Papers by Ibne Farabi Shihab
Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics (2026.acl-long)
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| Challenge: | Existing methods for activation compression are gradient-blind and preserve high-variance dimensions regardless of their impact on factual knowledge preservation. |
| Approach: | They propose a knowledge-aware compression framework that models activation-gradient coupling by directly modeling subspaces. |
| Outcome: | The proposed framework preserves 6–8% more accuracy on knowledge-intensive benchmarks compared to variance-based methods at 50% rank reduction. |
Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models increasingly require structured inference, says aaron sagar . meta-learning learns universal constraint propagation policies without task-specific training . standard schedulers are inexpensive but myopic, because they optimize local effects . |
| Approach: | MetaJuLS learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. |
| Outcome: | MetaJuLS achieves 1.5-2.0 speedups over GPU-optimized baselines while maintaining accuracy within 0.2% of state-of-the-art parsers. |
Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments (2025.emnlp-main)
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| Challenge: | State-space models struggle with quadratic computational complexity, limiting their use in long-context tasks and resource-constrained input data. |
| Approach: | They propose a pruning framework specifically tailored for Mamba that reduces parameter counts by 70% with only a 3–9% drop in performance. |
| Outcome: | The proposed pruning framework achieves up to 70% parameter reduction with only a 3–9% drop in performance. |
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are used as action proposers in reinforcement learning (RL) but they are expensive and require prohibitive computational costs. |
| Approach: | They propose a cache-efficient framework for Bayesian RL that leverages large language models as action proposers and optimizes meta-learning based on policy performance to enable efficient inference across text-based games and robotic control tasks. |
| Outcome: | The proposed framework achieves 3.8–4.7 reduction in LLM queries and 4.0–12.0 lower median latencies (85–93ms on consumer hardware) while retaining 96–98% of the uncached policy’s performance. |
Detecting Proxy Gaming in RL and LLM Alignment via Evaluator Stress Tests (2026.findings-acl)
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| Challenge: | Proxy optimization is a challenge spanning reinforcement learning and LLM alignment. |
| Approach: | They propose an invariance-based framework that detects proxy gaming by separating exploitable sensitivity from content-driven improvements using semantic validity audits. |
| Outcome: | The proposed framework achieves 78.4% precision and 81.7% recall across 15 environments and 5 algorithms. |