Why long-horizon tasks break simple evaluation
Long-horizon tasks—like guiding a robot through a multi-step request or reasoning over a week of first-person video—require a model to hold context, plan ahead, and execute a sequence of actions. That's fundamentally different from answering a single question about a single image, and it makes evaluation harder because you have to measure not just the final answer but the quality of the intermediate steps. For instance, in robotics, a model trained on diverse data (including human demonstrations) reduced the need for human intervention by 46% compared to a zero-shot baseline, but even then, the gap to real-world deployment remained large, showing that long-horizon success is about more than just recognizing objects [1].
The challenge is compounded by the need to track dependencies over time. In egocentric video reasoning, a model must connect events that happen hours or days apart—something that requires temporal abstraction and memory. A 2026 framework called Ego-R1 used a chain-of-tool-thought approach to break down long videos into manageable sub-questions, achieving 46% accuracy on a week-long benchmark, which beat Gemini-1.5-Pro (38.3%) but was still below a stronger closed-source model (53.7%) [3]. This shows that even with sophisticated tool use, long-horizon reasoning remains a hard problem, and evaluation must capture the full trajectory, not just the final answer.
The tool-use and composition problem
Long-horizon tasks often require using multiple tools in sequence—like calling a vision model to detect an object, then a retrieval system to find related information, then a language model to synthesize. Evaluating this is tricky because you need to check whether the model chose the right tools, in the right order, and whether it adapted when things went wrong. A 2026 benchmark called VTC-Bench tested 19 leading multimodal models on tasks that required composing up to 32 different visual operations. The best model, Gemini-3.0-Pro, only achieved 51% accuracy, and models struggled to generalize to unseen tool combinations, often falling back on a narrow subset of familiar functions [4]. This suggests that long-horizon evaluation must go beyond final accuracy to assess planning efficiency and tool selection—something that's hard to automate and even harder to compare across models.
The same issue appears in video understanding, where models must decide when to zoom in on a frame, when to retrieve a past segment, and when to summarize. Kangaroo, a video-language model, was designed to handle long videos by gradually increasing input frames during training, and it outperformed larger models on long-video benchmarks [2]. But the fact that it needed a special curriculum to handle long inputs highlights that standard evaluation—which often uses short clips—doesn't prepare or test models for the demands of long-horizon tasks. So, evaluation becomes a moving target: you're not just asking 'what's in this video?' but 'can the model figure out what to look at, when, and why?'
Data and benchmark limitations make evaluation harder
Long-horizon evaluation is also constrained by the lack of high-quality, diverse data. For robotics, a 2024 study collected 829,502 video-text pairs to train a model for long-horizon visual question answering, but even with that scale, the model still required human oversight to complete tasks [1]. This suggests that current benchmarks may not capture the full complexity of real-world long-horizon tasks, making it hard to know if a model is truly improving or just overfitting to specific scenarios.
Similarly, in chart understanding, a 2025 study found that models lack the ability to interpret domain-specific data like charts, largely due to a lack of relevant instruction-tuning data [5]. They created a dataset using GPT-4 to generate diverse chart tasks, and their model (ChartLlama) outperformed prior methods on several benchmarks. But this highlights a broader issue: if the data used for training and evaluation is narrow, then long-horizon performance—which requires generalization across many contexts—becomes even harder to assess. The same point applies to biodiversity forecasting, where a 2026 perspective argues that current species distribution models are trained and evaluated as static snapshots, ignoring long-term dynamics like dispersal and historical legacies [6]. This means that even if a model performs well on short-term benchmarks, it may fail on long-horizon tasks that require understanding change over centuries—something current evaluation methods don't capture.
About These Sources
This answer is built on 6 peer-reviewed studies — published from 2024 to 2026, 6 from 2024 or later, 1 in Q1 journals, collectively cited 144 times — selected as the most relevant from 6 studies that passed quality screening, drawn from 62 papers retrieved from a database of over 500 million.
Sources used in this answer
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics
RoboVQA collected 829,502 video-text pairs and showed that a video-conditioned model reduced human intervention by 46% compared to a zero-shot baseline, but still required oversight for long-horizon tasks.
Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input
Kangaroo, an 8B-parameter video-language model, used a curriculum training pipeline to handle long videos and outperformed larger models on long-video benchmarks.
Ego-R1: Agentic Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning.
Ego-R1, a 3B-parameter agent, achieved 46% accuracy on a week-long egocentric video benchmark, beating Gemini-1.5-Pro (38.3%) but trailing Gemini-3.1-Pro (53.7%).
VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining
VTC-Bench, with 680 problems and 32 visual tools, found that the best model (Gemini-3.0-Pro) only reached 51% accuracy, with models struggling to compose tools and generalize to unseen operations.
A Multimodal LLM for Chart Understanding and Generation
ChartLlama, trained on a GPT-4-generated dataset, outperformed prior methods on chart understanding benchmarks, but the study highlights the lack of domain-specific data as a key limitation.
Paleo-grounded biodiversity foundation models for long-horizon species distribution forecasting
A perspective paper argues that species distribution models are trained as static snapshots, and proposes a paleo-grounded foundation model to handle long-horizon forecasting, but notes current evaluation methods don't capture long-term dynamics.