Can Video Tools Give AI the Patience to Reason?
Long videos demand more than snapshots; they require memory, attention, and cross‑modal storytelling. A new wave of research is teaching AI how to reason through hours of footage, not just lines of text. The paper Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning, produced by a collaboration among Tsinghua University’s Shenzhen campus, the University of Chinese Academy of Sciences, and ByteDance Intelligent Creation, proposes a system that treats video content as a dynamic playground for thinking. It’s not just about seeing; it’s about deliberate, multi-step reasoning that can reach across dozens or hundreds of seconds of action. This is reasoning in the long form, where the brain–computer partnership must summon evidence precisely when it’s needed and forget the noise when it isn’t.
At the heart of the work are two ideas that feel almost counterintuitive: first, that thinking with videos needs tools, not just text; second, that learning to reason across many tasks (temporal grounding, question answering, and grounded QA) requires a disciplined way to balance difficulty across tasks. The researchers call their framework Video Intelligence via Tool-Augmented Learning, or VITAL. In practice, VITAL arms a multimodal language model with a visual toolbox and a round‑by‑round reasoning process that can call on the toolbox to fetch new frames, describe clips, or answer questions about a specific time window. The result is a model that can pause, probe a scene with a clip, and stitch a coherent chain of reasoning that links what it sees to what it’s asked to explain.
The paper’s lead authors, Haoji Zhang and Xin Gu, come from the collaboration between Tsinghua Shenzhen International Graduate School and the University of Chinese Academy of Sciences, with significant contributions from ByteDance Intelligent Creation. The work sits at the crossroads of visual understanding, language modeling, and reinforcement learning, and it targets long video understanding with a simple, practical aim: make AI’s reasoning more accurate, more interpretable, and less prone to hallucination when the video content stretches far beyond a few seconds.
Long Videos, Long Games: The Challenge of Temporal Reasoning
Reasoning through long videos is not just about counting frames; it’s about tracing cause and effect across time. Previous attempts to extend a model’s reasoning horizon often relied on compressing or sampling frames and hoping the essential events survive the squeeze. That approach can miss subtle cues that unfold over minutes: a plan hatched, a sequence of actions that only makes sense when you know what came before, or a single turn in a larger conversation that reframes the entire scene. The problem isn’t just memory; it’s how to keep cross‑modal signals—what the model reads as text and what it sees in video—faithfully aligned as the reasoning unfolds.
The VITAL framework tackles this by letting the model request new video frames on demand. Think of it as a practical “magnifying glass” that you can deploy in the middle of a thought, not after you’re done thinking. The model can issue a tool call to fetch a densely sampled clip for a specific time range, pull a caption or a targeted question‑answer pair about that clip, and then incorporate that evidence into its next thought. The result is a chain of multimodal reasoning steps that are not just text-based reflections but guided, evidence-backed narratives—a chain of thought that can be checked against the video itself.
Yet the system isn’t just about running a clever loop of calls and answers. The authors show that temporal grounding (pinpointing when an event happens) and temporal reasoning (understanding what happened and why) feed each other. When you know where an event occurs, you’re better at describing it; when you can describe it precisely, you’re more confident about its location in time. This mutual reinforcement becomes a practical advantage in long videos, where the risk of drifting off track grows with every additional moment to consider.
VITAL: A Visual Toolbox for Multimodal CoT
Imagine a librarian’s toolkit that slides in and out of a thinking machine, supplying just the right frames, captions, and clip QA on demand. VITAL integrates a visual toolbox into the large language model’s reasoning loop. The toolbox includes a video clipping tool (which returns dense visual tokens for a chosen time window), a clip captioning tool (describing what happens in a clip), and a clip QA tool (answering questions about a clip). The model’s multi-round reasoning process can call these tools multiple times, building a multimodal chain of thought (CoT) that combines textual reasoning with concrete visual evidence from the video.
Why is this important? Text-only CoT can become brittle when the chain draws incorrect assumptions, and as videos grow longer, the chances of getting hallucinations higher. The multimodal CoT, by contrast, anchors thinking in actual video segments. It’s not magic; it’s a disciplined negotiation between what the model can imagine and what the video proves. The paper’s demonstrations show that this approach reduces hallucinations and yields sharper, more reliable conclusions about what happens in long videos.
To teach the model to reason under real‑world video complexity, the authors built two large, multi‑task datasets: MTVR-CoT-72k for supervised fine-tuning and MTVR-RL-110k for reinforcement learning. The datasets cut across temporal grounding, video question answering, and grounded QA, with footage sourced from Charades-STA, ActivityNet, VidChapters, and others. The data generation pipeline includes a rollout filter to keep only “moderately difficult” samples—neither trivially easy nor hopelessly hard—so the model learns to navigate a realistic spectrum of challenges.
From CoT to Multimodal CoT: The Reasoning That Feels Human
Reasoning with video is not simply about what you see; it’s about how you connect scenes to questions, to evidence, to explanations. The shift from text-based chain-of-thought to multimodal chain-of-thought is more than a stylistic upgrade. It’s a structural change in how the model organizes its internal thinking. In a long video, you may need to “zoom in” on a moment, test a hypothesis with a clip QA, and then widen your lens to see how that moment relates to earlier or later events. The multimodal CoT makes that process visible and auditable, and it helps the model justify its conclusions with concrete clips and captions rather than vague textual arguments alone.
The researchers also add a twist: during reinforcement learning, they implement a difficulty-aware policy optimization method (DGRPO) that balances learning across tasks and across samples of varying difficulty. This matters because long video tasks aren’t equally hard all the time, and a naive training loop can bias the model toward easy cases or get stuck on hard ones. By adjusting rewards based on task difficulty and sample difficulty, DGRPO helps the model learn to reason robustly across a spectrum of challenges, which translates to better generalization when it sees new videos.
Put differently, VITAL’s multimodal CoT isn’t a gimmick. It’s a framework for structured, on‑demand evidence gathering that mirrors how humans study a scene: we ask questions, seek the exact moments that contain relevant information, and then integrate those moments into a coherent explanation. The model’s ability to call a clipping tool and then incorporate the resulting evidence into its thoughts makes its reasoning not only more accurate but more interpretable as it proceeds.
The Data Engine: MTVR-CoT‑72k and MTVR‑RL‑110k
Quality data, thoughtfully organized, is the quiet engine behind smarter reasoning. MTVR-CoT-72k and MTVR-RL-110k are multi-task datasets designed to train and measure long-video reasoning. MTVR-CoT-72k focuses on supervised fine-tuning for text‑based and multimodal CoT across tasks; MTVR-RL-110k drives reinforcement learning with the same multi-task mix. The data pipeline deliberately includes a rollout filtering step: for each sample, the model generates multiple reasoning trajectories, and only those with a balanced mix of challenge and success are kept. This produces training data that is informative enough to push the model forward without becoming monotonously easy or brutally hard.
In practice, this means the model learns to switch between thinking in text and thinking with evidence. It learns when to trust its internal textual reasoning and when to override a misstep with a direct look at the video through a tool call. It also learns to tokenize and compress video content in a way that preserves temporal cues without drowning in raw frames—a key practical constraint when working with hours of footage.
The datasets are not just about scale; they are about orchestration. They’re designed so that the model experiences a balanced curriculum across temporal grounding, VQA, and grounded QA, enabling shared representations and transferable skills. This cross‑task synergy is a recurring theme in the paper: success in one domain strengthens performance in the others, and vice versa.
The Learning Engine: Difficulty-Aware GRPO
Learning to reason across tasks is a bit like teaching a student to juggle different kinds of balls at once. The Difficulty-aware Group Relative Policy Optimization (DGRPO) is the paper’s answer to the multi‑task reinforcement learning challenge. DGRPO tailors the reward signal to two kinds of difficulty: task-level difficulty (how hard a given task is) and sample-level difficulty (how hard a particular example is within that task). By adjusting the reward scale, the model is encouraged to spend more learning effort on the tough shots without neglecting easier ones that still matter for general competence.
The reward design itself is a careful mix: accuracy rewards (IoU for grounding, exact-match, Rouge for text), a format reward (to encourage the model to adhere to the mandated multi-round response structure), and a tool reward (to incentivize successful tool use). The result is not a monkey with a reward stick but a trained agent that learns to call the toolkit prudently, reason across rounds, and ground its conclusions in verifiable video evidence.
In experiments, DGRPO demonstrated more stable learning and better performance on long-video benchmarks than traditional GRPO, especially when tool use was involved. The ablation studies underscore a simple truth: balancing difficulty is not just fair; it’s foundational for robust, generalizable long-video reasoning. When the model learns to navigate a spectrum of tasks—temporal grounding, VQA, grounded QA—with the right emphasis, it becomes better at everything that depends on long-range reasoning.
What the Experiments Reveal: A Leap in Long-Video Understanding
The numbers aren’t just impressive; they sketch a new baseline for long-video reasoning. On long-video benchmarks like LongVideo-Reason and VidChapters-7M, VITAL with the toolbox consistently outperforms strong baselines. The authors quantify gains from adding the visual toolbox as well as from the DGRPO training regime. The results are particularly striking in long-video scenarios where baseline models struggle to maintain accuracy as the reasoning horizon grows. The tool-augmented multimodal CoT approach delivers sharper temporal grounding, stronger grounded VQA, and more reliable long-form reasoning overall.
Across a wide battery of tasks—video temporal grounding, grounded VQA, and long-video question answering—the improvements stack up. In several benchmarks, enabling the visual toolbox yields measurable lifts in IoU, accuracy, and multi-task averages. The gains are not cosmetic; they reflect a qualitative shift: the model now leverages on-demand visual evidence to anchor its reasoning, reducing the risk of wildly speculative or hallucinated conclusions when the video stretches out in time and complexity.
Beyond raw scores, the researchers present qualitative cases showing how multimodal CoT produces cleaner, more faithful narratives of what happens in the video. Where text-only reasoning might drift or misinterpret, multimodal reasoning can correct course by checking the actual clip content, then re‑aligning the subsequent steps with the evidence. In short, the model isn’t merely faster; it’s more trustworthy about what it has seen and what it has inferred from it.
Implications: A Future Where AI Thinks with What It Sees
If AI can routinely bake evidence into its own thinking, we gain not just smarter assistants but more trustworthy partners for understanding the real world. The VITAL approach points toward AI systems that can handle long, messy streams of information—like lectures, documentaries, or surveillance footage—without collapsing into brittle summaries or hallucinations. The key is to treat the video as an active contributor to thought, not a passive backdrop for text. When the model can call a clip, read a caption, ask a targeted question about a segment, and then fold those results into its next reasoning step, it builds a narrative that can be audited against the video itself.
Another implication is in the realm of human-AI collaboration. By making reasoning steps multimodal and tool-supported, researchers pave the way for better explainability. If a model claims that a particular event happened at a certain second, it can point to the exact clip that informed the conclusion. This could be transformative for education, media analysis, and even fields like legal evidence review where temporal grounding matters as much as factual accuracy.
There are caveats, too. The current work focuses on visual content and temporal reasoning; audio and spatial grounding remain as frontiers to conquer. The team itself notes that broadening the toolbox to audio cues, spatial cues, and perhaps more interactive tools would unlock even richer multimodal reasoning. As with any tool, the usefulness hinges on how carefully it is designed, evaluated, and integrated into real-world workflows.
Limitations and the Road Ahead
No single system will solve every kind of video understanding problem, but this one points to a scalable blueprint. The authors acknowledge that VITAL currently emphasizes temporal grounding, VQA, and grounded QA for long videos. Spatial reasoning, audio cues, and cross-domain transfer to new video genres (sports, education, nature) are promising directions. In addition, while the toolset focuses on clipping and textual descriptions, future toolkits might incorporate more sophisticated visual analyses—object tracking over time, action recognition, or even scene graphs that capture relationships across segments.
Another frontier is efficiency. Long videos pose computational challenges, and dense frame sampling can be expensive. The authors’ approach of on-demand clipping helps, but there’s room to refine how to budget computation without sacrificing fidelity. Finally, while the datasets MTVR-CoT and MTVR-RL are robust, expanding their diversity—in terms of genres, languages, and real-world settings—will be crucial to ensure that multimodal CoT generalizes beyond curated benchmarks.
The Team, the Institutions, and the Human Element
The study springs from a collaboration among Tsinghua University’s Shenzhen International Graduate School, the University of Chinese Academy of Sciences, and ByteDance Intelligent Creation. The lead authors, Haoji Zhang and Xin Gu, bring together expertise in computer vision, language modeling, and reinforcement learning. The work reflects a broader trend: serious progress in AI happens not in silos but at intersections—where researchers from universities and industry labs combine forces to tackle problems that require both theory and practical engineering. That convergence is exactly what makes VITAL feel timely and potentially transformative.
Beyond the technical achievements, the paper is a reminder that progress in AI often travels through a language we can read: a multimodal chain of thought that shows how a model reasons step by step, with evidence in hand. It invites readers and practitioners to imagine AI that doesn’t just summarize a video, but truly reasons through it—piece by piece, clip by clip—while staying tethered to the actual footage it’s describing. In that sense, VITAL is less about a single model and more about a philosophy of thinking with visuals.
Closing Thoughts: Toward AI That Notices the Story in Time
The big idea is deceptively simple: teach AI to think with what it sees, not just what it reads. When a model can request a clip, interrogate it with a question, and fold the answer back into its mental model, it behaves less like a calculator and more like a careful, curious reader confronting a longer, richer narrative. This shift matters because many real-world tasks unfold over time and across modalities—think medical videos, educational content, or investigative journalism—and require a faithful chain of reasoning anchored in evidence.
As researchers push forward, we may see AI systems that can analyze multi-hour lectures for learning outcomes, review surveillance footage with context that makes sense of actions, or assist researchers in parsing complex documentary material with transparent, verifiable reasoning. The work presented in Thinking With Videos offers a concrete, scalable step toward that future: a framework that makes long-form video understanding not only possible but more trustworthy, interpretable, and useful in the real world.
The headline of the paper—VITAL as a tool for thinking with video—reads like a mission statement. It isn’t about replacing human insight but expanding it: giving AI a reliable method to sample, cite, and reason about the visual world in a way that respects time, context, and evidence. If this direction stays on course, the era of AI that can truly understand long videos—without getting lost in the noise—might be closer than we think.
