GenAI4ELab/papercli-papers huggingface dataset:
Neural Theorem Proving for Verification Conditions: A Real-World Benchmark
Authors: Qiyuan Xu; Xiaokun Luan; Renxi Wang; Joshua Ong Jun Leang; Peixin Wang; Haonan Li; Wenda Li; Conrad Watt
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Theorem proving is fundamental to program verification, where the automated proof of Verification Conditions (VCs) remains a primary bottleneck. Real-world program verification frequently encounters hard VCs that existing Automated Theorem Provers cannot prove, leading to a critical need for extensive manual proofs that burden practical application. While Neural Theorem Proving (NTP) has achieved significant success in mathematical competitions, demonstrating the potential of machine learning approaches to formal reasoning, its application to program verification—particularly VC proving—remains largely unexplored. 
Despite existing work on annotation synthesis and verification-related theorem proving, no benchmark has specifically targeted this fundamental bottleneck: automated VC proving.
This work introduces Neural Theorem Proving for Verification Conditions (NTP4VC) and presents the first real-world multi-lingual benchmark for this task. Specifically, from real-world projects such as Linux and Contiki-OS kernel, our benchmark leverages industrial pipelines (Why3 and Frama-C) to generate semantically equivalent test cases across formal languages of Isabelle, Lean, and Rocq. We evaluate large language models (LLMs), both general-purpose and those fine-tuned for theorem proving, on NTP4VC. Results indicate that although LLMs show promise in VC proving, significant challenges remain for program verification, highlighting a large gap and opportunity for future research.
Flow Along the $K$-Amplitude for Generative Modeling
Authors: weitao Du; Jiasheng Tang; Shuning Chang; Yu Rong; Fan Wang; Shengchao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this work, we propose K-Flow, a novel generative learning paradigm that flows along the $K$-amplitude domain, where $K$ is a scaling parameter that organizes projected coefficients (frequency bands), and amplitude refers to the norm of such coefficients. We instantiate K-Flow with three concrete $K$-amplitude transformations: Fourier transformation, Wavelet transformation, and PCA. By incorporating the $K$-amplitude transformations, K-Flow enables flow matching across the scaling parameter as time. We discuss six properties of K-Flow, covering its theoretical foundations, energy and temporal dynamics, and practical applications. Specifically, from the perspective of practical usage, K-Flow allows for steerable generation by controlling the information at different scales. To demonstrate the effectiveness of K-Flow, we conduct experiments on both unconditional and conditional image generation tasks, showing that K-Flow achieves competitive performance. Furthermore, we perform three ablation studies to illustrate how K-Flow leverages the scaling parameter for controlled image generation. Additional results, including scientific applications, are also provided.
TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows
Authors: Zhenglin Cheng; Peng Sun; Jianguo Li; Tao Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation.
These models are typically built upon multi-step frameworks like diffusion and flow matching, which inherently limits their inference efficiency (requiring 40-100 Number of Function Evaluations (NFEs)).
While various few-step methods aim to accelerate the inference, existing solutions have clear limitations.
Prominent distillation-based methods, such as progressive and consistency distillation, either require an iterative distillation procedure or show significant degradation at very few steps (< 4-NFE).
Meanwhile, integrating adversarial training into distillation (e.g., DMD/DMD2 and SANA-Sprint) to enhance performance introduces training instability, added complexity, and high GPU memory overhead due to the auxiliary trained models.
To this end, we propose TwinFlow, a simple yet effective framework for training 1-step generative models that bypasses the need for distillation from pre-trained models and avoids standard adversarial training, making  it ideal for building large-scale, efficient models.
On text-to-image tasks, our method achieves a GenEval score of 0.83 in 1-NFE, outperforming strong baselines like SANA-Sprint (a GAN loss-based framework) and RCGM (a consistency-based framework).
**Notably, we demonstrate the scalability of TwinFlow by transforming Qwen-Image-20B---the current largest open-source multi-modal generative model---into an efficient few-step generator**. With just 1-NFE, our approach matches the performance of the original 100-NFE model on both the GenEval and DPG-Bench benchmarks, reducing computational cost by $100\times$ with minor quality degradation.
Our code and models will be made publicly available.
Rethinking Reasoning in Document Ranking: Why Chain-of-Thought Falls Short
Authors: Xuan Lu; Haohang Huang; Rui Meng; Yaohui Jin; Wenjun Zeng; Xiaoyu Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Document reranking is a key component in information retrieval (IR), aimed at refining initial retrieval results to improve ranking quality for downstream tasks. Recent studies—motivated by large reasoning models (LRMs)—have begun incorporating explicit chain-of-thought (CoT) reasoning into LLM-based rerankers. 
However, the effectiveness of such reasoning for ranking tasks remains underexplored.
In this work, we present the first systematic study of reasoning in reranking across both logits-based pointwise and listwise settings, under both supervised fine-tuning and reinforcement learning. Using diverse benchmarks, including reasoning-intensive datasets BRIGHT and standard IR benchmarks BEIR, we find that reasoning-augmented rerankers consistently underperform their direct counterparts that predict rankings without CoT, despite substantially higher inference costs. Our analysis reveals three core limitations: (i) in pointwise rerankers, reasoning breaks calibration and biases models toward the positive class, raising TPR but lowering TNR, which inflates false positives and degrades ranking in negative-dominant pools; (ii) in listwise rerankers, explicit reasoning improves the fit during training but leads to higher variance and fails to improve performance in both in-domain and out-of-domain evaluations, even when reinforcement learning shortens rationales; and (iii) overall, directly fine-tuned rerankers remain more stable, effective, and robust.  These findings challenge the assumption that explicit reasoning is universally beneficial for reranking. We conclude by highlighting future directions, including calibration-aware scoring for pointwise rerankers and the design of concise, targeted reasoning strategies to mitigate overfitting and overthinking in listwise rerankers.
