11 papers
TxnQA: Benchmarking Analytical Reasoning and Evidence Grounding over Transaction Event Sequences
Rabimba Karanjai, Yiming Bian, Yun-Shiuan Chuang +9
Analyzing merchant transaction histories requires dynamic temporal filtering, entity resolution, and aggregation over irregular event streams--operations poorly captured by static…
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
Yun-Shiuan Chuang, Yirou Ge, Yixi Li +6
A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when…
CM-DPO: Constraint-Margin Direct Preference Optimization for LLM Planning
Rabimba Karanjai, Qun Gu, Hemanth Hegadehalli Madhavarao +8
Direct Preference Optimization (DPO) treats all constraint violations equally: a 1,000 overshoot induce the same training signal. It is also susceptible…
State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs
Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu +10
Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings…
MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai +6
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers. This openness introduces a security misalignment: agents implicitly trus…
Ada-RS: Adaptive Rejection Sampling for Selective Thinking
Yirou Ge, Yixi Li, Alec Chiu +8
Large language models (LLMs) are increasingly being deployed in cost and latency-sensitive settings. While chain-of-thought improves reasoning, it can waste tokens on simple reques…
Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents
Yun-Shiuan Chuang, Chaitanya Kulkarni, Alec Chiu +8
Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Benchmarks for these agents must bo…
NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agent
Sudhanshu Garg, Andrew Wang, Chaitanya Kulkarni +11
We present the development and optimization of PayPal's Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal plat…
Backdoor Collapse: Eliminating Unknown Threats via Known Backdoor Aggregation in Language Models
Liang Lin, Miao Yu, Moayad Aloqaily +5
Backdoor attacks are a significant threat to large language models (LLMs), often embedded via public checkpoints, yet existing defenses rely on impractical assumptions about trigge…
Automating Personalization: Prompt Optimization for Recommendation Reranking
Chen Wang, Mingdai Yang, Zhiwei Liu +4
Modern recommender systems increasingly leverage large language models (LLMs) for reranking to improve personalization. However, existing approaches face two key limitations: (1) h…
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
Linsey Pang, Amir Hossein Raffiee, Wei Liu +1
Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. It has since been found that moving beyond only using item ID and positi…