25 papers
Continual Test-Time Learning with Evolution Strategies
Guangya Hao, Yiting Duan, Longyan Tan +5
Large language models increasingly operate in open-ended environments, where they are expected to continually adapt to evolving tasks and distributions, often without access to ext…
Coupled Harness--Model Evolution for Scientific Equation Discovery
Pengze Li, Jiaquan Zhang, Encheng Su +8
Large language models (LLMs) are increasingly used to discover scientific equations from experimental data. Such a discovery system has two parts that must work together: a library…
How Much Evidence Should a Coding Agent's Self-Correction Carry? Adaptive Dirichlet Evidence for Self-Distillation
Yunbo Long, Guangya Hao, Yuhan Liu +4
Execution feedback lets coding agents revise programs and learn from their own corrections. A correction's learning weight should reflect both the transitions supported by its exec…
SPECTRUM: Proximal Spectral Modulation for Looped Self-Distillation
Yunbo Long, WenJie Chen, Jiaquan Zhang +4
A model that learns from its own outputs inherits more than their correctness: it inherits which solutions it produces. We formulate Looped Self-Distillation, a self-evolution fram…
ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometry
Yuhan Liu, Yuxuan Ou, Ruoxi Su +2
Speech delivery varies with both the requested paralinguistic attribute and the linguistic content. We introduce ParaGeo, a matched-content decomposition of paralinguistic variatio…
Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs
Yunbo Long, Haolang Zhao, Ge Zheng +1
LLM-based multi-agent systems have been widely adopted for knowledge retrieval and report generation, synthesizing known information through web search and textual reasoning. Howev…
Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning
Yunbo Long, Ge Zheng, Liming Xu +1
Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic data to support operational…
Self-Evolving Multi-Agent Systems via Decentralized Memory
Guangya Hao, Yunbo Long, Zhuokai Zhao
Self-evolving multi-agent systems (MAS) have emerged as a promising route to LLM agents that continually improve from experience, with persistent memory at their foundation. Howeve…
Self-Policy Distillation via Capability-Selective Subspace Projection
Guangya Hao, Yitong Shang, Yunbo Long +2
Self-distillation bootstraps large language models (LLMs) by training on their own generations. However, existing methods either rely on external signals to curate self-generated o…
Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
Yunbo Long, Tejumade Afonja, Guangya Hao +2
Tabular language models can generate synthetic tables by modeling rows as token sequences, but they are typically trained once with supervised fine-tuning and then used as static s…
EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation
Yunbo Long, Haolang Zhao, Lukas Beckenbauer +2
Post-trained LLMs are often optimized to produce helpful, polite, and accommodating responses. In adversarial negotiation, however, such behavior can become a vulnerability: emotio…
VeriTrace: Evolving Mental Models for Deep Research Agents
Haolang Zhao, Yunbo Long, Lukas Beckenbauer +1
Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but lea…
EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration
Yunbo Long, Yuhan Liu, Liming Xu
Large language models (LLMs) has been widely used for automated negotiation, but their high computational cost and privacy risks limit deployment in privacy-sensitive, on-device se…
AI-Supervisor: Autonomous AI Research Supervision via a Persistent Research World Model
Yunbo Long
Existing automated research systems operate as stateless, linear pipelines -- generating outputs without maintaining any persistent understanding of the research landscape they nav…
Topological Federated Clustering via Gravitational Potential Fields under Local Differential Privacy
Yunbo Long, Jiaquan Zhang, Xi Chen +1
Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy…
EQ-Negotiator: Dynamic Emotional Personas Empower Small Language Models for Edge-Deployable Credit Negotiation
Yunbo Long, Yuhan Liu, Alexandra Brintrup
The deployment of large language models (LLMs) in automated negotiation has set a high performance benchmark, but their computational cost and data privacy requirements render them…
EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation
Yunbo Long, Liming Xu, Lukas Beckenbauer +2
Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in \textit{complex}, \textit{multi-turn} negotiations, o…
SynDelay: A Synthetic Dataset for Delivery Delay Prediction
Liming Xu, Yunbo Long, Alexandra Brintrup
Artificial intelligence (AI) is transforming supply chain management, yet progress in predictive tasks -- such as delivery delay prediction -- remains constrained by the scarcity o…
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data
Yunbo Long, Liming Xu, Ge Zheng +1
Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. However, heterogeneous data across diverse regions, driven by…
Efficient and Privacy-Preserved Link Prediction via Condensed Graphs
Yunbo Long, Liming Xu, Alexandra Brintrup
Link prediction is crucial for uncovering hidden connections within complex networks, enabling applications such as identifying potential customers and products. However, this rese…
EmoDebt: Bayesian-Optimized Emotional Intelligence for Strategic Agent-to-Agent Debt Recovery
Yunbo Long, Yuhan Liu, Liming Xu +1
The emergence of autonomous Large Language Model (LLM) agents has created a new ecosystem of strategic, agent-to-agent interactions. However, a critical challenge remains unaddress…
LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion
Yunbo Long, Liming Xu, Alexandra Brintrup
Synthetic tabular data are increasingly being used to replace real data, serving as an effective solution that simultaneously protects privacy and addresses data scarcity. However,…
Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
Yunbo Long, Liming Xu, Alexandra Brintrup
Current evaluations of synthetic tabular data mainly focus on how well joint distributions are modeled, often overlooking the assessment of their effectiveness in preserving realis…
Random Walk Guided Hyperbolic Graph Distillation
Yunbo Long, Liming Xu, Stefan Schoepf +1
Graph distillation (GD) is an effective approach to extract useful information from large-scale network structures. However, existing methods, which operate in Euclidean space to g…