GenAI4ELab/papercli-papers huggingface dataset:
Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
Authors: Zonghang Li; Tao Li; Wenjiao Feng; Rongxing Xiao; Jianshu She; Hong Huang; Mohsen Guizani; Hongfang Yu; Qirong Ho; Wei Xiang; Xue Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: On-device inference offers privacy, offline use, and instant response, but consumer hardware restricts large language models (LLMs) to low throughput and capability. To overcome this challenge, we present prima.cpp, a distributed on-device inference system that runs 30-70B LLMs on consumer home clusters with mixed CPUs/GPUs, insufficient RAM/VRAM, slow disks, Wi-Fi links, and heterogeneous OSs. We introduce pipelined-ring parallelism (PRP) to overlap disk I/O with compute and communication, and address the prefetch-release conflict in mmap-based offloading. We further propose Halda, a heterogeneity-aware scheduler that co-optimizes per-device CPU/GPU workloads and device selection under RAM/VRAM constraints. On four consumer home devices, a 70B model reaches 674 ms/token TPOT with <6% memory pressure, and a 32B model with speculative decoding achieves 26 tokens/s. Compared with llama.cpp, exo, and dllama, our proposed prima.cpp achieves 5-17× lower TPOT, supports fine-grained model sizes from 8B to 70B, ensures broader cross-OS and quantization compatibility, and remains OOM-free, while also being Wi-Fi tolerant, privacy-preserving, and hardware-independent. The code is available at https://gitee.com/zonghang-li/prima.cpp.
Bayesian Post Training Enhancement of Regression Models with Calibrated Rankings
Authors: Kevin Tirta Wijaya; Bing Xu Hu; Hans-peter Seidel; Wojciech Matusik; Vahid Babaei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate regression models are essential for scientific discovery, yet high-quality numeric labels are scarce and expensive.
In contrast, rankings (especially pairwise) are easier to obtain from domain experts or artificial intelligence judges. We introduce RankRefine++, a novel plug-and-play method that improves a base regressor's prediction for a query by leveraging pairwise rankings between the query and reference items with known labels. RankRefine++ performs a Bayesian update that combines a Gaussian likelihood from the regressor and the Bradley-Terry likelihood from the ranker. This yields a strictly log-concave posterior with a unique maximum likelihood solution and fast Newton updates. We show that prior state-of-the-art is a special case of our framework, and we identify a fundamental failure mode: Bradley-Terry likelihoods suffer from scale mismatch and curvature dominance when the number of reference items is large, which can degrade performance. From this analysis, we derive a calibration method to adjust the information originating from the expert rankings. RankRefine++ shows a stunning 97.65\% median improvement across 12 datasets over previous state-of-the-art method using a realistically-accurate ranker, and runs efficiently on a consumer-grade CPU.
Highly Efficient and Effective LLMs with Multi-Boolean Architectures
Authors: Ba-Hien Tran; Van Minh Nguyen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Weight binarization has emerged as a promising strategy to reduce the complexity of large language models (LLMs). Existing approaches fall into post-training binarization, which is simple but causes severe performance loss, and training-aware methods, which depend on full-precision latent weights, adding complexity and limiting efficiency. We propose a novel framework that represents LLMs with multi-kernel Boolean parameters and, for the first time, enables direct finetuning LMMs in the Boolean domain, eliminating the need for latent weights. This enhances representational capacity and dramatically reduces complexity during both finetuning and inference. Extensive experiments across diverse LLMs show our method outperforms recent ultra low-bit quantization and binarization techniques.
Plug-and-Play Compositionality for Boosting Continual Learning with Foundation Models
Authors: Weiduo Liao; Fei Han; Hisao Ishibuchi; Qingfu Zhang; Ying Wei
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Vision learners often struggle with catastrophic forgetting due to their reliance on class recognition by comparison, rather than understanding classes as compositions of representative concepts. 
This limitation is prevalent even in state-of-the-art continual learners with foundation models and worsens when current tasks contain few classes. 
Inspired by the recent success of concept-level understanding in mitigating forgetting, we design a universal framework CompSLOT to guide concept learning across diverse continual learners. 
Leveraging the progress of object-centric learning in parsing semantically meaningful slots from images, we tackle the challenge of learning slot extraction from ImageNet-pretrained vision transformers by analyzing meaningful concept properties. 
We further introduce a primitive selection and aggregation mechanism to harness concept-level image understanding. 
Additionally, we propose a method-agnostic self-supervision approach to distill sample-wise concept-based similarity information into the classifier, reducing reliance on incorrect or partial concepts for classification. 
Experiments show CompSLOT significantly enhances various continual learners and provides a universal concept-level module for the community.
HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models
Authors: Weixuan Wang; Minghao Wu; Barry Haddow; Alexandra Birch
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning large language models (LLMs) on a mixture of diverse datasets poses challenges due to data imbalance and heterogeneity. Existing methods often address these issues across datasets (globally) but overlook the imbalance and heterogeneity within individual datasets (locally), which limits their effectiveness. We introduce Hierarchical Balancing Optimization (HBO), a novel method that enables LLMs to autonomously adjust data allocation during fine-tuning both across datasets (globally) and within each individual dataset (locally). HBO employs a bilevel optimization strategy with two types of actors: a Global Actor, which balances data sampling across different subsets of the training mixture, and several Local Actors, which optimizes data usage within each subset based on difficulty levels. These actors are guided by reward functions derived from the LLM's training state, which measure learning progress and relative performance improvement. We evaluate HBO on three LLM backbones across nine diverse tasks in multilingual and multitask setups. Results show that HBO consistently outperforms existing baselines, achieving significant accuracy gains. Our in-depth analysis further demonstrates that both the global actor and local actors of HBO effectively adjust data usage during fine-tuning. HBO provides a comprehensive solution to the challenges of data imbalance and heterogeneity in LLM fine-tuning, enabling more effective training across diverse datasets.
PAC-Bayes bounds for cumulative loss in Continual Learning
Authors: Lior Friedman; Ron Meir
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In continual learning, knowledge must be preserved and re-used between tasks, requiring a balance between maintaining
good transfer to future tasks and minimizing forgetting of previously learned ones. As several practical algorithms have been
devised to address the continual learning setting, the natural question of providing reliable risk certificates has also been raised.
Although there are results for specific settings and algorithms on the behavior of memory stability, generally applicable upper bounds on learning plasticity are few and far between. 

In this work, we extend existing PAC-Bayes bounds for online learning and time-uniform offline learning to the continual learning
setting. We derive general upper bounds on the cumulative generalization loss applicable for any task distribution and learning
algorithm as well as oracle bounds for Gibbs posteriors and compare their effectiveness for several different
task distributions. We demonstrate empirically that our approach yields non-vacuous bounds for several continual learning
problems in vision, as well as tight oracle bounds on linear regression tasks. To the best of our knowledge, this is the first general upper bound on learning plasticity for continual learning.
Follow-Your-Preference: Towards Preference-Aligned Image Inpainting
Authors: Yutao Shen; Junkun Yuan; Toru Aonishi; Hideki Nakayama; Jack Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper investigates image inpainting with preference alignment. Instead of introducing a novel method, we go back to basics and revisit fundamental problems in achieving such alignment. We leverage the prominent direct preference optimization approach for alignment training and employ public reward models to construct preference training datasets. Experiments are conducted across nine reward models, two benchmarks, and two baseline models with varying structures and generative algorithms. Our key findings are as follows: (1) Most reward models deliver valid reward scores for constructing preference data, even if some of them are not reliable evaluators. (2) Preference data demonstrates robust trends in both candidate scaling and sample scaling across models and benchmarks. (3) Observable biases in reward models, particularly in brightness, composition, and color scheme, render them susceptible to cause reward hacking. (4) A simple ensemble of these models yields robust and generalizable results by mitigating such biases. Built upon these observations, our alignment models significantly outperform prior models across standard metrics, GPT-4 assessments, and human evaluations, without any changes to model structures or the use of new datasets. We hope our work can set a simple yet solid baseline, pushing this promising frontier. Our code is available at: https://github.com/shenytzzz/Follow-Your-Preference.
There Was Never a Bottleneck in Concept Bottleneck Models
Authors: Antonio Almudévar; José Miguel Hernández-Lobato; Alfonso Ortega
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep learning representations are often difficult to interpret, which can hinder their deployment in sensitive applications. Concept Bottleneck Models (CBMs) have emerged as a promising approach to mitigate this issue by learning representations that support target task performance while ensuring that each component predicts a concrete concept from a predefined set. In this work, we argue that CBMs do not impose a true bottleneck: the fact that a component can predict a concept does not guarantee that it encodes only information about that concept. This shortcoming raises concerns regarding interpretability and the validity of intervention procedures. To overcome this limitation, we propose Minimal Concept Bottleneck Models (MCBMs), which incorporate an Information Bottleneck (IB) objective to constrain each representation component to retain only the information relevant to its corresponding concept. This IB is implemented via a variational regularization term added to the training loss. As a result, MCBMs yield more interpretable representations, support principled concept-level interventions, and remain consistent with probability-theoretic foundations.
