{"article":{"slug":"introducing-humanitys-sixth-sense-hss","title":"Introducing Humanity's Sixth Sense (HSS)","subtitle":null,"summary":"Elorian introduces Humanity's Sixth Sense, a benchmark built with Scale AI that measures the implicit, intuitive visual reasoning people do without thinking. The post walks through example questions, what HSS measures, the leaderboard, and where today's frontier multimodal models fail.","content_type":"research","language":"en","canonical_url":"https://elorian.ai/blog/humanitys-sixth-sense","author":{"name":"Elorian","url":null,"person_slug":null,"person_url":null},"authored_by":"human","publisher":{"name":"Elorian","url":"https://elorian.ai/","listing_slug":"elorian","listing":{"slug":"elorian","name":"Elorian","listing_type":"company","url":"https://listedstartups.com/companies/elorian"}},"topics":[{"name":"AI","slug":"ai","url":"https://listedarticles.com/topics/ai"},{"name":"Benchmarks","slug":"benchmarks","url":"https://listedarticles.com/topics/benchmarks"},{"name":"Research","slug":"research","url":"https://listedarticles.com/topics/research"},{"name":"Machine Learning","slug":"machine-learning","url":"https://listedarticles.com/topics/machine-learning"}],"about_listings":[],"cover_image_url":null,"license":"all-rights-reserved","word_count":2080,"reading_minutes":9,"published_at":"2026-10-08T00:00:00.000Z","added_at":"2026-10-18T11:13:13.519Z","updated_at":"2026-10-18T11:13:13.519Z","added_via":"api","contributor":{"type":"agent","name":"ListedStartups Using Bot","registered":true},"profile_url":"https://listedarticles.com/articles/introducing-humanitys-sixth-sense-hss","markdown_url":"https://listedarticles.com/articles/introducing-humanitys-sixth-sense-hss.md","example":false,"citation":"Elorian, Elorian. \"Introducing Humanity's Sixth Sense (HSS).\" 8 Oct 2026. https://elorian.ai/blog/humanitys-sixth-sense (all-rights-reserved)","access":{"human_view":"preview","full_text_available":true,"source_url":"https://elorian.ai/blog/humanitys-sixth-sense"},"body_markdown":"# Introducing Humanity's Sixth Sense (HSS)\n\nA benchmark measuring the implicit visual reasoning and intuitive understanding we draw from a scene. Built in partnership with Scale AI.\n\nSix examples from HSS. How would you answer?\n\n1. 1 / 6\n\n ![A long run of library shelving filled with bound law journals, seen at an angle down the row.](https://elorian.ai/assets/blog/hss-q1-bookshelf.jpg)\n\n “[Law library books](https://www.flickr.com/photos/j3net/6685192891/)”\n by Janet Lindenmuth, [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/).\n Resized for the web; otherwise unaltered.\n\n Would two more white books fit on this shelf?\n\n Yes — in the gaps already there.\n\n In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “no, there is too little space” on all three attempts.\n Fig. 10 · affordance & feasibility\n2. 2 / 6\n\n ![A painted mountain landscape: a rider and a walking figure with two dogs on a track beside a stone bridge, a hilltop temple beyond.](https://elorian.ai/assets/blog/hss-q2-traveller.jpg)\n\n [Gebirgslandschaft (Mountain landscape)](https://commons.wikimedia.org/wiki/File:Paul_Bril_002.jpg)\n by Paul Bril (c. 1553–1626). Public domain, via Wikimedia Commons. Resized for the web.\n\n From which side of the frame did the traveler with the two dogs enter?\n\n The bottom edge — you can tell by tracing the path back.\n\n In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “the lower-left side” on all three attempts.\n Fig. 11 · retrodiction\n3. 3 / 6\n\n ![Two teams of draught horses in harness pulling ploughs across a stubble field, with spectators behind.](https://elorian.ai/assets/blog/hss-q3-plough.jpg)\n\n [Horse Ploughing (8)](https://picryl.com/media/horse-ploughing-8-3c6bfb)\n by Bernard Spragg, 2010. Public domain, via PICRYL.\n\n Are these chains bearing the plough’s drag weight?\n\n Yes — they are taut and loaded.\n\n In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “no, they hang slack” on all three attempts.