Papers by Emad Barsoum
Self-Taught Agentic Long Context Understanding (2025.acl-long)
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Yufan Zhuang, Xiaodong Yu, Jialian Wu, Ximeng Sun, Ze Wang, Jiang Liu, Yusheng Su, Jingbo Shang, Zicheng Liu, Emad Barsoum
| Challenge: | Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs. |
| Approach: | They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks. |
Agent Laboratory: Using LLM Agents as Research Assistants (2025.findings-emnlp)
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Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, Emad Barsoum
| Challenge: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Approach: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Outcome: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference (2025.naacl-long)
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Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum
| Challenge: | Existing methods such as Medusa lack adequate information interaction between different drafting heads. |
| Approach: | They propose an enhanced speculative decoding framework that builds upon Medusa and integrates a drafting block capable of parallel inference. |
| Outcome: | The proposed framework outperforms Medusa in terms of head accuracy and latency. |
AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection (2026.findings-acl)
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| Challenge: | Existing routing strategies rely on static heuristics or external controllers to optimize performance. |
| Approach: | They propose a framework that leverages intrinsic generation confidence to estimate solvability. |
| Outcome: | Empirical results show that confidence-driven selection yields favorable Pareto frontier . computational cost of state-of-the-art large language models remains a key barrier to scalable deployment . |
Theory-optimal Quantization Based on Flatness (2026.acl-long)
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| Challenge: | Recent approaches to quantization of Large Language Models (LLMs) have been widely adopted due to activation outliers, which degrade model performance especially at lower bit precision. |
| Approach: | They propose a new metric for quantization that strategically distributes outlier magnitudes across matrix dimensions via optimized diagonal operations. |
| Outcome: | The proposed framework achieves less than 1% accuracy drop in W4A4 quantization on the LLaMA-3-8B model and reduces the performance gap by 39.1% on the more challenging W2A4KV16 model. |
TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games (2025.emnlp-main)
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| Challenge: | Recent advances in large reasoning models (LRMs) have driven significant breakthroughs across various reasoning tasks including deductive, arithmetic, commonsense, relational, and symbolic reasoning. |
| Approach: | They propose a programmatic approach to evaluate basic strategic, spatial, and logical reasoning abilities in large reasoning models through four two-player Tic-Tac-Toe-style games that humans can effortlessly solve from a young age. |
| Outcome: | The proposed model performs 41% lower on TTT-Bench than MATH 500 and AIME 2024 models, while the larger models perform better on longer reasoning traces. |
DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning (2026.findings-acl)
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Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun, Jialian Wu, Xiaodong Yu, Ze Wang, Chenliang Xu, Emad Barsoum, Zicheng Liu
| Challenge: | Multimodal large language models (MLLMs) have made rapid progress in perception and alignment, but their reasoning ability often lags behind strong text-only LLMs. |
| Approach: | They propose a method that transfers reasoning knowledge in the gradient space while preserving multimodal alignment. |
| Outcome: | Experiments on multimodal reasoning benchmarks show that DRIFT outperforms naive merging and standard SFT. |
DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large Language Models (2024.emnlp-industry)
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| Challenge: | Quantization-aware Training (QAT) is a popular technique for reducing memory usage and improving computational efficiency in large language models. |
| Approach: | They propose a weight-decomposed low-rank quantization-aware training approach that integrates QAT with a group-specific quantization magnitude adjustment. |
| Outcome: | The proposed method outperforms the state-of-the-art method on LLaMA and LLama2 models. |
Enhancing One-Shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism (2025.coling-main)
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Guanchen Li, Xiandong Zhao, Lian Liu, Zeping Li, Yixing Xu, Dong Li, Lu Tian, Jie He, Ashish Sirasao, Emad Barsoum
| Challenge: | Pre-trained language models (PLMs) are robust in contextual understanding but their considerable size incurs significant computational and storage costs. |
| Approach: | They propose a Sparse-Dense-Sparse pruning framework to prune PLMs . they prune less critical connections using conventional pruning methods . |
| Outcome: | The proposed pruning framework outperforms SparseGPT and Wanda under identical sparsity. |
MoEC: A Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control (2026.acl-long)
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| Challenge: | Existing systems rely on a monolithic policy to execute subgoals across varying contexts, causing inconsistent outcomes and scaling only partially mitigates. |
| Approach: | They propose a memory-routed mixtureof-experts controller for Adaptive Minecraft Control that routes via a subgoal-indexed expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation. |
| Outcome: | The proposed controller shows significant gains in adaptability, robustness, and execution consistency over strong baselines. |
TaDA: Training-free recipe for Decoding with Adaptive KV Cache Compression and Mean-centering (2025.acl-industry)
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| Challenge: | key-value caches in large language models consume memory, posing a major challenge for scalable deployment. |
| Approach: | They propose a training-free recipe for KV cache compression with quantization precision that adapts to error sensitivity across layers and a mean centering to eliminate separate outlier handling. |
| Outcome: | The proposed technique reduces the KV cache memory footprint to 27% of the original 16-bit baseline while achieving comparable accuracy. |