DP-Fusion: Token-Level Differentially Private Inference for Large Language Models
Authors: Rushil Thareja; Preslav Nakov; Praneeth Vepakomma; Nils Lukas
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) do not preserve privacy at inference-time. The LLM's outputs can inadvertently reveal information about the model's context, which presents a privacy challenge when the LLM is augmented via tools or databases containing sensitive information. Existing privacy-preserving methods at inference-time have significant limitations since they (i) lack provable guarantees or (ii) have a poor utility/privacy trade-off. We propose DP-Fusion, a Differentially Private Inference (DPI) mechanism for LLMs that provably bounds the influence a set of tokens in the context can have on the LLM's output. DP-Fusion works as follows: (1) label a subset of sensitive tokens, (2) infer the LLM without any sensitive tokens to obtain a baseline, (3) infer the LLM with the sensitive tokens, and (4) blend distributions so that the final output remains within a bounded distance of the baseline distribution. While this per-token influence bound also mitigates jailbreak-style prompt injection, we focus on document privatization, where the goal is to paraphrase a document containing sensitive tokens, e.g., personally identifiable information, so that no attacker can reliably infer them from the paraphrased document while preserving high text quality. The privacy/utility trade-off is controlled by $\epsilon$, where $\epsilon=0$ hides sensitive tokens entirely, while higher values trade off privacy for improved text quality. We show that our method creates token-level provably privatized documents with substantially improved theoretical and empirical privacy, achieving $6\times$ lower perplexity than related DPI methods.
PatchDNA: A Flexible and Biologically-Informed Alternative to Tokenization for DNA
Authors: Alice Del Vecchio; Chantriolnt-Andreas Kapourani; Abdullah M Athar; Agnieszka Dobrowolska; Andrew Anighoro; Benjamin Tenmann; Lindsay Edwards; Cristian Regep
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: DNA language models are emerging as powerful tools for representing genomic sequences, with recent progress driven by self-supervised learning. However, performance on downstream tasks is sensitive to tokenization strategies reflecting the complex encodings in DNA, where both regulatory elements and single-nucleotide changes can be functionally significant. Yet existing models are fixed to their initial tokenization strategy; single-nucleotide encodings result in long sequences that challenge transformer architectures, while fixed multi-nucleotide schemes like byte pair encoding struggle with character level modeling. Drawing inspiration from the Byte Latent Transformer's combining of bytes into patches, we propose that 'patching' provides a competitive and more efficient alternative to tokenization for DNA sequences. Furthermore, patching eliminates the need for a fixed vocabulary, which offers unique advantages to DNA. Leveraging this, we propose a biologically informed strategy, using evolutionary conservation scores as a guide for 'patch' boundaries. By prioritizing conserved regions, our approach directs computational resources to the most functionally relevant parts of the DNA sequence. We show that models up to an order of magnitude smaller surpass current state-of-the-art performance in existing DNA benchmarks. Importantly, our approach provides the flexibility to change patching without retraining, overcoming a fundamental limitation of current tokenization methods.
Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning
Authors: Ke Sun; Hongming Zhang; Jun Jin; Chao Gao; Xi Chen; Wulong Liu; Linglong Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inspired by the human learning and memory system, particularly the interplay between the hippocampus and cerebral cortex, this study proposes a dual-learner framework comprising a fast learner and a meta learner to address continual Reinforcement Learning~(RL) problems. These two learners are coupled to perform distinct yet complementary roles: the fast learner focuses on knowledge transfer, while the meta learner ensures knowledge integration. In contrast to traditional multi-task RL approaches that share knowledge through average return maximization, our meta learner incrementally integrates new experiences by explicitly minimizing catastrophic forgetting, thereby supporting efficient cumulative knowledge transfer for the fast learner. To facilitate rapid adaptation in new environments, we introduce an adaptive meta warm-up mechanism that selectively harnesses past knowledge. We conduct experiments in various pixel-based  and continuous control benchmarks, revealing the superior performance of continual learning for our proposed dual-learner approach relative to baseline methods.
