Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

Fatemeh Afghah; Hossein Kashiani; Niloufar Alipour Talemi

arxiv: 2606.04434 · v1 · pith:DWIK5Z6Rnew · submitted 2026-06-03 · 💻 cs.CV · cs.LG

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

Niloufar Alipour Talemi , Hossein Kashiani , Fatemeh Afghah This is my paper

Pith reviewed 2026-06-28 07:21 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords multimodal in-context learningattention calibrationhyperbolic distillationdemonstration-free inferencelow-rank adapterLorentz distancequery-adaptive modulationmultimodal large language models

0 comments

The pith

Hyper-ICL trains a low-rank logit adapter to reproduce demonstration-induced attention shifts without any demonstrations present at inference time.

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

The paper develops a training-based method that lets multimodal large language models perform in-context learning on new queries alone. It does so by learning an adapter that adjusts attention distributions to match the redistribution that would have been caused by a set of image-text demonstrations. The adapter incorporates a query-dependent modulation that scales its effect per token, layer, and head. Training relies on a distillation objective that pulls student features toward those of a teacher model conditioned on the demonstrations, measured with Lorentz geodesic distance in hyperbolic space. Experiments across six benchmarks show the resulting system matches or exceeds the accuracy and stability of standard demonstration-based ICL.

Core claim

Hyper-ICL learns a parameter-efficient low-rank logit-level adapter together with a query-adaptive modulation mechanism that reconstructs the attention redistribution induced by interleaved image-text demonstrations; the adapter is trained by a layer-wise hyperbolic anchor distillation loss that aligns intermediate features to a demonstration-conditioned teacher via Lorentz geodesic distance, enabling demonstration-free inference while preserving task performance.

What carries the argument

Layer-wise hyperbolic anchor distillation loss that aligns intermediate student features to a demonstration-conditioned teacher via Lorentz geodesic distance, combined with query-adaptive modulation of intervention strength at the token level.

If this is right

Inference latency drops because no demonstrations need to be processed at test time.
Sensitivity to demonstration ordering, formatting, and content is removed.
The same adapter can be applied across multiple tasks after a single training stage.
Performance remains competitive on VQAv2, OK-VQA, and COCO Caption relative to full demonstration-based ICL.

Where Pith is reading between the lines

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

The same distillation approach might be tested on purely textual ICL to see whether hyperbolic alignment transfers beyond vision-language settings.
If the adapter generalizes across model scales, it could be used as a lightweight post-training module for any frozen multimodal LLM.
The query-adaptive modulation might be inspected to determine whether certain heads or layers consistently require stronger calibration than others.

Load-bearing premise

A low-rank adapter trained once on demonstration pairs can faithfully reproduce the attention effects of those demonstrations for entirely new queries that never see the demonstrations again.

What would settle it

Replace the Lorentz geodesic term in the distillation loss with a Euclidean distance term and check whether accuracy on VQAv2 and OK-VQA falls back to the level of vanilla ICL without any adapter.

Figures

Figures reproduced from arXiv: 2606.04434 by Fatemeh Afghah, Hossein Kashiani, Niloufar Alipour Talemi.

**Figure 1.** Figure 1: Overview of Hyper-ICL. A frozen teacher processes the full demonstration-conditioned prompt, while the student receives only the query at inference time. Hyper-ICL reconstructs demonstration effects by calibrating attention through a parameter-efficient low-rank logit-level adapter, whose strength is controlled token-wise by a query-adaptive gate gl,h across layers and heads. A layer-wise hyperbolic anchor… view at source ↗

**Figure 2.** Figure 2: Comparison of Hyper-ICL with LoRA, LIVE, and MimIC across training sample sizes on VQAv2 and OK-VQA using Idefics-9B and Idefics2-8B-base. Dashed horizontal lines denote standard ICL baselines, 32-shot for Idefics-9B and 8-shot for Idefics2-8B-base. highlighting its robustness and stability across diverse architectures and evaluation benchmarks. We further evaluate Hyper-ICL on additional MLLM backbones i… view at source ↗

**Figure 3.** Figure 3: Ablation of Hyper-ICL hyperparameters and architectural choices on Idefics-9B (VQAv2). (a) Effect of the low-rank adapter rank r for logit-level attention calibration, comparing static interventions (layer-wise, layer & head-wise) with our queryadaptive token-wise modulated Hyper-ICL. (b) Sensitivity of the hyperbolic anchor distillation to curvature κ and the supervision weight λ, showing the best perfo… view at source ↗

