GenAI4ELab/papercli-papers huggingface dataset:
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Authors: Jiaru Zou; Soumya Roy; Vinay Kumar Verma; Ziyi Wang; David Wipf; Pan Lu; Sumit Negi; James Zou; Jingrui He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. 
Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising text-only reasoning steps, struggle with table-specific operations such as sub-table retrieval and schema interaction, leading to critical performance bottlenecks. To address this limitation, we propose TaTToo, a novel table-grounded PRM framework that (i) reasons explicitly over tabular reasoning steps and (ii) integrates tool-based verification to provide precise reward supervision. Concretely, we first design a scalable data curation pipeline that constructs over 60k high-quality step-level annotations by integrating table verification rationales with tool-based executions. Building on the collected data, we train TaTToo with a dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use reasoning patterns, followed by reinforcement learning with tool-grounded reward shaping to align our model with table-based verification.
We provide a comprehensive evaluation of the policy improvement induced by our newly designed PRM.
Across 5 challenging tabular reasoning benchmarks covering numerical reasoning, fact-checking, and data analysis, TaTToo improves downstream policy LRMs by 30.9\% at inference, surpasses strong PRM baselines such as Qwen-2.5-Math-PRM-72B with only 8B parameters, and demonstrates strong generalizability across diverse TTS strategies.
SumRA: Parameter Efficient Fine-tuning with Singular Value Decomposition and Summed Orthogonal Basis
Authors: Chin Yuen Kwok; Yongsen Zheng; Jia Qi Yip; Kwok-Yan Lam; Eng Siong Chng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Parameter-efficient fine-tuning (PEFT) aims to adapt large pretrained speech models using fewer trainable parameters while maintaining performance. Low-Rank Adaptation (LoRA) achieves this by decomposing weight updates into two low-rank matrices, $A$ and $B$, such that $W'=W_0+BA$. Previous studies showed that freezing $A$ and only updating $B$ can reduce trainable parameters and achieve performance close to standard LoRA, where $A$ is initialized using the principal singular vectors of $W_0$ obtained via singular value decomposition (SVD). However, because $A$ is typically initialized with only the leading singular vectors, its representation capacity is confined to a narrow subspace of the model’s knowledge. To overcome this limitation, we propose SumRA, which initializes each row of $A$ as a sum of multiple singular vectors chosen from beyond the leading components, thereby enabling $A$ to influence a larger portion of the model’s knowledge space. Experiments on multilingual automatic speech recognition (ASR) tasks show that by adapting Whisper to five new languages from Common Voice with only 10 hours of data each, our method improves word error rate from 14.42\% to 12.41\% over LoRA baselines while using 50\% less trainable parameters.
TOUCH: Text-guided Controllable Generation of Free-Form Hand-Object Interactions
Authors: Guangyi Han; Wei Zhai; Yuhang Yang; Yang Cao; Zheng-Jun Zha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hand-object interaction (HOI) is fundamental for humans to express intent. Existing HOI generation research is predominantly confined to fixed grasping patterns, where control is tied to physical priors such as force closure or generic intent instructions, even when expressed through elaborate language. Such an overly general conditioning imposes a strong inductive bias for stable grasps, thus failing to capture the diversity of daily HOI. To address these limitations, we introduce $\textbf{Free-Form HOI Generation}$, which aims to generate controllable, diverse, and physically plausible HOI conditioned on fine-grained intent, extending HOI from grasping to free-form interactions, like pushing, poking, and rotating. To support this task, we construct $\textbf{WildO2}$, an in-the-wild diverse 3D HOI dataset, which includes diverse HOI derived from internet videos. Specifically, it contains 4.4k unique interactions across 92 intents and 403 object categories, each with detailed semantic annotations. Building on this dataset, we propose $\textbf{TOUCH}$, a three-stage framework centered on a multi-level diffusion model that facilitates fine-grained semantic control to generate versatile hand poses beyond grasping priors. This process leverages explicit contact modeling for conditioning and is subsequently refined with contact consistency and physical constraints to ensure realism. Comprehensive experiments demonstrate our method's ability to generate controllable, diverse, and physically plausible hand interactions representative of daily activities.
