance. We structure the workshop around three key research challenges: 1) Quantification of capabilities across task levels: How can we move from scores to formal, quantitative guarantees on performance across task levels? 2) Foundations of generalization and composition: Which mathematical frameworks can explain when and why models generalize? 3) Reliable and structured empirical evaluation: How should benchmarks be constructed to evaluate reasoning, robustness under distribution shift, and calibrated uncertainty? This workshop will convene researchers across mathematics, statistics, machine learning, and industry to catalyze a new research agenda that tightly couples theory and empirical evaluation of foundation models. By advancing frameworks that make performance predictable and quantifiable, we aim to influence: (i) how benchmarks are designed, (ii) how models are stress-tested, and (iii) how reliability claims are substantiated. These developments have direct implications for large-scale deployment, evaluation pipelines, and red-teaming practices in industry. More broadly, the workshop will help accelerate the emergence of a principled science of foundation models grounded in predictive theory, structured evaluation, and rigorous performance guarantees.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventFrom Frames to Stories (F2S): Toward Reliable, Controllable and Trustworthy Long-Horizon Video GenerationUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMVideo generation has advanced rapidly for short clips, but minutes-long, multi-shot generation remains unreliable due to compounding errors, identity drift, and weak long-range coherence. Long-horizon video therefore provides a demanding testbed for long-context multimodal modeling, inference-time computation, and interactive generation, aligning closely with core ICML interests. We focus on three bottlenecks: (i) persistent state representation, what to store (and how to compress it) to ensure that identities, scene dynamics, and narrative facts remain consistent; (ii) interactive control that steers future states with rich and compositional signals (shot plans, localized edits, multimodal constraints, actions) over long horizons; and (iii) trustworthy evaluation via minutes-scale protocols that measure consistency and control adherence in a reproducible, hard-to-game way. We highlight research directions where conceptual and methodological advances, rather than model scale alone, drive progress under realistic academic compute budgets. The program combines invited talks, contributed spotlights, posters/demos, a panel, and breakout groups on open problems with report-back.PanelDeliberation+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisMediumRead from source
all eventGraph Foundation Models: A New Era for Graph Machine LearningUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMGraph-structured data are ubiquitous across science and industry, yet today’s graph machine learning (GML) pipelines remain largely task- and dataset-specific, limiting robustness and transferability. This workshop brings together researchers and practitioners to advance graph foundation models (GFMs): models that pretrain once and adapt broadly across heterogeneous, temporal, and multimodal graphs. We will catalyze exchange on core questions spanning: architectural choices (GNNs, Transformers, and LLM-integrated pipelines), graph tokenization and structural encodings, pretraining objectives and scaling laws, and principled evaluation for cross-graph transfer. The scope covers diverse domains, including knowledge graphs, molecular and biological networks, relational databases, recommender systems, and social networks, emphasizing both methodological rigor and real-world impact. Through invited keynotes, contributed talks, posters, and panel discussions, the workshop aims to (i) consolidate design principles for GFMs, (ii) establish shared datasets, metrics, and reproducible protocols, and (iii) chart a community roadmap for scalable, transferable, and trustworthy graph learning.KeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
all eventAI as a Tool for Mathematics, Computer Science, and Machine LearningUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 10, AUDITORIUMUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventPlanning in The Era of Language Models (LM4Plan)UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 11, GRAND BALLROOM 101-102UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventLanguage Models (LMs) are a disruptive force, changing how research was done in many subareas of AI. Planning is one of the last bastions that remain standing. The focus of this workshop is on the questions in the intersection of these areas. Some of the specific areas we would like to gain a better understanding in include: what LMs can contribute to planning, how LMs can/should be used, what are the pitfalls of using LMs, what are the guarantees that can be obtained. This would be a third edition of the LM4Plan workshop, which started at AAAI 2025 and had its second edition at ICAPS 2025. The workshop series website is at https://llmforplanning.github.io/WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventPhilosophy Meets Machine Learning: What Counts as Trustworthy?UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMPhilosophers have long thought deeply about many concepts that are used colloquially in the machine learning (ML) community such as epistemology, counterfactuals, explainability, reliability, uncertainty and causality. As ML systems are now embedded in high-stakes decisions across science, industry, and public life, it is urgent that when ML researchers claim properties such as "explainability", "reliability", "intelligence" or "cognition", these claims are made with awareness of what practitioners, policymakers, and affected users mean by those terms. In particular, we argue that the ML community needs to take a step back and review whether the mathematical objectives used in optimisation and evaluation procedures truly take into account how philosophers have analysed them - analyses that explicitly aim to connect notions like explanation, evidence, and uncertainty to human understanding, justification, and use. Philosophers of science and psychologists are more actively engaged than ever in such questions; however, their interaction with ML researchers remains sparse and fragmented. The goal of the proposed workshop is to facilitate a lively dialogue between the two otherwise largely separate communities, to promote more principled and grounded advances in ML and artificial intelligence.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventAdaptFM: Resource-Adaptive Foundation Model InferenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 11, ASEM Ballroom 202UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventAI for Science: AI Scientists -- Tools, Co-authors, or Founders?UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 11, HALL CUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all event2nd Workshop on Compositional Learning: Safety, Interpretability, and AgentsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 11, AUDITORIUMUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventCompositionality, defined as the ability to construct and reason about complex concepts from reusable components, is a hallmark of human cognition and the key to robust generalization. Despite the astonishing progress of modern AI systems, it remains an open question whether they truly capture and leverage the compositional nature of many real-world domains. The workshop will explore this pressing challenge across multiple critical dimensions. We will invite contributions focusing on the theoretical foundations of compositionality, its central role in the age of foundation models and agents, and its impact on achieving robustness and systematic out-of-domain generalization. Through interdisciplinary dialogue, we aim to catalyze new research directions that push the boundaries of compositional learning in advanced AI systems.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventWorkshop on Human-AI Co-Creativity: Advances, Opportunities, and ChallengesWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMThis workshop will bring together researchers and practitioners interested in topics of generative AI, creativity, and human-AI co-creation. On the one hand, we will explore opportunities in how recent advances in generative AI can support people in open-ended creative tasks. On the other hand, we will identify unique challenges in integrating generative AI into creative workflows, including design fixation, idea homogeneity, and issues of authorship. By fostering collaboration between different communities and stakeholders, we aim to facilitate the development of next-generation technologies that enhance human-AI co-creativity.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventGame Theory in Nature: From Optimality to EquilibriumUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMThis workshop explores nature as a massive distributed learning system to bridge the gap between biological adaptation and multi-agent machine learning. While modern AI often relies on centralized optimization and global objectives, natural systems like microbial colonies and animal societies attain stability and collective intelligence through local strategic interactions. As the machine learning community moves toward decentralized environments and uses large scale models for ecological data, understanding the tension between individual goals and population level stability becomes critical. By investigating core themes such as evolutionary stability, environmental feedback, and emergent communication, this workshop aims to identify biological mechanisms that can inform the design of more efficient and scalable artificial systems. We bring together researchers in game theory, animal behavior, and machine learning to address the challenges of collective decision making and systemic robustness in the face of the current explosion in ecological sensor data.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventICML 2026 Hypothesis Testing WorkshopWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMHypothesis testing, while much-maligned, remains a key component of scientific practice. Machine learning contributes to helping develop testing methodology, with many key advances in testing coming from the ML community, from widely used nonparametric tests to recent work on e-values. Machine learning practice can also benefit from the usage of hypothesis testing techniques, whether for checking or ensuring model reliability and robustness, or practical methods for helping detect subgroup shifts in medical applications. This workshop will explore advances both in testing methodology and in its impacts across ML.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventForecasting as a New Frontier of IntelligenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
8:00 AM - 5:00 PMJul 11, GRAND BALLROOM 104-105UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
all eventForecasting has a rich literature in machine learning (ML), ranging from classical time-series analysis, to significant recent interest from both ML theory (e.g., forecasting and calibration) and applied ML research (e.g., benchmarking and advancing forecasting capabilities of foundation models). Despite its deep roots and recent trends in ML research, a dedicated workshop to forecasting is still missing, to our best knowledge. Led by a diverse team of seasoned organizers and featuring a compelling lineup of confirmed invited speakers, this workshop will provide a platform for interdisciplinary dialogue on AI forecasting, and will bring together researchers from varied perspectives (theoretical vs applied) and application domains (tech, finance, policy making, etc.).WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventAI4Physics: An ICML 2026 Workshop on AI for PhysicsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMAI is rapidly reshaping physics research, but progress is often fragmented across subfields and stages of the scientific workflow. This workshop, AI4Physics at ICML 2026, will bring together machine learning researchers and physicists to develop and evaluate trustworthy AI methods that support physics discovery end to end - from physics-centric reasoning with LLMs and tool-using agents, to high-fidelity generative and surrogate simulators, to inverse problems and uncertainty-aware inference under systematics, and finally to data scarcity, dataset-building, and closed-loop experimental design and control. Spanning high-energy physics, astrophysics and cosmology, condensed matter, plasma and fusion, and quantum science, the workshop will highlight shared structure and bottlenecks across domains, consolidate best practices for physical consistency and robust evaluation, and catalyze cross-disciplinary collaboration through invited talks, posters, and contributed presentations.WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all event3rd AI for Math Workshop: Toward Self-Evolving Scientific AgentsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
8:00 AM - 5:00 PMJul 11, HALL D1UnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 48/100 and verified 45/100, then capped for no visible follow-up, tracking, or impact mechanism.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as an applied learning or working session, with the strongest visible signal in participation architecture and learning transfer and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence.
Strongest signals: Participation Architecture, Learning Transfer, and Problem Specificity. Weakest signals: Follow Through, Network Design, and Evidence Maturity.
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.
- Add baseline measurement, comparison logic, tracking, or post-event impact reporting so effectiveness is not inferred only from format.
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 icml.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, Personalization.
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.
- No baseline measurement is visible.
Score caps
- 42: No visible follow-up, tracking, or impact mechanism.
Review flags
- Fourth-loop score cap: No visible follow-up, tracking, or impact mechanism.
- No source-backed follow-up, validation, baseline, tracking, or impact evidence.
- No tracking, validation, feedback, or impact measurement found in the visible source text.
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_2026_icml_wca4z_platform_accelerating_legacy_code_modernization_using_ai_agents_439259"><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 Academic / Research / Science events scored on the same eight pillars.