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REVIEW 3 major objections 5 minor 30 references

Bidirectional Action Sequence Learning for Long-term Action Anticipation with Large Language Models

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read BiAnt claims that adding a backward action-prediction task during LLM training improves forward-only long-term action anticipation on Ego4D.

desk verdict A reasonable extension that needs a proper ablation before we can believe the bidirectional training actually causes the gain. read the letter →

arxiv 2508.00374 v1 pith:F3DQWLX7 submitted 2025-08-01 cs.CV

Yuji Sato , Yasunori Ishii , Takayoshi Yamashita This is my paper
classification cs.CV
keywords long-termactionanticipationbidirectionallearninglargelanguagemodelsEgo4Deditdistancesequencepredictionprompt-basedvideounderstanding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

BiAnt claims that long-term action anticipation improves when the model is trained not only to predict future actions from the past, but also to predict past actions from a reversed future context. The paper argues that forward-only training leaves the model blind to scene continuity, so small errors cascade when repeated but semantically distinct sub-actions shift. Adding a backward prediction task during training, then discarding it at inference, lowers action edit distance on Ego4D from 0.8770 (AntGPT) to 0.8655. The reason a reader should care is that this is a training-only change: inference stays forward-only, so it costs nothing at deployment.

What carries the argument

The mechanism is a pair of task prompts sharing one LLM. A control token at the start tells the model whether this example is forward or backward. The backward prompt reverses the temporal order of both the observation interval and the future interval, so the model must produce earlier actions from later context. The training loss combines forward and backward cross-entropy terms, L = Lfwd + Lbwd. At inference the backward branch is dropped entirely; the claim is that the shared weights have learned a bidirectional consistency that suppresses cascade errors during forward generation.

What would settle it

Train the same LLM with the forward objective only but with doubled effective data (for instance, presenting each forward sample twice or running twice as many optimizer steps) using identical hyperparameters; if action edit distance matches 0.8655, the backward direction is not the cause. A second check: replace the backward sequence with a randomly shuffled sequence of the same labels; if the gain persists, the reversed temporal order is not what matters.

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Extended reading notes

Core claim

The paper's central claim is that bidirectional sequence learning, predicting future actions forward and past actions backward from the same video, produces a stronger forward-only anticipator than training on forward prediction alone. The method, BiAnt, feeds action labels into an LLM with two prompt templates: a forward prompt asking for the next 20 actions, and a backward prompt built by reversing the observed and future segments and asking for the next 12 actions in reverse temporal order. The two cross-entropy losses are summed with equal weight, and the model is fine-tuned with LoRA. At inference only the forward prompt is used. On the Ego4D v2 test set, BiAnt reports action edit distance 0.8655 versus 0.8770 for AntGPT, the forward-only LLM baseline; verb and noun scores edge slightly downward, so the gain is specific to the joint action metric.

Load-bearing premise

The load-bearing premise is that the backward prediction loss is what caused the edit-distance improvement, but the paper reports no ablation that removes that loss, so the gain could instead come from having more training data or from the specific instruction wording.

Editorial extensions

If this is right

  • If BiAnt is right, any LLM-based anticipator can gain accuracy without changing how it runs at inference, since the backward task is used only during training.
  • The gain is measured on the joint action edit distance, not on verb or noun scores alone, so the method targets the coherence of the whole predicted action sequence.
  • Because visual encoders and action recognition models are frozen, BiAnt's improvement is additive and can be combined with better feature extractors.
  • The backward prompt reuses the same action labels already available for forward training, so the method adds no annotation cost.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper never ablates the backward loss itself, so an equally consistent reading is that the improvement comes from approximately doubled training signal; a forward-only run with duplicated samples or twice the steps would separate these explanations.
  • If the true mechanism is sequence-level consistency rather than reversed causality, the same prompt-based auxiliary task could be replaced by any structured auxiliary loss, and similar gains should appear.
  • One extension the authors leave implicit is testing on other long-term anticipation benchmarks and reporting per-position error growth; that would show whether the backward task specifically reduces cascade errors late in the sequence.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes BiAnt, a framework for long-term action anticipation on the Ego4D benchmark. BiAnt fine-tunes an LLM (Llama2-7B) with LoRA on two tasks: standard forward prediction from observed actions to future actions, and a backward task in which the model predicts a reversed past sequence from a reversed context. At inference only forward prediction is used. The authors report that BiAnt improves action edit distance (0.8655) over AntGPT (0.8770) on the Ego4D v2 test set, and an ablation varying a task-description control token. The central claim is that joint forward/backward training improves forward anticipation.

Significance. If the bidirectional-training effect is genuine, the finding is a modest but potentially useful contribution to LLM-based action anticipation: the method is simple, requires no new architecture, and uses standard supervised training. The authors are also to be credited for testing on a held-out test set rather than tuning on test labels. However, the study as presented has no code release, no error bars, and no ablation that isolates the backward loss, so the evidence is currently insufficient to establish the claimed cause. The paper's contribution is specifically bidirectional training, which makes the missing control experiment load-bearing rather than cosmetic.

