GenAI4ELab/papercli-papers huggingface dataset:
RankFlow: Property-aware Transport for Protein Optimization
Authors: Lu Yu; Wei Xiang; Kang Han; Gaowen Liu; Ramana Rao Kompella
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A key step in protein optimization is modeling the fitness landscape, which maps proteins to functional assay readouts. Existing methods typically either use property-agnostic likelihoods/embeddings from pretrained protein language models (PLMs) for fitness prediction, or assume independent mutational effects, limiting their ability to capture higher-order interactions. In this work, we introduce RankFlow, a conditional flow framework that refines PLM representations to be a property-aligned distribution via a tailored energy function and captures multi-mutation interactions through learnable embeddings. To align optimization with evaluation protocols, we propose the Rank-Consistent Conditional Flow Loss (RC$^2$), a differentiable ranking objective that enforces the correct order of mutants rather than absolute values, which improves out-of-distribution generalization. Finally, we introduce a Property-guided Steering Gate (PSG) that concentrates learning on positions carrying signals for the target property while suppressing unrelated evolutionary biases. Across the ProteinGym, PEER, and FLIP benchmarks, RankFlow obtains state-of-the-art ranking accuracy and superior generalization performance.
Unifying Stable Optimization and Reference Regularization in RLHF
Authors: Li He; Qiang Qu; He Zhao; Stephen Wan; Dadong Wang; Lina Yao; Tongliang Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning from Human Feedback (RLHF) has advanced alignment capabilities significantly but remains hindered by two core challenges: reward hacking and stable optimization. Current solutions independently address these issues through separate regularization strategies, specifically a KL-divergence penalty against a supervised fine-tuned model ($\pi_0$) to mitigate reward hacking, and policy ratio clipping towards the current policy ($\pi_t$) to promote stable alignment. However, the implicit trade-off arising from simultaneously regularizing towards both $\pi_0$ and $\pi_t$ remains under-explored. In this paper, we introduce a unified regularization approach that explicitly balances the objectives of preventing reward hacking and maintaining stable policy updates. Our simple yet principled alignment objective yields a weighted supervised fine-tuning loss with a superior trade-off, which demonstrably improves both alignment results and implementation complexity. Extensive experiments across diverse benchmarks validate that our method consistently outperforms RLHF and online preference learning methods, achieving enhanced alignment performance and stability.
MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task
Authors: Yuchen Yan; Yongliang Shen; Yang Liu; Jin Jiang; Xin Xu; Mengdi Zhang; Jian Shao; Yueting Zhuang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mathematical reasoning represents a critical frontier in advancing large language models (LLMs). While step-by-step approaches have emerged as the dominant paradigm for mathematical problem-solving in LLMs, the quality of reasoning steps in training data fundamentally constrains the performance of the models. 
Recent studies have demonstrated that more detailed intermediate steps can enhance model performance, yet existing methods for step expansion either require more powerful external models or incur substantial computational costs. 
In this paper, we introduce MathFimer, a novel framework for mathematical reasoning step expansion inspired by the ''Fill-in-the-middle'' task from code reasoning.
By decomposing solution chains into prefix-suffix pairs and training models to reconstruct missing intermediate steps, we develop a specialized model, MathFimer-7B, on our carefully curated NuminaMath-FIM dataset. 
We then apply these models to enhance existing mathematical reasoning datasets by inserting detailed intermediate steps into their solution chains, creating MathFimer-expanded versions.
Through comprehensive experiments on multiple mathematical reasoning datasets, including MathInstruct, MetaMathQA and etc., we demonstrate that models trained on MathFimer-expanded data consistently outperform their counterparts trained on original data across various benchmarks such as GSM8K and MATH.
Our approach offers a practical, scalable solution for enhancing mathematical reasoning capabilities in LLMs without relying on powerful external models or expensive inference procedures.
ManipEvalAgent: Promptable and Efficient Evaluation Framework for Robotic Manipulation Policies
Authors: Yiteng Chen; Huiping Zhuang; WenboLi; Shiyi Wang; Xiangyu Zhao; Qingyao Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In recent years, robotic manipulation policies have made substantial progress. However, evaluating these policies typically requires large-scale sampling in simulation benchmarks, leading to high time costs. Moreover, existing evaluation pipelines are usually fixed, do not account for user needs, and report only a single scalar score, lacking interpretability. In contrast, human experts can quickly form an intuitive impression of a policy’s capabilities from just a handful of executions. We therefore propose ManipEvalAgent, an efficient, promptable, and dynamically multi-round evaluation framework for robotic manipulation policies. The framework conducts small-batch, multi-round evaluations and adaptively plans subsequent evaluation steps based on intermediate observations from each round. Via code generation, it constructs tasks and evaluation functions within simulator. By generating evaluation functions and leveraging vision–language models (VLMs) for video understanding, ManipEvalAgent provides user-instruction-centric, fine-grained analysis. Our approach offers three key advantages: (1) efficiency, no need for massive sampling; (2) promptable, planning the evaluation process according to user queries; and (3) interpretability, providing diagnostic text that goes beyond a single score. Across multiple settings, our evaluation method significantly shortens the overall time compared with traditional simulation benchmarks, while reaching conclusions comparable to those from large-scale simulation benchmarks.