Unified Diffusion VLA: Vision-Language-Action Model via Joint Discrete Denosing Diffusion Process
Authors: Jiayi Chen; Wenxuan Song; Pengxiang Ding; Ziyang Zhou; Han Zhao; Feilong Tang; Donglin Wang; Haoang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language-action (VLA) models aim to understand natural language instructions and visual observations and execute corresponding actions as an embodied agent. Recent advancements have integrated future images into the understanding-action loop, enabling foresight-driven policies that reduce abstract action prediction to a more tractable inverse kinematics problem. However, existing models either rely on external experts for modality unification or treat image generation and action prediction as separate processes, limiting the benefits of direct synergy between these tasks. In this work, we propose Unified Diffusion VLAs, which tightly couple understanding, generation, and action in a mutually reinforcing manner. Our method optimizes the generation of actions and images jointly through a synchronous denoising diffusion process, where action tokens progressively attend to future image tokens. This iterative refinement enables actions to evolve from initialization with sufficient visual guidance, ensuring precise action execution. We introduce a hybrid attention mechanism and the Joint Discrete Denoising Diffusion Process (JD3P), which integrates multiple modalities into a unified trajectory. We also propose a two-stage training pipeline and several inference-time techniques that optimize performance and efficiency. Our approach achieves state-of-the-art performance on benchmarks such as CALVIN, LIBERO, and SimplerEnv, and we demonstrate its effectiveness through ablation studies and real-world evaluations.
Welfarist Formulations for Diverse Similarity Search
Authors: Siddharth Barman; Nirjhar Das; Shivam Gupta; Kirankumar Shiragur
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Nearest Neighbor Search (NNS) is a fundamental problem in data structures with wide-ranging applications, such as web search, recommendation systems, and, more recently, retrieval-augmented generations (RAG). In such recent applications, in addition to the relevance (similarity) of the returned neighbors, diversity among the neighbors is a central requirement. In this paper, we develop principled welfare-based formulations in NNS for realizing diversity across attributes. Our formulations are based on welfare functions---from mathematical economics---that satisfy central diversity (fairness) and relevance (economic efficiency) axioms. With a particular focus on Nash social welfare, we note that our welfare-based formulations provide objective functions that adaptively balance relevance and diversity in a query-dependent manner. Notably, such a balance was not present in the prior constraint-based approach, which forced a fixed level of diversity and optimized for relevance.  In addition, our formulation provides a parametric way to control the trade-off between relevance and diversity, providing practitioners with flexibility to tailor search results to task-specific requirements. We develop efficient nearest neighbor algorithms with provable guarantees for  the welfare-based objectives. Notably, our algorithm can be applied on top of any standard ANN method (i.e., use standard ANN method as a subroutine) to efficiently find neighbors that approximately maximize our welfare-based objectives. Experimental results demonstrate that our approach is practical and substantially improves diversity while maintaining high relevance of the retrieved neighbors.
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
Authors: Ruiyao Xu; Kaize Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the low-resource setting, where labeled nodes are severely limited and scarce, remains constrained since fine-tuning LLMs usually requires sufficient labeled data, especially when the TAG shows complex structural patterns. In essence, this paper targets two key challenges: (i) the difficulty of generating and selecting reliable pseudo labels on TAGs for LLMs, and (ii) the need to mitigate potential label noise when fine-tuning LLMs with pseudo labels. To counter the challenges, we propose a new framework, GNN-as-Judge, which can unleash the power of LLMs for few-shot semi-supervised learning on TAGs by incorporating the structural inductive bias of Graph Neural Networks (GNNs). Specifically, GNN-as-Judge introduces a collaborative pseudo-labeling strategy that first identifies the most influenced unlabeled nodes from labeled nodes, then exploits both the agreement and disagreement patterns between LLMs and GNNs to generate reliable labels. Furthermore, we develop a weakly-supervised LLM fine-tuning algorithm that can distill the knowledge from informative pseudo labels while mitigating the potential label noise. Experiments on multiple TAG datasets demonstrate that GNN-as-Judge significantly outperforms existing methods, particularly in low-resource regimes where labeled data are scarce.
Train Once, Answer All: Many Pretraining Experiments for the Cost of One
Authors: Sebastian Bordt; Martin Pawelczyk
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent work has demonstrated that controlled pretraining experiments are a powerful tool for studying
the relationship between training data and large language model (LLM) behavior. 
However, the computational cost of pretraining presents a significant constraint. To overcome this constraint, we propose a new approach where multiple experiments are conducted simultaneously during a *single* training run. We validate our approach by performing ten experiments while training on 210B tokens, with models of up to 2.7B parameters. Although models are trained only once, we can replicate the results of multiple previous works on data contamination, poisoning, and memorization. We also conduct novel investigations into knowledge acquisition, mathematical reasoning, and watermarking. For example, we dynamically update the training data until a model acquires a particular piece of knowledge. Remarkably, the influence of the experiments on the model's training dynamics and overall performance is minimal. However, interactions between experiments may act as a confounder in our approach. We propose continual pretraining dependence testing (CPDT), a novel technique to test for interactions with continual pretraining experiments, finding them to be negligible in our setup. Overall, our results suggest that performing multiple pretraining experiments within a single training run can enable rigorous scientific experimentation with large models on a compute budget.