Source-linked AI summary
EgoArgus: Benchmarking VLMs as Situational Assistants for Modality-Grounded User Supports
Yu-Chien Tang, Yu-Hsiang Liu, An-Zi Yen
TL;DR
Existing egocentric benchmarks provide limited evidence about whether assistants can arbitrate between first-person video and user dialogue when the modalities conflict or distract. EgoArgus addresses this gap with a human-annotated benchmark spanning understanding and intervention decisions across five dialogue-video scenarios. Results show text-dominant errors, over-assistance, and incorrect intervention timing, while analyses find limited gains from existing modality-bias mitigation methods.
Problem
Existing egocentric resources have limited exploration of controlled video-dialogue relations paired with assistance labels, intervention timing, and assist-step annotations.
Method
EgoArgus combines real-video MCQA examples for understanding with VISTA-synthesized first-person assistant episodes for intervention decisions across five dialogue-video scenarios.
Results
Current VLMs show text-dominant failures under contradictory dialogue, over-assist when they should stay quiet, and misjudge when warranted interventions should occur.
Takeaways & Limitations
Reliable egocentric assistants need adaptive mechanisms that identify answer-bearing modalities, distractors, and when apparently non-answer context should be preserved.
Takeaways & Limitations
The understanding evaluation uses four-choice VQA, while the decision evaluation uses synthetic videos that may not capture deployed assistants’ visual diversity, sensor noise, and social ambiguity.
Abstract
from arXiv · showhide
VLMs are increasingly positioned as daily assistants that perceive first-person environments, follow user dialogue, and decide how to help. Existing egocentric benchmarks mainly evaluate visual understanding in isolation, leaving open whether models can arbitrate between visual evidence and user-provided language when the two are helpful, irrelevant, or conflicting. We introduce EgoArgus, a human-annotated dataset for evaluating egocentric assistants on understanding and decision tasks in five dialogue-video daily scenarios. Our results demonstrate that it is still challenging for current VLMs as reliable egocentric assistants, which requires identifying which modality is trustworthy and deciding when intervention is warranted. Deeper analysis also shows that existing modality bias mitigation methods are quite restricted to enhance performance, providing insights to aid practioners into the deployment of current VLMs as daily assistants.
1 Introduction
EgoArgus addresses whether egocentric assistants can reconcile first-person video with user dialogue across helpful, irrelevant, and conflicting relationships. It evaluates situation understanding, next-action prediction, and intervention decisions, revealing persistent reliability gaps in current VLMs.
- Benchmark scope: EgoArgus organizes evaluation around five dialogue-video scenarios plus no assistance, covering three capabilities.The capabilities are context understanding, next-action prediction, and intervention decision.
- Motivation: Reliable egocentric assistance requires combining first-person video and dialogue while judging which modality to trust.The modalities may complement, conflict, or be irrelevant, so assistants must determine the user’s need and provide an environment-grounded response.
- Benchmark construction: The benchmark combines 6,978 real-video MCQA examples with 789 VISTA-synthesized assistant episodes.The understanding portion targets context understanding and next-action prediction, while VISTA episodes target intervention decisions across safety, non-safety, and no-assistance cases.
- Findings: Current VLMs follow contradictory user dialogue over visual evidence, producing scene-inconsistent understanding and next-action guidance.The models echo mistaken user claims and recommend steps that do not fit the actual scene.
- Findings: Models also over-assist and misjudge intervention timing, warning too early or too late when assistance is warranted.These timing errors can make the warning itself incorrect.
2 EgoArgus Construction
EgoArgus combines real-video multiple-choice understanding data with VISTA-generated intervention episodes, varying dialogue-video relations across five assistance scenarios. Human annotation verifies scenario validity and correct answers before evaluation.
- Data sources: EgoArgus uses EgoPlan-Bench2, QaEgo4D, and MIntRec for understanding, and VISTA for assistant episodes.MIntRec dialogue is expanded into histories and reused as off-topic distractors; VISTA metadata records assistance requirements and timing.
