publications
A selected list with short digests. An asterisk (*) denotes equal contribution; see my Google Scholar for the full list.
2026
- Rethinking the Evaluation of Efficiency Methods for Multi-Agent SystemsIn Conference on Empirical Methods in Natural Language Processing (EMNLP), 2026TL;DREvaluationMulti-AgentEfficiency
Many methods claim to make LLM multi-agent systems (MAS) cheaper by pruning agents or communication edges – but do the gains really come from the method? Reported improvements are often measured under method-specific prompts and starting topologies, and many appear in settings where a single agent or even random pruning already does well. We introduce a controlled, MAS-demanding diagnostic benchmark that evaluates representative efficiency methods under shared model, agent, and runtime settings across topology, scale, depth, and tool use, and find that many reported gains are setup-dependent – arising from structural collapse, disabled tool pathways, or easy pruning rather than genuine efficiency improvements.
- PreprintSPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform CodingarXiv preprint arXiv:2608.07915, 2026TL;DREfficiencyQuantizationLong Context
As LLMs read longer inputs, inference memory is dominated by the KV cache, and the usual fix – quantizing every stored value to the same low precision – stalls at a 2-bit cliff, where four levels are too few for the cache’s outlier-heavy values. Spending more bits on the channels that matter would help, but the raw cache offers no handle: its channels are strongly correlated, so none stands out. We show the handle appears once the cache is rotated into a coordinate system computed from its own statistics – there a small fraction of channels carries almost all the information. SPECTRA is a training-free, drop-in codec built on this observation: on Llama-3.1-8B and Qwen2.5-7B long-context benchmarks it is near-lossless at 4x compression, competitive at 8x where uniform quantization has collapsed, and reaches up to 12x.
- PreprintWiSP: A Working-Set View of Mixture-of-Experts Serving on Extremely Low-Resource HardwarePreprint (work in progress), 2026TL;DREfficiencySystemsMoE
Mixture-of-Experts models put most of their parameters in experts, yet only a few fire per token – and on commodity GPUs the usual layer-level CPU offload streams every expert across PCIe on each forward pass, throwing away MoE’s sparsity. WiSP treats low-resource MoE serving as a GPU working-set problem, paging in only the experts a workload actually reuses, and reaches up to 1.95x the decode throughput of static offload at the same memory budget with byte-identical outputs. A companion allocator, MV-WSA, splits VRAM between experts and the KV cache by equalizing marginal latency benefit per byte.
- Under ReviewRecognizing and Restructuring Latent Experts for Large Language Model CompressionUnder review, 2026TL;DREfficiencyCompressionMoE
Static compression like pruning permanently removes parameters deemed globally unimportant – but in LLMs, importance is highly input- and task-dependent, so static removal is brittle under distribution shift. Inspired by conditional computation in Mixture-of-Experts, MoRE (Mixture-of-Recognized-Experts) is a training-free paradigm that turns static redundancy in pretrained Transformers into input-dependent sparse computation. We find that pretrained MLP layers have a hybrid structure – a shared backbone that is always active plus input-dependent residual components that enable specialization – and MoRE exploits it through training-free routing, activating different subsets of pretrained parameters per input without modifying any weights. Across model families and benchmarks, MoRE matches or beats structured pruning on accuracy–efficiency trade-offs while staying robust under distribution shift and knowledge-intensive QA.
2025
- Flexible Group Count Enables Hassle-Free Structured PruningIn IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025TL;DREfficiencyPruning
Structured (grouped-kernel) pruning normally forces you to pre-commit to a rigid group count, making it painful to reuse across architectures. We make the group count flexible and hassle-free, so a single procedure prunes convolutional models cleanly while preserving the dense, hardware-friendly structure that delivers real speedups.
- Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsTransactions on Machine Learning Research (TMLR), 2025TL;DRSurveyEfficiencyLLM
Long chain-of-thought reasoning boosts accuracy but burns a lot of test-time compute – the “overthinking” phenomenon, where a model may spend hundreds of tokens on a trivial question. This is the first structured survey of efficient reasoning for LLMs, organizing the fast-growing literature into model-, output-, and prompt-level directions. It has been widely covered by AI media (e.g., 新智元, 科技行者, and Towards AI) and reshared across X and RED, with citations climbing fast.
- Redefining Language Model Evaluation with Ground-Truth-Free Ranking FrameworksIn Findings of the Association for Computational Linguistics (ACL Findings), 2025TL;DREvaluationLLM
Evaluating LLMs usually leans on ground-truth labels that are expensive and often unavailable. We rank models without any ground truth, using a reasoning-consistency framework that yields reliable, label-free rankings well correlated with traditional benchmarks.
- In-Progress: Structured Pruning in the Wild: Benchmarking Practical Robustness Under Real-World CorruptionsIn IEEE Security and Privacy Workshops (SPW), 2025TL;DRRobustnessEvaluation
Structured-pruned models often look fine on clean data but degrade under real-world corruptions. We benchmark the practical robustness of pruned models under realistic corruptions, surfacing when and how pruning quietly trades away reliability.
- arXivThe Science of Evaluating Foundation ModelsarXiv preprint arXiv:2502.09670, 2025TL;DREvaluation
Foundation-model evaluation is fragmented and frequently ad hoc. This work lays out principles and a structured framework for evaluating foundation models rigorously – what to measure, how, and why – toward assessments that are trustworthy and comparable.
- Context Matching is not Reasoning: Assessing Generalized Evaluation of Generative Language Models in Clinical Settingsnpj Digital Medicine, 2025TL;DREvaluationHealthcare
Generative LLMs can appear competent in clinical QA by matching surface context rather than genuinely reasoning. Through a generalized clinical evaluation, we show that strong benchmark numbers can mask a real lack of reasoning in medical settings.
2024
- GNNs Also Deserve Editing, and They Need It More Than OnceIn International Conference on Machine Learning (ICML), 2024TL;DRRobustnessGraphs
Model editing – fixing mistakes postmortem – is common everywhere except graphs, which are theoretically unfriendly to edits. We trace the root cause to over-accommodation of editing targets and present the first graph-editing method that lives up to real-life scrutiny of sequential edits.
2023
- One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel PruningIn Advances in Neural Information Processing Systems (NeurIPS), 2023TL;DREfficiencyRobustness
Pruned convolutional models can match clean accuracy yet collapse under perturbed inputs. We fix this essentially for free with a theoretically motivated grouped-kernel-pruning procedure that leverages kernel smoothness – a property known to promote frequency robustness – at no extra cost.