Jisheng Dang

Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning

Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. In many current training setups, temporal supervision is applied through local objectives such as boundary regression or span generation, while verification is used mainly to rerank candidate segments at inference time. We study whether a frozen verifier can also guide training. Our multi-agent framework couples a trainable \emph{Grounder} with a frozen \emph{Verifier}: the Grounder samples candidate trajectories and evidence segments, the Verifier assigns query-conditioned segment scores, a group-relative policy-gradient objective favors trajectories that outperform their within-input peers, and a bootstrapped calibration loss steers temporal predictions toward verifier-preferred spans. Trained on source tasks and evaluated without target-dataset fine-tuning, a two-billion-parameter instantiation transfers zero-shot across grounded question answering, temporal grounding, and long-video question answering, reaching 28.7\% intersection-over-union and 25.4\% answer-grounding accuracy on a grounded-question-answering benchmark, 46.1\% intersection-over-union on a temporal-grounding benchmark, and 54.1\% on a long-video question-answering benchmark. Relative to a strong same-scale baseline, the gains are modest but consistent, with the clearest improvements on relevance-oriented metrics such as intersection-over-union and moderate-overlap recall. Within the tested benchmarks and transfer setting, the results support frozen verification as a training signal for evidence selection, while showing that strict boundary precision remains comparatively weaker. Code and models are available at https://anonymous.4open.science/r/MASIRL-E50C/

SafeSteer: Adaptive Subspace Steering for Efficient Jailbreak Defense in Vision Language Models

As the capabilities of Vision Language Models (VLMs) continue to improve, they are increasingly targeted by jailbreak attacks. Existing defense methods face two major limitations: (1) they struggle to ensure safety without compromising the model’s utility; and (2) many defense mechanisms significantly reduce the model’s generation efficiency. To address these challenges, we propose SafeSteer, a lightweight inference-time steering framework that effectively defends against diverse jailbreak attacks without modifying model weights. At the core of SafeSteer is the innovative use of singular value decomposition (SVD) to purify a low-dimensional “safety subspace” from noisy activation differences. By projecting the raw steering vector into this subspace, SafeSteer isolates the core safety signal from noise, adaptively removing harmful influences while preserving the model’s ability to handle benign inputs. SafeSteer avoids iterative response generation and introduces only limited overhead compared with other single-pass activation-steering defenses. Extensive experiments show that SafeSteer reduces the attack success rate by over 60% while maintaining the model’s utility on benign tasks, without introducing significant inference latency. These results demonstrate that robust and practical jailbreak defense can be achieved through simple, efficient inference-time control.

Tightly coupled equivariant flow matching for molecular docking with multimodal physical constraints.

PhysMLLMs: Spatial Priors for Unified Referring Segmentation and Grounded Reasoning of Images and Videos

PhysMLLMs is a training-stage prior injection architecture that injects physics-inspired spatial continuity priors into Video MLLMs, demonstrating that the injected spatial prior improves video consistency without compromising image-level grounding or general multimodal capability.