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STEER: Structured Event Evidence for Video Reasoning via Multi-Objective Reinforcement Learning

Zinuo Li, Yongxin Guo, Jun Liu, Jiawei Zhan, Xi Jiang, Chengjie Wang, Mohammed Bennamoun, Farid Boussaid, Feng Zheng, Qiuhong Ke · Apr 6, 2026 · Citations: 0

Automatic Metrics
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning. In contrast, existing Video-LLMs apply unstructured chain-of-thought directly to raw visual tokens, where critical temporal cues are buried in verbose narration and event-level structure is largely overlooked. We propose Structured Event Evidence, which represents a video as a compact, time-ordered event schema capturing salient events with key attributes and inter-event temporal dependencies, enabling evidence-grounded reasoning through a constrained verification process. This design promotes concise, interpretable reasoning while reducing the drift typical of unconstrained chain-of-thought. To train models under this paradigm, we introduce STEER-60K, a dataset with a four-stage progressive pipeline: evidence training, format warm-start, thinking warm-start, and RL post-training. During RL, CoT length and task accuracy often conflict while rewards for hard samples are too sparse, causing the policy to neglect challenging instances. We formulate this as a multi-objective Pareto optimality problem and propose Pareto-Frontier guided Advantage Balancing (P-FAB), which dynamically resolves reward conflicts and identifies balanced optimization directions along the Pareto frontier. The resulting model STEER-4B rivals 7B-scale baselines on video understanding tasks with half the input frames Code and data will be released.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning."

Reported Metrics

partial

Accuracy

Useful for evaluation criteria comparison.

"During RL, CoT length and task accuracy often conflict while rewards for hard samples are too sparse, causing the policy to neglect challenging instances."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: Coding

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

accuracy

Research Brief

Metadata summary

Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning.
  • In contrast, existing Video-LLMs apply unstructured chain-of-thought directly to raw visual tokens, where critical temporal cues are buried in verbose narration and event-level structure is largely overlooked.
  • We propose Structured Event Evidence, which represents a video as a compact, time-ordered event schema capturing salient events with key attributes and inter-event temporal dependencies, enabling evidence-grounded reasoning through a constrained verification process.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Accuracy

Research Summary

Contribution Summary

  • Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning.
  • We propose Structured Event Evidence, which represents a video as a compact, time-ordered event schema capturing salient events with key attributes and inter-event temporal dependencies, enabling evidence-grounded reasoning through a…
  • To train models under this paradigm, we introduce STEER-60K, a dataset with a four-stage progressive pipeline: evidence training, format warm-start, thinking warm-start, and RL post-training.

Why It Matters For Eval

  • Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: accuracy

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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