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

A state-based framework for attacks and defenses in tool-using agents, pairing execution-guided trajectory search with state-aware pre-execution defense.

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.