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

• 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

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

• 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