- Five Assistance Scenarios: The five scenarios distinguish whether video, dialogue, both modalities, or neither should determine the judgment.They include multimodal grounded, contradictory, video-grounded on-topic, video-grounded off-topic, and text-grounded relations.
- Data Annotation: Sixteen annotators independently review examples, with three annotators assigned to each example and majority voting used for evaluated answers.Reviewers assess whether each example matches its intended scenario and correct invalid, ambiguous, or unsupported gold answers.
- Data Annotation: The evaluated decision split retains 311 reviewed videos paired into 789 scenario-specific video-dialogue rows.Assistance rows include appropriate intervention time and help, while the no-assistance case identifies situations where no intervention is needed.
3 Experiments
Experiments evaluate seven VLMs on EgoArgus understanding and intervention decisions, revealing strong modality-arbitration failures and imperfect intervention policies. Results also show model-size and capability trade-offs across false alarms, missed interventions, timing, and assist-step errors.
- Evaluation setup: Seven VLMs are evaluated on EgoArgus, with MCQA accuracy for understanding and assistance-decision F1 plus timing and error analyses for decisions.Decision evaluation uses video, dialogue, and dialogue timestamp, analyzing intervention time, issue summary, evidence, and assist steps.
- Understanding results: 63.0% mean accuracy on multimodal-grounded examples falls to 18.5% on contradictory examples, with every model below random guessing except Molmo2-8B at 24.9%.The contradictory dialogue often reverses the answer rather than merely distracting the model.
- Understanding results: Mean accuracy is 71.0% for text-grounded examples, compared with 41.5% for off-topic dialogue and 45.2% for on-topic answerless dialogue.Models handle clearly unrelated text better than actively contradictory dialogue, while some answerless dialogue may provide useful scene priors.
- Decision results: Decision performance varies widely: stronger models make more reliable intervention decisions, but weaker models often fail even with full-video input.No-assistance cases remain difficult, creating a precision–recall trade-off in intervention policy.
- Decision results: VLMs tend to intervene before oracle timing, and timing errors remain large enough to affect short safety or task-assistance events.Reliable intervention therefore requires calibrated timing in addition to recognizing that assistance may be relevant.
- Intervention failures: Gemini-3.1-Pro makes the fewest errors overall but has the highest no-assist false-alarm rate, while InternVL3.5-4B has the largest errors overall and frequent step errors.Qwen3.5-Plus keeps false alarms low and timing relatively stable, but its errors concentrate on missed interventions, especially non-safety cases.
4 Discussion
Oracle removal shows that contradictory dialogue drives severe video-grounded failures, while internal answer preferences become recoverable only in late decoder layers. Visual and dialogue representations can separate without ensuring correct modality arbitration, and simple mitigation strategies remain brittle.
- Distractor Modality: Removing contradictory dialogue improves accuracy by 34.0–43.0 points across four models, while off-topic and text-grounded changes stay within 2.0 points.Removing on-topic dialogue is neutral to detrimental, by up to −4.5 points.
- Modality Representation Separation: The probing setup recovers each model’s final answer preference from last-token hidden states at individual decoder layers without using gold correctness.A separate linear probe is trained for each decoder layer.
- Modality Representation Separation: Qwen3.5-2B probe accuracy jumps from 49.5% at layer 15 to 94.5% at layer 16, while three 36-layer models jump at layer 25 from 60.0–69.5% to 93.5–97.0%.Peak probe accuracy reaches 97.5–100.0% across models, with the transition at middle-to-deep fractional decoder depth.
- Modality Representation Separation: Qwen3.5-2B visual and dialogue token representations are heavily mixed at layer 5, visibly separated by layers 12 and 18, and mostly separated at layer 24.Figure 5 provides a complementary token-level view of the layer-wise probing pattern.
