Qi Zhang

AgentCompass transforms agent evaluation by providing a modular, open-source framework that supports over 20 benchmarks and enables nuanced failure analysis.

Even state-of-the-art language models struggle significantly in real-world tasks, exposing critical shortcomings in their deployment readiness.

T2LDM++ generates realistic LiDAR scenes with rich geometric details, overcoming the limitations of existing models that struggle with insufficient training data and controllability.

JEPA-based world models face a fundamental trade-off between approximation and sample errors that could redefine their application in predictive tasks.

Even the top-performing language models struggle with archive-grounded reasoning, achieving only 59.4% accuracy on a benchmark designed to test their agentic capabilities across diverse workplace documents.

Transforming Poisson noise into Gaussian noise can boost image denoising performance by up to 0.75 dB, even in challenging conditions.

G2PO redefines agent actions and leverages a global state-transition graph, leading to a 22.2% boost in success rates for long-horizon tasks.

MemGUI-Agent achieves unprecedented long-horizon task performance by proactively managing context, outperforming traditional methods that struggle with prompt dilution.

Achieving lossless processing of 256K contexts, Keye-VL-2.0 transforms how we approach long-video understanding and agentic intelligence.

Superficial rephrasing can inflate AI peer review scores by over 1.3 points, revealing a dangerous vulnerability in AI-assisted scientific evaluation.

ISPO reduces critical reasoning failures in RLVR by transforming reward structures, leading to superior performance on complex reasoning tasks.

SkillComposer enables language models to self-evolve skills in real-time, achieving up to +4.5 improvements on agent tasks compared to larger models.

Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.

LLMs' reliance on specific knowledge sources during question answering can now be reliably estimated without extensive perturbation, enabling better error detection and risk screening.

Naive distributed inference on edge devices can be *slower* than local execution due to CPU-GPU communication bottlenecks, but a profiling-driven adaptive approach can flip the script for significant gains.

Compressing 3D Gaussian splats by operating on intermediate feature representations slashes storage by an order of magnitude without sacrificing rendering quality.

RFT's impressive in-domain performance masks surprisingly weak generalization to new environments, highlighting a critical challenge for deploying LLM agents in the real world.