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20242026
most citedJustice or Prejudice? Quantifying Biases in LLM-as-a-Judge

8 citations · 19 across the 25 of their papers we have counts for

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7 papers · 1 filter

cs.CR2026

Adaptive Traffic Camouflage: Causal and Resource-Aware Defense Against IoT Fingerprinting

Daniel Adu Worae, Spyridon Mastorakis, Nuno Moniz +1

Encryption hides IoT payloads, but traffic shape can still reveal device identity through packet sizes, timing, direction, and packetization. We present Adaptive Traffic Camouflage…

cs.LG2026

Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents

Yujun Zhou, Kehan Guo, Haomin Zhuang +8

Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated…

cs.LG2026

Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla +1

Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative mod…

cs.CL2026

PRISM: A Multi-Dimensional Benchmark for Evaluating LLM Peer Reviewers

Ngoc Phan Phuoc Loc, Toan Huynh La Viet, Thanh Tran Khanh +8

The rapid growth in submissions to machine learning venues has strained the scientific peer-review system and intensified interest in LLM-based automated peer reviewers. However, h…

cs.MA2026

Emergent Risks in Generative Multi-Agent Systems

Yue Huang, Yu Jiang, Wenjie Wang +12

Multi-agent systems composed of large generative models are rapidly moving from laboratory prototypes to real-world deployments, where they jointly plan, negotiate, and allocate sh…

cs.CL2026

CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era

Kaiwen Shi, Weixiang Sun, Zheyuan Zhang +3

Scientific research relies on citation integrity, yet large language models (LLMs) have introduced a critical risk: fabricated references that appear plausible but correspond to no…