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新论文 · v1arXiv:2611.15652

Taming Neural Fields with a Frequency-Lipschitz Curriculum for Sparse-View CT Reconstruction

Songtao Hu, Jingrui Wu, Hong Ding, Fanhao Zhu, Yunhao Liu, Suyang Xi

原文摘要

Sparse-view computed tomography (SVCT) reduces radiation dose by acquiring fewer projection views, but the resulting under-determined inverse problem requires careful regularization to prevent reconstruction algorithms from fitting streak artifacts and hallucinated structures. Existing implicit neural representation (INR) methods rely on implicit spectral bias or iterative external denoising, yet they provide limited control over when high-frequency capacity is exposed during optimization. To address this limitation, we propose FLIC (Frequency-Lipschitz INR Curriculum), a capacity-controlled self-prior INR framework that makes the network's representational capacity explicit and schedulable. The core of our method is a joint frequency-Lipschitz curriculum that couples random Fourier feature encoding with spectrally normalized MLP decoding under non-uniform Lipschitz budgets, guiding reconstruction along a coarse-to-fine trajectory from stable global anatomy to fine details. The terminal capacity is automatically calibrated from FBP gradient statistics, enabling scan-adaptive regularization without per-scan manual tuning of the terminal Lipschitz budget. We further introduce a prior-guided adaptive ray sampling strategy that concentrates integration samples near high-gradient anatomical boundaries, improving forward-model fidelity. The entire framework operates in a single training stage without iterative denoising, enabling efficient per-image reconstruction. Extensive experiments on four public CT datasets under noise-free and low-dose settings show that our method achieves state-of-the-art unsupervised reconstruction quality with consistent performance across diverse anatomical structures.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11790

FMA-Net: Flexible Modality Adaptation for Robust Multimodal Face Presentation Attack Detection

Yuhao Gao, Yichen Shi, Zitong Yu, Hongyang Wang, Hongrui Li, Ruixin Zhang, Shouhong Ding, Jun Feng

原文摘要

Multimodal face presentation attack detection (fPAD) improves robustness using complementary RGB, depth, and infrared (IR) cues, but deployment remains challenging when modalities are unavailable or incomplete. Existing flexible-modality methods mainly use zero-filling or modality omission, potentially discarding cross-modal information and introducing modality-dependent bias. We propose the Flexible Modality Adaptation Network (FMA-Net), a unified framework that compensates for missing modalities through instruction-guided latent-space translation. The Face Modality Instruct Translator (FMIT) estimates target modality observations from RGB inputs, while context-aware category enhancement uses learnable text prompts as modality-invariant semantic anchors. A Facial Feature Adapter (F2A) integrates global facial structure and local spoof-relevant cues to enhance discriminative representations. Synthetic-to-Real Face Domain Alignment (SR-FDA) reduces feature discrepancies between translated and real modalities, with benefits depending on the missing-modality pattern and data domain. Experiments on WMCA and PADISI-Face validate the framework. On WMCA with a 70% modality missing ratio, FMA-Net achieves a mean ACER of 3.22% across four intra-domain missing-modality protocols, compared with 4.24% for the strongest baseline by the same criterion. When both depth and IR are entirely unavailable, it achieves 5.23% ACER, compared with 5.99% for the best competing result. Cross-domain experiments also reveal limitations of synthetic-to-real alignment under dataset shifts. These results support FMA-Net as a practical approach to fPAD with flexible and incomplete modalities.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06046

DeepfakeGenome: Toward Next-Generation Deepfake Attribution

Shunli Wang, Xinyu Zhou, Yandan Zhao, Wenbin Liang, Taiping Yao, Ke-Yue Zhang, Zhiyuan Yan, Shouhong Ding 等 9 位作者

原文摘要

In recent years, AIGC technologies are capable of generating hyper-realistic forged facial images, posing severe threats to facial security safety. To tackle these challenges, early Deepfake research primarily centered on real-fake classification. Recently, growing efforts have been devoted to the Deepfake Attribution (DFA) of generated content. Nevertheless, existing research on Deepfake detection and attribution faces saturated performance in binary classification, limited diversity in datasets and algorithms, and imperfect evaluation protocols, which severely impede practical application. To address these limitations, we propose a comprehensive deepfake detection and attribution benchmark named DeepfakeGenome (DFG). It contains 100 facial forgery algorithms and 2M images in total, achieving 4x to 100x larger than prior DFA benchmarks. We further design 4 protocols for practical evaluation, including a novel retrieval-based attribution paradigm. Unlike previous open-set evaluation metrics, the proposed retrieval metrics are more aligned with the real-world active defense situation of blacklist registration mechanisms. Based on these elaborate designs, we investigate the performance ceiling of deepfake attribution task. Over 2k experimental evaluations are conducted, and 10 insightful findings are derived. We believe this work can provide new insights into the DFA research field. The Code and Benchmark of DFG are avaliable on https://huggingface.co/spaces/DeepfakeGenome/DFG_Page.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06076

Breaking the One-Player Extension Barrier: History-Aware Repair for Multiplayer Approximate Nash Equilibria

