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Shangding Gu

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#artificial intelligence Review Oct 2026

Groundability, Not Scale Alone: When Weak Reviewers Can Audit Strong Coding Agents

Coding agents can return plausible patches that omit required behavior. These failures are hard to review because long traces and confident summaries often hide what was missed. We ask when a nominally weaker reviewer can reliably decide whether a patch solves its issue. We study 411 execution-labeled traces from three...

Jun-Yu Guo, Shangding Gu, Ming Jin et al. · 1 citation
#artificial intelligence Preprint Sep 2026

When Context Changes: Understanding Update Failures in LLMs

As preferences, goals, and facts change, LLM agents must use the current state while earlier versions remain in context. Yet they can answer with an old value of the same variable, a failure that we call stale binding. To study when models use outdated information and why, we introduce Controlled In-Context Memory (CIC...

Jun-Yu Guo, Yu-Cheng Fang, Shangding Gu et al. · 0 citations
Preprint Sep 2026

TRACE: Governing Memory Validity in Evolving Multi-Agent Systems

TRACE is presented, a training-free layer that treats re-entry as an eligibility decision rather than a storage or retrieval operation, reconciling a departure checkpoint against absence-period updates, resolving explicit and implicit invalidation, and releasing a bounded Return View only when it covers the returning r...

Wen-Jun Xiong, Shengtao Zhang, Shangding Gu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

HarnessPAI: An Evolving Harness for Physical AI

The results suggest that the frontier of Physical AI depends not only on stronger action models, but also on executable harnesses that integrate perception, task understanding and reasoning, and action execution into a unified, verifiable, and feedback-driven system.

X. Wang, Wen-Hao Wu, Meng-Hao Zhang et al. · 1 citation
#artificial intelligence Preprint Sep 2026

WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies

Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which later decisions are made. We study how collective failure changes with harmful-agent fraction and society size in a controlled financial agent society, where agents commu...

Le-Jun Zhang, Sarah Lu-Liang, Xin Jiang et al. · 0 citations
Jun 2026

Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens

SafeClawArena is developed, a benchmark of 406 adversarial tasks executed in containerized replicas of real agent platforms with canary-marked credentials and evaluated via automated taint tracking across nine output channels, exposing the inadequacy of current defenses and suggesting directions for future hardening.

Peizhi Niu, Wenjie Qu, Shangding Gu et al. · 2 citations

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