arxiv: 2604.24893 · v1 · submitted 2026-04-27 · 💻 cs.CV

Recognition: unknown

Interactive Episodic Memory with User Feedback

Nikesh Subedi , Loris Bazzani , Ziad Al-Halah

Authors on Pith no claims yet

Pith reviewed 2026-05-08 04:29 UTC · model grok-4.3

classification 💻 cs.CV

keywords episodic memoryegocentric videouser feedbackinteractive vision-languageplug-and-play modulenatural language queriesfeedback alignmentvideo retrieval

0 comments

The pith

A plug-and-play module turns one-shot episodic memory models interactive by incorporating user feedback.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets the gap in episodic memory systems where natural language queries on long egocentric videos are handled in a single pass, ignoring that queries are often ambiguous or incomplete. It defines a new interactive task called EM-QnF in which users can correct an initial prediction or supply extra details to refine the result. The authors collect supporting feedback datasets and develop a lightweight training approach plus a Feedback Alignment Module that attaches to existing models without full retraining or sequential optimization. If successful, this setup delivers higher accuracy on three benchmarks than prior one-shot methods while staying efficient and competitive with large commercial vision-language models. A sympathetic reader would see practical value for real-world personal memory assistants that can adapt on the fly to user input.

Core claim

The central claim is that the Episodic Memory with Questions and Feedback task, supported by newly collected feedback datasets, allows a lightweight plug-and-play Feedback Alignment Module to let existing EM-NLQ models incorporate user corrections and additional information effectively. This is achieved through a training scheme that avoids expensive sequential optimization or per-interaction retraining, yielding significant gains over state-of-the-art one-shot approaches on three challenging benchmarks, performance that matches or exceeds commercial large vision-language models in efficiency, and strong generalization when tested with human-generated feedback in realistic scenarios.

What carries the argument

The Feedback Alignment Module (FALM), a lightweight plug-and-play component that aligns user feedback with an existing model's initial output to refine predictions for natural language queries on egocentric video.

If this is right

Existing one-shot episodic memory models can be upgraded to handle interactive refinement without starting training over each time.
Retrieval accuracy rises substantially on three established egocentric video benchmarks.
The system stays efficient enough to remain competitive with or beat commercial large vision-language models.
Performance holds when the feedback comes from real humans rather than simulated data.
The approach directly addresses ambiguity in natural language queries by allowing iterative clarification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same lightweight alignment idea could transfer to other vision-language tasks such as interactive image search or conversational video QA.
Widespread adoption might lower the compute barrier for building personalized, continuously adapting memory assistants.
Multi-turn feedback loops could emerge as a natural next extension for handling complex or evolving user needs.
The method hints that plug-and-play adaptation may reduce reliance on ever-larger pretrained models for domain-specific refinement.

Load-bearing premise

User feedback can be effectively aligned and incorporated via a lightweight plug-and-play module without requiring expensive sequential optimization or full model retraining for each interaction.

What would settle it

An experiment in which attaching the FALM to a current top EM-NLQ model produces no accuracy gain on a held-out set of human-corrected feedback queries, or where the gains require retraining the full base model from scratch.

Figures

Figures reproduced from arXiv: 2604.24893 by Loris Bazzani, Nikesh Subedi, Ziad Al-Halah.

**Figure 1.** Figure 1: We introduce the interactive episodic memory with user view at source ↗

**Figure 2.** Figure 2: Our feedback generation recipe. For a query view at source ↗

**Figure 3.** Figure 3: Our (a) Feedback ALignment Module (FALM) module and (b) its integration with an EM-NLQ model, ReFocus. FALM is view at source ↗

**Figure 4.** Figure 4: Multi-Turn Feedback evaluation of our ReFo view at source ↗

**Figure 5.** Figure 5: Qualitative results for GroundNLQ and our ReFocus(GroundNLQ) when given feedback. Examples (a) and (b) show cases where view at source ↗

**Figure 6.** Figure 6: Multi-Turn Feedback evaluation of our ReFo view at source ↗

**Figure 7.** Figure 7: Noisy Multi-Turn Feedback evaluation of our ReFo view at source ↗

**Figure 8.** Figure 8: Additional Qualitative Results. These examples show our method improving on the baseline. view at source ↗

**Figure 9.** Figure 9: Additional Failure Cases. These examples show few failure cases of our method. view at source ↗

**Figure 10.** Figure 10: Example prompts used with Gemini-2.5-flash. (a) shows inference with NLQ only. (b) shows inference with NLQ and user view at source ↗

**Figure 11.** Figure 11: Human User Feedback Collection UI. Users were given a short tutorial and advised not to directly answer the question. view at source ↗

**Figure 12.** Figure 12: Example prompt used to generate GoalStep-Q and HD-EPIC-Q datasets and output generated by Qwen-3-8B. Text after view at source ↗

**Figure 13.** Figure 13: Example Span Captioning prompt and output generated by Qwen-2.5-VL-Instruct during user feedback generation. view at source ↗

**Figure 14.** Figure 14: Example Response Span Explanation prompt and output generated by Qwen-2.5-VL-Instruct during user feedback generation. view at source ↗

**Figure 15.** Figure 15: Example Feedback Generation prompt and output generated by Qwen-QwQ-32B-AWQ during user feedback generation. view at source ↗

**Figure 16.** Figure 16: Example Clauses Extraction prompt and output generated by Qwen3-8B. view at source ↗

read the original abstract

In episodic memory with natural language queries (EM-NLQ), a user may ask a question (e.g., "Where did I place the mug?") that requires searching a long egocentric video, captured from the user's perspective, to find the moment that answers it. However, queries can be ambiguous or incomplete, leading to incorrect responses. Current methods ignore this key aspect and address EM-NLQ in a one-shot setup, limiting their applicability in real-world scenarios. In this work, we address this gap and introduce the Episodic Memory with Questions and Feedback task (EM-QnF). Here, the user can provide feedback on the model's initial prediction or add more information (e.g., "Before this. I'm looking for the big blue mug not the white one"), helping the model refine its predictions interactively. To this end, we collect datasets for feedback-based interaction and propose a lightweight training scheme that avoids expensive sequential optimization. We also introduce a plug-and-play Feedback ALignment Module (FALM) that enables existing EM-NLQ models to incorporate user feedback effectively. Our approach significantly improves over the state of the art on three challenging benchmarks and is better than or competitive with commercial large vision-language models while remaining efficient. Evaluation with human-generated feedback shows that it generalizes well to real-world scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper adds interactivity to episodic memory via a new task and lightweight module, but experimental details are missing which weakens the performance claims.

read the letter

The main things to know about this paper are that it introduces the EM-QnF task for interactive feedback in episodic memory queries and proposes FALM as a plug-and-play module to handle user clarifications efficiently. What is actually new is the shift from one-shot to multi-turn interaction with feedback, along with datasets for it and the lightweight training scheme. The paper does well in identifying a real-world limitation and offering a practical solution that aims to stay efficient compared to large models. Collecting human feedback for evaluation is also a solid move toward applicability. The soft spots are in the experimental side. The abstract reports improvements on three benchmarks and competitiveness with large VLMs, but provides no details on experimental setup, baselines, error bars, or data splits, making the central claims hard to verify. The key assumption that user feedback can be aligned via FALM without expensive sequential optimization or retraining needs clear demonstration in the paper; if it relies on hidden tuning, the efficiency and interactivity advantages would not hold up. While the human-generated feedback evaluation suggests good generalization, more analysis on different feedback types or potential failure modes would strengthen it. This paper is for researchers in computer vision and AI who focus on egocentric video understanding and building interactive personal assistants. A reader interested in extending memory systems to handle ambiguity through natural interaction would get value from the task definition and the module proposal. The work demonstrates clear thinking by addressing a genuine gap in prior one-shot methods and engages honestly with the need for interactivity. I recommend sending this to peer review. The idea is worth referee time, but the experiments will need expansion and the lightweight claim will need careful checking.

Referee Report

2 major / 2 minor

Summary. The paper introduces the Episodic Memory with Questions and Feedback (EM-QnF) task, extending one-shot EM-NLQ to support iterative natural-language user feedback (e.g., clarifications like 'the big blue mug') on egocentric video queries. It proposes a lightweight training scheme that avoids expensive sequential optimization and a plug-and-play Feedback Alignment Module (FALM) that can be added to existing EM-NLQ models. The central claims are significant improvements over the state of the art on three benchmarks, competitiveness with commercial large vision-language models, and effective generalization when evaluated with human-generated feedback.

Significance. If the performance and efficiency claims hold, the work would advance practical interactive systems for personal memory retrieval by enabling natural, iterative refinement without full model retraining. The modular, lightweight design addresses a key deployment barrier in real-world assistive vision-language applications.

major comments (2)

[Abstract and experimental results section] Abstract and experimental results section: The manuscript asserts 'significant improvements over the state of the art' and 'better than or competitive with commercial large vision-language models' but supplies no details on baselines, data splits, error bars, training procedures, or the identity of the three benchmarks. This absence prevents verification of the central performance claims.
[Method section describing FALM and training scheme] Method section describing FALM and training scheme: The description does not demonstrate that the module and training operate without any per-interaction optimization or hidden fine-tuning. Because the efficiency and 'plug-and-play' advantages rest on this property, explicit ablations or measurements confirming zero per-query cost are required to support the claim that the approach avoids 'expensive sequential optimization'.

minor comments (2)

[Abstract] The abstract should name the three benchmarks explicitly instead of referring to them generically.
[Method] A figure or pseudocode illustrating the FALM integration and feedback loop would improve clarity of the proposed architecture.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each of the major comments below and plan revisions to improve clarity and support for our claims.

read point-by-point responses

Referee: [Abstract and experimental results section] Abstract and experimental results section: The manuscript asserts 'significant improvements over the state of the art' and 'better than or competitive with commercial large vision-language models' but supplies no details on baselines, data splits, error bars, training procedures, or the identity of the three benchmarks. This absence prevents verification of the central performance claims.