**Figure 4.** Figure 4: Qualitative comparison of hallucination behavior across methods on VQA. Hyper-ICL stays visually grounded on representative queries (e.g., sign text, purse color, and road direction), while standard ICL, LoRA, and LIVE produce ungrounded content [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

read the original abstract

Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks. Despite its flexibility, multimodal ICL incurs high inference latency and suffers from instability due to sensitivity to demonstration formatting, ordering, and content. To address these limitations, we propose Hyper-ICL, a lightweight, training-based framework for demonstration-free multimodal ICL that reconstructs demonstration effects directly without requiring ICDs at inference time. Hyper-ICL learns a parameter-efficient low-rank logit-level adapter that calibrates attention distributions to better match demonstration-induced attention redistribution. To capture how demonstration influence varies across queries, we introduce a query-adaptive modulation mechanism that adaptively controls intervention strength at token level across layers and heads based on the current query. Finally, we propose a layer-wise hyperbolic anchor distillation loss that aligns intermediate student features to a demonstration-conditioned teacher via Lorentz geodesic distance. This loss encourages the student to reconstruct the demonstration-query relationships induced by ICDs. Extensive experiments across six different multimodal benchmarks (including VQAv2, OK-VQA, and COCO Caption) demonstrate that Hyper-ICL consistently improves accuracy and stability over vanilla ICL and existing state-of-the-art methods.

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

Hyper-ICL trains a low-rank logit adapter with query-adaptive modulation and Lorentz geodesic distillation to replace multimodal in-context demos at inference, but the abstract gives no direct check that attention redistribution is actually reconstructed on unseen queries.

read the letter

The paper's core move is to train a parameter-efficient adapter so that a multimodal model can skip the demonstrations entirely at test time while still getting some of their effect. It does this with a low-rank update at the logit level, a per-query modulation that scales the change across layers and heads, and a distillation objective that pulls student features toward a demo-conditioned teacher using Lorentz geodesic distance.

The combination of those three pieces for demonstration-free multimodal ICL does not appear in the cited prior work, so the technical package is new even if the individual parts are familiar. The authors correctly flag the real deployment costs—extra latency from processing image-text pairs and sensitivity to demo order or content—and target them with a lightweight training step that leaves the base model frozen.

The soft spot is the central assumption. The distillation aligns intermediate features layer by layer, but the abstract does not report whether the resulting attention maps or output logits on held-out queries actually match what the teacher produces when demonstrations are present. If the low-rank adapter mainly learns average or training-set-specific adjustments rather than internalizing the full demo-query interaction, the claimed accuracy and stability gains across the six benchmarks could be narrower than presented. No ablation numbers, attention-map comparisons, or out-of-distribution checks are visible.

This is aimed at groups working on efficient inference for vision-language models who already run ICL and want to cut the demo overhead. A reader who needs a concrete adapter recipe might extract useful implementation details once the full paper is available.

I would send it to peer review. The idea is clear enough and the problem is practical, but the reconstruction claim needs the full results and controls before anyone can judge how far it generalizes.

Referee Report

2 major / 2 minor

Summary. The paper proposes Hyper-ICL, a training-based framework for demonstration-free multimodal in-context learning. It introduces a parameter-efficient low-rank logit-level adapter trained with a query-adaptive modulation mechanism and a layer-wise hyperbolic anchor distillation loss (using Lorentz geodesic distance) to calibrate attention distributions so that they reconstruct the effects of in-context demonstrations without requiring ICDs at inference. The abstract states that extensive experiments on six multimodal benchmarks (VQAv2, OK-VQA, COCO Caption and three others) show consistent gains in accuracy and stability over vanilla ICL and prior SOTA methods.

Significance. If the core reconstruction claim holds, the work would provide a practical route to the benefits of multimodal ICL (task adaptation via demonstrations) while removing its inference-time costs and instabilities. The combination of low-rank adaptation with hyperbolic feature alignment is technically distinctive and, if shown to generalize, could influence future work on efficient conditioning of large multimodal models.

major comments (2)

