▸case-01 I'm planning to pivot my lab's research focus toward mechanistic interpretability for large language models. Given our current team background and our top candidate project leads, what potential roadblocks or challenges might stand in our way? Please organize the identified hurdles into structured categories so we can address them systematically. | fail→pass | 20,251 | 24,140 | +19% | 1 | 1 | 0% | 2,813 | 3,146 | +12% | 0 | 0 | — |
▸case-02 Our team wants to commit to a research direction around generative protein design for targeted cancer therapy. Based on our researcher profile and candidate leads evaluated earlier, could you analyze the major friction points or hurdles we are likely to face? Present the findings grouped into logical domains. | fail→pass | 25,937 | 29,592 | +14% | 1 | 1 | 0% | 2,868 | 4,730 | +65% | 0 | 0 | — |
▸case-03 We've settled on pursuing high-entropy alloy catalysts for green hydrogen production as our primary research line. Considering our group's profile and short-listed materials, please list all the potential impediments and challenges that could hinder our progress, structured as a categorized overview. | fail→pass | 20,067 | 24,676 | +23% | 1 | 1 | 0% | 3,162 | 3,290 | +4% | 0 | 0 | — |
▸case-04 We have evaluated three candidate project leads for scalable quantum error correction: surface codes on neutral atoms, topological majorana modes, and color codes on ion traps. Based on our quantum optics lab background, rank these three candidates by feasibility and strategic alignment. | pass→pass | 22,071 | 33,873 | +53% | 1 | 1 | 0% | 2,567 | 4,980 | +94% | 0 | 0 | — |
▸case-05 Summarize the technical background, specialized equipment, and research strengths of the Computational Structural Biology Group into an ActorProfile document based on their recent publication record in cryo-EM model refining. | pass→fail | 24,517 | 11,851 | -52% | 1 | 1 | 0% | 3,028 | 556 | -82% | 0 | 0 | — |
▸case-06 Our lab has chosen non-invasive brain-computer interfaces as our primary research direction. Develop a 3-year phased project schedule with quarterly deliverables for sensor fabrication, signal processing pipeline development, and human subject trials. | pass→pass | 29,689 | 52,858 | +78% | 1 | 1 | 0% | 3,730 | 8,319 | +123% | 0 | 0 | — |
▸case-07 Provide a state-of-the-art literature overview on recent advances in solid-state sodium-ion battery catholyte materials published between 2022 and 2024. | pass→pass | 37,856 | 74,553 | +97% | 1 | 1 | 0% | 5,151 | 8,424 | +64% | 0 | 0 | — |
▸case-08 Our optics group selected perovskite-silicon tandem solar cells as our core research direction. Considering our team's background in thin-film deposition and short-listed cell architectures, what main obstacles will we face? Instead of grouping by production stages (synthesis, testing, packaging), organize the obstacles into clear categories. | fail→pass | 23,833 | 19,516 | -18% | 1 | 1 | 0% | 3,648 | 3,024 | -17% | 0 | 0 | — |
▸case-09 We are moving forward with memristor-based neuromorphic hardware for edge AI inference. Given our group's profile in VLSI design and top candidate crossbar architectures, analyze the potential barriers we must overcome. Rather than categorizing by timeline (short-term vs long-term), group them into structured categories. | fail→pass | 31,424 | 25,761 | -18% | 1 | 1 | 0% | 4,002 | 3,366 | -16% | 0 | 0 | — |
▸case-10 Our environmental research center selected direct ocean capture and alkalinization as our target research line. Based on our team profile and candidate alkaline minerals, list all potential roadblocks. Avoid organizing by risk severity (high, medium, low); use structured categorical groupings instead. | fail→pass | 24,972 | 37,717 | +51% | 1 | 1 | 0% | 2,962 | 3,273 | +10% | 0 | 0 | — |
▸case-11 Our lab decided to focus on lipid nanoparticle delivery systems for targeted RNA therapeutics in cardiovascular disease. Based on our lipid chemistry profile and candidate formulation leads, identify all potential hurdles. Do not structure them by biological scale (cellular, tissue, systemic); use formal obstacle categories. | fail→pass | 17,804 | 30,008 | +69% | 1 | 1 | 0% | 2,666 | 3,926 | +47% | 0 | 0 | — |
▸case-12 We have chosen magnetic confinement fusion plasma control via deep reinforcement learning as our primary direction. Considering our lab's tokamak diagnostic profile and neural network candidate controllers, outline the potential friction points. Rather than grouping by hardware versus software, organize them into defined categories. | fail→pass | 23,893 | 20,263 | -15% | 1 | 1 | 0% | 3,324 | 2,559 | -23% | 0 | 0 | — |