Authors: We agree with the referee that the abstract is high-level and that the experimental results section would benefit from a clearer summary of the experimental setup. In the revised version, we will modify the abstract to include the names of the three benchmarks and add explicit details at the beginning of the experimental results section regarding the baselines, data splits, error bars, and training procedures to facilitate verification of the performance claims. revision: yes
Referee: [Method section describing FALM and training scheme] Method section describing FALM and training scheme: The description does not demonstrate that the module and training operate without any per-interaction optimization or hidden fine-tuning. Because the efficiency and 'plug-and-play' advantages rest on this property, explicit ablations or measurements confirming zero per-query cost are required to support the claim that the approach avoids 'expensive sequential optimization'.

Authors: We appreciate this point. Our lightweight training scheme is performed once upfront, and the FALM module is plug-and-play without requiring per-interaction optimization or fine-tuning. To better demonstrate this, we will include in the revised method section explicit ablations comparing our approach to sequential optimization methods and measurements of per-query inference costs to confirm they remain zero beyond the initial setup. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper defines a new task (EM-QnF), collects dedicated datasets, introduces an additive plug-and-play FALM module, and reports empirical gains on three benchmarks plus human feedback. No equations or claims reduce a prediction to a fitted input by construction, no self-citation is load-bearing for the core result, and the lightweight-training claim is presented as an empirical engineering choice rather than a derived necessity. The derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that feedback can be aligned effectively with a lightweight module; no free parameters or invented entities beyond the module itself are detailed in the abstract.

axioms (1)

domain assumption Existing EM-NLQ models can be extended with user feedback via a plug-and-play alignment module without full retraining.
Invoked in the proposal of FALM and lightweight training scheme.

invented entities (1)

Feedback Alignment Module (FALM) no independent evidence
purpose: To enable existing EM-NLQ models to incorporate user feedback interactively.
New module introduced to address the interactive task.

pith-pipeline@v0.9.0 · 5533 in / 1234 out tokens · 27463 ms · 2026-05-08T04:29:54.306305+00:00 · methodology

discussion (0)

Reference graph

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[49]

What oil did I add to the pasta in the pot?

Supplementary Material In this supplementary material, we provide further informa- tion about dataset generation, model evaluation, and limi- tations of our work. All code and datasets can be found on our project page:https://nsubedi11.github. io/refocus. The supplementary material is sectioned as follows: • Sec 6.1: Supplementary Video with qualitative r...

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[50]

The query is primarily asking for action of placing some object inside location X

Do not Answer the Query Directly. The query is primarily asking for action of placing some object inside location X. Do not provide any information about the object that was placed in the given location
[51]

Feedback should be either questions or first person focused statements unless the query focuses on someone else

Give Feedback as if You are Asking the Query. Feedback should be either questions or first person focused statements unless the query focuses on someone else. You should give feedback as if you are the person who is asking the query and is looking for the correct video clip
[52]

Attributes of the Given Location. Since the query is asking for what kind of object was placed in given location X, you can provide any information about the given location, from its attribute to its surrounding objects
[53]

Use memorable contrastive information about object or action that are present/absent in the incorrect video clip but absent/present in correct video clip or the query

Contrast between the Video Clips. Use memorable contrastive information about object or action that are present/absent in the incorrect video clip but absent/present in correct video clip or the query
[54]

feedback

Relative Time. You can use the relative time of the video clips to provide time related feedback but do not be too specific. ... Now, I will provide some examples of good and bad feedback for given query and the video descriptions. [IN-CONTEXT EXAMPLES] [RESPONSE SPAN DESCRIPTION] [RESPONSE SPAN EXPLANATION] [REFERENCE SPAN DESCRIPTION] Query: What oil di...
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Some information related to the query action or object to be searched
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Some information that should not be searched for
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contains

Temporal information regarding where the query action/object is located in the video timeline (either before or after or null if not specified) Your job is to extract these 3 potential types of information from the feedback. Some feedback may not have all 3 types of information. Here are some examples to help you understand the task: [IN-CONTEXT EXAMPLES]...