Behavioral Reliability
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Related Articles from SNS
When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding
Announce Type: new Abstract: High accuracy does not necessarily make an LLM a faithful coder. This issue matters because many social-science studies rely on expert-written codebooks to turn text into structured data. We study this problem in political event coding, a challenging source-target relation classification task beyond ordinary sentence-level classification, where models must determine what one actor did to another using detailed coding rules.
From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing
Announce Type: new Abstract: Existing LLM routing methods typically treat a model's single response to a query as its capability label for training routers. However, because LLM generation is inherently stochastic, such single-shot supervision provides only a noisy observation of a query-model pair's behavior rather than a reliable capability estimate. We show that this assumption introduces systematic noise into routing supervision, making learned routing policies less reliable.
Vehicle Overacceleration -- A Fundamental Microscopic Mechanism for Traffic Breakdown Control Using Automated Vehicles and AI
Announce Type: replace Abstract: This review article addresses a fundamental controversial question in traffic theory: Is the nucleation character of traffic breakdown at a bottleneck governed by vehicle overdeceleration (overbraking) or by discontinuous vehicle acceleration, referred to as vehicle overacceleration. This question is of particular importance in the context of automated vehicles and AI, whose individual dynamic behavior can enable reliable strategies for traffic breakdown...
Toward Agentic Governance: What Shapes LLM-Agent Intervention in Public Forums?
arXiv:2606.00603v2 Announce Type: replace Abstract: LLM agents are increasingly used in moderation-relevant public forum workflows, where their choices to answer, acknowledge, repair, or decline are routinely challenged by users, platforms, and regulators. The same agent often returns different responses on identical content, so any defense based on the agent's behavior cannot be reliably reproduced. The variation is structural.
Vehicle Overacceleration -- A Fundamental Microscopic Mechanism for Traffic Breakdown Control Using Automated Vehicles and AI
Announce Type: new Abstract: This review article addresses a fundamental controversial question in traffic theory: Is the nucleation character of traffic breakdown at a bottleneck governed by vehicle overdeceleration (overbraking) or by discontinuous vehicle acceleration, referred to as vehicle overacceleration. This question is of particular importance in the context of automated vehicles and AI, whose individual dynamic behavior can enable reliable strategies for traffic breakdown control...
Human Psychometric Questionnaires Mischaracterize LLM Behavior
Announce Type: replace Abstract: We examine whether human psychometric questionnaires can serve as reliable tools for characterizing and predicting LLM behavior in everyday user interactions. We analyze eight open-source LLMs by comparing their value and personality profiles derived from two different methods: Likert self-reports on established questionnaires (PVQ-40/21 and BFI-44/10) and generation probabilities over value-laden responses to everyday user queries. The two profiles diverge...
Bridging Domain Expertise and Generalization for Performance Estimation
arXiv:2606.06335v1 Announce Type: new Abstract: Performance estimation under distribution shift aims to predict how a model behaves on an unlabeled test set whose distribution differs from the training data, a scenario that requires reliable indicators that can faithfully reflect model behavior without ground-truth labels. Existing approaches rely solely on the outputs of the given model whose biases are amplified once the distribution shifts, weakening the correlation with the true...
Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization
arXiv:2605.30928v1 Announce Type: new Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward-driven and often exhibit behaviors that differ from humans, limiting interpretability and reliability. In this work, we introduce a novel human-like RL framework that predicts action sequences closely aligned with human behaviors while maximizing rewards.
Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
arXiv:2606.02417v1 Announce Type: new Abstract: Multi-behavior recommendation improves target-behavior prediction by exploiting heterogeneous auxiliary feedback (e.g., view, collect, and cart), yet its robustness is undermined by behavior-dependent noise and inconsistency. We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities. First, intra-behavior representation entanglement arises when multi-hop propagation blends incidental signals with...
AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification
new Abstract: As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware.