Wei Ye

ToolHazard reveals that injection timing and placement are critical factors in exploiting vulnerabilities of LLM-based agents, leading to new insights in adversarial robustness.

SHIFT transforms LLMs into reasoning-efficient retrievers, achieving superior performance on complex retrieval tasks by rethinking how we align retrieval objectives with implicit reasoning.

Disentangling high-level cross-modal reasoning from low-level modality-specific refinement in talking head generation yields superior lip-sync accuracy, video quality, and audio quality compared to entangled approaches.

Executable visual transformations enable MLLMs to achieve continuous self-evolution without the pitfalls of pseudo-labels, leading to superior performance in dynamic VQA tasks.

Forget sifting through mountains of MCTS trajectories – contrasting successful and failed reasoning paths distills 10x more effective training data for reasoning models.

Forget external retrieval controllers: GRIP lets your language model decide when and how to retrieve information, all within its own token-level decoding process.

Noisy multi-turn dialogue data hurts instruction tuning, but selecting entire conversations based on topic grounding and information flow yields surprisingly robust models.

LLMs can now leverage visual structure, not just text, to pinpoint bugs in multimodal programs, thanks to a novel graph alignment approach that bridges the gap between GUI screenshots and code.

Turns out, what makes for good code pre-training data depends heavily on the downstream task you're targeting.