Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 32/100, so the next move is to benchmark the weakest pillars before repeating the format.
academic research science agenda agenda analysis
USENIX
This academic research science agenda in Academic / Research / Science shows 37 visible agenda rows from usenix.org and scores 32/100: a thin but inspectable design signal. The clearest public signals sit in Problem Specificity and Participation Architecture; the main limits are Follow Through and Network Design. Visible mechanisms include Participant work, Feedback, Impact evidence, and Network design. Follow-through or tracking is at least visible enough to inspect, though causal proof still depends on stronger... A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as an academic research science agenda, with the strongest visible signal in problem specificity and participation architecture and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to execution quality beyond the public agenda. 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 thin outcome architecture band. The strongest visible pillars are Problem Specificity, Participation Architecture, and Learning Transfer; the thinnest visible pillars are Follow Through, Network Design, and Personalization. Visible mechanisms include Participant work, Feedback, Impact evidence, Network design, and Learning transfer. The extracted agenda preview includes 50 visible rows. The most common formats are Demo, Training, and Break; the most common inferred purposes are Showcase, Skill Building, and Wellbeing.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?52
Participation Architecture - 52/100. 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?59
Problem Specificity - 59/100. A clear costly problem, objective, decision, or performance target.
Personalization?34
Personalization - 34/100. Role, path, goal, preparation, or connection tailoring for participants.Missing: Create role-based paths, prepared questions, tailored breakouts, or participant-specific next steps.
Network Design?16
Network Design - 16/100. 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?50
Learning Transfer - 50/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.
Evidence Maturity?39
Evidence Maturity - 39/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?41
Future-of-Work Fit - 41/100. 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
Network Design (16/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Personalization (34/100)
Exercises that strengthen it: Journey Mapping • WorkshopBank · Group Energiser & Perspective-Changing Exercise - Paradigm... · 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
54 percent of the 50 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventPoster SessionPoster sessionShowcase+
Format · ShowcasePoster sessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisOutcome inferred from formatInferred from format
all eventCall for 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 eventTowards Understanding and Enhancing Security of Proof-of-Training for DNN Model Ownership VerificationTrainingSkill 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 eventPAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMsPresentationPacing+
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 eventJBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and ManipulationPresentationPacing+
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 eventRevisiting Training-Inference Trigger Intensity in Backdoor AttacksTrainingSkill 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 eventThe Cost of Performance: Breaking ThreadX with Kernel Object Masquerading AttacksPresentationPacing+
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 eventSelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical MannerPresentationPacing+
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 eventExposing the Guardrails: Reverse-Engineering and Jailbreaking Safety Filters in DALL·E Text-to-Image PipelinesExhibitionPacing+
Format · ShowcaseExhibitionA showcase or expo floor. Browsing and light interaction rather than structured participant work.
Evidence basisOutcome inferred from formatInferred from format
all eventGreat, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak AttackPresentationPacing+
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 eventMore is Less: Extra Features in Contactless Payments Break SecurityBreakPacing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisOutcome inferred from formatInferred from format
all eventCertPHash: Towards Certified Perceptual Hashing via Robust TrainingTrainingSkill 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 eventFLOP: Breaking the Apple M3 CPU via False Load Output PredictionsPresentationPacing+
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 eventPoster SessionPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventCall for PostersPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventTowards Understanding and Enhancing Security of Proof-of-Training for DNN Model Ownership VerificationTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventTask-Oriented Training Data Privacy Protection for Cloud-based Model TrainingTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventCloud-based model training presents significant privacy challenges, as users must upload personal data for training high-performance models. Once uploaded, this data goes beyond the user's control and could be misused for other purposes. Users need tools to control the usage scope of the uploaded training data, preventing unauthorized training without compromising authorized training. Unfortunately, existing solutions overlook this issue.