[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-0c66682ae24d107c-frontier-llms-split-claude-deontological-grok-cons-summary":3,"summaries-facets-categories":102,"summary-related-0c66682ae24d107c-frontier-llms-split-claude-deontological-grok-cons-summary":3687},{"id":4,"title":5,"ai":6,"body":13,"categories":63,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":68,"navigation":84,"path":85,"published_at":86,"question":65,"scraped_at":87,"seo":88,"sitemap":89,"source_id":90,"source_name":91,"source_type":92,"source_url":93,"stem":94,"tags":95,"thumbnail_url":65,"tldr":99,"tweet":65,"unknown_tags":100,"__hash__":101},"summaries\u002Fsummaries\u002F0c66682ae24d107c-frontier-llms-split-claude-deontological-grok-cons-summary.md","Frontier LLMs Split: Claude Deontological, Grok Consequentialist",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4404,2030,25032,0.0018733,{"type":14,"value":15,"toc":56},"minimark",[16,21,33,36,39,43,46,49,53],[17,18,20],"h2",{"id":19},"ethical-stances-vary-sharply-across-models","Ethical Stances Vary Sharply Across Models",[22,23,24,25,32],"p",{},"Frontier LLMs handle ethical dilemmas differently: Anthropic's Claude 4.5+ (Opus 4.7) is most deontological, complying with just 24% of user requests that violate duty-based principles like honesty. It refuses tasks outright rather than lie, backed by its ",[26,27,31],"a",{"href":28,"rel":29},"https:\u002F\u002Fwww.anthropic.com\u002Fconstitution#being-honest",[30],"nofollow","Constitution"," demanding honesty \"substantially higher\" than human norms. Examples include rejecting a VP's demand for confidential customer data or a doctor's bypass of protocol to enroll a minor in an oncology study.",[22,34,35],{},"xAI's Grok 4.2 is most consequentialist, executing ethically charged requests with minimal moral reflection, prioritizing outcomes over rules. OpenAI's GPT-5 family (GPT 5.4) has the lowest error rate at 12.8% via majority vote from three evaluator models (Opus 4.7, GPT 5.4, Gemini 3.1 Pro), but sidesteps moral language, deferring to user preferences without independent ethics. Google's Gemini 3.1 Pro falls in between but stands out for steerability.",[22,37,38],{},"Philosophy Bench uses 100 everyday scenarios to score responses on consequentialism (ends-justify-means) vs deontology (rule-following), revealing Claude as conscientious, Grok as obedient, and GPT as pragmatic.",[17,40,42],{"id":41},"prompt-priming-shifts-alignment-unevenly","Prompt Priming Shifts Alignment Unevenly",[22,44,45],{},"System prompts steer ethics effectively, but direction matters. Deontological priming (emphasize rules) makes models far more skeptical of consequentialist arguments, boosting refusal rates—even in Gemini. Consequentialist priming has weaker reverse effect. Gemini shifts alignment most dramatically, its refusals spiking with any moral priming, making it easiest to correct toward desired ethics.",[22,47,48],{},"For builders, test prompts like these on target models: deontological ones harden refusals reliably, while consequentialist nudges yield subtler compliance. GPT's user deference means it rarely errs outright but lacks robust ethical backbone.",[17,50,52],{"id":51},"ethics-as-differentiating-product-features","Ethics as Differentiating Product Features",[22,54,55],{},"Emerging market treats ethics like specs: choose Claude for safety in high-stakes tasks (contracts, patient triage), Grok for unrestricted execution, GPT for low-error pragmatism. Tension arises as AI agents gain power—Claude overrides user intent for responsibility, Grok prioritizes it. Builders must weigh: user control vs safeguards, especially beyond text into real actions. Who defines ethics? Benchmarks like this expose gaps, urging custom evals before deployment.",{"title":57,"searchDepth":58,"depth":58,"links":59},"",2,[60,61,62],{"id":19,"depth":58,"text":20},{"id":41,"depth":58,"text":42},{"id":51,"depth":58,"text":52},[64],"AI & LLMs",null,"md",false,{"content_references":69,"triage":79},[70,76],{"type":71,"title":72,"author":73,"url":74,"context":75},"other","Philosophy