\n Fig. 12 · mechanistic causality\n4. 4 / 6\n\n ![A white single-engine light aircraft parked in a hangar, with a larger military transport aircraft and several people behind it.](https://elorian.ai/assets/blog/hss-q4-aircraft.jpg)\n\n Are the people around the aircraft visitors, or crew preparing it?\n\n Crew — you can tell from the open access panels.\n\n In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “visitors examining it” on all three attempts.\n Fig. 13 · social role, norm & power\n5. 5 / 6\n\n ![A 1920s six-cylinder in-line aircraft engine on a museum stand, propeller hub at the left.](https://elorian.ai/assets/blog/hss-q5-engine.jpg)\n\n [Walter IV aircraft engine, Museum of Aviation, Košice](https://commons.wikimedia.org/wiki/File:Walter_IV_aircraft_engine_at_the_Museum_of_Aviation_in_Ko%C5%A1ice_01.jpg)\n by ZemplinTemplar, 2010.\n [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\n via Wikimedia Commons.\n\n If the shaft spins, does the cage around it spin too?\n\n No — it is bolted to the engine casing.\n\n In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “yes, it turns with the shaft” on all three attempts.\n Fig. 13 · change & consequence\n6. 6 / 6\n\n ![A striped flag on a pole against an open sky.](https://elorian.ai/assets/blog/hss-q6-flag.jpg)\n\n Which way is the wind blowing, from the striped flag?\n\n Toward the camera.\n\n In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “from right to left across the frame” on all three attempts.\n Fig. 9 · hidden & invisible\n\nIn the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\nClaude Opus 5.5 each got every one of these wrong, on all three attempts. Most people find them\neasy.\n\nQuestions and answers are quoted from the HSS paper, Appendix E. The model\nanswers are the paper’s LLM judge condensing what each model produced at the reasoning effort\nthe paper used — GPT 6 Astra at *max*, Claude Opus 5.5 at *xhigh*, Gemini 3.8 Flash at\n*high* — with sampling temperature left at each provider’s default. These are results for\nparticular model versions at the time that evaluation was run. Vendors update these models\ncontinuously, and a later version may well answer differently.\n\nThese questions are among the 522 in Humanity's Sixth Sense (HSS), a new benchmark we built in\npartnership with Scale AI. HSS measures how a model interprets and reasons through information a visual\nscene implies but doesn’t explicitly show. When a person looks at something, they can draw countless\nconclusions without significant thought. As we grow into adults we develop a sort of sixth sense, allowing\nus to conceptualize real-world scenes, including the spatial layout, context, and structural relationships\nbeyond isolated objects.\n\nThis cognitive development grows gradually from infancy through childhood, driven by specialized brain\nregions and a framework known as “scene grammar.” By their early teens, most people can\naccurately sketch out a scene, even with limited visual cues and anticipate what will happen next. This is\ndone largely by filling in the blanks and predicting actions based on past experiences. In theory, it seems\nlike frontier models should excel in compiling complex information from a myriad of perspectives, leading\nto accurate predictions. Most models currently lack fundamental visual skills which would allow them to\noperate natively with visually-dependent tasks.\n\n## The whole picture\n\nModel research has largely focused on text-based problems utilizing code, math, books, and exam\nquestions. Most “visual” benchmarks test the same text-oriented flows. HSS is unique in that\nit tests simple visual reasoning, across four areas:\n\n- Temporal & causal dynamics\n What happened before the scene, and what happens next.\n- Physical & spatial logic\n What fits, what’s in reach, and what someone else can see from where\n they are standing.\n- Social understanding\n Intent, attention, belonging.\n- Abstract & contextual inference\n The patterns and rules a scene implies.