[§3 (method) and Experiments] The central claim (abstract and §3) is that the low-rank adapter plus hyperbolic distillation internalizes demonstration-induced attention redistribution so that the same effect occurs on arbitrary unseen queries without ICDs. However, the manuscript provides no quantitative verification (e.g., attention-map cosine similarity, KL divergence on attention weights, or logit-level agreement) between student and teacher on held-out queries outside the distillation training set. Feature alignment alone does not guarantee that the final attention redistribution or output behavior matches the teacher on new inputs.
[§3.2 and ablation tables] The query-adaptive modulation is described as controlling intervention strength at token level, yet no ablation isolates its contribution versus a fixed-strength baseline, nor is there analysis showing that the modulation actually varies meaningfully with query content rather than acting as a learned scalar per layer/head.

minor comments (2)

[§3.1] Notation for the Lorentz geodesic distance and the low-rank adapter matrices should be defined once in a single preliminary subsection rather than re-introduced inline.
[Experiments] The six benchmarks are listed but the exact train/validation/test splits used for adapter training versus final evaluation are not tabulated; a supplementary table would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting opportunities to strengthen the empirical support for our core claims. We address each major comment below and will incorporate the suggested analyses in the revised manuscript.

read point-by-point responses

Referee: [§3 (method) and Experiments] The central claim (abstract and §3) is that the low-rank adapter plus hyperbolic distillation internalizes demonstration-induced attention redistribution so that the same effect occurs on arbitrary unseen queries without ICDs. However, the manuscript provides no quantitative verification (e.g., attention-map cosine similarity, KL divergence on attention weights, or logit-level agreement) between student and teacher on held-out queries outside the distillation training set. Feature alignment alone does not guarantee that the final attention redistribution or output behavior matches the teacher on new inputs.

Authors: We agree that direct quantitative verification of attention redistribution on held-out queries would provide stronger evidence. The layer-wise hyperbolic anchor distillation aligns intermediate features to reconstruct demonstration effects, and the consistent downstream gains on six benchmarks support the claim. However, to directly address the concern, we will add attention-map cosine similarity, KL divergence on attention weights, and logit-level agreement metrics comparing student and teacher on held-out queries in the revision. revision: yes
Referee: [§3.2 and ablation tables] The query-adaptive modulation is described as controlling intervention strength at token level, yet no ablation isolates its contribution versus a fixed-strength baseline, nor is there analysis showing that the modulation actually varies meaningfully with query content rather than acting as a learned scalar per layer/head.

Authors: We will add an ablation study isolating the query-adaptive modulation against a fixed-strength baseline. We will also include analysis (e.g., statistics and visualizations of modulation values across queries) to demonstrate that it varies meaningfully with query content rather than acting as a static per-layer scalar. revision: yes

Circularity Check

0 steps flagged

No circularity; standard supervised distillation with independent empirical validation

full rationale

The paper describes a training procedure in which a low-rank adapter is optimized via a hyperbolic distillation loss to align student features with a demonstration-conditioned teacher model. The reconstruction claim is the explicit training objective rather than a post-hoc renaming of fitted outputs, and the reported gains are measured on six held-out multimodal benchmarks. No equations reduce by construction to inputs, no self-citation chain bears the central result, and no uniqueness theorem or ansatz is imported from prior author work. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities beyond the general statement that a new adapter is trained; the ledger is therefore empty except for the implicit assumption that attention redistribution is transferable via distillation.

pith-pipeline@v0.9.1-grok · 5786 in / 1115 out tokens · 23703 ms · 2026-06-28T07:21:22.705515+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 4 canonical work pages · 2 internal anchors

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Li, Arnab Sen Sharma, Aaron Mueller, Byron C

Todd, E., Li, M. L., Sharma, A. S., Mueller, A., Wallace, B. C., and Bau, D. Function vectors in large language models.arXiv preprint arXiv:2310.15213,

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These results further demonstrate that the proposed logit-level attention calibration and hyperbolic distillation generalize effectively across diverse MLLM architectures

and OK-VQA (Marino et al., 2019). These results further demonstrate that the proposed logit-level attention calibration and hyperbolic distillation generalize effectively across diverse MLLM architectures. Table 7.Results evaluated on additional MLLMs. Backbone Method VQAv2 OK-VQA LLaV A-Interleave-7B Zero-shot 13.02 5.10 8-shot ICL 68.19 43.84 MimIC 74.4...

2019
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For a fair comparison, the 8-shot ICL baseline also uses demonstrations drawn from the source dataset rather than the target dataset

and evaluated on Flickr30k (Young et al., 2014). For a fair comparison, the 8-shot ICL baseline also uses demonstrations drawn from the source dataset rather than the target dataset. As shown in Table 8, Hyper-ICL substantially improves over both zero-shot inference and standard 8-shot ICL on unseen benchmarks within the same task family. These results in...