Jinqi Tang, Shihong Ding, Cong Fang

原文摘要

Computation of Nash equilibria is a central problem in algorithmic game theory, yet exact equilibrium computation is $\mathsf{PPAD}$-complete even for two-player games. This hardness motivates the study of polynomial-time algorithms for approximate Nash equilibria. While substantial progress has been made for two-player and three-player games, existing algorithms for general $m$-player games have largely relied on repeatedly applying one-player extension starting from improved fixed-player algorithms. Such improvements can at best strengthen the classical $1-1/m$ guarantee to $1-1/(m-c)$, for some constant $c$ independent of $m$. We break this long-standing barrier by developing an iterative algorithm that directly tackles general $m$-player games and achieves a $(1-(σ_{\star}-ε)/m)$-approximation for fixed $ε> 0$ and sufficiently large $m$, where $σ_\star\approx1.5867$ is the unique root in $(1,2)$ of $σ_\star^3-19σ_\star^2+27σ_\star+1=0$. Our approach rests on a novel iterative descent-and-repair framework that allows multiple rounds of repair. At the heart of our analysis is a new potential function inspired by continuous-time dynamics, which certifies progress across iterations and ensures polynomial-time termination. Our results highlight the power of history-aware, iterative repair in breaking approximation barriers for multiplayer games.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.12309

G$^2$TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

Junxian Li, Kai Liu, Zizhong Ding, Zhixin Wang, Zhikai Chen, Renjing Pei, Yulun Zhang

原文摘要

The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. In this paper, we focus on understanding-side visual token reduction for improving the efficiency of separate-encoder UMMs. While this topic has been widely studied for MLLMs, existing methods typically rely on attention scores, text-image similarity and so on, implicitly assuming that the final objective is discriminative reasoning. This assumption does not hold for UMMs, where understanding-side visual tokens must also preserve the model's capabilities for editing images. We propose G$^2$TR, a generation-guided visual token reduction framework for separate-encoder UMMs. Our key insight is that the generation branch provides a task-agnostic signal for identifying understanding-side visual tokens that are not only semantically relevant but also important for latent-space image reconstruction and generation. G$^2$TR estimates token importance from consistency with VAE latent, performs balanced token selection, and merges redundant tokens into retained representatives to reduce information loss. The method is training-free, plug-and-play, and applied only after the understanding encoding stage, making it compatible with existing UMM inference pipelines. Experiments on image understanding and editing benchmarks show that G$^2$TR substantially reduces visual tokens and prefill computation by 1.94x while maintaining both reasoning accuracy and editing quality, outperforming baselines on almost all benchmarks. Code is at: https://github.com/lijunxian111/G2TR.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.24279

ContextEcho：长程智能体编程会话中人格漂移的基准

ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions

Xianzhong Ding, Yangyang Yu, Changwei Liu, Bill Zhao, Le Chen, Tao Chen

AI 速读 基于摘要

作者提出 ContextEcho 基准与可复用测试框架，用于刻画长程智能体编程会话中编程助手人格的漂移现象。

cs.CL · cs.SE

arXiv ↗PDF ↗
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Frontier language models are trained to exhibit stable programming assistant personas, yet those personas may drift over long agentic coding sessions in production. After a long real-world coding session, a model that hedges preferences in a fresh chat ("I don't have preferences") may instead express them directly ("Python -- the feedback loop is instant..."). Existing persona-stability studies focus on scripted chat of tens of turns, leaving long production coding sessions involving thousands of tool-using agent steps, repeated compaction, and hours of interaction largely uncharacterized. We introduce ContextEcho, a benchmark and reusable harness with a 25-probe identity suite, a snapshot-then-probe protocol that evaluates conversation state without perturbing the main session, judge-based and judge-free metrics, and a corpus of 57 long agentic-coding sessions collected with consent and redacted for release across Claude Code and Codex CLI. We use one representative session for a 23-model cross-provider study and all 57 sessions for corpus-scale trajectory analysis across frontier and open-weight models. Results show that persona drift occurs across providers and emerges early in real agentic interaction, while in-session compaction neither reliably resets nor amplifies it. A single-shot anchor restores the original assistant persona, though placebo controls suggest a generic register-reset effect. On same-task continuations, drifted prefixes improve tool-call fidelity -- which we attribute to retained task context rather than drift itself. In tool-free chat, however, the same prefixes break output-format contracts and inflate output length. These findings motivate ContextEcho as an open-source framework for auditing persona drift in long coding sessions without retraining.

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.259ACL Anthology · ACL 2026

Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations

Pengqi Li, Lizhong Ding Beijing Institute of Technology, Zhehao Zhou, Chunhui Zhang, Jiarun Fu, Hao Li Beijing Institute of Technology, Ye Yuan Beijing Institute of Technology, Guoren Wang Beijing Institute of Technology

原文摘要

Hallucinations arise when large language models (LLMs) guess rather than acknowledge their underlying uncertainty. Existing static strategies for mitigating hallucinations have been only partially successful, largely because they do not explicitly model the information gain from interacting with the external environment. Researchers need a general method to proactively steer users toward informative clarifications, thereby unlocking the model’s effective capacity under underspecified inputs. We model the uncertainty of LLMs in interactive settings and uncover the mechanism of active calibration between model concepts and human evaluations, improving reliability. We show that calibration error in LLMs density estimation admits a non-vanishing lower bound under non-interactive learning, while interaction empirically reduces it. We further characterize that calibration error identifies informative queries and that calibration can be accelerated by shifting query distributions from imbalanced to balanced regimes. Guided by these insights, we propose a calibration-driven Interactive Learning Strategy (ILS) that selects clarification queries by optimizing calibration error, providing both theoretical guarantees and empirical gains for reliability. Code and data are available at https://github.com/zhouyeah215/Demystifying_Uncertainty.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表