More Than What Was Chosen: LLM-based Explainable Recommendation Beyond Noisy User Preferences
Authors: Chung Park; Hyeongjun Yun; Taesan Kim; Junui Hong; Dongjoon Hong; Mira Myong; Jihoon Oh; MinCheol Cho; Kijung Park; Min sung Choi; Jihwan Seok; Jaegul Choo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recommender systems traditionally rely on the principle of Revealed Preference (RP), which assumes that observed user behaviors faithfully reflect underlying interests.
While effective at scale, this assumption is fragile in practice, as real-world choices are often noisy and inconsistent.
Thus, even LLM-based recommendation models (LLM-Rec) equipped with advanced reasoning capabilities may fail to capture genuine user preferences and often produce rationales of limited persuasiveness.
To address this issue, we introduce the concept of Coherent Preference (CP), which complements RP by favoring items that are logically and causally coherent with user interaction history.
Building on this perspective, we propose Conflict-Aware Direct Preference Optimization (C-APO), an LLM-Rec framework that jointly optimizes RP and CP while adaptively reconciling their agreement and conflict, delivering robust recommendation performance and logically consistent rationales. 
We construct a unified ordering approach that combines the RP signal, based on chosen versus unobserved items, with the CP signal, which ranks items by their logical consistency with past interaction history.
In this unified preference ordering, we dynamically adjust the influence of each signal depending on whether RP and CP agree or conflict, allowing the model to better capture user intent and generate more plausible recommendations.
On the Amazon Review dataset, our approach consistently outperforms approximately 20 state-of-the-art baseline models in both recommendation performance and rationale quality, achieving a 1.65$\times$ relative improvement in click-through rate during deployment, thereby demonstrating its practical utility.
The code and dataset are available at https://github.com/cpark88/C-APO.
Learning linear state-space models with sparse system matrices
Authors: Yasen Wang; Kaiqi Fang; Guijun Ma; Junlin Li; Mengyu Sun; Zhilan Huang; Gang Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Due to tractable analysis and control,  linear state-space  models (LSSMs) provide a fundamental mathematical tool for time-series data modeling in various disciplines. In particular, many LSSMs have sparse system matrices because interactions among variables are limited or only a few significant relationships exist.  However, current learning algorithms for LSSMs lack the ability to learn system matrices with the sparsity constraint due to the similarity transformation. To address this issue, we impose sparsity-promoting priors on system matrices to balance modeling error and  model complexity. By taking hidden states of LSSMs as latent variables, we then explore the expectation-maximization (EM) algorithm to derive a maximum a posteriori (MAP) estimate of both hidden states and system matrices from noisy observations. Based on the Global Convergence Theorem, we further demonstrate that the proposed learning algorithm yields a sequence  converging to a local maximum or saddle point of the joint posterior distribution. Finally, experimental results on simulation and real-world problems illustrate that the proposed algorithm can preserve the inherent topological structure among variables and significantly improve prediction accuracy over classical learning algorithms.
Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
Authors: Annan Yu; Danielle C. Maddix; Boran Han; Xiyuan Zhang; Abdul Fatir Ansari; Oleksandr Shchur; Christos Faloutsos; Andrew Gordon Wilson; Michael W. Mahoney; Bernie Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time series foundation models (TSFMs) are a potential class of powerful, general-purpose tools for forecasting and related temporal tasks, but their behavior is strongly shaped by subtle inductive biases in their design. 
Rather than developing a new model and claiming that it is better than existing TSFMs, e.g., by winning on existing benchmarks, our objective is to understand how the various "knobs" of the training process affect model quality. 
Using a mix of theory and controlled empirical evaluation, we identify and show how various design choices (e.g., patch size, embedding choice, training objective, etc.) lead to implicit biases in fundamental model properties (e.g., temporal behavior, geometric structure, how aggressively or not the model regresses to the mean, etc.), and how these biases can be intuitive or counterintuitive, depending on properties of the model and data. 
We illustrate in a case study on outlier handling how multiple biases interact in complex ways.
