11 citations · 12 across the 8 of their papers we have counts for
10 papers
Counterexamples to the total-current Hohenberg-Kohn conjecture
Andre Laestadius, Mihály A. Csirik
The total-current Hohenberg-Kohn conjecture asserts that the ground-state particle density and total current determine the scalar potential up to an additive constant and the vecto…
Non-Gaussianity Reveals Interpretable Directions in Language Models
Sida Liu, Feijiang Han, Sarah Wiegreffe +1
Finding human-interpretable directions in language-model activations is important for understanding and controlling model behavior. These directions are usually discovered through…
ThinknCheck: Grounded Claim Verification with Compact, Reasoning-Driven, and Interpretable Models
Delip Rao, Feijiang Han, Chris Callison-Burch
We present ThinknCheck, a 1B-parameter verifier for grounded claim verification that first produces a short, structured rationale and then a binary verdict. We construct LLMAggreFa…
DeepAFL: Deep Analytic Federated Learning
Jianheng Tang, Yajiang Huang, Kejia Fan +8
Federated Learning (FL) is a popular distributed learning paradigm to break down data silo. Traditional FL approaches largely rely on gradient-based updates, facing significant iss…
Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification
Feijiang Han
Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in sectors such as healthcare and financ…
APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
Kejia Fan, Jianheng Tang, Zhirui Yang +8
Personalized Federated Learning (PFL) has presented a significant challenge to deliver personalized models to individual clients through collaborative training. Existing PFL method…
ZeroTuning: Unlocking the Initial Token's Power to Enhance Large Language Models Without Training
Feijiang Han, Xiaodong Yu, Jianheng Tang +3
Token-level attention tuning, a class of training-free methods including Post-hoc Attention Steering (PASTA) and Attention Calibration (ACT), has emerged as a promising approach fo…
Can LLMs Handle WebShell Detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework
Feijiang Han, Jiaming Zhang, Chuyi Deng +2
WebShell attacks - where adversaries implant malicious scripts on web servers - remain a persistent threat. Prior machine-learning and deep-learning detectors typically depend on t…
Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Reading
Feijiang Han, Hengtao Cui, Licheng Guo +2
Large Language Models (LLMs) often fail on complex reasoning tasks due to flawed question comprehension, not just flawed logic. This paper presents a systematic investigation into…
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Jianheng Tang, Kejia Fan, Wenxuan Xie +6
The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies either assume that the qualities of workers are known in advance, o…