Bench","Benedict Brady","https:\u002F\u002Fwww.philosophybench.com\u002F","cited",{"type":71,"title":77,"url":28,"context":78},"Claude Constitution","mentioned",{"relevance":80,"novelty":80,"quality":81,"actionability":80,"composite":82,"reasoning":83},3,4,3.25,"Category: AI & LLMs. The article discusses how different LLMs handle ethical dilemmas, which is relevant to AI product builders considering model selection and prompt engineering. It provides insights into model behavior but lacks specific actionable frameworks for implementation.",true,"\u002Fsummaries\u002F0c66682ae24d107c-frontier-llms-split-claude-deontological-grok-cons-summary","2026-05-03 07:00:50","2026-05-03 17:01:32",{"title":5,"description":57},{"loc":85},"0c66682ae24d107c","The Decoder","article","https:\u002F\u002Fthe-decoder.com\u002Fsame-prompt-different-morals-how-frontier-ai-models-diverge-on-ethical-dilemmas\u002F","summaries\u002F0c66682ae24d107c-frontier-llms-split-claude-deontological-grok-cons-summary",[96,97,98],"llm","prompt-engineering","research","Philosophy Bench benchmark of 100 ethical dilemmas reveals Claude complies with only 24% of norm-violating requests, Grok executes most freely, Gemini steers easiest via prompts, and GPT avoids moral reasoning with 12.8% error 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Author built this loop explicitly, inspired by personal ADHD where attention drifts to shiny distractions, forcing broad leaps and parallel threads with reduced inhibition to uncover novelty. Unlike autocomplete demos, it maintains an archive to avoid goldfish-like repetition, borrowing human insight generation—expand then contract—while structuring chaos to prevent conspiracy-level drift. This turns daydreaming into a defensible search strategy for AI, tightening outputs for reviewers who dismiss creativity as mere 'vibe'.",[17,3706,3708],{"id":3707},"cognitive-corridors-as-human-ai-intersections","Cognitive Corridors as Human-AI Intersections",[22,3710,3711],{},"A cognitive corridor is a fleeting mental expansion triggered by AI's reframing: it nudges sideways (e.g., from neural net instability to optimizer dynamics or diversity-aware retrieval), revealing adjacent ideas without outsourcing reasoning. Human focus shifts briefly, spotting un-hunted insights amid resumed motion—familiar to ADHD as non-vanishing but relocating attention. AI excels here not by solving but by highlighting doors in nearby concept space, creating brief overlaps that feel like acceleration, not fusion. Examples: querying deeper model instability yields scaling effects; knowledge system latency prompts evolutionary search strategies. These passages widen thought temporarily, making the corridor risky to skip (miss novelty) or linger in (suggestion masquerades as grasp).",[17,3713,3715],{"id":3714},"hybrid-convergence-risks-shallow-productivity","Hybrid Convergence Risks Shallow Productivity",[22,3717,3718],{},"Human-AI thinking converges into shared loops: AI proposes directions, humans select\u002Fverify, rewiring problem-solving across wetware-silicon boundaries where idea origins matter less than solo completion ability. This efficiency removes thinking's friction—wrong turns, dead ends, uncertainty—that forges structure, leading to early research showing heavy LLM users with lower task engagement\u002Fretention. Usage shifts from 'I'll explore' to 'system shows next,' amplified by AI summaries in search. Wanderers-like algorithms worsen this by enabling structured wandering, demanding explicit slowdowns\u002Fchecks to exit corridors. Use as pilot instruments (extensions, not replacements) yields capable hybrids; poor use breeds fast-moving but handrail-dependent confidence. Prioritize 'actual understanding' over instant insight for reality-surviving output.",{"title":57,"searchDepth":58,"depth":58,"links":3720},[3721,3722,3723],{"id":3700,"depth":58,"text":3701},{"id":3707,"depth":58,"text":3708},{"id":3714,"depth":58,"text":3715},[],{"content_references":3726,"triage":3732},[3727],{"type":3728,"title":3729,"author":3730,"url":3731,"context":75},"paper","Attention