1 citations · 1 across the 16 of their papers we have counts for
28 papers
CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades
Miaobo Hu, Shuhao Hu, Xiaobo Guo +4
We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read semantics, binds successful read…
FedRIC: Heterogeneity-Aware Federated Learning with Hard Deadline Constraints in Open RAN
Adhwaa Al-Chaab, Ayman Younis, Dario Pompili
Open Radio Access Networks (O-RANs) bring intelligence to disaggregated RANs through virtualized platforms and open interfaces, but the stringent latency budgets of the RAN Intelli…
Quantizer Redundancy and Exact Index Recoding in a Frozen Neural Audio Codec
Youssef Hemimy
Residual scalar quantization can produce highly dependent indices when successive stages reuse identical, unscaled grids. This case study examines that behavior in a frozen neural…
FSPO: Policy-Consistent Risk and Pareto-Feasible Control for Budgeted LLM RL Post-Training
Miaobo Hu, Shuhao Hu, Xiaobo Guo +4
Adaptive LLM reinforcement-learning post-training changes multiple training actuators online, including rollout temperature, group size, clipping, KL regularization, verifier alloc…
ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Adaptive multi-verifier systems are commonly compared through endpoint quality-cost gaps, even when the verifier catalog, availability, accounting, information filtration, or score…
FAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and downstream learner utility are d…
ContractRL: Shielded Group-Relative Policy Optimization for Auditable Tool-Call Repair
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Structured tool calls often fail after only a small number of fields violate a schema or an execution contract. Regenerating the complete object enlarges the action surface and mak…
Actions with Receipts: Jointly Binding Claims, Evidence, and Execution for Replayable Tool-Agent Auditing
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Tool-using agents can expose citations and execution logs while leaving a critical association unaudited: whether the claim shown to a user is the claim emitted by the committed ex…
VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Repeated verifier calls are useful only when they contribute conditional information. We introduce VStress, an auditable replay contract, and VStress-CA, a correlation-aware alloca…
RSD-Poker: Structure-Adaptive and Shift-Robust Risk-Utility Certification for Residual Policies in Imperfect-Information Games
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Residual policy adaptation provides a lightweight way to modify a strong reference policy, but a shared scale and a fixed subgroup partition can hide heterogeneous degradation and…
Audit-First VAPO: Risk-Certified Selective Updates under Imperfect Verification
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Imperfect verifiers can assign a harmful update direction even when clipping and regularization bound its magnitude. We introduce Audit-First VAPO, which separates discrete directi…
OSCC: Certified Observation-Safe Coupling Optimization for Gradient-Noise Control in Imperfect-Information Learning
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Coupled rollouts can reduce the noise of counterfactual action comparisons, but two issues prevent standard common-random-number constructions from serving as a general learning pr…
QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides
Zhengyang Zhuge, Hao Yu, Xin Wang +4
Rollout generation is a major bottleneck in Reinforcement Learning (RL) for Mixture-of-Experts (MoE) Large Language Models, motivating low-precision rollout acceleration such as FP…
Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
Sophia Xiao Pu, Zhaotian Weng, Chengzhi Liu +4
Self-play reinforcement learning trains language models on their own generated tasks, co-evolving a proposer and solver without human labels. Recent systems report strong reasoning…
ECPO: Evidence-Coupled Policy Optimization for Verifier-Audited Candidate Ranking
Miaobo Hu, Shuhao Hu, BoKun Wang +5
Ranking systems used for decision support should expose evidence that can be audited independently of the model's ranking scores. We study a closed-roster setting in which a policy…
Towards Context-Invariant Safety Alignment for Large Language Models
Yixu Wang, Yang Yao, Xin Wang +4
Preference-based post-training aligns LLMs with human intent, yet safety behavior often remains brittle. A model may refuse a harmful request in a standard prompt but comply when t…
AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback
Miaobo Hu, Shuhao Hu, Bokun Wang +5
Reinforcement learning improves LLM reasoning, but PPO/GRPO typically use fixed clipping and decoding temperature, which makes training brittle and tuning-heavy. We propose Adaptiv…
SAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction
Miaobo Hu, Shuhao Hu, Bokun Wang +5
Multimodal IE in social media is difficult because a post may attach multiple images that are weakly related, redundant, or even misleading with respect to the text. In this settin…
TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
Xin Wang, Yixu Wang, Jiaming Zhang +6
Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to imperceptible adversarial perturbation…
DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models
Ye Sun, Xin Wang, Jiaming Zhang +7
While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversarial attacks. However, tradition…
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan +5
Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing co…
The Impact of Quantization on Large Reasoning Model Reinforcement Learning
Medha Kumar, Zifei Xu, Xin Wang +1
Strong reasoning capabilities can now be achieved by large-scale reinforcement learning (RL) without any supervised fine-tuning. Although post-training quantization (PTQ) and quant…
Early Attentive Sparsification Accelerates Neural Speech Transcription
Zifei Xu, Sayeh Sharify, Hesham Mostafa +3
Transformer-based neural speech processing has achieved state-of-the-art performance. Since speech audio signals are known to be highly compressible, here we seek to accelerate neu…
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
Utkarsh Saxena, Sayeh Sharify, Kaushik Roy +1
Post-training quantization (PTQ) of large language models (LLMs) holds the promise in reducing the prohibitive computational cost at inference time. Quantization of all weight, act…