Pay Less Attention to Function Words for Free Robustness of Vision-Language Models
Authors: Qiwei Tian; Chenhao Lin; Zhengyu Zhao; Chao Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against cross-modal adversarial attacks, and propose Function-word De-Attention (FDA) accordingly to mitigate the vulnerability brought by function words. Inspired by differential transformers, our FDA calculates the original and the function-word cross-attention within attention heads, and differentially subtracts the latter from the former for more robust alignment. Comprehensive experiments include 2 SOTA baselines under 6 different attacks on 2 downstream tasks, 3 datasets, and 3 models. Overall, our FDA yields an average 18/13/53\% ASR drop with only 0.2/0.3/0.6\% performance drops on the 3 tested models on retrieval, and a 90\% ASR drop with a 0.3\% performance gain on visual grounding. We demonstrate the scalability, generalization, and zero-shot performance of FDA experimentally, as well as in-depth ablation studies and analysis. Code is available at https://github.com/michaeltian108/FDA.
Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
Authors: Annan Yu; Danielle C. Maddix; Boran Han; Xiyuan Zhang; Abdul Fatir Ansari; Oleksandr Shchur; Christos Faloutsos; Andrew Gordon Wilson; Michael W. Mahoney; Bernie Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformers are widely used across data modalities, and yet the principles distilled from text models often transfer imperfectly. In this paper, we analyze Transformers through the lens of rank structure. Our focus is on the time series setting, where the structural properties of the data remarkably differ from those of text or vision. Time-series embeddings, unlike text or vision, exhibit sharply decaying singular spectra: small patch sizes and smooth continuous mappings concentrate the data into low-rank subspaces. From this, we prove that the associated $Q/K/V$ projections admit accurate low-rank approximations, and that attention layers become compressible in proportion to the decay of the embedding spectrum. We introduce the concept of *flow-of-ranks*, a mechanism by which nonlinear mixing across depth inflates the rank, explaining why early layers are most amenable to compression and why rank schedules should grow with depth. Guided by these results, we compress Chronos, a large time series foundation model, achieving a reduction of $65\\%$ in inference time and $81\\%$ in memory without loss of accuracy. These findings provide principled guidance for allocating width, depth, and heads in time series foundation models, and for exploiting their inherent compressibility. Our code is available at https://github.com/amazon-science/tsfm-compression.
Spike-based Digital Brain: a novel fundamental model for brain activity analysis
Authors: Shaolong Wei; Qiyu Sun; Mingliang Wang; Liang Sun; Weiping Ding; Jiashuang Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling the temporal dynamics of the human brain remains a core challenge in computational neuroscience and artificial intelligence. Traditional methods often ignore the biological spike characteristics of brain activity and find it difficult to reveal the dynamic dependencies and causal interactions between brain regions, limiting their effectiveness in brain function research and clinical applications. To address this issue, we propose a Spike-based Digital Brain (Spike-DB), a novel fundamental model that introduces the spike computing paradigm into brain time series modeling. Spike-DB encodes fMRI signals as spike trains and learns the temporal driving relationships between anchor and target regions to achieve high-precision prediction of brain activity and reveal underlying causal dependencies and dynamic relationship characteristics. Based on Spike-DB, we further conducted downstream tasks including brain disease classification, abnormal brain region identification, and effective connectivity inference. Experimental results on real-world epilepsy datasets and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that Spike-DB outperforms existing mainstream methods in both prediction accuracy and downstream tasks, demonstrating its broad potential in clinical applications and brain science research. Our code is available at https://github.com/UAIBC-Brain/Spike-DB.
Near-Optimal Online Deployment and Routing for Streaming LLMs
Authors: Shaoang Li; Jian Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid pace at which new large language models (LLMs) appear, and older ones become obsolete, forces providers to manage a streaming inventory under a strict concurrency cap and per-query cost budgets. We cast this as an online decision problem that couples *stage-wise deployment* (at fixed maintenance windows) with *per-query routing* among live models. We introduce *StageRoute*, a hierarchical algorithm that (i) optimistically selects up to $M_{\max}$ models for the next stage using reward upper-confidence and cost lower-confidence bounds, and (ii) routes each incoming query by solving a budget- and throughput-constrained bandit subproblem over the deployed set. We prove a regret of $\tilde{\mathcal{O}}(T^{2/3})$ with a matching lower bound, establishing near-optimality, and validate the theory empirically: *StageRoute* tracks a strong oracle under tight budgets across diverse workloads.
RL Grokking Recipe: How Does RL Unlock and Transfer New Algorithms in LLMs?
Authors: Yiyou Sun; Yuhan Cao; Pohao Huang; Haoyue Bai; Hannaneh Hajishirzi; Nouha Dziri; Dawn Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: It remains an open question whether LLMs can acquire or generalize genuinely new reasoning strategies, beyond the sharpened skills encoded in their parameters during pre-training or post-training.