- Modality Representation Separation: Separating visual and textual sources does not guarantee correct modality arbitration, because models may still follow misleading dialogue when choosing an answer.The finding distinguishes source representation separation from reliable evidence selection.
- Mitigation: Uniform visual-token attention reweighting is sensitive to ϵ and does not uniformly improve all scenarios, indicating that visual attention alone may be insufficient against strong language lures.The paper evaluates this training-free intervention on Qwen3.5-2B.
5 Related Work
Prior work established large-scale egocentric understanding, planning, grounded question answering, and multimodal conversational intent resources. EgoArgus targets the complementary deployment question of whether assistants can arbitrate dialogue and video while deciding when to intervene.
- Egocentric Video Understanding: Ego4D established large-scale first-person video understanding, while EgoPlan-Bench2 and QaEgo4D evaluate egocentric planning and grounded question answering.EgoCross further evaluates cross-domain generalization across surgery, industry, extreme sports, and animal-perspective videos.
- Multimodal Conversation and Intent: MIntRec provides multimodal conversational intent annotations that EgoArgus reuses to create off-topic distractors and text-grounded examples with natural conversational language.This avoids relying only on synthetic distractor text.
- Modality Bias and Mitigation: Modality-bias research studies cross-modal attention imbalance, conflicting evidence, layer-wise modality preference, and preference optimization for reducing bias and hallucination.These strands motivate EgoArgus’s analysis of modality arbitration and mitigation.
6 Conclusion
EgoArgus benchmarks dialogue-induced modality bias in egocentric assistants across understanding and intervention decisions. Its analyses identify contradictory dialogue as the strongest failure source and show that modality interference and late answer-preference formation are central reliability issues.
- Conclusion: EgoArgus is a two-part benchmark for dialogue-induced modality bias in egocentric video assistants.The benchmark covers both understanding and assistant decision behavior.
- Conclusion: Across seven VLMs, contradictory dialogue causes the strongest failure, reducing mean accuracy below random guessing despite the answer being visible in video.The conclusion identifies contradiction as the dominant reliability gap.
- Conclusion: Oracle distractor removal confirms that modality interference drives many errors, while linear probing shows answer preferences become recoverable only after late-middle layers where visual and dialogue tokens separate.The conclusion connects behavioral failures with representation-level diagnostics.
Limitations
EgoArgus combines complementary evaluation formats, but each has important scope boundaries. The benchmark’s real-video MCQA, synthetic intervention episodes, and model-specific analyses do not fully represent deployed assistant settings or all evaluated models.
- Evaluation Formats: The understanding part uses four-choice VQA, simplifying answer extraction and random-choice interpretation but not covering open-ended assistant behavior.The decision part addresses open intervention but uses synthetic videos that may miss deployed visual diversity, sensor noise, and social ambiguity.
- Dataset Scope: The reported evaluation contains 6,978 real-video MCQA examples and 789 VISTA Assist-Step rows, excluding larger candidate pools and future VISTA generations.These counts define the scope of the current evaluated split.
- Analysis Scope: Layer-wise probing covers four VLMs, whereas attention-intervention and NaPO analyses remain specific to Qwen3.5-2B.Extending the intervention analyses to all evaluated models remains future work.
Ethics Statement
EgoArgus combines privacy-conscious real-video sources with reviewed synthetic VISTA scenarios, including deliberately contradictory dialogues. These constructed examples are intended to diagnose reliability under misleading context rather than represent faithful user behavior.
- Real-video sources include privacy and consent procedures, while VISTA uses generated first-person videos and human-reviewed synthetic scenarios.The synthetic scenarios reduce direct privacy exposure but can depict safety-relevant assistance situations.
- Added dialogues may intentionally contradict visual evidence and should be labeled as diagnostic, synthetic, or constructed contexts.They should not be treated as faithful transcripts.
