arXiv AI
Jun 6
Towards One-to-Many Temporal Grounding
arXiv:2606. 06294v1 Announce Type: cross Abstract: Temporal Grounding (TG) aims to localize video segments corresponding to a textual query.
Grounding with Confidence: Controllable Generative Video Temporal Grounding
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arXiv Computation and Language
Sep 1
Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning
arXiv:2608.28675v1 Announce Type: cross Abstract: Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. I...
Hugging Face Trending Papers
Jun 8
Temporal-Aware Reasoning Optimization for Video Temporal Grounding
Multi-modal Large Language Models (MLLMs) have achieved remarkable progress in video temporal grounding with reinforcement learning for generating reasoning paths. However, existing models often produce superficial reasoning, which offers limited guidance for precise temporal localization.
arXiv AI
2d ago
LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identification
LineupRL introduces a reinforcement learning framework with verifiable rewards for time series captioning, using a frozen large language model to identify the correct time series from a set of distractors based on a generated caption. This approach bypasses the limitations of supervised fine‑tuning and traditional RL rewards that poorly transfer to open‑ended time series generation. Experiments on two captioning benchmarks, as well as forecasting and reconstruction tasks, show that LineupRL outperforms both SFT and RL baselines across all metrics, and its trained 3B vision‑language model surpasses a 72B model distilled from SFT captions. The method also demonstrates resistance to reward hacking and produces captions that accurately trace trends and name key values.
arXiv Computer Vision
3d ago
No Corners Cut: State-Grounded Transitions for Mid-Stream Prompt Switches in Video Generation
arXiv:2609.38691v1 Announce Type: new Abstract: Streaming video generators allow users to dynamically modulate video synthesis via mid-stream prompt switching. Existing streaming methods can respond...
arXiv AI
Jun 30
TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
arXiv:2508. 07683v2 Announce Type: replace-cross Abstract: Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries.
arXiv AI
Aug 18
FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy
arXiv:2608. 16697v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogeneous architectures.
arXiv AI
Jun 3
Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification
arXiv:2606. 03608v1 Announce Type: cross Abstract: Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner.
arXiv AI
Aug 21
VISD: Enhancing Video Reasoning via Structured Self-Distillation
arXiv:2605. 06094v5 Announce Type: replace-cross Abstract: Training VideoLLMs for complex reasoning remains challenging due to sparse sequence level rewards and the lack of fine grained credit assignment over long, temporally grounded reasoning trajectories.
Hugging Face Trending Papers
Aug 5
Predict, Then Retrieve: Cross-Instance Future-State Retrieval from Video Prefixes
We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instances from other videos or images that depict that state. Unlike action anticipation, which predicts a label, moment retrieval, which localizes an observed event within a video, or video generation, which synthesizes pixels, PSR combines anticipation with cross-instance retrieval across multiple temporal horizons.
Hugging Face Trending Papers
Sep 21
VideoGen-Agent: Reinforcing Video Generation Agents
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, sp...
arXiv Computer Vision
Sep 22
VideoGen-Agent: Reinforcing Video Generation Agents
VideoGen-Agent is a multimodal agent that uses multitask agentic reinforcement learning to coordinate external tools for video generation. It learns to augment, generate, and verify videos through multi‑turn interactions, guided by prompts and intermediate observations. On the new VABench benchmark, the agent improves base text‑to‑video performance by 19.1 points, and further upgrades to generation tools raise the score to 86.1, with human raters favoring the upgraded configuration in 84.3% of comparisons.