Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints. Current guardrail solutions often suffer from rigidity, relying on fixed policy sets and offering limited transparency or reasoning flexibility. We prese...
RecAP is introduced, a benchmark that measures continual-learning phenomena at the constraint level under a strictly proactive adapt-then-test protocol: prompt optimization methods receive only the constraint specification and must generalize before seeing any test data.
Harsh Deshpande, Kushal Chawla, Sangwoo Cho et al.· arXiv.org· 0 citations
This work introduces AcCoRD, a user-agent collaboration benchmark requiring agents to handle diverse user preference dynamics in two domains: online shopping and travel planning, and evaluates five frontier LLMs under two prompting strategies: vanilla ReAct, and an uncertainty-guided variant that prompts models to iden...
SINKFLEX-RL, a modular training system for RL in dual-control tool-use environments that combines a Gymnasium-compatible environment wrapper, a VERL-style rollout dataflow, group-relative policy optimization without a separate value model, and a sink-aware FlexAttention path designed to preserve model-specific sink sca...
Zelei Cheng, Amritansh Mishra, Sambit Sahu et al.· 0 citations
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