35 papers
When Is Benchmark Contamination Detectable? Information Limits and Power-Calibrated Audits
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Behavioral contamination detectors can return "no evidence" either because a benchmark is clean or because the audit has little power. We formalize this distinction for a benchmark…
Private Anytime Selective-Risk Certification for Federated Retrieval-Augmented Generation: Guarantees and Empirical Limits
Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma
Selective-risk certificates promise that accepted outputs meet a declared error target. We develop Fed-SRC, a score-agnostic certificate for federated, differentially private, adap…
CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning
Ibne Farabi Shihab, Sanjeda Akter, Abu Sa-Adat Mohamed Moon-Im Al Ahsan +2
Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can r…
EST-PRM: Stress-Testing Process Reward Models Before They Become Load-Bearing
Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1
Process reward models (PRMs) are widely used in language-model training with dense step-level supervision. They assume PRM scores are stable proxies for step correctness under labe…
Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG
Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1
As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large language models may still priori…
Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a common structural pattern in n…