Hugging Face Trending Papers
Have I Seen Enough? Frozen Video-Language Models Encode Evidence Readiness
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arXiv Machine Learning
5d ago
Have I Seen Enough? Frozen Video-Language Models Encode Evidence Readiness
The paper investigates whether frozen video‑language models inherently encode a signal indicating whether sufficient evidence has been observed to answer a question. By training linear probes on seven byte‑identical models, the authors demonstrate that these models contain a readable evidence‑readiness signal with AUROC ranging from 0.733 to 0.905, even when the probe is trained without any footage from the benchmark family. The signal is question‑conditioned, remains robust when the model answers incorrectly, and outperforms traditional uncertainty estimators; it can be leveraged as a Readiness Gating policy that improves answer accuracy by up to 9.75 percentage points without extra computational cost.
arXiv AI
Sep 30
Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning
The paper introduces Watch-Think-Interact (WTI), a closed-loop framework for multi-question streaming video reasoning that maintains compact natural-language memory entries linked to video time ranges. WTI decides whether to answer, continue watching, or recall relevant past intervals for each question, avoiding replay of the full history. The authors build a large dataset, WTI-82K, and a training method, Stream-GDPO, achieving state‑of‑the‑art performance on StreamingBench and OVO-Bench.
arXiv Computation and Language
Sep 28
TRACE: Temporal Audit and Condition-aware Evaluation of Streaming Video Understanding
TRACE (Temporal Audit and Condition-aware Evaluation) is a new benchmark and evaluation framework for streaming video understanding that explicitly records when evidence becomes valid, how visual history is maintained, and how responses are triggered. It combines temporally audited visual tasks, evidence timing, instruction-dependent trigger annotations, a unified causal Core–Adapter protocol, and multidimensional reporting of answer quality, timeliness, response-selection behavior, workload, completion, and reliability. Using 1,240 records from 517 videos, TRACE evaluated eight publicly available models in eight configurations, revealing that similar QA accuracy can hide significant differences in completion, answer validity, generation workload, and proactive performance metrics such as response delay, false alarms, and missed target windows.
arXiv AI
Jul 16
Accuracy Without Grounding: Diagnosing Visual Dependency Dissociation in Video LLM Benchmarks
arXiv:2607. 13305v1 Announce Type: cross Abstract: Benchmark accuracy in video large language models (LLMs) is often treated as evidence of visual understanding.
arXiv AI
Jun 16
What Should a Streaming Video Model Remember?
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.
arXiv Computer Vision
4d ago
VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification
arXiv:2604.01569v2 Announce Type: replace Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can...