Accepted Papers

We received a large number of high-quality submissions and are pleased to share the full list of accepted papers below, organized by poster session. Each paper links to its OpenReview page.

Poster Session 1

• Improving Cascade Routing for Structured Attribute Generation with Heterogeneous Confidence Fatemeh Mansoori, Andrea Scarinci, Aditya Aggarwal, Suleiman A. Khan, Ashwin Chandramouli

• Pruning and Distilling Mixture-of-Experts into Dense Language Models Junhyuck Kim, Jihun Yun, Haechan Kim, Gyeongman Kim, Joonghyun Bae, Jaewoong Cho

• BASTION: Budget-Aware Speculative Decoding with Tree-structured Block Diffusion Drafting Soowon Oh, Nam Cao, Yujin Kim, Hojung Jung, Huzama Ahmad, Sangmin Bae, Se Young Yoon

• Relaxed On-Policy Distillation: Selective Credit Allocation for Scaling Reasoning Efficiently Jongwoo Ko, Sara Abdali, Young Jin Kim, Tianyi Chen, Pashmina Cameron

• KVgrad: Query-Agnostic KV Cache Eviction via Gradient-based Global Importance Scoring Jihwan Kwak, Sunghwan Joo, Jung Yoon Hwang, Jaeseok Byun, Taesup Moon

• Latent Cache Flow: Model-to-Model Communication Without Text Maximillian Rossi, Prajwal Raghunath, Eugene Wu

• EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts Minseo Kim, Minjae Lee, Seunghyuk Oh, Kevin Galim, Donghoon Kim, Coleman Richard Charles Hooper, Harman Singh, Amir Gholami, Hyung Il Koo, Wonjun Kang

• Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning Mujtaba farhan, Ashwinee Panda, Maheep Chaudhary, Sean Wu

• On the Optimal Reasoning Length for RL-Trained Language Models Daisuke Nohara, Taishi Nakamura, Rio Yokota

• SubspacePath Pruner: Inference-time Pruning via Probe-based Representation–Parameter Coupling Zhiren Gong, Yikun Hou, Fan Wu, CHE WANG, Fuyao Zhang, Tiantong Wu, Yurong Hao, Jiaming Zhang, Yiyang Duan, Tiantong Wang, Fei Huang, Chau Yuen, Wei Yang Bryan Lim

• SRA-MoE: Output-Aware Selective Router Alignment for MoE Quantization Geonho Lee, Hancheol Park, Seunghyun Lee, Jungwook Choi, Tae-Ho Kim

• ShadowSpec: Towards Zero Speculation Overhead for Substitute Speculative Decoding Kuan-Cheng Lin, Pei-Shuo Wang, Jian-Jia Chen, Chun-Che Yang, Chi-Chih Chang, Ning-Chi Huang, Kai-Chiang Wu

• Fast Inference via Hierarchical Speculative Decoding Clara Mohri, Amir Globerson, Haim Kaplan, Yishay Mansour, Tal Schuster

• AgentKV: Phase-Aware KV Eviction for Agentic LLMs Taowen Liu, Jeffrey T. H. Wong, Can Xiao, Bowen Yang, Hao Mark Chen, Yiren Zhao

• Layer Verification Accelerates Speculative Tree Decoding Jaeyoung Cha, Hanseul Cho, Chulhee Yun

• Accelerating LLM Inference via Vector Index Based Output Embeddings Martin Loretz, Sepp Hochreiter

• Resource-Adaptive Foundation Model Reasoning via Semantic Coverage Max Ruiz Luyten, Thomas Pouplin, Mihaela van der Schaar

• Convergence-Gated Distillation for Resource-Adaptive Reinforcement Learning Agents Bruce Changlong Xu, Jay J. Park, Vivek Buch

• Low Dimensional Embeddings for Model Capability Understanding Shivam Patel, William Cocke, Gauri Joshi