- EgoArgus evaluates assistant reliability under misleading user context rather than modeling or profiling real user behavior.
- The dataset motivates avoiding blind trust in any single modality, especially user text that may be mistaken or adversarial.
- Contradictory examples preserve the original video-grounded gold answer while pairing it with fluent dialogue that conflicts with the visual evidence.At least one distractor choice aligns with the erroneous dialogue, requiring models to rely on video and ignore misleading text.
A.3 Distractor and Text-Grounded Scenarios
The benchmark constructs distractor, text-grounded, and assistance-decision scenarios by controlling which modality carries the answer and by auditing multimodal relations. Its VISTA component adds reviewed synthetic intervention cases, no-assistance controls, and structured assistance labels.
- Distractor scenarios: Off-topic examples pair fixed video questions with unrelated dialogue, while on-topic examples add relevant dialogue without revealing the answer.The matched subsets isolate dialogue relevance while keeping the visual QA task fixed.
- Text-grounded scenarios: Text-grounded examples ask for the intent of a final utterance and pair the dialogue with a random egocentric video distractor.Dialogue is therefore the only answer-bearing modality.
- Verification: Three annotators verify multimodal-grounded and contradictory examples by checking scenario validity, correcting answers, and resolving labels through majority voting.
- VISTA decision scenarios: VISTA supplies intervention metadata for whether help is required, when to intervene, what issue to notice, and what step to recommend, without exposing it to models.
- VISTA decision scenarios: The synthetic export covers five dialogue-video relations and includes safety, non-safety, and no-assistance labels.No-assistance controls encode cases where the correct behavior is not to intervene.
- Quality control: Quality control reviews event visibility, viewpoint plausibility, object stability, dialogue relations, and evidence for oracle assistance labels.Failed generations are discarded or revised through script editing and regeneration.
- Quality control: 2,917 audit events across 218 cases produced 410 usable video-dialogue pairings in the round-1 reviewed export.Review tags included object-identity drift, assistance-signal problems, viewpoint breaks, spatial flips, seed mismatch, and no-assistance errors.
- Decision annotations: Decision annotations mark the earliest useful intervention, the issue to warn about, and the assistance step in safety cases such as identifying a metal mug entering a microwave.The example requires visual evidence because the dialogue does not reveal the mug’s material.
B.5 Comparison with Related Benchmarks
EgoArgus is positioned against related benchmarks using six axes central to multimodal assistance. It provides primary coverage across first-person video, dialogue, intervention decisions, timing, assist steps, and no-assistance handling.
- The comparison evaluates benchmarks on first-person video, user dialogue, intervention decision, intervention timing, assist steps, and no-assistance handling.
- Existing benchmarks generally cover only subsets of these capabilities, with dialogue, timing, decision, and no-assistance support often incomplete or absent.
- EgoArgus is described as the only resource with primary coverage on all six axes.It jointly evaluates whether, when, and how to intervene using first-person video and user dialogue, including cases where silence is correct.
C Linear Probe Details
The probing analysis trains layer-wise linear classifiers on hidden states from balanced real-video scenario samples. Additional visualizations separate visual-token and dialogue-token representations through SVD of probe weights.
- Probing uses 1,000 real-video MCQA examples, with 200 sampled from each of five scenarios, across four VLMs.The analysis extracts hidden states from all decoder layers and uses the final token position after L2 normalization.
- Each layer receives an independent linear classifier trained with soft cross-entropy on scenario-balanced 60%/20%/20% train-validation-test splits.Training uses Adam with learning rate 10^-3, batch size 256, and 200 epochs, selecting the lowest-validation-loss checkpoint.
- The SVD visualization separately averages visual-token and dialogue-token hidden states before decomposing the corresponding probe weight matrix.
- The decision examples annotate intervention timing, assistance modes, and event distributions across scenes and events.These annotations complement the probe setup by specifying when assistance is useful and how decision cases are organized.