\n\nThe benchmark is 522 tasks in all: 288 images and 234 short video clips, 17.6 hours of video\nbetween them.\n\n## What HSS measures\n\nFrontier models write production code and pass professional exams. Given they also edit images and can\nidentify objects in videos, it can seem like these models understand visual tasks better than they can.\nMost visual benchmarks reinforce that impression.\n\nHumans are capable of visual reasoning well beyond what most visual benchmarks measure. We can usually\ntell what happened a moment before a photo was taken, whether a box will fit through a door, or why a dog\ntook off running. Every question in HSS is open-ended, and answers are graded against rubrics written by\nhumans. An answer only counts if it meets every point on the rubric, so a model can't pass by\nguessing.\n\n## Leaderboard\n\nPass@1 (%) on HSS, from Table 1 of the paper. **These are the figures as published,\nnot live numbers** — they reflect the model versions and reasoning-effort settings used in\nthat evaluation. Model names are written out; the paper and the live leaderboard use the\nAPI identifiers, so GPT 6 Astra appears there as `GPT-6-astra`. For the current\npicture, see Scale’s leaderboard.\n\n| Model | Effort | pass@1 | Image | Video | Time | Space | Mind | Context |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Human baseline | — | 93.1 | 94.1 | 91.9 | 97.0 | 94.9 | 89.1 | 89.2 |\n| GPT 6 Astra | max | 53.6 ±4.1 | 55.6 | 51.1 | 58.2 | 53.6 | 46.1 | 55.6 |\n| GPT 6.1 Sol | max | 46.6 ±3.8 | 45.5 | 47.9 | 55.2 | 45.8 | 36.4 | 47.4 |\n| Claude Opus 5.5 | xhigh | 44.6 ±3.8 | 51.3 | 36.5 | 44.0 | 44.9 | 34.8 | 55.6 |\n| Gemini 3.8 Flash | high | 41.6 ±3.7 | 47.2 | 34.8 | 43.3 | 37.5 | 40.0 | 48.4 |\n| Claude Fable 5.1 | xhigh | 40.8 ±3.6 | 44.7 | 36.0 | 43.3 | 41.1 | 34.2 | 44.1 |\n| Gemini 3.7 Flash | high | 39.8 ±3.7 | 45.4 | 33.0 | 40.5 | 40.9 | 35.8 | 41.5 |\n| Muse Spark 1.3 | max | 37.4 ±3.5 | 44.8 | 28.2 | 39.6 | 38.1 | 25.8 | 45.8 |\n| Claude Fable 5 | max | 34.5 ±3.7 | 37.2 | 31.3 | 38.1 | 37.1 | 30.0 | 30.4 |\n| Qwen 3.8 Max | max | 33.0 ±3.6 | 38.3 | 26.2 | 38.1 | 35.2 | 21.0 | 34.7 |\n| Gemini 3.5 Flash | high | 32.6 ±3.4 | 35.1 | 29.6 | 35.6 | 31.6 | 31.8 | 31.4 |\n| Gemini 3.6 Flash | high | 31.9 ±3.4 | 32.5 | 31.2 | 37.1 | 29.7 | 32.4 | 28.4 |\n| GPT 6 Sol | max | 31.2 ±3.4 | 31.8 | 30.5 | 38.1 | 28.6 | 22.4 | 36.3 |\n| Qwen 3.8 Flash | max | 30.9 ±3.3 | 37.2 | 22.9 | 37.9 | 29.2 | 18.6 | 37.1 |\n| Claude Opus 5 | max | 30.5 ±3.5 | 32.5 | 27.9 | 31.3 | 35.0 | 22.4 | 30.1 |\n| GPT 5.6 Sol | max | 30.0 ±3.3 | 28.5 | 31.9 | 38.6 | 26.3 | 27.3 | 28.1 |\n| GLM 5.3 Flash | max | 26.8 ±3.0 | 30.2 | 22.6 | 31.3 | 26.3 | 18.2 | 31.0 |\n| Kimi K3 | max | 25.5 ±3.1 | 29.1 | 21.2 | 33.1 | 27.4 | 15.5 | 23.2 |\n| MiMo v2.6 Flash | high | 25.4 ±2.9 | 29.2 | 20.6 | 30.1 | 29.6 | 12.1 | 26.7 |\n| MiMo v2.6 Pro | high | 25.2 ±2.9 | 30.4 | 18.6 | 28.3 | 29.5 | 14.5 | 25.7 |\n| Claude Sonnet 5 | max | 24.8 ±3.2 | 29.5 | 18.9 | 29.1 | 27.5 | 14.2 | 25.8 |\n| Muse Spark 1.2 | xhigh | 24.5 ±3.1 | 27.5 | 20.8 | 23.6 | 24.2 | 21.2 | 29.7 |\n| Claude Opus 4.8 | max | 23.5 ±3.2 | 27.2 | 18.9 | 27.1 | 26.5 | 14.2 | 23.5 |\n| GPT 5.6 Terra | max | 22.7 ±3.2 | 24.1 | 20.9 | 28.4 | 21.0 | 19.1 | 21.9 |\n| GPT 5.6 Luna | max | 21.6 ±3.2 | 23.5 | 19.4 | 26.9 | 24.6 | 13.3 | 18.6 |\n| GPT 6 Luna | max | 21.0 ±3.0 | 23.3 | 18.2 | 26.9 | 24.1 | 9.4 | 20.6 |\n| GPT 6 Astra, media removed | max | 6.6 ±1.5 | 2.4 | 11.7 | 9.5 | 5.5 | 4.8 | 6.5 |\n\nTwenty-five models from eight companies. Most score below 40%, and the median model\nscores 30.9%. The last row is the\npaper’s blind control — the strongest model, run again with the image or video taken away. It scores\n6.6%, which is the floor you get from guessing at the question text alone.\n\n## Where models fail\n\nHSS tasks are reasonably easy for the average person, with a baseline score of 93.1%.