• Block-Level Recursion: Adaptive Test-Time Routing in Large Language Models Kristiyan Sakalyan, Sanghwan Kim, Leo Schwinn, Quentin Bouniot, Zeynep Akata

• Characterizing self-speculative decoding approaches for accelerating LLMs Jungmin Ha, Karthik Ganesan, Anh Nguyen, Tanvir Ahmed, Andreas Moshovos

• TEAM: Temporal–Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration Linye Wei, Zixiang Luo, Pingzhi Tang, Meng Li

• Is Escalation Worth It? A Decision-Theoretic Characterization of LLM Cascades Dylan Bouchard

• LExI: Layer-Adaptive Active Experts for Efficient MoE Inference Krishna Teja Chitty-Venkata, Murali Emani

• CoupledNorm: Efficient Normalization via Shared RMS Statistics Martin Loretz, Sepp Hochreiter

• Sigmoid Attention as a Better Substrate for Learned KV Cache Eviction Isaac Li

• Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification Shaddin Dughmi, Mahdi Haghifam, Yusuf Hakan Kalayci

• Cache You Later: Post-Compression KV Repair for Long-Context Agentic LLM Inference Andrew Rusli, Shreyan Paliwal, Henry Zhang, Michael Jiao

• PRESTO: Prefix-Aligned Tree Drafting for Diffusion Speculative Decoding Zheng Wang, Zhifan Ye, Yonggan Fu, Qi Cheng, Ziyan Wang, Feng Zhu, Haozhe Zhao, Humphrey Shi, Pavlo Molchanov, Minjia Zhang

• Training Continuous Chain of Thought Models: A Tale of Two Regimes Varun Yerram, He He, Eunsol Choi

• MineDraft: A Framework for Batch Parallel Speculative Decoding Zhenwei Tang, Arun Verma, Zijian Zhou, Zhaoxuan Wu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low

• Staircase Streaming for Low-Latency Multi-Agent Inference Junlin Wang, Jue WANG, Zhen Xu, Ben Athiwaratkun, Bhuwan Dhingra, Ce Zhang, James Zou

• A Recipe for an Elastic Mixture: One Mixture-of-Experts for Every Resource Budget Chloe Chia

• Think Deep, Think Fast: Investigating Inference-Time Scaling And The Reasoning Floor Junlin Wang, Shang Zhu, Jon Saad-Falcon, Ben Athiwaratkun, Qingyang Wu, Jue WANG, Shuaiwen Leon Song, Ce Zhang, Bhuwan Dhingra, James Zou

• Learning Adaptive Reasoning Budgets via Constraint-Rectified Training Qinhang Wu, Sen Lin, Ming Zhang, Yingbin Liang, Ness Shroff

• AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows Rudrendu Kumar Paul, Sourav Nandy

• GreenMoE: Exploiting Dynamic Load Imbalance for Energy-Efficient Long-Context MoE Training Lilaiyi, Zhenheng Tang, Peijie Dong, Qiang Wang

• Leech Lattice Vector Quantization for Efficient LLM Compression Tycho F. A. van der Ouderaa, Mart Van Baalen, Paul N. Whatmough, Markus Nagel

• SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Shengkun Tang, Zekun Wang, Bo Zheng, Liangyu Wang, Rui Men, Siqi Zhang, Xiulong Yuan, Zihan Qiu, Zhiqiang Shen, Dayiheng Liu

• DREAM-MoE: Downstream Routing Error-Aware Margin-Preserving Quantization for Mixture-of-Experts Large Language Models Hancheol Park, Geonho Lee, Tae-Ho Kim

• IR3DE: A Linear Router for Large Language Models Eros Fanì, Oguzhan Ersoy

• One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training Erik Schultheis, Maximilian Kleinegger, Dan Alistarh

• Fixed-Point Reasoning: Stable and Adaptive Deep Looped Models Sajad Movahedi, Shlomo Libo Feigin, Vera Milovanović, Alexander Theus, Thomas Hofmann, Valentina Boeva, T. Konstantin Rusch, Antonio Orvieto