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventPAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMsBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventIn this study, we systematically analyze security threats associated with 3D printing, focusing specifically on vulnerabilities caused by G-Code commands. We introduce attacks and attacker models that assume a less powerful adversary than traditionally considered, broadening the scope of potential security threats. Our findings show that even minimal access to the 3D printer can result in significant security breaches, such as unauthorized access to subsequent print jobs or persistent misconfiguration of the printer. We identify 278 potentially malicious G-Codes across the attack categories Information Disclosure, Denial of Service, and Model Manipulation. Our evaluation demonstrates the applicability of these attacks across various 3D printers and their firmware. Our findings underscore the need for a better standardization process of G-Codes and corresponding security best practices.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventJBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and ManipulationBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventAutomated program repair (APR) techniques, which aim to triage and fix software bugs autonomously, have emerged as powerful tools against vulnerable code. Recent advancements in large language models (LLMs) have further shown promising results when applied to APR, especially on patch generation. However, without effective fault localization and patch validation, APR tools specialized in patching alone cannot handle a more practical and end-to-end setting - given a concrete input that triggers a vulnerability, how to patch the program without breaking existing tests?BreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventRevisiting Training-Inference Trigger Intensity in Backdoor AttacksTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventIn this paper, we systematically reveal a range of adversarial threats to such E2EE-PHM systems, leading to regulatory failures. Unlike previous case studies, our attack is a more realistic threat - uniformly fooling the famous Microsoft PhotoDNA, Facebook PDQ, Apple NeuralHash, and pHash, even with higher success rates and less training rounds. Here, we validate the above proposition in both scenarios of escaping and triggering regulation.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventThe Cost of Performance: Breaking ThreadX with Kernel Object Masquerading AttacksBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventSelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical MannerBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventIn a single secret leader election (SSLE) protocol, all parties collectively and obliviously elect one leader. No one else should learn its identity unless it reveals itself as the leader. The problem is first formalized by Boneh et al. (AFT '20), which proposes an efficient construction based on the Decision Diffie-Hellman (DDH) assumption. Considering the potential risk of quantum computers, several follow-ups focus on designing a post-quantum secure SSLE protocol based on pure lattices or fully homomorphic encryption. However, no concrete benchmarks demonstrate the feasibility of deploying such heavy cryptographic primitives.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventFinally, through experiments, we demonstrate that our approach produces tighter privacy lower bounds on common differentially private mechanisms while requiring significantly fewer observations. We also provide a case study illustrating that our method successfully detects privacy violations in flawed implementations of private algorithms.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventExposing the Guardrails: Reverse-Engineering and Jailbreaking Safety Filters in DALL·E Text-to-Image PipelinesBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventGreat, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak AttackBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventMore is Less: Extra Features in Contactless Payments Break SecurityBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventRowhammer attacks are pervasive in client systems when launched natively. The biggest Rowhammer threat for such systems, however, lies in the browser. Our large-scale evaluation of browser-based Rowhammer attacks shows that they can only trigger bit flips on a small fraction of DRAM devices. Postponing refresh commands that trigger in-DRAM mitigations can boost the performance of Rowhammer attacks, but it has never been demonstrated in practice.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventMachine Learning (ML) models are vulnerable to membership inference attacks (MIAs), where an adversary aims to determine whether a specific sample was part of the model's training data. Traditional MIAs exploit differences in the model's output posteriors, but in more challenging scenarios (label-only scenarios) where only predicted labels are available, existing works directly utilize the shortest distance of samples reaching decision boundaries as membership signals, denoted as the shortestBD. However, they face two key challenges: low distinguishability between members and non-members due to sample diversity, and high query requirements stemming from direction diversity.