HiCache: A Plug-in Scaled-Hermite Upgrade for Taylor-Style Cache-then-Forecast Diffusion Acceleration
Authors: Liang Feng; Shikang Zheng; Jiacheng Liu; Yuqi Lin; Qinming Zhou; Peiliang Cai; Xinyu Wang; Junjie Chen; Chang Zou; Yue Ma; Linfeng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have achieved remarkable success in content generation but suffer from prohibitive computational costs due to iterative sampling. While recent feature caching methods tend to accelerate inference through temporal extrapolation, these methods still suffer from severe quality loss due to the failure in modeling the complex dynamics of feature evolution. To solve this problem, this paper presents HiCache (Hermite Polynomial-based Feature Cache), a training-free acceleration framework that fundamentally improves feature prediction by aligning mathematical tools with empirical properties. Our key insight is that feature derivative approximations in Diffusion Transformers exhibit multivariate Gaussian characteristics, motivating the use of Hermite polynomials, the potentially theoretically optimal basis for Gaussian-correlated processes. Besides, we introduce a dual-scaling mechanism that ensures numerical stability while preserving predictive accuracy, which is also effective when applied standalone to TaylorSeer. Extensive experiments demonstrate HiCache's superiority: achieving \$5.55\times\$ speedup on FLUX.1-dev while exceeding baseline quality, maintaining strong performance across text-to-image, video generation, and super-resolution tasks. Moreover, HiCache can be naturally added to the previous caching methods to enhance their performance, e.g., improving ClusCa from \$0.9480\$ to \$0.9840\$ in terms of image rewards. Our code is included in the supplementary material, and will be released on GitHub.
FARTrack: Fast Autoregressive Visual Tracking with High Performance
Authors: Guijie Wang; Tong Lin; Yifan Bai; Anjia Cao; Shiyi Liang; Wangbo Zhao; Xing Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inference speed and tracking performance are two critical evaluation metrics in the field of visual tracking. However, high-performance trackers often suffer from slow processing speeds, making them impractical for deployment on resource-constrained devices. To alleviate this issue, we propose $\textbf{FARTrack}$, a $\textbf{F}$ast $\textbf{A}$uto-$\textbf{R}$egressive $\textbf{T}$racking framework. Since autoregression emphasizes the temporal nature of the trajectory sequence, it can maintain high performance while achieving efficient execution across various devices. FARTrack introduces $\textbf{Task-Specific Self-Distillation}$ and $\textbf{Inter-frame Autoregressive Sparsification}$, designed from the perspectives of $\textbf{shallow-yet-accurate distillation}$ and $\textbf{redundant-to-essential token optimization}$, respectively. Task-Specific Self-Distillation achieves model compression by distilling task-specific tokens layer by layer, enhancing the model's inference speed while avoiding suboptimal manual teacher-student layer pairs assignments. Meanwhile, Inter-frame Autoregressive Sparsification sequentially condenses multiple templates, avoiding additional runtime overhead while learning a temporally-global optimal sparsification strategy. FARTrack demonstrates outstanding speed and competitive performance. It delivers an AO of 70.6\% on GOT-10k in real-time. Beyond, our fastest model achieves a speed of 343 FPS on the GPU and 121 FPS on the CPU. Source code is available at: https://github.com/MIV-XJTU/FARTrack.git
LLM Pretraining with Continuous Concepts
Authors: Jihoon Tack; Jack Lanchantin; Jane Yu; Andrew Cohen; Ilia Kulikov; Janice Lan; Shibo Hao; Yuandong Tian; Jason E Weston; Xian Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perplexity. We propose Continuous Concept Mixing (CoCoMix), a novel pretraining framework that combines discrete next token prediction with continuous concepts. Specifically, CoCoMix predicts ``continuous concepts'' learned from a pretrained sparse autoencoder and mixes them into the model's hidden state by interleaving with token hidden representations. Through experiments on multiple benchmarks, including language modeling and downstream reasoning tasks, we show that CoCoMix is more sample efficient and consistently outperforms standard next token prediction and knowledge distillation. We find that combining both concept learning and interleaving in an end-to-end framework is critical to performance gains. Furthermore, CoCoMix enhances interpretability and steerability by allowing direct inspection and modification of the predicted concept, offering a transparent way to guide the model’s internal reasoning process.