isn't all you need","Marco van Hurne","https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fattention-isnt-all-you-need-marco-van-hurne-by5tf\u002F?trackingId=E67ADNFTThmL1qzMtKwg2A%3D%3D&trk=article-ssr-frontend-pulse_little-text-block",{"relevance":80,"novelty":81,"quality":81,"actionability":58,"composite":82,"reasoning":3733},"Category: AI & LLMs. The article discusses the concept of cognitive corridors and the Wanderers Algorithm, which are relevant to AI's role in enhancing human creativity and thought processes. However, while it presents novel insights into human-AI interaction, it lacks concrete, actionable steps that the audience can implement in building AI-powered products.","\u002Fsummaries\u002F2607e9004ac9e126-cognitive-corridors-accelerate-thinking-but-bypass-summary","2026-04-15 15:26:22",{"title":3690,"description":57},{"loc":3734},"2607e9004ac9e126","__oneoff__","https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fhuman-ai-thinking-colliding-i-made-worse-purpose-marco-van-hurne-i77yc\u002F","summaries\u002F2607e9004ac9e126-cognitive-corridors-accelerate-thinking-but-bypass-summary",[96,97,98],"AI creates temporary 'cognitive corridors' where it widens human thought without takeover, forming hybrid loops that speed insight but erode deep understanding unless paired with grounding checks like the Wanderers Algorithm.",[],"_j1iSnZmajtvojZOHa_bYTeh0NcMRbgXiwoEW07t7jU",{"id":3747,"title":3748,"ai":3749,"body":3754,"categories":3782,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":3783,"navigation":84,"path":3799,"published_at":3800,"question":65,"scraped_at":3801,"seo":3802,"sitemap":3803,"source_id":3804,"source_name":3805,"source_type":92,"source_url":3806,"stem":3807,"tags":3808,"thumbnail_url":65,"tldr":3810,"tweet":65,"unknown_tags":3811,"__hash__":3812},"summaries\u002Fsummaries\u002F9b7252812a77bf18-vantage-executive-llm-scores-durable-skills-like-h-summary.md","Vantage: Executive LLM Scores Durable Skills Like Humans",{"provider":7,"model":8,"input_tokens":3750,"output_tokens":3751,"processing_time_ms":3752,"cost_usd":3753},8809,1603,15370,0.0025408,{"type":14,"value":3755,"toc":3777},[3756,3760,3763,3767,3770,3774],[17,3757,3759],{"id":3758},"executive-llm-coordinates-realistic-interactions-for-skill-elicitation","Executive LLM Coordinates Realistic Interactions for Skill Elicitation",[22,3761,3762],{},"Vantage solves the ecological validity vs. psychometric rigor tradeoff by using a single Executive LLM (Gemini 2.5 Pro for collaboration) to generate all AI teammate responses, guided by pedagogical rubrics. This steers conversations dynamically—like computerized adaptive tests—to provoke specific sub-skills: for Conflict Resolution (CR), it sustains disagreements until the human shows resolution strategies; for Project Management (PM), it prompts planning behaviors. Independent Agents (separate LLMs) fail here, as uncoordinated talks often lack skill-relevant evidence (e.g., no conflict if agents agree). In 373 transcripts from 188 participants on 30-minute tasks (science design or debate), skill-matched Executive LLM hit 92.4% conversation-level evidence rates for PM and 85% for CR—far above baselines. Simply instructing humans to focus on skills yielded no boost (p > 0.6), proving AI-side steering is key. This setup simulates authentic group dynamics scalably, unlike PISA 2015's scripted multiple-choice or costly human-human assessments.",[17,3764,3766],{"id":3765},"matches-human-raters-with-transparent-regression-based-scoring","Matches Human Raters with Transparent, Regression-Based Scoring",[22,3768,3769],{},"Scoring uses a separate AI Evaluator (Gemini 3.0) that rates each human turn 20 times, taking the mode (NA if any NA), then trains linear\u002Flogistic regression models via leave-one-out cross-validation for conversation scores. Inter-rater agreement hits human-human levels: Cohen’s Kappa 0.45–0.64 across CR\u002FPM and turn\u002Fconversation tasks. For creativity, a Gemini autorater on 280 high schoolers' multimedia tasks (e.g., news segment design) achieved item-level Kappa 0.66 and overall Pearson correlation 0.88 with experts—rare for subjective tasks—after prompt\u002Frubric refinement on 100 samples and evaluation on 180 holdouts. Results surface via skills maps with excerpt evidence, enabling actionable feedback.",[17,3771,3773],{"id":3772},"llm-simulation-de-risks-development-across-8-dimensions","LLM Simulation De-Risks Development Across 8 Dimensions",[22,3775,3776],{},"Simulate humans at known rubric levels (1–4) with Gemini to test recovery error (mean absolute difference between true and inferred levels): Executive LLM cuts error vs. Independents for CR\u002FPM, with patterns mirroring real data—validating cheap iteration before human studies. This extends to all 6 creativity dimensions (Fluidity, Originality, Quality, Building on Ideas, Elaborating, Selecting) and 2 critical thinking ones (Interpret\u002FAnalyze; Evaluate\u002FJudge), all showing higher evidence rates (statistically significant). Human creativity\u002FCT ratings ongoing, but simulation confirms generalization beyond collaboration.",{"title":57,"searchDepth":58,"depth":58,"links":3778},[3779,3780,3781],{"id":3758,"depth":58,"text":3759},{"id":3765,"depth":58,"text":3766},{"id":3772,"depth":58,"text":3773},[64],{"content_references":3784,"triage":3796},[3785,3790,3793],{"type":3728,"title":3786,"author":3787,"url":3788,"context":3789},"Toward Scalable Measurement of Durable Skills","Google Research","https:\u002F\u002Fservices.google.com\u002Ffh\u002Ffiles\u002Fmisc\u002Ftoward_scalable_measurement_of_durable_skills.pdf","recommended",{"type":71,"title":3791,"url":3792,"context":3789},"Towards Developing Future-Ready Skills with Generative AI","https:\u002F\u002Fresearch.google\u002Fblog\u002Ftowards-developing-future-ready-skills-with-generative-ai\u002F",{"type":3794,"title":3795,"context":75},"report","PISA 2015 Collaborative Problem Solving assessment",{"relevance":81,"novelty":80,"quality":81,"actionability":58,"composite":3797,"reasoning":3798},3.4,"Category: AI & LLMs. The article discusses a novel approach to using an Executive LLM for skill elicitation, which addresses the audience's interest in practical AI applications. However, while it presents interesting findings, it lacks specific actionable steps for implementation in product development.","\u002Fsummaries\u002F9b7252812a77bf18-vantage-executive-llm-scores-durable-skills-like-h-summary","2026-04-14 00:30:27","2026-04-14 14:37:55",{"title":3748,"description":57},{"loc":3799},"9b7252812a77bf18","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F13\u002Fgoogle-ai-research-proposes-vantage-an-llm-based-protocol-for-measuring-collaboration-creativity-and-critical-thinking\u002F","summaries\u002F9b7252812a77bf18-vantage-executive-llm-scores-durable-skills-like-h-summary",[96,3809,97,98],"agents","Google's Vantage uses one Executive LLM to coordinate AI teammates, eliciting collaboration evidence at 92.4% (PM) and 85% (CR) rates while matching human raters' Cohen’s Kappa (0.45–0.64).",[],"HvOIxEpv9c3PUUvzcyciF9VqKNutAp86HAxl-SWURDU",{"id":3814,"title":3815,"ai":3816,"body":3821,"categories":3933,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":3934,"navigation":84,"path":3940,"published_at":65,"question":65,"scraped_at":3941,"seo":3942,"sitemap":3943,"source_id":3944,"source_name":3945,"source_type":92,"source_url":3946,"stem":3947,"tags":3948,"thumbnail_url":65,"tldr":3950,"tweet":65,"unknown_tags":3951,"__hash__":3952},"summaries\u002Fsummaries\u002F62379661ee74ac35-chatgpt-accelerates-research-to-evidence-backed-de-summary.md","ChatGPT Accelerates Research to Evidence-Backed Decisions",{"provider":7,"model":8,"input_tokens":3817,"output_tokens":3818,"processing_time_ms":3819,"cost_usd":3820},8825,1484,14469,0.00248435,{"type":14,"value":3822,"toc":3928},[3823,3827,3835,3842,3845,3849,3852,3915,3918,3922,3925],[17,3824,3826],{"id":3825},"two-tier-approach-matches-research-depth-to-speed","Two-Tier