2014
[12]

First, we compute the mean gate value in early layers(0-7) and late layers (24-31), together with the average per-layer standard deviation over heads and query tokens

using subsets of VQAv2 and OK-VQA. First, we compute the mean gate value in early layers(0-7) and late layers (24-31), together with the average per-layer standard deviation over heads and query tokens. This evaluates whether the gate saturates near0or1, or instead preserves meaningful variation across the model. Second, we examine whether the learned gat...

2024

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D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. Language models are few-shot learners. Advances in neural information processing systems, 33: 1877–1901,

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Chen, X., Fang, H., Lin, T.-Y ., Vedantam, R., Gupta, S., Doll´ar, P., and Zitnick, C. L. Microsoft COCO captions: Data collection and evaluation server.arXiv preprint arXiv:1504.00325,

work page internal anchor Pith review Pith/arXiv arXiv

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A survey on in- context learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. A survey on in- context learning. InProceedings of the 2024 conference on empirical methods in natural language processing, pp. 1107–1128,

2024

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SEED-Bench: Benchmarking multimodal large language models

Li, B., Ge, Y ., Ge, Y ., Wang, G., Wang, R., Zhang, R., and Shan, Y . SEED-Bench: Benchmarking multimodal large language models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13299–13308, 2024a. Li, B., Zhang, Y ., Guo, D., Zhang, R., Li, F., Zhang, H., Zhang, K., Zhang, P., Li, Y ., Liu, Z., and Li, C. LLaV A- ...

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B., Carin, L., and Chen, W

Liu, J., Shen, D., Zhang, Y ., Dolan, W. B., Carin, L., and Chen, W. What makes good in-context examples for GPT- 3? InProceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd workshop on knowledge extraction and integration for deep learning architectures, pp. 100–114,

2022

[6] [6]

Efficient multi- modal long context learning for training-free adaptation

Ma, Z., Zhang, S., Wei, L., and Tian, Q. Efficient multi- modal long context learning for training-free adaptation. arXiv preprint arXiv:2505.19812,

work page arXiv

[7] [7]

Object Hallucination in Image Captioning

Rohrbach, A., Hendricks, L. A., Burns, K., Darrell, T., and Saenko, K. Object hallucination in image captioning. arXiv preprint arXiv:1809.02156,

work page internal anchor Pith review Pith/arXiv arXiv

[8] [8]

Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning

Shukor, M., Rame, A., Dancette, C., and CORD, M. Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning. In International Conference on Learning Representations, volume 2024, pp. 21756–21786,

2024

[9] [9]

Li, Arnab Sen Sharma, Aaron Mueller, Byron C

Todd, E., Li, M. L., Sharma, A. S., Mueller, A., Wallace, B. C., and Bau, D. Function vectors in large language models.arXiv preprint arXiv:2310.15213,

work page arXiv

[10] [10]

These results further demonstrate that the proposed logit-level attention calibration and hyperbolic distillation generalize effectively across diverse MLLM architectures

and OK-VQA (Marino et al., 2019). These results further demonstrate that the proposed logit-level attention calibration and hyperbolic distillation generalize effectively across diverse MLLM architectures. Table 7.Results evaluated on additional MLLMs. Backbone Method VQAv2 OK-VQA LLaV A-Interleave-7B Zero-shot 13.02 5.10 8-shot ICL 68.19 43.84 MimIC 74.4...

2019

[11] [11]

For a fair comparison, the 8-shot ICL baseline also uses demonstrations drawn from the source dataset rather than the target dataset

and evaluated on Flickr30k (Young et al., 2014). For a fair comparison, the 8-shot ICL baseline also uses demonstrations drawn from the source dataset rather than the target dataset. As shown in Table 8, Hyper-ICL substantially improves over both zero-shot inference and standard 8-shot ICL on unseen benchmarks within the same task family. These results in...

2014

[12] [12]

First, we compute the mean gate value in early layers(0-7) and late layers (24-31), together with the average per-layer standard deviation over heads and query tokens

using subsets of VQAv2 and OK-VQA. First, we compute the mean gate value in early layers(0-7) and late layers (24-31), together with the average per-layer standard deviation over heads and query tokens. This evaluates whether the gate saturates near0or1, or instead preserves meaningful variation across the model. Second, we examine whether the learned gat...

2024