major comments (3)
  1. [Section 4, Table 1 and Section 3.3, Eq. (3)] The central claim—that the backward loss L_bwd causes the improved forward prediction—is never tested by an ablation that removes L_bwd. The only ablation, Table 2, varies the task-description token, not the backward objective. Because BiAnt trains on both forward and backward sequences, it sees roughly twice as many supervised action-label tokens per video as AntGPT, and its prompts differ in wording and include a '[forward]'/'[backward]' control token. The 0.0115 action-ED improvement over AntGPT could therefore be due to doubled training data, prompt differences, or other implementation differences from AntGPT. A forward-only variant trained with identical prompts, identical token counts, and the same LoRA schedule is required to support the attribution in the title and abstract.
  2. [Section 4, Table 1] No error bars or significance tests are reported. The main result, action edit distance 0.8655 vs. 0.8770, is a difference of 0.0115; without variance over seeds or a paired test it is unclear whether this difference is distinguishable from noise. Since the authors use K=5 candidate selection and report a single test-set number, I recommend reporting mean and standard deviation over at least three training runs with different seeds, plus a paired significance test on the validation subset.
  3. [Section 4, Table 2] The ablation table is internally unclear: the first row shows '3' in the 'Task description token' column, and the two rows are not labeled as 'with token' and 'without token'. In addition, the text claims that adding the token improves action ED by 0.0125, which matches the difference between 0.8741 and 0.8866, but the table formatting makes it impossible to verify which row is which. Please label the rows explicitly and reconcile the numbers with the text.
minor comments (5)
  1. [Figure 2(c)] The inference prompt in Figure 2(c) contains unreadable garbled characters and is not a usable example; please replace it with clean text.
  2. [Section 3.3, Equations (1)-(2)] The notation is inconsistent: the same symbols (\vec{y}_i, \vec{p}_i) are used with and without tildes in different places, and the definition of 'predicted probability at position i' for autoregressive generation is not specified. Please clarify the exact token-level loss and the role of the control token in the loss.
  3. [Section 3.2] The variables N, \tilde{N}_{obs}, Z, and \tilde{Z} are introduced with overlapping roles; in particular, the relationship between \tilde{N}_{obs}, \tilde{Z}, and the reversed sequence length is stated only in prose. A formal definition or a small diagram would greatly improve reproducibility.
  4. [Section 4, Implementation Details] The comparison to AntGPT is not controlled for prompt template, number of training tokens, or LoRA configuration, and no details of the AntGPT reproduction are given. Even if the main ablation is added, please also state exactly what differed between BiAnt and the AntGPT baseline.
  5. [Abstract and Conclusion] The abstract and conclusion state that BiAnt 'improves performance in terms of edit distance compared to baseline methods,' but the paper does not report statistical evidence. Please soften these claims or add the missing significance analysis.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the claimed improvement is an empirical test-set comparison; the missing L_bwd ablation is an attribution concern, not a circular reduction.

full rationale

The central derivation is not circular. BiAnt trains an LLM with two supervised objectives, L_fwd (Eq. 1) and L_bwd (Eq. 2), combined in Eq. 3, and evaluates forward-only inference on the Ego4D test set using edit distance. No parameter is fitted to test labels, and the claimed improvement (action ED 0.8655 vs 0.8770 for AntGPT) is an empirical outcome rather than a quantity constructed from its inputs. The backward loss uses ground-truth actions only as training targets, which is standard supervision; it is not a renamed version of the evaluation metric. The only self-citation is reference [14] (PsMsEgoAI), used as a baseline in Table 1 and related work; it is not load-bearing for the paper's contribution. The main weakness, the absence of an ablation that removes L_bwd from Eq. (3), is an attribution or soundness concern because the gain could be due to extra training data or prompt differences, but it is not circularity because the reported number is not forced by construction.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

There are no invented physical entities. The free parameters are hyperparameters chosen by hand or from the original Ego4D protocol. The key assumption is the utility of the backward task, which is the paper's core idea.

free parameters (7)
  • alpha (forward loss weight) = 1.0
    Chosen by hand, not tuned. Appears in Eq. (3).
  • beta (backward loss weight) = 1.0
    Chosen by hand, not tuned. Appears in Eq. (3).
  • Observation length N_obs = 8
    Set to 8, following Ego4D protocol (Section 4).
  • Future prediction length Z = 20
    Set to 20, following Ego4D protocol (Section 4).
  • Reversed observation length = 16
    Chosen by hand for backward task (Section 3.2).
  • Reversed future length = 12
    Chosen by hand for backward task (Section 3.2).
  • Number of candidate sequences K = 5
    Used for minimum edit distance selection (Section 4).
assumptions (4)
  • standard math Cross-entropy loss with teacher forcing is an appropriate training objective for sequence generation.
    Used in Eq. (1) and (2); standard in autoregressive generation.
  • domain assumption Edit distance on actions, nouns, and verbs is the official Ego4D evaluation metric.
    Used for all quantitative comparisons (Section 4).
  • domain assumption The frozen action recognition model provides sufficiently accurate labels for both forward and backward training.
    Relied on in Section 3.2; noisy labels could affect the backward task.
  • domain assumption Predicting the past from a reversed future sequence provides a useful training signal for forward prediction.
    This is the central hypothesis of the paper, stated in Section 3.1 and 3.3; not independently justified.

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Cite this review

Pith. "Pith review of Bidirectional Action Sequence Learning for Long-term Action Anticipation with Large Language Models." pith.science (2026). https://pith.science/paper/F3DQWLX7

@misc{pith2026250800374,
  author       = {Pith},
  title        = {Pith review of: Bidirectional Action Sequence Learning for Long-term Action Anticipation with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F3DQWLX7}},
  note         = {Machine review of arXiv:2508.00374}
}
read the original abstract

Video-based long-term action anticipation is crucial for early risk detection in areas such as automated driving and robotics. Conventional approaches extract features from past actions using encoders and predict future events with decoders, which limits performance due to their unidirectional nature. These methods struggle to capture semantically distinct sub-actions within a scene. The proposed method, BiAnt, addresses this limitation by combining forward prediction with backward prediction using a large language model. Experimental results on Ego4D demonstrate that BiAnt improves performance in terms of edit distance compared to baseline methods.

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