FLAS turns model behavior into a continuous dial, from fixing creature-metaphor bugs to controlled brand voice, all at inference time without retraining.

LLM Fail to Acquire Context

Generate diverse, stateful agentic RL environments with verifiable rewards by deriving both dynamics and evaluation from solved mechanisms.

A reproducible 365-day benchmark for agents operating online stores under negotiation, delayed outcomes, market shocks, and persistent memory demands.

A response-aware monitor trained to intervene at the first harm-enabling turn while preserving benign exploratory dialogue.

An interactive audiovisual benchmark showing that current VLMs struggle to turn privacy cues into situated behavior.

Introduce CKA-Agent, a framework that reformulates jailbreaking as an adaptive tree search over the target LLM’s correlated knowledge, achieving 96-99% attack success rates against state-of-the-art commercial LLMs.

A comprehensive evaluation benchmark for assessing privacy awareness of large language models in physical environments, revealing significant gaps when privacy is grounded in real-world contexts across four evaluation tiers.

With increasing demands for efficiency, information retrieval has developed a branch of sparse retrieval, further advancing towards inference-free retrieval where the documents are encoded during indexing time and there is no model-inference for queries. Existing sparse retrieval models rely on FLOPS regularization for sparsification, while this mechanism was originally designed for Siamese encoders, it is considered to be suboptimal in inference-free scenarios which is asymmetric. Previous attempts to adapt FLOPS for inference-free scenarios have been limited to rule-based methods, leaving the potential of sparsification approaches for inference-free retrieval models largely unexplored. In this paper, we explore ℓ0 inspired sparsification manner for inference-free retrievers. Through comprehensive out-of-domain evaluation on the BEIR benchmark, our method achieves state-of-the-art performance among inference-free sparse retrieval models and is comparable to leading Siamese sparse retrieval models. Furthermore, we provide insights into the trade-off between retrieval effectiveness and computational efficiency, demonstrating practical value for real-world applications.

Propose a cross-view approximation on Grassman manifold (CAGM) model to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources.