[GH Actions] Automatically add papers from authors (#108)

Co-authored-by: RSTZZZ <RSTZZZ@users.noreply.github.com>

Author: github-actions[bot]

_posts/papers/2026-05-01-2605.00423.md

@@ -0,0 +1,29 @@
+---
+title: 'GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection'
+venue: ''
+openAccessPdf:
+  url: ''
+  status:
+  license:
+  disclaimer: 'Notice: Paper or abstract available at https://arxiv.org/abs/2605.00423,
+    which is subject to the license by the author or copyright owner provided with
+    this content. Please go to the source to verify the license and copyright information
+    for your use.'
+names: Qincheng Lu, Sitao Luan, Xiao-Wen Chang
+tags:
+- ''
+link: https://arxiv.org/abs/2605.00423
+author: Sitao Luan
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+In wireless communications, recovering the optimal solution to the multiple-input multiple-output (MIMO) detection problem is NP-hard. Obtaining high-quality suboptimal solutions with a favorable performance-complexity trade-off is particularly challenging in under-determined systems with $N_t$ transmit antennas and $N_r<N_t$ receive antennas. Recent diffusion-based MIMO detectors have shown promise, but they require extensive sampling iterations at inference time, and their performance degrades in under-determined scenarios. We propose GD4, a graph-based discrete denoising diffusion method for MIMO detection. Unlike existing diffusion-based detectors that operate in a continuous relaxed space, GD4 performs denoising directly in the discrete symbol space and enables fast inference with one or a few denoising evaluations. Numerical results show that, under a similar inference-time compute budget, GD4 produces higher-quality suboptimal solutions than existing diffusion-based detectors and some widely used classical baseline including box-constrained Babai point and the $K$-best box-constrained randomized Klein-Babai point in both under-determined and overdetermined settings.

_posts/papers/2026-05-01-2605.00513.md

@@ -0,0 +1,31 @@
+---
+title: 'ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis
+  on Social Networks'
+venue: ''
+openAccessPdf:
+  url: ''
+  status:
+  license:
+  disclaimer: 'Notice: Paper or abstract available at https://arxiv.org/abs/2605.00513,
+    which is subject to the license by the author or copyright owner provided with
+    this content. Please go to the source to verify the license and copyright information
+    for your use.'
+names: Ta Thanh Thuy, Jiaqi Zhu, Xuan Liu, Linjing Shang, Reihaneh Rabbany, Guillaume
+  Rabusseau, Lihui Chen, Zheng Yilun, Sitao Luan
+tags:
+- ''
+link: https://arxiv.org/abs/2605.00513
+author: Reihaneh Rabbany
+categories: Publications
+
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+## Abstract
+
+Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and content moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.

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