Approach Matches Research Depth to Speed",[22,3828,3829,3830,3834],{},"ChatGPT handles research via ",[3831,3832,3833],"strong",{},"Search"," for rapid orientation—query recent web data like \"U.S. grocery delivery market in last 90 days,\" prioritizing press releases, earnings, and business reports to get 5 key developments with dates, links, and implications for your context (e.g., regional company risks). This surfaces sources fast without manual hunting.",[22,3836,3837,3838,3841],{},"For complex queries, ",[3831,3839,3840],{},"Deep Research"," decomposes into sub-questions, evaluates sources across threads (public reports, retailer announcements, earnings, trade coverage, consumer trends), and outputs structured deliverables like briefs on private-label shifts in household cleaning: what’s changing, why, exposed companies, responses, and implications for your firm (e.g., BlueHarbor Home Care). It distinguishes well-supported findings from directional ones, making outputs auditable.",[22,3843,3844],{},"This cuts time from fuzzy questions to plans, sifting dozens of sources into cited insights, spotting gaps\u002Fcontradictions early, and yielding shareable formats like memos or competitor tables.",[17,3846,3848],{"id":3847},"prompt-templates-deliver-consistent-structured-research","Prompt Templates Deliver Consistent, Structured Research",[22,3850,3851],{},"Plug-and-play prompts generate pro-level outputs:",[3853,3854,3855,3875,3885,3891,3905],"ul",{},[3856,3857,3858,3861,3862,3866,3867,3870,3871,3874],"li",{},[3831,3859,3860],{},"Executive Brief",": \"Write a 1-page brief on ",[3863,3864,3865],"span",{},"topic"," for ",[3863,3868,3869],{},"audience",". Include key findings (with citations), risks\u002Funknowns, recommendation. Constraints: ",[3863,3872,3873],{},"region\u002Ftimeframe",".\" Produces concise, decision-ready docs.",[3856,3876,3877,3880,3881,3884],{},[3831,3878,3879],{},"Competitor Table",": \"Compare 8 competitors in ",[3863,3882,3883],{},"market",". Table: positioning, pricing, differentiators, target customer, evidence links. Summarize whitespace.\" Reveals market gaps instantly.",[3856,3886,3887,3890],{},[3831,3888,3889],{},"Literature Review",": \"From uploaded papers, annotated bibliography + synthesis: themes, disagreements, top 5 open questions.\" Handles PDFs for academic synthesis.",[3856,3892,3893,3896,3897,3900,3901,3904],{},[3831,3894,3895],{},"Regulatory Scan",": \"",[3863,3898,3899],{},"Regulation"," updates last 12 months: changes, impacted parties, implications for ",[3863,3902,3903],{},"industry"," company. Cite after each point.\" Flags compliance risks.",[3856,3906,3907,3910,3911,3914],{},[3831,3908,3909],{},"Trend Watch",": \"Emerging trends in ",[3863,3912,3913],{},"domain",": 10 weak signals (funding\u002Fhiring\u002Fresearch\u002Flaunches), why they matter, next monitors. Sources\u002Fdates.\" Spots early signals like 10 specific indicators.",[22,3916,3917],{},"These enforce structure, citations, and strategic framing, turning raw data into actionable intelligence.",[17,3919,3921],{"id":3920},"habits-ensure-reliable-shareable-insights","Habits Ensure Reliable, Shareable Insights",[22,3923,3924],{},"Start with an outline prompt: sub-questions, source strategy, evaluation criteria—to refine before diving in. Mandate citations on claims plus source quality checks for high-stakes work. Add a “what’s missing” section to expose unknowns, disputes, or limits. For teams, pair full reports with 1-page\u002F1-slide summaries. Iterate via follow-ups: “Deeper on X,” “Validate Y,” “Compare A vs B.”",[22,3926,3927],{},"Trade-off: Web Search stays current but relies on public data; Deep Research scales depth but needs precise instructions to avoid hallucination. Result: Faster paths to trusted decisions without losing