Context Tokens are Anchors: Understanding the Repeat Curse in dMLLMs from an Information Flow Perspective
Authors: Qiyan Zhao; Xiaofeng Zhang; Shuochen Chang; Qianyu Chen; Xiaosong Yuan; Xuhang Chen; Luoqi Liu; Jiajun Zhang; Xu-Yao Zhang; Da-Han Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent diffusion-based Multimodal Large Language Models (dMLLMs) suffer from high inference latency and therefore rely on caching techniques to accelerate decoding. However, the application of cache mechanisms often introduces undesirable repetitive text generation, a phenomenon we term the Repeat Curse. To better investigate underlying mechanism behind this issue, we analyze repetition generation through the lens of information flow. Our work reveals three key findings: (1) context tokens aggregate semantic information as anchors and guide the final predictions; (2) as information propagates across layers, the entropy of context tokens converges in deeper layers, reflecting the model’s growing prediction certainty; (3) Repetition is typically linked to disruptions in the information flow of context tokens and to the inability of their entropy to converge in deeper layers. Based on these insights, we present CoTA, a plug-and-play method for mitigating repetition. CoTA enhances the attention of context tokens to preserve intrinsic information flow patterns, while introducing a penalty term to the confidence score during decoding to avoid outputs driven by uncertain context tokens. With extensive experiments, CoTA demonstrates significant effectiveness in alleviating repetition and achieves consistent performance improvements on general tasks. Code is available at https://github.com/ErikZ719/CoTA
Learning Collective Variables from BioEmu with Time-Lagged Generation
Authors: Seonghyun Park; Kiyoung Seong; Soojung Yang; Rafael Gomez-Bombarelli; Sungsoo Ahn
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Molecular dynamics is crucial for understanding molecular systems but its applicability is often limited by the vast timescales of rare events like protein folding. Enhanced sampling techniques overcome this by accelerating the simulation along key reaction pathways, which are defined by collective variables (CVs). However, identifying effective CVs that capture the slow, macroscopic dynamics of a system remains a major bottleneck. This work proposes a novel framework coined BioEmu-CV that learns these essential CVs automatically from BioEmu, a recently proposed foundation model for generating protein equilibrium samples. In particular, we re-purpose BioEmu to learn time-lagged generation conditioned on the learned CV, i.e., predict the distribution of molecular states after a certain amount of time. This training process promotes the CV to encode only the slow, long-term information while disregarding fast, random fluctuations. We validate our learned CV on fast-folding proteins with two key applications: (1) estimating free energy differences using on-the-fly probability enhanced sampling and (2) sampling transition paths with steered molecular dynamics. Our empirical study also serves as a new systematic and comprehensive benchmark for MLCVs on fast-folding proteins larger than Alanine Dipeptide.
Unlocking Full Efficiency of Token Filtering in Large Language Model Training
Authors: Di Chai; LI Pengbo; Feiyuan Zhang; Yilun Jin; Han Tian; Kaiqiang Xu; Binhang Yuan; Dian Shen; Junxue Zhang; Kai Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Token filtering has been proposed to enhance the utility of large language models (LLMs) by eliminating inconsequential tokens during training. While using fewer tokens is expected to reduce computational workloads, existing methods have not yet achieved a real-world efficiency boost. This is primarily due to two factors: (1) existing work has inadequate sparsity for speedup, and (2) token filtering operates within a sparsity range that is non-standard in existing machine learning (ML) libraries and thus cannot be efficiently supported. This paper presents Centrifuge, a system that leverages algorithm and system co-design to unleash the full efficiency of token filtering in LLM training. At the algorithm level, Centrifuge filters activations of inconsequential tokens in the attention backward kernel to amplify the sparsity in backward computation. At the system level, Centrifuge proposes an automatic workflow that transforms sparse GEMM into dimension-reduced dense GEMM for optimized efficiency using standard ML libraries. Evaluations on models with various scales—from 1.1B to 40B—demonstrate that Centrifuge reduces backpropagation time by up to 49.9\% and end-to-end training time by up to 34.7\% when filtering 50\% of tokens. Utility assessments indicate that Centrifuge preserves the utility benefits of token filtering and significantly enhances model performance by up to 26.6\% compared to standard training. Centrifuge is designed for seamless integration into existing LLM training frameworks, enabling systems already utilizing token filtering to accelerate training with just one line of code.
FrameThinker: Learning to Think with Long Videos via Multi-Turn Frame Spotlighting
Authors: Zefeng He; Xiaoye Qu; Yafu Li; Siyuan Huang; Daizong Liu; Yu Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application to long video reasoning is hindered by uniform frame sampling and static textual reasoning, which are inefficient and struggle to handle visually intensive video tasks. 
To overcome these challenges, in this paper, we introduce 
the concept of thinking with long videos and propose a novel framework FrameThinker. Within this framework, LVLMs are able to iteratively interrogate video content. 
Developing such video reasoning capabilities in LVLMs presents notable challenges, particularly in adapting the model to new video actions (e.g. select frame), and designing reward functions to guide LVLMs to adopt the newly introduced action. 
