PrivacyAlign: Contextual Privacy Alignment for LLM Agents
AI agents acting on behalf of users are constantly making decisions, and for users to trust their agents, those decisions must align with what they actually…
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What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics
Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training. While most defenses operate at…
Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation
Continual Test-Time Adaptation (CTTA) aims to maintain model performance under evolving target domains by adapting online without labeled data. However, practical deployments often cannot retain the…
ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillation
On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated outputs, but standard OPD treats all student-generated outputs (SGOs) equally regardless…
Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints
Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains insufficiently understood. This paper reports…
MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation
Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the reference…
ShutterMuse: Capture-Time Photography Guidance with MLLMs
Real-world photography requires capture-time guidance for both camera framing and subject pose. Yet existing aesthetic cropping benchmarks mainly evaluate post-hoc crop prediction and overlook subject-side recommendations,…
RL-Index: Reinforcement Learning for Retrieval Index Reasoning
Retrieving external knowledge is essential for solving real-world tasks, yet it remains challenging when the relationship between a query and its relevant knowledge involves implicit and…
CAVEWOMAN: How Large Language Models Behave Under Linguistic Input and Output Compression
"Talk short. Drop grammar. Save token." This caveman style is widely promoted as a way to cut inference cost, but whether it actually saves anything depends…
When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents
As LLM agents increasingly select tools autonomously, their choices among tools with different privileges become safety-relevant. However, prior tool-selection studies focus on safety-agnostic metadata preferences, leaving…