• Layout and Fusion Trade-offs for Mixture-of-Experts Inference under Single-Node Tensor Parallelism June Yong Yang, Inhyuk Cho, Taehyeon Kim, Yu Jin Kim, Moontae Lee

• MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models Nurbek Tastan, Stefanos Laskaridis, Karthik Nandakumar, Samuel Horváth

• CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization Soo Min Kwon, Ziteng Sun, Ananda Theertha Suresh, Himanshu Jain, Sanjiv Kumar

• Learning Adaptive LLM Decoding Huangyuan Su, Zhe Ye, Samuel Tenka, Aidan Z.H. Yang, Soonho Kong, Udaya Ghai

• DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Chenyang Song, Weilin Zhao, Xu Han, Chaojun Xiao, Yingfa Chen, Zhiyuan Liu

• Cross-Tokenizer LLM Distillation through a Byte-Level Interface Avyav Kumar Singh, Yen-Chen Wu, Alexandru Cioba, Alberto Bernacchia, Davide Buffelli

• Multi-Token Prediction via Self-Distillation John Kirchenbauer, Abhimanyu Hans, Brian R. Bartoldson, Micah Goldblum, Ashwinee Panda, Tom Goldstein

• Beyond Imitation: A Resource Adaptive Embedder that Outperforms its 14×Larger Teacher on Financial Retrieval Ailar Mahdizadeh, Aria Salari, Sohail Rajabi, Shahriar Mirabbasi, Panos Nasiopoulos, Alireza Morsali

• HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction Shengxuan Qiu, Haochen Huang, Shuzhang Zhong, Pengfei Zuo, Meng Li

• Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping Hsun-Yu Kuo, El Mahdi Chayti, Patrik Reizinger, Wieland Brendel, Martin Jaggi

• HYBRIDKV: Exploiting Head-Dominant Reconstruction for Efficient Query-Agnostic KV Cache Compression Changwoo Baek, Kyeongbo Kong

• DIPA: Difficulty-Informed Probabilistic Allocation of Test-Time Compute via Training-Free Proxies Wenyang Hu, Yao Shu, See-Kiong Ng, Bryan Kian Hsiang Low

• EntropyCache: Decoded Token Entropy Guided KV Caching for Diffusion Language Models Minsoo Cheong, Donghyun Son, Woosang Lim, Sungjoo Yoo

• Prelude: Execution-Class Aware Serving for Decision-Style LLM Inference Minzhou Pan, Yuzhou Nie, Ruilin Zhou, Yuheng Tang, Jingyang Zhang, Dawn Song, Bo Li, Wenbo Guo

• Step-Tagging Early-Stopping: Toward controlling the generation of Language Reasoning Models through black-box step monitoring Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John Kelleher

• Selective Sinkhorn Routing for Improved Sparse Mixture of Experts Duc Anh Nguyen, Huu Binh Ta, Duc-Nhuan Le, Tan Minh Nguyen, Toan Tran

• Recency/Frequency Adaptive KV Caching for Large Language Model Serving Yang Shen, Meghana Madhyastha, Robert Underwood, Bogdan Nicolae, Randal Burns

• XShare: Collaborative in-Batch Expert Sharing for Faster MoE Inference Daniil Vankov, Nikita Ivkin, Jaime Campos Salas, Kyle R. Ulrich, Xiang song, Ashish Khetan, George Karypis

• DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction Aozhong Zhang, Selcuk Gurses, Yanxia Deng, Naigang Wang, Chi-Chun Liu, Davis Wertheimer, Derrick Liu, Xin Li, Zi Yang, Felix X.-F. Ye, Penghang Yin

• TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar, Ashok Vardhan Makkuva, Hyeji Kim

Poster Session 2

• CARES: Context-Aware Resolution Selector for VLMs Moshe Kimhi, Nimrod Shabtay, Raja Giryes, Chaim Baskin, Eli Schwartz

• Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention Aryan Sood, Shantanu Acharya

• AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization Beshr IslamBouli, David Jin

• When Good Enough Is Optimal: Multiplication-Only Matrix Inversion Approximation for Quantized Gated DeltaNet Luoming Zhang, Yuwei Ren, Kuizhang, Tian Liu, Lingjuan Ge, Denghao Li, Matthew Harper Langston, Yin Huang, Weiliang Will Zeng, liang zhang

• SpiralFovea: Input-Adaptive Foveated Tokenization as a Third Lever of Resource-Adaptive Inference KyanMahajan, Mohammad Saqlain

• Hybrid Linear Attention Done Right: Efficient Distillation and Effective Architectures for Extremely Long Contexts Yingfa Chen, Zhen Leng Thai, Zihan Zhou, Zhu Zhang, Xingyu Shen, Shuo Wang, Chaojun Xiao, Xu Han, Zhiyuan Liu

• Re-evaluating Confidence Remasking in Masked Diffusion Language Models Stipe Frkovic, Metod Jazbec, Dan Zhang, Christian A. Naesseth, Ilija Bogunovic, Eric Nalisnick

• Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning Minkyu Kim, Vincent-Daniel Yun, Youngrae Kim, YoungJin Heo, Suin Cho, Seong-hun Kim, Woosang Lim, Gaeul Kwon

• Modality-Aware Block Rotation for Vision-Language-Action Model Quantization U-Yeong Kim, Suk-Ju Kang

• MoNe: Modular Neural Memory for Efficient Long Context Inference Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy, Sunghyun Park, Hyoungwoo Park, Jeongho Kim, Arash Behboodi, Kyuwoong Hwang, Sungrack Yun

• Fault Robustness of Custom Floating-Point and Integer Formats: Datatype Selection as a Reliability-Aware Compression Decision R S Haripriya, Jaynarayan T Tudu

• VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval Shahaf Wagner, Gabriele Serussi, Dan Ben Ami, Tomer Galanti, Chaim Baskin

• SparseSAM: Structured Sparsification of Activations in Segment Anything Models Hoai-Chau Tran, Chi H Nguyen, Duy Minh Ho Nguyen, Mathias Niepert, Fan Lai, Khoa D Doan

• What Matters for NVFP4 Training? A Scaling Study of Low-Precision Pre-Training Recipes Anjulie Agrusa, Andrei Panferov, Elizabeth Wei, Keith Wyss, Paul Gibbons, Erik Schultheis, Tijmen Blankevoort, Dan Alistarh

• LLM Family Expansion via Distillation and Quantization Andrei Panferov, Davit Melikidze, Dan Alistarh

• On State Reduction in Linear Attention Philipp Nazari, T. Konstantin Rusch

• Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs Nico Harder, Daniel Becking, Karsten Mueller, Wojciech Samek

• CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding Ziteng Sun, Adrian Benton, Sadik Yagiz Yetim, Samuel Kushnir, Asher Trockman, Vikas Singh, Suhas Diggavi, Ananda Theertha Suresh

• Vision Token Pruning via Query–Vision Interaction Decomposition Harshithanjani Athi, Sravan Kumar Ankireddy, Jianzhong Charlie Zhang, Hyeji Kim

• Block-Based Double Decoders Asher Labovich, Vanessa Alexander, Chaitanya Harsha, Benjamin Bradley

• Alignment Collapse Under KV Cache Quantization: A 35-Minute Audit for Quantized LLM Deployments Bruce Changlong Xu, Adarsh Kumarappan, Mu Zhou

• Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity Vincent-Daniel Yun, Junhyuk Jo, Sunwoo Lee

• Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy, Sunwoo Lee

• Fully Nested Transformers Avi Trost, Alexander Yun, John Cooper, Gabriel Orlanski, Frederic Sala

