PIA achieves lower attack success rates on persona-based jailbreaks via self-play co-evolution of attacks (PLE) and defenses (PICL) that structurally decouples safety from persona context using unilateral KL-divergence.
Free dolly: Introducing the world’s first truly open instruction-tuned llm
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
WARD is a guard model trained on 177K web samples and adversarially hardened via attacker-guard co-evolution to achieve high recall on prompt injections with low false positives and no added latency.
GaMMA unifies global and temporal music understanding in a single LMM via MoE audio encoders and progressive training, achieving new state-of-the-art accuracies on music benchmarks including 79.1% on MuchoMusic.
citing papers explorer
-
Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment
PIA achieves lower attack success rates on persona-based jailbreaks via self-play co-evolution of attacks (PLE) and defenses (PICL) that structurally decouples safety from persona context using unilateral KL-divergence.
-
WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections
WARD is a guard model trained on 177K web samples and adversarially hardened via attacker-guard co-evolution to achieve high recall on prompt injections with low false positives and no added latency.
-
GaMMA: Towards Joint Global-Temporal Music Understanding in Large Multimodal Models
GaMMA unifies global and temporal music understanding in a single LMM via MoE audio encoders and progressive training, achieving new state-of-the-art accuracies on music benchmarks including 79.1% on MuchoMusic.