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventTo overcome these limitations, we propose a novel label-only attack called DHAttack, designed for Higher performance and Higher stealth, focusing on the boundary distance of individual samples to mitigate the effects of sample diversity, and measuring this distance toward a fixed point to minimize query overhead. Empirical results demonstrate that DHAttack consistently outperforms other advanced attack methods. Notably, in some cases, DHAttack achieves more than an order of magnitude improvement over all baselines in terms of TPR @ 0.1% FPR with just 5 to 30 queries. Furthermore, we explore the reasons for DHAttack's success, and then analyze other crucial factors in the attack performance. Finally, we evaluate several defense mechanisms against DHAttack and demonstrate its superiority over all baseline attacks.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventSpecifically, we demonstrate that these models are highly susceptible to DIFF2, a simple yet effective attack, which substantially diminishes their robustness assurance. Essentially, DIFF2 integrates a malicious diffusion-sampling process into the diffusion model, guiding inputs embedded with specific triggers toward an adversary-defined distribution while preserving the normal functionality for clean inputs. Our case studies on adversarial purification and robustness certification show that DIFF2 can significantly reduce both post-purification and certified accuracy across benchmark datasets and models, highlighting the potential risks of relying on pre-trained diffusion models as defensive tools. We further explore possible countermeasures, suggesting promising avenues for future research.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventCertPHash: Towards Certified Perceptual Hashing via Robust TrainingTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventPerceptual hashing (PHash) systems - e.g., Apple's NeuralHash, Microsoft's PhotoDNA, and Facebook's PDQ - are widely employed to screen illicit content. Such systems generate hashes of image files and match them against a database of known hashes linked to illicit content for filtering. One important drawback of PHash systems is that they are vulnerable to adversarial perturbation attacks leading to hash evasion or collision. It is desirable to bring provable guarantees to PHash systems to certify their robustness under evasion or collision attacks. However, to the best of our knowledge, there are no existing certified PHash systems, and more importantly, the training of certified PHash systems is challenging because of the unique definition of model utility and the existence of both evasion and collision attacks.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventIn this paper, we propose CertPHash, the first certified PHash system with robust training. CertPHash includes three different optimization terms, anti-evasion, anti-collision, and functionality. The anti-evasion term establishes an upper bound on the hash deviation caused by input perturbations, the anti-collision term sets a lower bound on the distance between a perturbed hash and those from other inputs, and the functionality term ensures that the system remains reliable and effective throughout robust training. Our results demonstrate that CertPHash not only achieves non-vacuous certification for both evasion and collision with provable guarantees but is also robust against empirical attacks. Furthermore, CertPHash demonstrates strong performance in real-world illicit content detection tasks.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventRetrieval-augmented generation (RAG) systems respond to queries by retrieving relevant documents from a knowledge database and applying an LLM to the retrieved documents. We demonstrate that RAG systems that operate on databases with untrusted content are vulnerable to denial-of-service attacks we call jamming. An adversary can add a single "blocker" document to the database that will be retrieved in response to a specific query and result in the RAG system not answering this query, ostensibly because it lacks relevant information or because the answer is unsafe.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventWe evaluate jamming attacks on several embeddings and LLMs and demonstrate that the existing safety metrics for LLMs do not capture their vulnerability to jamming. We then discuss defenses against blocker documents.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventIn this work, we analyze the risks associated with the PJC functionality output. We consider an adversary that is a participating party of PJC and describe four practical attacks that break the other party's input privacy, and which are able to recover both membership of keys in the intersection and their associated values. Our attacks consider the privacy threats associated with deployment and highlight the need to include the functionality output as part of the MPC security model.BreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventIn this paper, we fill this gap and present Lyso, the first multi-target, multi-step guided fuzzer that leverages semantic information (i.e., program flows) and correlations (i.e., shared root causes) derived from static analysis. By concurrently handling multiple alarms and prioritizing seeds that cover these root causes, Lyso efficiently explores multiple alarms. For each alarm, Lyso breaks down the goal of reaching an alarm into a sequence of manageable steps. By progressively following these steps, Lyso refines its search to reach the final step, significantly improving its ability to trigger challenging alarms.BreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventWe compared Lyso to eight state-of-the-art (directed) fuzzers. Our evaluation demonstrates that Lyso outperforms existing approaches, achieving an average 12.17x speedup while finding the highest absolute number of bugs. Additionally, we applied Lyso to verify static analysis results for real-world programs, and it successfully discovered eighteen new vulnerabilities.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventOur results are significant: for the first time, we have demonstrated that attackers are indeed using Tor to conceal their identities while targeting cloudless IoT devices. Over a period of 12 months, TORCHLIGHT analyzed 26 TB of traffic, revealing 45 vulnerabilities, including 29 zero-day exploits with 25 CVE-IDs assigned (5 CRITICAL, 3 HIGH, 16 MEDIUM, and 1 LOW) and an estimated value of approximately $312,000. These vulnerabilities affect around 12.71 million devices across 148 countries, exposing them to severe risks such as information disclosure, authentication bypass, and arbitrary command execution. The findings have attracted significant attention, sparking widespread discussion in cybersecurity circles, reaching the top 25 on Hacker News, and generating over 190,000 views.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventTo demonstrate the feasibility of our approach, we design, implement, and benchmark an anonymous reputation system with better-than-state-of-the-art performance and features, supporting asynchronous reputation updates, banning, and reputation-dependent rate limiting to better protect against Sybil attacks.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventFLOP: Breaking the Apple M3 CPU via False Load Output PredictionsBreakWellbeing+
Format · LogisticsBreakA pacing or recovery block between sessions.
Evidence basisMediumRead from source
all eventUnbalanced case: We present the first unbalanced enhanced PSU, which achieves sublinear communication complexity in the size of the large set. Experimental results demonstrate that the larger the difference between the two set sizes, the better our protocol performs. For unbalanced set sizes (2^10, 2^20) with single thread in 1Mbps bandwidth, our protocol requires only 2.322 MB of communication. Compared with the state-of-the-art enhanced PSU, there is 38.1x shrink in communication and roughly 17.6x speedup in the running time.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventApple's Find My network, leveraging over a billion active Apple devices, is the world's largest device-locating network. We investigate the potential misuse of this network to maliciously track Bluetooth devices. We present nRootTag, a novel attack method that transforms computers into trackable "AirTags" without requiring root privileges. The attack achieves a success rate of over 90% within minutes at a cost of only a few US dollars. Or, a rainbow table can be built to search keys instantly. Subsequently, it can locate a computer in minutes, posing a substantial risk to user privacy and safety. The attack is effective on Linux, Windows, and Android systems, and can be employed to track desktops, laptops, smartphones, and IoT devices. Our comprehensive evaluation demonstrates nRootTag's effectiveness and efficiency across various scenarios.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventJava Web applications are of great importance for information systems deployed across critical sections of our society as demonstrated in the severe impacts caused by notorious log4j vulnerability. One major challenge in detecting Java Web Application vulnerabilities is cross-thread dataflows, which are caused by shared Java objects and triggered by multiple web requests in the same session. To the best of our knowledge, none of the prior works can handle such cross-thread dataflows in Java Web applications.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventRecently, image generation models like Stable Diffusion have gained significant popularity due to their remarkable achievements. However, their widespread use has raised concerns about potential misuse, particularly regarding acquiring training data, including using copyright-protected material. Various schemes have been proposed to address these concerns by introducing inconspicuous perturbations (poisons) to prevent models from utilizing these samples for training.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 37/100 and verified 37/100 with no fourth-loop cap.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as an academic research science agenda, with the strongest visible signal in problem specificity and participation architecture and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to execution quality beyond the public agenda.
Strongest signals: Problem Specificity, Participation Architecture, and Learning Transfer. Weakest signals: Follow Through, Network Design, and Personalization.
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.
- Create role-based paths, prepared questions, tailored breakouts, or participant-specific next steps.
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 usenix.org. 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, Impact evidence, 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 baseline measurement is visible.
Score caps
- No fourth-loop score cap applied.
Review flags
- No tracking, validation, feedback, or impact measurement found in the visible source text.
- High cleanup rate: many extracted rows were hidden or merged as fragments.
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.
Where To Go Next
Compare this agenda against other Academic / Research / Science events scored on the same eight pillars.