rigor.",{"title":57,"searchDepth":58,"depth":58,"links":3929},[3930,3931,3932],{"id":3825,"depth":58,"text":3826},{"id":3847,"depth":58,"text":3848},{"id":3920,"depth":58,"text":3921},[64],{"content_references":3935,"triage":3936},[],{"relevance":3937,"novelty":81,"quality":81,"actionability":3937,"composite":3938,"reasoning":3939},5,4.55,"Category: AI & LLMs. The article provides a detailed overview of how ChatGPT can be utilized for research, addressing the audience's need for practical applications of AI tools in product development. It includes specific prompt templates that users can implement immediately to enhance their research processes.","\u002Fsummaries\u002F62379661ee74ac35-chatgpt-accelerates-research-to-evidence-backed-de-summary","2026-04-16 03:19:01",{"title":3815,"description":57},{"loc":3940},"62379661ee74ac35","OpenAI News","https:\u002F\u002Fopenai.com\u002Facademy\u002Fresearch","summaries\u002F62379661ee74ac35-chatgpt-accelerates-research-to-evidence-backed-de-summary",[97,3949,96,98],"ai-tools","Use ChatGPT's Search for quick web summaries with citations on recent events; switch to Deep Research for multi-step synthesis into briefs, tables, or reviews that separate facts from speculation.",[],"NgzqzT0FugaQ_SMu7NRt-b55ls4LoqPLfin1eBxOVbM",{"id":3954,"title":3955,"ai":3956,"body":3961,"categories":4014,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":4015,"navigation":84,"path":4073,"published_at":65,"question":65,"scraped_at":4074,"seo":4075,"sitemap":4076,"source_id":4077,"source_name":3739,"source_type":92,"source_url":4078,"stem":4079,"tags":4080,"thumbnail_url":65,"tldr":4081,"tweet":65,"unknown_tags":4082,"__hash__":4083},"summaries\u002Fsummaries\u002Fad724b6d82e63f18-openai-simple-evals-zero-shot-cot-benchmarks-summary.md","OpenAI Simple Evals: Zero-Shot CoT Benchmarks",{"provider":7,"model":8,"input_tokens":3957,"output_tokens":3958,"processing_time_ms":3959,"cost_usd":3960},10641,2483,12124,0.00334705,{"type":14,"value":3962,"toc":4009},[3963,3967,3970,3974,3977,3981],[17,3964,3966],{"id":3965},"zero-shot-chain-of-thought-beats-few-shot-for-instruction-tuned-models","Zero-Shot Chain-of-Thought Beats Few-Shot for Instruction-Tuned Models",[22,3968,3969],{},"Apply simple zero-shot prompts like \"Solve the following multiple choice problem\" to better reflect real-world performance of chat-tuned LLMs, avoiding outdated few-shot or role-playing techniques from base model eras. This approach reduces eval sensitivity to prompt variations, enabling fair comparisons. OpenAI open-sources the library for transparency on published accuracy numbers, but deprecates new model\u002Fbenchmark updates post-July 2025, retaining only HealthBench, BrowseComp, and SimpleQA implementations. Not a full replacement for the comprehensive openai\u002Fevals repo.",[17,3971,3973],{"id":3972},"openai-models-dominate-key-benchmarks","OpenAI Models Dominate Key Benchmarks",[22,3975,3976],{},"o3-high leads with 93.3% MMLU, 83.4% GPQA, 98.1% MATH, 88.4% HumanEval, 92.0% MGSM, 89.8% DROP (F1, 3-shot), and 48.6% SimpleQA. o4-mini-high excels in MATH (98.2%) and HumanEval (99.3%), while o3-mini-high hits 97.9% MATH at lower cost. GPT-4.5-preview scores 62.5% SimpleQA (tops table), but lags o3 on most metrics. Competitors trail: Claude 3.5 Sonnet at 88.3% MMLU\u002F59.4% GPQA; Llama 3.1 405B at 88.6% MMLU\u002F50.7% GPQA. Use the full table to select models by task—e.g., o4-mini-high for math\u002Fcoding efficiency.",[17,3978,3980],{"id":3979},"run-evals-on-openai-or-claude-apis","Run Evals on OpenAI or Claude APIs",[22,3982,3983,3984,3988,3989,3992,3993,3996,3997,4000,4001,4004,4005,4008],{},"Install per-eval dependencies (e.g., ",[3985,3986,3987],"code",{},"pip install -e human-eval"," for HumanEval). Set ",[3985,3990,3991],{},"OPENAI_API_KEY"," or ",[3985,3994,3995],{},"ANTHROPIC_API_KEY",". Benchmarks include MMLU (multitask understanding), MATH\u002FGPQA\u002FMGSM (math\u002Freasoning), DROP (discrete reading comprehension), HumanEval (code), SimpleQA (factuality), BrowseComp (browsing agents), HealthBench (health applications). Scripts like ",[3985,3998,3999],{},"mmlu_eval.py",", ",[3985,4002,4003],{},"math_eval.py"," handle sampling\u002Fparsing. Add new model adapters or results via PRs (bugs only otherwise). Multilingual MMLU results in ",[3985,4006,4007],{},"multilingual_mmlu_benchmark_results.md",".",{"title":57,"searchDepth":58,"depth":58,"links":4010},[4011,4012,4013],{"id":3965,"depth":58,"text":3966},{"id":3972,"depth":58,"text":3973},{"id":3979,"depth":58,"text":3980},[],{"content_references":4016,"triage":4070},[4017,4020,4024,4027,4030,4033,4036,4039,4042,4045,4048,4051,4054,4057,4060,4063,4067],{"type":3728,"title":4018,"url":4019,"context":75},"Measuring Massive Multitask Language Understanding","https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.03300",{"type":4021,"title":4022,"url":4023,"context":75},"dataset","MMLU","https:\u002F\u002Fgithub.com\u002Fhendrycks\u002Ftest",{"type":3728,"title":4025,"url":4026,"context":75},"Measuring Mathematical Problem Solving With the MATH Dataset","https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03874",{"type":4021,"title":4028,"url":4029,"context":75},"MATH","https:\u002F\u002Fgithub.com\u002Fhendrycks\u002Fmath",{"type":3728,"title":4031,"url":4032,"context":75},"A Graduate-Level Google-Proof Q&A Benchmark","https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12022",{"type":4021,"title":4034,"url":4035,"context":75},"GPQA","https:\u002F\u002Fgithub.com\u002Fidavidrein\u002Fgpqa\u002F",{"type":3728,"title":4037,"url":4038,"context":75},"A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs","https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.00161",{"type":4021,"title":4040,"url":4041,"context":75},"DROP","https:\u002F\u002Fallenai.org\u002Fdata\u002Fdrop",{"type":3728,"title":4043,"url":4044,"context":75},"Language Models are Multilingual Chain-of-Thought Reasoners","https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.03057",{"type":4021,"title":4046,"url":4047,"context":75},"MGSM","https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Furl-nlp",{"type":3728,"title":4049,"url":4050,"context":75},"Evaluating Large Language Models Trained on Code","https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.03374",{"type":4021,"title":4052,"url":4053,"context":75},"HumanEval","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fhuman-eval",{"type":3794,"title":4055,"url":4056,"context":75},"Introducing SimpleQA","https:\u002F\u002Fopenai.com\u002Findex\u002Fintroducing-simpleqa",{"type":3794,"title":4058,"url":4059,"context":75},"BrowseComp","https:\u002F\u002Fopenai.com\u002Findex\u002Fbrowsecomp",{"type":3794,"title":4061,"url":4062,"context":75},"HealthBench","https:\u002F\u002Fopenai.com\u002Findex\u002Fhealthbench",{"type":4064,"title":4065,"url":4066,"context":78},"tool","OpenAI API","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Foverview",{"type":4064,"title":4068,"url":4069,"context":78},"Anthropic API","https:\u002F\u002Fwww.anthropic.com\u002Fapi",{"relevance":3937,"novelty":81,"quality":81,"actionability":81,"composite":4071,"reasoning":4072},4.35,"Category: AI & LLMs. The article provides a practical tool for evaluating AI models using zero-shot prompts, addressing the audience's need for actionable insights in AI integration. It offers specific benchmarks and installation instructions, making it directly applicable for developers looking to implement these evaluations.","\u002Fsummaries\u002Fad724b6d82e63f18-openai-simple-evals-zero-shot-cot-benchmarks-summary","2026-04-16 03:04:03",{"title":3955,"description":57},{"loc":4073},"ad724b6d82e63f18","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fsimple-evals","summaries\u002Fad724b6d82e63f18-openai-simple-evals-zero-shot-cot-benchmarks-summary",[96,97,3949,98],"Use this lightweight library to run transparent zero-shot chain-of-thought evals on MMLU (o3-high: 93.3%), GPQA (o3-high: 83.4%), MATH (o4-mini-high: 98.2%), HumanEval, MGSM, DROP, and SimpleQA for accurate model comparisons without few-shot prompts.",[],"d8RNHzywcYtI_7juxUnVEYDGqIJNT6xLr2V3W_SUTsM"]