1 citations · 4 across the 59 of their papers we have counts for
73 papers
Margin Certificates for Voting Classifiers under Mixing Dependence
Adria Binte Habib, Ibne Farabi Shihab
Margin bounds explain why weighted votes generalize after training error vanishes, but dependent observations require concentration arguments that keep the dependence model and fai…
Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth
Adria Binte Habib, Ibne Farabi Shihab
Calibrating a conformal prediction set at each early exit does not preserve coverage once a data-dependent exit is selected. We construct one terminal path score from prefix maxima…
From Spectral Saliency to Finite Deletion: Evaluating Selective Language Model Edits
Ibne Farabi Shihab, Joyanta Jyoti Mondal
Deleting singular components offers a precise way to remove a capability from a language model, yet the first-order spectral saliency used to rank components predicts only a local…
One-Step Curvature Probes Miss the Fitting Operator: Retained Capacity and Terminal Null-Space Correction for Continual Learning
Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Ibne Farabi Shihab, Md Najmus Swaqeeb
A one-step curvature probe evaluates an initial direction, whereas continual learners are judged after reaching comparable new-task fit. In an overparameterized linearization, proj…
When Can Prefixes Compile LoRA? Exact Resource-Capped Tests for Frozen Attention
Joyanta Jyoti Mondal, Ibne Farabi Shihab
Can a fixed continuous prefix replace a given low-rank adapter while the attention head stays frozen? In this research, we show that the answer depends on the adapter's target thro…
Certification Frontiers for Gaussian LoRA: Independent Priors, Posterior Risk, and Prediction-Preserving Balancing
Joyanta Jyoti Mondal, Ibne Farabi Shihab
Post-hoc Bayesian fine-tuning places Gaussians around trained low-rank adapters, yet a calibrated posterior does not by itself yield a useful generalization certificate. Such a pos…
Before Answering: Evidence Sufficiency under Size-Matched Memory Construction
Joyanta Jyoti Mondal, Md. Shifatul Ahsan Apurba, Mridul Banik +2
Agents that answer questions from compressed or retrieved memory must recognize when the evidence a query needs is no longer in memory. Benchmarks for this task usually create insu…
StepCOPS: Closed-Testing Lower-Tail Certificates for Language-Model Policy Selection
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Post-training pipelines must select one language-model policy from many checkpoints, prompts, and decoding rules. Mean evaluator scores can conceal rare failures, whereas simultane…
When Is Language-Model Advice Worth Buying? A Matched-Budget Study of Predictive Gating in Reinforcement Learning
Ibne Farabi Shihab, Md Najmus Swaqeeb, Abu Sa-Adat Mohamed Moon-Im Al Ahsan +1
Language-model advice can accelerate reinforcement learning, but every call has a price, and a returned suggestion may be unparsable, unsafe to execute, or no better than the agent…
BLADE: Distilled LLM Regularization for Calibrated Knowledge Graph Completion
Ibne Farabi Shihab, Rabeya Bosri Tamanna, Abdo El Karaky +2
Knowledge graph completion models optimize ranking, although many downstream applications require calibrated probabilities. We present BLADE, a variational model that separates lat…
Spectral-Guided Diffusion: Accelerating Inference via Static Spectral Layer Scheduling
Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj Sharma
Diffusion inference repeatedly evaluates the same large network. We ask whether pretrained weights alone can identify residual branches that need not be recomputed throughout the t…
Task-Aware Spectral Pruning: A Mixture-of-Masks Framework for Efficient LLM Inference
Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1
Static pruning imposes one sparse structure on every prompt, even though reasoning, retrieval, generation, coding, and translation can depend on different parts of a language model…
FlowNeg: GFlowNet-Guided Diverse Hard Negative Sampling for Knowledge Graph Embedding
Ibne Farabi Shihab, Naoshin Anzum Hridi, Joyanta Jyoti Mondal
Negative sampling determines whether a knowledge graph embedding (KGE) model learns from informative counterexamples or wastes updates on implausible corruptions. Uniform negatives…
Candidate-Label Saliency for Efficient Conformal Pruning of Text Classifiers
Ibne Farabi Shihab, Adria Binte Habib, Anuj Sharma +1
Recalibration can preserve conformal coverage after pruning while enlarging prediction sets and leaving more alternatives for downstream decisions. Directly searching for deletions…
CS-WCP: Robust Conformal Sets for LLM-Judge Traffic Shifts with Uncertain Group Proportions
Ibne Farabi Shihab, Fariya Afrin
Prediction sets built from an LLM judge can undercover when deployment traffic changes the prevalence of task or policy groups. Weighted conformal prediction is exact under covaria…
When Accuracy Gaps Fail to Certify: Auditing Cross-Domain Recalibration of LLM Judges
Fariya Afrin, Ibne Farabi Shihab
A scalar recalibration map fitted for an LLM judge on one task can fail when the task distribution changes, but the source-target accuracy gap is often treated as a proxy for that…
Spectral Tail Interventions in Decoder-Only Language Models: Reasoning-Sensitive Weight Structure from Controlled Surgery
Ibne Farabi Shihab, Sanjida Akhter, Md Najmus Swaqeeb +2
Weight-space structure often correlates with language-model behavior, but correlation alone does not establish computational involvement. We study concentrated upper spectral tails…
The Cost of Adaptivity: Matching Lower Bounds Across Learning Problems
Ibne Farabi Shihab, Adria Binte Habib
Adaptive procedures must work without nuisance information an oracle may use, such as a gradient scale or smoothness index, and robust procedures may have to answer queries whose c…
Stateful CARS: Exact Cross-History Reuse for Policy-Constrained LLM Agents
Ibne Farabi Shihab, Md Najmus Swaqeeb, Abu Sa-Adat Mohamed Moon-Im Al Ahsan
Tool-using language-model agents face constraints whose meaning changes with observations and prior actions. We study exact sampling from the model distribution conditioned on a ha…
Opportunity Is Not Realizability: Selection-Valid Diagnostics for Multi-LLM Routing
Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Md Najmus Swaqeeb
Oracle routing measures how much a pool of language models could gain from per-query selection, but the diagnostic has two flaws: testing against a best fixed model selected on the…
Quality-Diversity Stress Tests for Process Reward Models:What Archive Coverage Can and Cannot Certify
Ibne Farabi Shihab, Fariya Afrin
Process reward models (PRMs) score intermediate reasoning steps and are widely used for search, ranking, and training, but optimization can exploit these learned proxies by increas…
Evaluator Ensembles Under Reward Hacking: Covariance Geometry and Finite-Search Guarantees
Fariya Afrin, Ibne Farabi Shihab
Language-model judges and reward models enable scalable supervision, but finite optimization can exploit evaluator errors rather than improve response quality. We characterize this…
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…