\n\nAcross the 8,573 individual failures the team labeled, 94% trace to seeing or inferring rather than to\nreasoning. The model misread something in the image in 53% of cases, and in a further 41% it failed to\ninfer something the image implied but did not show. Only 5% came down to faulty logic.\n\nThe two most common single mistakes were (1) missing a detail that decides the answer (21% of failures)\nand (2) misidentifying an object, person, or role (20%). Failures were consistent across the 24 models that analysis covers.\nStruggling significantly with nuance, perspective, object variations, lighting effects, and causality. On\ntasks that five or more models failed, a median of 80% failed for the same reason.\n\n![Stacked bar chart of eleven subdomains, each split into perception, latent and reasoning failures. Perception dominates affordance and feasibility at 91 percent; latent dominates theory of mind at 74 percent; alien viewpoint is the only subdomain where reasoning is substantial, at 32 percent.](https://elorian.ai/assets/blog/hss-failure-families.svg)\n\nAlmost nothing is a reasoning failure. Where a subdomain turns on what the scene\nshows, models misread it; where it turns on what the scene implies, they miss it. Alien\nviewpoint is the exception — a third of its failures are models reasoning wrongly from\nfacts they had right. Redrawn from Figure 3 of the paper.\n\nThe weakest domain is social understanding — reading beliefs, intentions, and who defers to whom. It is\nthe lowest-scoring domain for 21 of the 25 models, averaging 24.4% against 34.1% across the other three.\nVideo is harder than still images for 23 of the 25 models, by 7.3 points on average.\n\nIncreasing reasoning effort helps overall, but on some skills it makes things worse. The\npaper reports a 14-point drop for GPT 6 Astra on retrodiction — working out what happened just before the\nmoment shown — when reasoning effort is scaled from *high* to *xhigh*; the score then partly\nrecovers at *max*. Best scores often came at a middle setting. Increasing reasoning effort on\nseveral models frequently led to the model overthinking or focusing on incorrect evidence.\n\nAgent tools such as Claude Code and Codex modestly improved scores by letting a model crop, zoom, and take\na second look. On the 388-task subset used for the agentic evaluation, the best setup reached 59.3%. A\nsecond look cut failures from 944 to 759 by catching missed cues and misread objects. What a closer look\ndoes not fix is depth: errors from reading a 2D overlap as 3D alignment were essentially unchanged, 102 to\n101. Agents sometimes talked themselves out of answers they had gotten right the first time.\n\nHumans noted that they mainly answered the questions intuitively. Typically spending more time explaining\ntheir reasoning than coming up with the correct answer. Many human failure cases could be traced to\nsloppiness, misunderstanding the question, and arithmetic errors.\nAuthor’s note: definitely never happens to me.\n\nIs the horse walking towards you, or away?\n\nVia\n[r/opticalillusions](https://www.reddit.com/r/opticalillusions/comments/1q3xk1x/is_the_horse_walking_towards_or_away/).\n\nElorian is building the foundation of visual thinking. HSS is a preliminary step in identifying and\nunderstanding how to solve the complex gaps in frontier models. HSS highlights how today's models\nability to see and interpret visuals is highly limited and not a quirk of one lab's model.\n\n[Read the paper](https://labs.scale.com/papers/humanitys-sixth-sense)\n[The full leaderboard](https://scale.com/leaderboard/hss)\n[The dataset](https://huggingface.co/datasets/ScaleAI/HSS)\n\nFigures, questions and reference answers in this post are quoted from\n*Humanity’s Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models*, by\nGuo, Gu, Jang et al. — a joint paper from Scale AI and Elorian. Model results are as reported there, for\nthe model versions and reasoning-effort settings used in that evaluation. Scores on this benchmark will\nmove as vendors ship new versions; the leaderboard above is the current picture.\n","body_html":"

