AVERE: Improving Audiovisual Emotion Reasoning with Preference Optimization.

Ashutosh Chaubey, Jiacheng Pang, Maksim Siniukov, Mohammad Soleymani

Abstract read

In one paragraph

Article in ... International Conference on Learning Representations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ashutosh ChaubeyInstitute for Creative Technologies, University of Southern California, Los Angeles, CA 90007, USA.

Jiacheng PangInstitute for Creative Technologies, University of Southern California, Los Angeles, CA 90007, USA.

Maksim SiniukovInstitute for Creative Technologies, University of Southern California, Los Angeles, CA 90007, USA.

Mohammad SoleymaniInstitute for Creative Technologies, University of Southern California, Los Angeles, CA 90007, USA.

Funding

Novel multimodal neural, physiological, and behavioral sensing and machine learning for mental statesR61MH135407 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Maryam Shanechi · 2024 to 2026

$2.8M

NIMH NIH HHS R61 MH135407

6 · The paper itself

Abstract

Emotion understanding is essential for building socially intelligent agents. Although recent multimodal large language models (MLLMs) have shown strong performance on this task, two key challenges remain: (i) spurious associations between emotions and irrelevant audiovisual cues (

Identifiers

PMID42801257

PMCPMC13615894

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.