To solve these challenges, 
we propose a two-phase training strategy, first employing Supervised Fine-Tuning (SFT) to instill fundamental action capabilities, followed by Reinforcement Learning (RL) to optimize a strategic decision-making policy.
Notably, in this RL phase, we conduct an in-depth and comprehensive exploration of the reward design for each action and format reward. 
Extensive experiments on reasoning benchmarks like Video-Holmes, LongVideo-Reason, and long-video understanding benchmarks such as LongVideoBench, MLVU, VideoMME, and LVBench, demonstrate that FrameThinker gets a significant average improvement of +10.4\% over baselines while drastically reducing the number of processed frames. 
Most notably, our 7B model, FrameThinker establishes a new state-of-the-art on LongVideo-Reason, achieving 76.1\% accuracy using an average of only 20.6 frames. This not only outperforms the competitive LongVILA-R1 (72.0\%) but does so with over 20x fewer frames (vs. 512), demonstrating unparalleled efficiency and effectiveness. 
Our code is available at:
\url{https://github.com/lcqysl/FrameThinker}.
UniCA: Unified Covariate Adaptation for Time Series Foundation Model
Authors: Lu Han; Yu Liu; Lan Li; Qiwen Deng; Jian Jiang; Yinbo sun; Zhe Yu; Binfeng Wang; Xingyu Lu; Lintao Ma; Han-Jia Ye; De-Chuan Zhan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often \emph{heterogeneous covariates}—such as categorical variables and multimodal data (e.g., images, text)—which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs. Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios. Code: https://github.com/hanlu-nju/UniCA.
Flow-Disentangled Feature Importance
Authors: Xingshu Chen; Yifeng Guo; Jin-Hong Du
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Quantifying feature importance with valid statistical uncertainty is central to interpretable machine learning, yet classical model-agnostic methods often fail under feature correlation, producing unreliable attributions and compromising inference. Statistical approaches that address correlation through feature decorrelation have shown promise but remain restricted to $\ell_2$ loss, limiting their applicability across diverse machine learning tasks. We introduce Flow-Disentangled Feature Importance (FDFI), a model-agnostic framework that resolves these limitations by combining principled statistical inference with computational flexibility. FDFI leverages flow matching to learn flexible disentanglement maps that not only handle arbitrary feature distributions but also provide an interpretable pathway for understanding how importance is attributed through the data's correlation structure. The framework generalizes the decorrelation-based attribution to general differentiable loss functions, enabling statistically valid importance assessment for black-box predictors across regression and classification. We establish statistical inference theory, deriving semiparametric efficiency of FDFI estimators, which enables valid confidence intervals and hypothesis testing with Type I error control. Experiments demonstrate that FDFI achieves substantially higher statistical power than removal-based and conditional permutation approaches, while maintaining robust and interpretable attributions even under severe interdependence. These findings hold across synthetic benchmarks and a broad collection of real datasets spanning diverse domains.
Seeing What’s Not There: Negation Understanding Needs More Than Training
Authors: Bhuvan Aggarwal; Amit More; Mudit Soni; S Divakar Bhat
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding the negation in a sentence is an important part of compositional
understanding and logic in natural language. Many practical AI applications, such
as autonomous driving, include precise instruction with negations. For example,
following instruction to an AI assistant ”locate a parking spot without a vehicle”
requires the assistant to not confuse between presence and absence of vehicles. Al-
though joint embedding-based Vision Language Models (VLMs) like CLIP have
revolutionized multi-modal tasks, they struggle to interpret negation. To address
this limitation, recently many works proposed to solve the problem through a data-
centric approach by introducing additional datasets with hard-negative samples for
both image and text data. Contrary to these approaches, we present a zero-shot
approach to tackle the negation understanding problem. We probe the properties
of CLIP text embeddings and show that they follow compositional arithmetic op-
erations, which allow the addition or removal of semantic information directly in
the embedding space. We then present a rule-based approach to extract negated
text from given caption and then use it to explicitly remove corresponding se-
mantic information from original embedding, improving negation understanding
in VLMs. Our approach does not require expensive training process to induce
negation understanding into the model, and achieves the state-of-the-art perfor-
mance on popular benchmark for negation understanding. We improve baseline
CLIP model performance on NegBench from 25.5% to 67.0% for MCQ and from
50.9% to 56.1% for retrieval tasks. Even NegCLIP model which is fine-tuned on
negtion datasets, our approach boosts its MCQ accuracy from 54.03% to 66.22%
and retrieval accuracy from 59.25% to 60.1% showing strong performance.