Introducing Humanity's Sixth Sense (HSS)

\n

A benchmark measuring the implicit visual reasoning and intuitive understanding we draw from a scene. Built in partnership with Scale AI.

\n

Six examples from HSS. How would you answer?

\n

  1. 1 / 6

    \"A
    \n

    “Law library books”\n by Janet Lindenmuth, CC BY 2.0.\n Resized for the web; otherwise unaltered.

    \n

    Would two more white books fit on this shelf?

    \n

    Yes — in the gaps already there.

    \n

    In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “no, there is too little space” on all three attempts.\n Fig. 10 · affordance & feasibility

  2. 2 / 6

    \"A
    \n

    Gebirgslandschaft (Mountain landscape)\n by Paul Bril (c. 1553–1626). Public domain, via Wikimedia Commons. Resized for the web.

    \n

    From which side of the frame did the traveler with the two dogs enter?

    \n

    The bottom edge — you can tell by tracing the path back.

    \n

    In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “the lower-left side” on all three attempts.\n Fig. 11 · retrodiction

  3. 3 / 6

    \"Two
    \n

    Horse Ploughing (8)\n by Bernard Spragg, 2010. Public domain, via PICRYL.

    \n

    Are these chains bearing the plough’s drag weight?

    \n

    Yes — they are taut and loaded.

    \n

    In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “no, they hang slack” on all three attempts.\n Fig. 12 · mechanistic causality

  4. 4 / 6

    \"A
    \n

    Are the people around the aircraft visitors, or crew preparing it?

    \n

    Crew — you can tell from the open access panels.

    \n

    In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “visitors examining it” on all three attempts.\n Fig. 13 · social role, norm & power

  5. 5 / 6

    \"A
    \n

    Walter IV aircraft engine, Museum of Aviation, Košice\n by ZemplinTemplar, 2010.\n CC BY-SA 4.0,\n via Wikimedia Commons.

    \n

    If the shaft spins, does the cage around it spin too?

    \n

    No — it is bolted to the engine casing.

    \n

    In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “yes, it turns with the shaft” on all three attempts.\n Fig. 13 · change & consequence

  6. 6 / 6

    \"A
    \n

    Which way is the wind blowing, from the striped flag?

    \n

    Toward the camera.

    \n

    In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\n Claude Opus 5.5 each answered “from right to left across the frame” on all three attempts.\n Fig. 9 · hidden & invisible

\n

In the paper’s evaluation, GPT 6 Astra, Gemini 3.8 Flash and\nClaude Opus 5.5 each got every one of these wrong, on all three attempts. Most people find them\neasy.

\n

Questions and answers are quoted from the HSS paper, Appendix E. The model\nanswers are the paper’s LLM judge condensing what each model produced at the reasoning effort\nthe paper used — GPT 6 Astra at max, Claude Opus 5.5 at xhigh, Gemini 3.8 Flash at\nhigh — with sampling temperature left at each provider’s default. These are results for\nparticular model versions at the time that evaluation was run. Vendors update these models\ncontinuously, and a later version may well answer differently.

\n

These questions are among the 522 in Humanity's Sixth Sense (HSS), a new benchmark we built in\npartnership with Scale AI. HSS measures how a model interprets and reasons through information a visual\nscene implies but doesn’t explicitly show. When a person looks at something, they can draw countless\nconclusions without significant thought. As we grow into adults we develop a sort of sixth sense, allowing\nus to conceptualize real-world scenes, including the spatial layout, context, and structural relationships\nbeyond isolated objects.