To attempt to answer this debate, we introduce DELTA — Distributional Evaluation of Learnability and Transferrability in Algorithmic Coding, a controlled benchmark of synthetic coding problem families designed to probe two fundamental aspects: learnability—can LLMs, through reinforcement learning (RL), solve problem families where pretrained models exhibit failure with large enough attempts (pass@K=0)?—and transferability—if learnability happens, can such skills transfer systematically to out-of-distribution (OOD) test sets? Unlike prior public coding datasets, DELTA isolates reasoning skills through templated problem generators and introduces fully OOD problem families that demand novel strategies rather than tool invocation or memorized patterns.
Our experiments reveal a striking grokking phase transition: after an extended period with near-zero reward, RL-trained models abruptly climb to near-perfect accuracy.
To enable learnability on previously unsolvable problem families, we explore key training ingredients such as staged warm-up with dense rewards, experience replay, curriculum training, and verification-in-the-loop.
Beyond learnability, we use DELTA to evaluate transferability or generalization along exploratory, compositional, and transformative axes, as well as cross-family transfer. Results show solid gains within families and for recomposed skills, but persistent weaknesses in transformative cases. DELTA thus offers a clean testbed for probing the limits of RL-driven reasoning and for understanding how models can move beyond existing priors to acquire new algorithmic skills.
T-TAMER: Provably Taming Trade-offs in ML Serving
Authors: Yuanyuan Chloe Yang; Ruimin Zhang; Jamie Heather Morgenstern; Haifeng Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives. Multi-model serving further complicates these trade-offs; for example, in cascaded models, each early-exit decision balances latency reduction against potential accuracy loss. Despite the pervasiveness and importance of such trade-offs, current strategies remain largely heuristic and case-specific, limiting both their theoretical guarantees and general applicability.

We present a general framework, T-Tamer, which formalizes this setting as a multi-stage decision process, where the objective is to determine both when to exit and which model to consult. Our main result shows that recall (i.e., the ability to revisit earlier models) is both necessary and sufficient for achieving provable performance guarantees. In particular, we prove that strategies without recall cannot obtain any constant-factor approximation to the optimal trade-off, whereas recall-based strategies provably attain the optimal trade-off in polynomial time.

We validate our analysis through experiments on synthetic datasets and early-exit workloads for vision and NLP benchmarks. The results show that recall-based strategies consistently yield efficient accuracy–latency trade-offs. We hope this work provides a principled foundation for bridging heuristic practice with theoretical guarantees in the design of early-exit and cascaded models.
Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models
Authors: Bowei Chen; Sai Bi; Hao Tan; He Zhang; Tianyuan Zhang; Zhengqi Li; Yuanjun Xiong; Jianming Zhang; Kai Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this work, we propose aligning pretrained visual encoders to serve as tokenizers for latent diffusion models in image generation. Unlike training a variational autoencoder (VAE) from scratch, which primarily emphasizes low-level details, our approach leverages the rich semantic structure of foundation encoders.
We introduce a three-stage alignment strategy called AlignTok: (1) freeze the encoder and train an adapter and a decoder to establish a semantic latent space; (2) jointly optimize all components with an additional semantic preservation loss, enabling the encoder to capture perceptual details while retaining high-level semantics; and (3) refine the decoder for improved reconstruction quality.
This alignment yields semantically rich image tokenizers that benefit diffusion models.
On ImageNet 256$\times$256, our tokenizer accelerates the convergence of diffusion models, reaching a gFID of 1.90 within just 64 epochs, and improves generation both with and without classifier-free guidance. Scaling to LAION, text-to-image models trained with our tokenizer consistently outperforms FLUX VAE and VA-VAE under the same training steps. Overall, our method is simple, scalable, and establishes a semantically grounded paradigm for continuous tokenizer design.
3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras
Authors: Zixun Huang; Cho-Ying Wu; Yuliang Guo; Xinyu Huang; Liu Ren
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: 3D Gaussian Splatting (3DGS) achieves an appealing balance between rendering quality and efficiency, but relies on approximating 3D Gaussians as 2D projections—an assumption that degrades accuracy, especially under generic large field-of-view (FoV) cameras. 
Despite recent extensions, no prior work has simultaneously achieved both projective exactness and real-time efficiency for general cameras. We introduce 3DGEER, a geometrically exact and efficient Gaussian rendering framework. From first principles, we derive a closed-form expression for integrating Gaussian density along a ray, enabling precise forward rendering and differentiable optimization under arbitrary camera models. To retain efficiency, we propose the Particle Bounding Frustum (PBF), which provides tight ray–Gaussian association without BVH traversal, and the Bipolar Equiangular Projection (BEAP), which unifies FoV representations, accelerates association, and improves reconstruction quality. Experiments on both pinhole and fisheye datasets show that 3DGEER outperforms prior methods across all metrics, runs 5x faster than existing projective exact ray-based baselines, and generalizes to wider FoVs unseen during training—establishing a new state of the art in real-time radiance field rendering.