1 citations · 1 across the 31 of their papers we have counts for
36 papers
Topology optimization of shipboard components for Navy shock certification via the Dynamic Design Analysis Method
Miguel A. Aguilo
Shipboard equipment must survive the transient loads of underwater explosions. The U.S. Navy certifies such equipment with the Dynamic Design Analysis Method (DDAM), a modal respon…
Adaptive Decoding Methods for Token-Efficient Reinforcement Learning
Zhaonan Li, Zelei Cheng, Amritansh Mishra +5
Rollout decoding affects both which trajectories are used for reinforcement learning with verifiable rewards (RLVR) and how many completion tokens are spent collecting them. We stu…
Beyond Corrected Actions: Training LLM Agents on Textual Critiques
Thomas Zeng, Zelei Cheng, Mingyang Zhou +3
Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequences of actions. A common a…
AdaGuard: Enhancing Safety and Policy Compliance with Reasoning-Enabled LLM-As-A-Judge Guardrails
Melissa Kazemi Rad, Sihui Dai, Isha Slavin +6
Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints. Current guard…
When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models
Berkcan Kapusuzoglu, Connor Pryor, Sangwoo Cho +4
Expert pruning reduces the memory and serving cost of Mixture-of-Experts (MoE) models by removing low-importance experts identified by the router, assuming router probabilities pro…
The Rise of Verbal Reinforcement Learning
Kshitij Tayal, Arun Sharma, Genta Indra Winata +2
Natural language is emerging as a primary feedback channel for improving language agents, capable of conveying intent, preferences, and causal structure in forms interpretable by b…
AREAs-Lab: An Interactive Environment for AI-driven Requirement Elicitation for AI Systems
Pengshan Cai, Zihao Zhang, Ting Jin +8
Building effective AI systems increasingly depends on writing high-quality task requirements, yet users often struggle to articulate the constraints, preferences, and edge cases th…
StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
Zefang Liu, Chenyang Zhu, Sangwoo Cho +3
Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We p…
Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks
Zelei Cheng, Amritansh Mishra, Sambit Sahu +1
Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcement learning (RL) is a natural f…
Know When to Stop: Segment-Level Credit Assignment for Reducing Overthinking
Chia-Hsuan Lee, Sihui Dai, Mingyang Zhou +5
Reasoning language models frequently overthink: generating extended chains of behaviors such as hedging, approach abandonment, and self contradiction that consume tokens without im…
Structured Thoughts For Improved Reasoning And Context Pruning
Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu +5
Large language models (LLMs) excel at generating long chains of thought, but long reasoning traces are often verbose and memory-inefficient. In this work, we introduce Structured T…
SEAD: Competence-Aware On-Policy Distillation via Entropy-Guided Supervision
Chia-Hsuan Lee, Zelei Cheng, Yu Wang +4
On-policy distillation (OPD) has a property absent in offline distillation and RL: teacher supervision quality depends on student competence. Incoherent rollouts yield noisy gradie…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Sangwoo Cho, Kushal Chawla, Pengshan Cai +4
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, an…
SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation
Chenyang Zhu, Jiayu Yao, Kushal Chawla +10
As autonomous agents tackle increasingly complex multi-step, multi-agent tasks, their execution trajectories have scaled beyond the constraints of even the largest context windows.…
Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
Paresh Dashore, Shreyas Kulkarni, Uttam Gurram +5
Large language model (LLM)-based multi-agent systems demonstrate strong performance on complex reasoning and task execution, enabling broad enterprise applications. However, produc…
A History-Aware Visually Grounded Critic for Computer Use Agents
Jaewoo Lee, Zaid Khan, Archiki Prasad +7
Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in compl…
T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin +12
Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain li…
MemGym: a Long-Horizon Memory Environment for LLM Agents
Wujiang Xu, Yu Wang, Kai Mei +8
Memory is a central capability for LLM agents operating across long-horizon tasks. Existing memory benchmarks predominantly evaluate retention of personalized information in multi-…
AVSD: Adaptive-View Self-Distillation by Balancing Consensus and Teacher-Specific Privileged Signals
Duy Nguyen, Hanqi Xiao, Archiki Prasad +7
Self-distillation enables language models to learn on-policy from their own trajectories by using the same model as both student and teacher, with the teacher being conditioned on…
CoT-Guard: Small Models for Strong Monitoring
Nirav Diwan, Han Wang, Berkcan Kapusuzoglu +6
Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code generation tasks. While large m…
Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?
Chia-Hsuan Lee, Mingyang Zhou, Renkun Ni +6
Preference optimization methods such as DPO and KTO are widely used for aligning language models, yet little is understood about what properties of preference data drive downstream…
Know Thy Reasoner: Not All Language Models Explore Alike
Moulik Choraria, Argyrios Gerogiannis, Anirban Das +4
Compute scaling for LLM reasoning trades off exploring solution approaches (\emph{breadth}) against refining promising ones (\emph{depth}), yet why a given trade-off works, and why…
DIAL-SUMMER: A Structured Evaluation Framework of Hierarchical Errors in Dialogue Summaries
Sahana Ramnath, Nima Chitsazan, Mingyang Zhou +8
Dialogues are a predominant mode of communication for humans, and it is immensely helpful to have automatically generated summaries of them (e.g., to revise key points discussed in…
SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation
Yuxin Yang, Gangda Deng, Ömer Faruk Akgül +6
Retrieval-Augmented Generation (RAG) grounds large language model outputs in external evidence, but remains challenged on multi-hop question answering that requires long reasoning.…