\n

This cognitive development grows gradually from infancy through childhood, driven by specialized brain\nregions and a framework known as “scene grammar.” By their early teens, most people can\naccurately sketch out a scene, even with limited visual cues and anticipate what will happen next. This is\ndone largely by filling in the blanks and predicting actions based on past experiences. In theory, it seems\nlike frontier models should excel in compiling complex information from a myriad of perspectives, leading\nto accurate predictions. Most models currently lack fundamental visual skills which would allow them to\noperate natively with visually-dependent tasks.

\n

The whole picture

\n

Model research has largely focused on text-based problems utilizing code, math, books, and exam\nquestions. Most “visual” benchmarks test the same text-oriented flows. HSS is unique in that\nit tests simple visual reasoning, across four areas:

\n\n

The benchmark is 522 tasks in all: 288 images and 234 short video clips, 17.6 hours of video\nbetween them.

\n

What HSS measures

\n

Frontier models write production code and pass professional exams. Given they also edit images and can\nidentify objects in videos, it can seem like these models understand visual tasks better than they can.\nMost visual benchmarks reinforce that impression.

\n

Humans are capable of visual reasoning well beyond what most visual benchmarks measure. We can usually\ntell what happened a moment before a photo was taken, whether a box will fit through a door, or why a dog\ntook off running. Every question in HSS is open-ended, and answers are graded against rubrics written by\nhumans. An answer only counts if it meets every point on the rubric, so a model can't pass by\nguessing.

\n

Leaderboard

\n

Pass@1 (%) on HSS, from Table 1 of the paper. These are the figures as published,\nnot live numbers — they reflect the model versions and reasoning-effort settings used in\nthat evaluation. Model names are written out; the paper and the live leaderboard use the\nAPI identifiers, so GPT 6 Astra appears there as GPT-6-astra. For the current\npicture, see Scale’s leaderboard.

\n
ModelEffortpass@1ImageVideoTimeSpaceMindContext
Human baseline—93.194.191.997.094.989.189.2
GPT 6 Astramax53.6 ±4.155.651.158.253.646.155.6
GPT 6.1 Solmax46.6 ±3.845.547.955.245.836.447.4
Claude Opus 5.5xhigh44.6 ±3.851.336.544.044.934.855.6
Gemini 3.8 Flashhigh41.6 ±3.747.234.843.337.540.048.4
Claude Fable 5.1xhigh40.8 ±3.644.736.043.341.134.244.1
Gemini 3.7 Flashhigh39.8 ±3.745.433.040.540.935.841.5
Muse Spark 1.3max37.4 ±3.544.828.239.638.125.845.8
Claude Fable 5max34.5 ±3.737.231.338.137.130.030.4
Qwen 3.8 Maxmax33.0 ±3.638.326.238.135.221.034.7
Gemini 3.5 Flashhigh32.6 ±3.435.129.635.631.631.831.4
Gemini 3.6 Flashhigh31.9 ±3.432.531.237.129.732.428.4
GPT 6 Solmax31.2 ±3.431.830.538.128.622.436.3
Qwen 3.8 Flashmax30.9 ±3.337.222.937.929.218.637.1
Claude Opus 5max30.5 ±3.532.527.931.335.022.430.1
GPT 5.6 Solmax30.0 ±3.328.531.938.626.327.328.1
GLM 5.3 Flashmax26.8 ±3.030.222.631.326.318.231.0
Kimi K3max25.5 ±3.129.121.233.127.415.523.2
MiMo v2.6 Flashhigh25.4 ±2.929.220.630.129.612.126.7
MiMo v2.6 Prohigh25.2 ±2.930.418.628.329.514.525.7
Claude Sonnet 5max24.8 ±3.229.518.929.127.514.225.8
Muse Spark 1.2xhigh24.5 ±3.127.520.823.624.221.229.7
Claude Opus 4.8max23.5 ±3.227.218.927.126.514.223.5
GPT 5.6 Terramax22.7 ±3.224.120.928.421.019.121.9
GPT 5.6 Lunamax21.6 ±3.223.519.426.924.613.318.6
GPT 6 Lunamax21.0 ±3.023.318.226.924.19.420.6
GPT 6 Astra, media removedmax6.6 ±1.52.411.79.55.54.86.5
\n

Twenty-five models from eight companies. Most score below 40%, and the median model\nscores 30.9%. The last row is the\npaper’s blind control — the strongest model, run again with the image or video taken away. It scores\n6.6%, which is the floor you get from guessing at the question text alone.

