# Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation

arXiv: https://arxiv.org/abs/2508.01742 · HTML: https://mlsanity.com/paper/2508.01742/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [Understanding Human Hands in Contact at Internet Scale](https://mlsanity.com/paper/2006.06669/) (2020)
- [VideoLLM: Modeling Video Sequence with Large Language Models](https://mlsanity.com/paper/2305.13292/) (2023)
- [EGO-TOPO: Environment Affordances from Egocentric Video](https://mlsanity.com/paper/2001.04583/) (2020)
- [Timeception for Complex Action Recognition](https://mlsanity.com/paper/1812.01289/) (2018)
- [AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?](https://mlsanity.com/paper/2307.16368/) (2023)
- [VideoGraph: Recognizing Minutes-Long Human Activities in Videos](https://mlsanity.com/paper/1905.05143/) (2019)
- [EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation](https://mlsanity.com/paper/2406.18070/) (2024)
- [Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models](https://mlsanity.com/paper/2405.20305/) (2024)
- [Helping Hands: An Object-Aware Ego-Centric Video Recognition Model](https://mlsanity.com/paper/2308.07918/) (2023)
- [PALM: Predicting Actions through Language Models](https://mlsanity.com/paper/2311.17944/) (2023)
- [Object-centric Video Representation for Long-term Action Anticipation](https://mlsanity.com/paper/2311.00180/) (2023)
- [Video + CLIP Baseline for Ego4D Long-term Action Anticipation](https://mlsanity.com/paper/2207.00579/) (2022)
- [Palm: Predicting Actions through Language Models @ Ego4D Long-Term Action Anticipation Challenge 2023](https://mlsanity.com/paper/2306.16545/) (2023)
- [EgoPCA: A New Framework for Egocentric Hand-Object Interaction Understanding](https://mlsanity.com/paper/2309.02423/) (2023)
- [Technical Report for Ego4D Long-Term Action Anticipation Challenge 2025](https://mlsanity.com/paper/2506.02550/) (2025)
- [Technical Report for Ego4D Long Term Action Anticipation Challenge 2023](https://mlsanity.com/paper/2307.01467/) (2023)
- [Vision and Intention Boost Large Language Model in Long-Term Action Anticipation](https://mlsanity.com/paper/2505.01713/) (2025)

## Built on by (most cited first, 10 of 10)

- [SpeechLess: Micro-utterance with Personalized Spatial Memory-aware Assistant in Everyday Augmented Reality](https://mlsanity.com/paper/2602.00793/) (2026)
- [VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026](https://mlsanity.com/paper/2605.20901/) (2026)
- [JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026](https://mlsanity.com/paper/2605.20904/) (2026)
- [EgoIntent: A Pre-Outcome Micro-Step Benchmark for Understanding What, Why, and Next](https://mlsanity.com/paper/2603.12147/) (2026)
- [MARS: Technical Report for the CASTLE Challenge at EgoVis 2026](https://mlsanity.com/paper/2605.18176/) (2026)
- [TAP-JEPA: Frozen Future-Latent Probing and Two-Stage Score Fusion for EPIC-KITCHENS-100 Action Anticipation](https://mlsanity.com/paper/2606.00662/) (2026)
- [FROST-STA: Frozen Dense Features for the Ego4D Short-Term Object Interaction Anticipation](https://mlsanity.com/paper/2606.00694/) (2026)
- [Reliable Egocentric Action Anticipation via Temporal Reliability Suppression and Compositional Graph Decoding](https://mlsanity.com/paper/2609.13293/) (2026)
- [Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior](https://mlsanity.com/paper/2609.17064/) (2026)
- [Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges](https://mlsanity.com/paper/2609.31665/) (2026)

## Abstract (arXiv)

Long-term action anticipation from egocentric video is critical for applications such as human-computer interaction and assistive technologies, where anticipating user intent enables proactive and context-aware AI assistance. However, existing approaches suffer from three key limitations: 1) underutilization of fine-grained visual cues from hand-object interactions, 2) neglect of semantic dependencies between verbs and nouns, and 3) lack of explicit cognitive reasoning, limiting generalization and long-term forecasting ability. To overcome these challenges, we propose INSIGHT, a unified two-stage framework for egocentric action anticipation. In the first stage, INSIGHT focuses on extracting semantically rich features from hand-object interaction regions and enhances action representations using a verb-noun co-occurrence matrix. In the second stage, it introduces a reinforcement learning-based module that simulates explicit cognitive reasoning through a structured process: visual perception (think) -> intention inference (reason) -> action anticipation (answer). Extensive experiments on Ego4D, EPIC-Kitchens-55, and EGTEA Gaze+ benchmarks show that INSIGHT achieves state-of-the-art performance, demonstrating its effectiveness and strong generalization capability.