Yike Guo

A Theory of Reliable Self-Evolution for Agent Harnesses

In harness self-evolution, agents modify their own prompts, code, tools, and orchestration while keeping the underlying language model fixed. Recent work has shown that agents can improve themselves in response to task failures and achieve substantial performance gains. However, gains on failed tasks do not automatical...

OMP-MoE: Efficient Expert Pruning for Mixture-of-Experts LLMs via Orthogonal Matching Pursuit

Mixture-of-Experts (MoE) models enable efficient scaling of large language models but face critical deployment challenges due to massive memory requirements. Existing pruning methods either incur prohibitive search costs or neglect the dynamic interdependencies between experts. To address these challenges, we present O...

Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations

Visual modality has recently been explored as a way to compress textual tokens, including rendering code as images for static code understanding. We study whether this representation can serve as operational context for agentic coding, where an agent must navigate repositories, edit source files, and verify executable...

MD-VAE: Concepts embedded variational autoencoder with multiple decoders

LexAgentHallu: A Hierarchical Benchmark for Profiling Hallucinations in Legal Agents

As large language models are increasingly deployed as tool-augmented legal agents, they introduce agentic hallucinations where tool-call and reasoning errors cascade into fabricated holdings and miscited authority. However, existing legal benchmarks evaluate only single-turn QA with outcome-level metrics, while agentic...

LUNA: Learning Universal 3D Human Animation Beyond Skinning

LUNA is the first end-to-end 3D animatable model that supports implicit 2D driving and introduces hybrid supervision that distills soft structural priors from an LBS teacher and a loss that supports training on both limited fitted data and large in-the-wild unlabeled videos.

Life Operators: a self-evolving framework for multiscale life modelling

Life Operators is proposed: task-bounded mappings that define three scientific roles: Perception operators infer task-relevant biological states from multimodal observations, Evolution operators propagate these states under natural or intervention-conditioned dynamics, and Generation operators map them to measurable si...

Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence

This analysis provides a structured account of current approaches to scaling LRMs beyond human supervision and the open problems involved in developing self-sustaining learning systems toward superintelligence.

Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment

Zero2Skill is presented, a human-robot symbiotic agentic system in which corrections are retained and reused across rounds, and policies fine-tuned on Zero2Skill data match teleoperation-trained policy success at a fraction of collection human cost.

Learning Ophthalmologist Clinical Reasoning for Glaucoma Diagnosis from Fundus Images

A reasoning-driven vision-language framework that explicitly models the ophthalmologist's diagnostic workflow by generating structured clinical reasoning prior to diagnosis is developed, demonstrating that explicitly modeling expert clinical reasoning simultaneously improves interpretability and diagnostic performance.

ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships

This work proposes ReBind, a systematic framework that introduces semantic instructions with embedded reference tokens as the intermediate representation for multi-reference image-conditioned video editing and develops ReBind-Instruct, a specialized MLLM that learns to establish explicit bindings between visual attribu...

SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

SkillProx is introduced, a proximal-gradient-inspired forward--backward framework that couples closed-loop diagnostic evolution with utility-aware proximal refinement and demonstrates the complementary effects of closed-loop diagnosis and proximal refinement.