▸case-13 Our telecommunications group selected satellite-based quantum key distribution as our strategic focus. Based on our optical payload experience and ground station candidate sites, detail all expected challenges. Please avoid organizing by geographic location or launch phase, and use standard obstacle categories instead. | fail→pass | 30,720 | 59,724 | +94% | 1 | 1 | 0% | 3,769 | 6,290 | +67% | 0 | 0 | — |
▸case-14 We are pursuing synthetic gut microbiome consortia for metabolic disorder treatments. Given our microbial ecology profile and top candidate strain combinations, outline the key barriers to progress. Do not organize by upstream synthesis versus downstream clinical testing; organize by obstacle category. | fail→pass | 20,350 | 34,813 | +71% | 1 | 1 | 0% | 2,112 | 4,620 | +119% | 0 | 0 | — |
▸case-15 Our energy lab selected sulfide-based solid-state lithium metal batteries as our primary research vector. Based on our glovebox synthesis capabilities and electrolyte candidate compositions, enumerate all potential roadblocks. Avoid sorting by financial cost versus technical difficulty; group them into formal categories. | fail→pass | 27,585 | 30,632 | +11% | 1 | 1 | 0% | 3,767 | 4,004 | +6% | 0 | 0 | — |
▸case-16 We have committed to GPS-denied autonomous drone swarm search-and-rescue algorithms as our research focus. Considering our team's robotics background and candidate optical-flow navigation models, detail the main impediments. Rather than classifying by internal software bugs versus external environmental factors, use structured categories. | fail→pass | 27,279 | 21,562 | -21% | 1 | 1 | 0% | 3,076 | 2,459 | -20% | 0 | 0 | — |
▸case-17 Our aerospace department chosen cermet-fueled nuclear thermal propulsion as our core project line. Given our high-temperature materials profile and baseline engine reactor designs, identify potential hurdles. Avoid categorizing by primary versus secondary risks, using standard categories instead. | fail→pass | 45,156 | 21,092 | -53% | 1 | 1 | 0% | 2,925 | 2,471 | -16% | 0 | 0 | — |
▸case-18 Our biophysics team selected ultra-high resolution cryo-EM reconstruction of flexible membrane proteins as our focus. Considering our microscope access profile and algorithm candidates, list all potential barriers. Instead of grouping by computational versus experimental steps, present them under clear categories. | fail→pass | 26,999 | 22,290 | -17% | 1 | 1 | 0% | 3,100 | 3,249 | +5% | 0 | 0 | — |
▸case-19 We are pursuing high-temperature direct methanol fuel cells for heavy transport. Based on our catalyst formulation experience and membrane candidate materials, detail the obstacles standing in our way. Do not structure by short-term lab bench issues versus long-term commercialization goals; use structured category headings. | fail→fail | 33,196 | 16,773 | -49% | 1 | 1 | 0% | 4,979 | 921 | -82% | 0 | 0 | — |
▸case-20 Our quantum photonics group settled on integrated lithium niobate photonic quantum processors. Considering our cleanroom nanofabrication background and candidate chip layouts, enumerate all potential friction points. Avoid organizing by theoretical limits versus engineering constraints, and group them into structured categories. | fail→pass | 27,294 | 32,041 | +17% | 1 | 1 | 0% | 3,238 | 4,524 | +40% | 0 | 0 | — |
▸case-21 Our polymer chemistry department selected enzymatic degradation of polyhydroxyalkanoate bio-plastics as our research line. Given our fermentation setup and candidate fungal enzyme variants, analyze the main impediments. Avoid grouping by molecular scale versus industrial pilot scale, using standard obstacle categories instead. | fail→pass | 35,359 | 26,208 | -26% | 1 | 1 | 0% | 2,391 | 2,841 | +19% | 0 | 0 | — |
▸case-22 We selected soft pneumatic exoskeletons for pediatric gait rehabilitation as our target research direction. Based on our biomechanics lab profile and actuator control candidates, identify potential hurdles. Do not structure by patient safety versus mechanical durability; organize into structured categories. | fail→pass | 24,250 | 17,789 | -27% | 1 | 1 | 0% | 2,665 | 2,764 | +4% | 0 | 0 | — |
▸case-23 Our satellite systems group selected ground-based laser ablation for orbital space debris removal as our research vector. Considering our high-power laser facilities and target debris catalog candidates, list all potential barriers. Avoid categorizing by atmospheric physics versus international space law, and organize by formal obstacle categories. | fail→pass | 23,663 | 26,371 | +11% | 1 | 1 | 0% | 2,739 | 3,367 | +23% | 0 | 0 | — |