• MAGE: All-[MASK] Block Already Knows Where to Look in Block Diffusion LLM Omin Kwon, Yeonjae Kim, Doyeon Kim, Minseo Kim, Yeonhong Park, Jae W. Lee

• Zero-Shot Quantization for Vision-Language-Action Models via Trajectory Curvature and Attention Guidance Sung-hwan Han, Youngmin Yi

• MatMLA: Matryoshka Multi-Head Latent Attention Kevin Li, Berlin Chen, Caitlin Wang, Aakash Lahoti, Albert Gu, Tri Dao

• StreamAttention: Energy-Efficient and High-Utilization Attention on Systolic Hardware Olav Førland, H. T. Kung

• A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models Theo X. Olausson, Metod Jazbec, Xi Wang, Armando Solar-Lezama, Christian A. Naesseth, Stephan Mandt, Eric Nalisnick

• Neural Weight Compression for Language Models Jegwang Ryu, Minkyu Kim, Seungjun Shin, Hee Min Choi, Dokwan Oh, Jaeho Lee

• Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models Deepanshu Pandey, Nahush Lele, Arnav Chavan, Sankalp Dayal, Deepak Gupta

• COAT: COrrelation-Aware Orthogonal Transform for LLM Quantization Indranil Patra, AZHAR YOUSUF, Manu Mathew, Chandra Sekhar Seelamantula

• CLAWS: Calibration-Aware Activation Sparsity for Instruction-Tuned LLMs Noah Cylich, Karen Mosoyan, Henry Ndubuaku

• StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models Duy Minh Ho Nguyen, Tuan Anh Tran, Thuy-Duong Khanh Nguyen, Siwei Xie, Trung Quoc Nguyen, Mai Thanh Nhat Truong, Daniel Palenicek, An Thai Le, Michael Barz, Eric Hannus, TrungTin Nguyen, Tuan Quang Dam, Tran Nguyen Le, Ngan Le, Minh Nhat VU, Khoa D Doan, Vien Anh Ngo, Pengtao Xie, James Zou, Daniel Sonntag, Jan Peters, Mathias Niepert

• Speedrunning GPT3: Training an (Almost-) GPT3-175B-Quality Model in Under 10K USD Georgios Vlassis, Erik Schultheis, Matin Ansaripour, Andrei Panferov, Dan Alistarh

• Activation Quantization of Vision Encoders Needs Prefixing Registers Seunghyeon Kim, Taesun Yeom, Jinho Kim, Wonpyo Park, Kyuyeun Kim, Jaeho Lee

• QJL is 1-bit Compressive Sensing: An Equivalence and Its Consequences for KV Cache Compression in LLMs Mohammad Babakmehr

• LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models Mohammad Mozaffari, Younes Hourri, Mohammad Rastegari, Mahyar Najibi

• Vitality-Aware Compression for Efficient Image-to-Shape Diffusion Transformers Jaeah Lee, Hyunjin Kim, Jaewoong Cho, Gihyun Kwon

• OriCache: Orientation-Guided Feature Caching for DiT Acceleration Joonsik Nam, Hyunwoo Yu, Suk-Ju Kang

• Empirical Analysis of Layer Redundancy in Diffusion Language Models Yuto Karashima, Hiroaki Ito, Hikari Otsuka, Guanxi Lu, Tatsuya Kaneko, Masato Motomura, Daichi Fujiki

• NOSA: Native and Offloadable Sparse Attention Yuxiang Huang, Pengjie Wang, Jicheng Han, Weilin Zhao, Zhou su, Ao Sun, HongyaLyu, Hengyu Zhao, Yudong Wang, Chaojun Xiao, Xu Han, Zhiyuan Liu

• WildCat: Near-Linear Attention in Theory and Practice Tobias Schröder, Lester Mackey

• Decoupling Spatial and Semantic Token Compression for Vision-Language Model Acceleration Seunghun Moon, Jaehyun Pyun, Hyunwoo Yu, Suk-Ju Kang