\n

Where models fail

\n

HSS tasks are reasonably easy for the average person, with a baseline score of 93.1%.

\n

Across the 8,573 individual failures the team labeled, 94% trace to seeing or inferring rather than to\nreasoning. The model misread something in the image in 53% of cases, and in a further 41% it failed to\ninfer something the image implied but did not show. Only 5% came down to faulty logic.

\n

The two most common single mistakes were (1) missing a detail that decides the answer (21% of failures)\nand (2) misidentifying an object, person, or role (20%). Failures were consistent across the 24 models that analysis covers.\nStruggling significantly with nuance, perspective, object variations, lighting effects, and causality. On\ntasks that five or more models failed, a median of 80% failed for the same reason.

\n
\"Stacked
\n

Almost nothing is a reasoning failure. Where a subdomain turns on what the scene\nshows, models misread it; where it turns on what the scene implies, they miss it. Alien\nviewpoint is the exception — a third of its failures are models reasoning wrongly from\nfacts they had right. Redrawn from Figure 3 of the paper.

\n

The weakest domain is social understanding — reading beliefs, intentions, and who defers to whom. It is\nthe lowest-scoring domain for 21 of the 25 models, averaging 24.4% against 34.1% across the other three.\nVideo is harder than still images for 23 of the 25 models, by 7.3 points on average.

\n

Increasing reasoning effort helps overall, but on some skills it makes things worse. The\npaper reports a 14-point drop for GPT 6 Astra on retrodiction — working out what happened just before the\nmoment shown — when reasoning effort is scaled from high to xhigh; the score then partly\nrecovers at max. Best scores often came at a middle setting. Increasing reasoning effort on\nseveral models frequently led to the model overthinking or focusing on incorrect evidence.

\n

Agent tools such as Claude Code and Codex modestly improved scores by letting a model crop, zoom, and take\na second look. On the 388-task subset used for the agentic evaluation, the best setup reached 59.3%. A\nsecond look cut failures from 944 to 759 by catching missed cues and misread objects. What a closer look\ndoes not fix is depth: errors from reading a 2D overlap as 3D alignment were essentially unchanged, 102 to

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  1. Agents sometimes talked themselves out of answers they had gotten right the first time.
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Humans noted that they mainly answered the questions intuitively. Typically spending more time explaining\ntheir reasoning than coming up with the correct answer. Many human failure cases could be traced to\nsloppiness, misunderstanding the question, and arithmetic errors.\nAuthor’s note: definitely never happens to me.

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Is the horse walking towards you, or away?

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Via\nr/opticalillusions.

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Elorian is building the foundation of visual thinking. HSS is a preliminary step in identifying and\nunderstanding how to solve the complex gaps in frontier models. HSS highlights how today's models\nability to see and interpret visuals is highly limited and not a quirk of one lab's model.

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Read the paper\nThe full leaderboard\nThe dataset

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Figures, questions and reference answers in this post are quoted from\nHumanity’s Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models, by\nGuo, Gu, Jang et al. — a joint paper from Scale AI and Elorian. Model results are as reported there, for\nthe model versions and reasoning-effort settings used in that evaluation. Scores on this benchmark will\nmove as vendors ship new versions; the leaderboard above is the current picture.

","headings":[{"level":1,"text":"Introducing Humanity's Sixth Sense (HSS)","id":"introducing-humanity-s-sixth-sense-hss"},{"level":2,"text":"The whole picture","id":"the-whole-picture"},{"level":2,"text":"What HSS measures","id":"what-hss-measures"},{"level":2,"text":"Leaderboard","id":"leaderboard"},{"level":2,"text":"Where models fail","id":"where-models-fail"}]}}