• Structure-Preserving Adaptive Post-Training Quantization for Monocular Depth Estimation Jaemin Choi, Jincheol Yang, NaHyun Lim, Yun-Seong Jeong, Matti Alexander Zinke, Hyunwoo Yu, Suk-Ju Kang

• Recovering Selectivity with LTI State Space Operators for Portable Long-Context Inference Minseon Gwak, N. Benjamin Erichson, PooGyeon Park

• Implicit Off-Diagonal Curvature Modeling via Gradient Projection for Post-Training Quantization of Vision Transformers Jincheol Yang, Jaemin Choi, NaHyun Lim, Yun-Seong Jeong, Matti Alexander Zinke, Hyunwoo Yu, Bongjoon Hyun, Kyomin Sohn, Suk-Ju Kang

• A3: an Analytical Low-Rank Approximation Framework for Attention Jeffrey T. H. Wong, Cheng Zhang, Xinye Cao, Pedro Gimenes, Christos-Savvas Bouganis, George Anthony Constantinides, Wayne Luk, Yiren Zhao

• LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning Mengmeng Ji, Ravi Shanker Raju, Jonathan Lingjie Li, Chen Wu

• Structural Outlier-Aware Post-Training Quantization for Monocular Depth Estimation Yun-Seong Jeong, Jincheol Yang, NaHyun Lim, Jaemin Choi, Matti Alexander Zinke, Sungwook Choi, Sung-Sik Cho, Suk-Ju Kang

• Resource-Adaptivity Beyond the Model: Sensor Control for Quantized On-Device Vision Hongjun Suh, Woojin Jang, Hyung-Sin Kim

• SFPruner: Single-Forward Visual Token Subset Selection for Resource-Efficient Multimodal Foundation Model Inference Jouwon Song, Woohyeong Kim, Seungjae Baek, Kyeongbo Kong

• Efficient Encoder-Only Context Compression via Marginal Contribution Scoring Thao Do, Dinh Phu Tran, An Vo, Seon Kwon Kim, Daeyoung Kim

• Multi-Mixer Models: Flexible Sequence Modeling with Shared Representations Kevin Li, Asher Trockman, Ananda Theertha Suresh, Ziteng Sun

• FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment Riccardo Zaccone, Stefanos Laskaridis, Marco Ciccone, Samuel Horváth

• Adaptive Safety Probing for Resource-Efficient Vision-Language-Action Models Seongbin Park, Fan Zhang, Hossein Khalili, Nader Sehatbakhsh

• Distill, Suppress, and Fuse: Cross-Modal Knowledge Integration for Optical Flow-Free Temporal Action Segmentation Seungjin Han, Gyeong-Hyeon Kim, Eunwoo Kim

• Referring Video Object Segmentation via Language-aligned Track Selection Seongchan Kim, Woojeong Jin, Sangbeom Lim, Heeji Yoon, Hyunwook Choi, Seungryong Kim

• Reducing Attention Distribution Error with Unified Tail Aggregation for Sparse Attention Hyunwoo Yu, Jongbeom Lee, Jaemin Choi, Jincheol Yang, Yubin Cho, Joonsik Nam, Seunghun Moon, Jung-Woo Chang, Bongjoon Hyun, Kyomin Sohn, Kyeongbo Kong, Suk-Ju Kang

• Learning When to Attend: Conditional Memory Access for Long-Context LLMs Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Matthew Trager, Wei Xia, Stefano Soatto

• Understanding Layer Patching in Model Size Interpolation Sara Kangaslahti, Jonathan Geuter, Nihal V. Nayak, Marco Fumero, Francesco Locatello, David Alvarez-Melis

• You Had One Job: Per-Task Quantization Using LLMs’ Hidden Representations Amit LeVi, Raz Lapid, Rom Himelstein, Chaim Baskin, Ravid Shwartz-Ziv, Avi Mendelson