Reliable Evaluation of MRI Motion Correction: Dataset and Insights
Authors: Kun Wang; Tobit Klug; Stefan Ruschke; Jan Kirschke; Reinhard Heckel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Correcting motion artifacts in scientific and medical imaging is important, as they significantly impact image quality. 
However, evaluating deep learning-based and classical motion correction methods remains fundamentally difficult due to the lack of accessible ground-truth target data. 
To address this challenge, we study three evaluation approaches: real-world evaluation based on reference scans, simulated motion, and reference-free evaluation, each with its merits and shortcomings. 
To enable evaluation with real-world motion artifacts, we release PMoC3D, a dataset consisting of unprocessed $\textbf{P}$aired $\textbf{Mo}$tion-$\textbf{C}$orrupted $\textbf{3D}$ brain MRI data. 
To advance evaluation quality, we introduce MoMRISim, a  feature-space metric trained for evaluating motion reconstructions. 
We assess each evaluation approach and find real-world evaluation together with MoMRISim, while not perfect, to be most reliable. 
Evaluation based on simulated motion systematically exaggerates algorithm performance, and reference-free evaluation overrates oversmoothed deep learning outputs.
SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions
Authors: Jeongyong Yang; Seunghwan Jang; SooJean Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative planners based on flow matching (FM) produce high-quality paths in a single or a few ODE steps, but their sampling dynamics offer no formal safety guarantees and can yield incomplete paths near constraints. We present SafeFlowMatcher, a planning framework that couples FM with control barrier functions (CBFs) to achieve both real-time efficiency and certified safety. SafeFlowMatcher uses a two-phase prediction-correction (PC) integrator: (i) a prediction phase integrates the learned FM once (or a few steps) to obtain a candidate path without intervention; (ii) a correction phase refines this path with a vanishing time‑scaled vector field and a CBF-based quadratic program that minimally perturbs the vector field. We prove a barrier certificate for the resulting flow system, establishing forward invariance of a robust safe set and finite-time convergence to the safe set. In addition, by enforcing safety only on the executed path—rather than all intermediate latent paths—SafeFlowMatcher avoids distributional drift and mitigates local trap problems. Moreover, SafeFlowMatcher attains faster, smoother, and safer paths than diffusion- and FM-based baselines on maze navigation, locomotion, and robot manipulation tasks. Extensive ablations corroborate the contributions of the PC integrator and the barrier certificate.
Lightweight Spatio-Temporal Modeling via Temporally Shifted Distillation for Real-Time Accident Anticipation
Authors: Patrik Patera; Yie-Tarng Chen; Wen-Hsien Fang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Anticipating traffic accidents in real time is critical for intelligent transportation systems, yet remains challenging under edge-device constraints. We propose a lightweight spatio-temporal framework that introduces a temporally shifted distillation strategy, enabling a student model to acquire predictive temporal dynamics from a frozen image-based teacher without requiring a video pre-trained teacher. The student combines a RepMixer spatial encoding with a RWKV-inspired recurrent module for efficient long-range temporal reasoning. To enhance robustness under partial observability, we design a masking memory strategy that leverages memory retention to reconstruct missing visual tokens, effectively simulating occlusions and future events. In addition, multi-modal vision-language supervision enriches semantic grounding. Our framework achieves state-of-the-art performance on multiple real-world dashcam benchmarks while sustaining real-time inference on resource-limited platforms such as the NVIDIA Jetson Orin Nano. Remarkably, it is 3-7$\times$ smaller than leading approaches yet delivers superior accuracy and earlier anticipation, underscoring its practicality for deployment in intelligent vehicles.
ToolTree: Efficient LLM Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning
Authors: Shuo Yang; Caren Han; Yihao Ding; Shuhe Wang; Eduard Hovy
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Model (LLM) agents are increasingly applied to complex, multi-step tasks that require interaction with diverse external tools across various domains. However, current LLM agent tool planning methods typically rely on greedy, reactive tool selection strategies that lack foresight and fail to account for inter-tool dependencies.
In this paper, we present ToolTree, a novel Monte-Carlo tree search-inspired planning paradigm for tool planning. ToolTree explores possible tool usage trajectories using a dual-stage LLM evaluation and bidirectional pruning mechanism that enables the agent to make informed, adaptive decisions over extended tool-use sequences while pruning less promising branches before and after the tool execution. Empirical evaluations across both open-set and closed-set tool planning tasks on 4 benchmarks demonstrate that ToolTree consistently improves performance while keeping the highest efficiency, achieving an average gain of around 10\% compared to the state-of-the-art planning paradigm.
SpareTrain: Fault-Tolerant LLM Training via Low-Cost Dual Modular Redundancy
Authors: Rihae Park; Yeonjae Kim; Seung Yul Lee; Yeonhong Park; Jae W. Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dual Modular Redundancy (DMR) is a highly effective mechanism for detecting silent data corruption (SDC)—a critical reliability concern in large language model (LLM) training—by executing each operation twice. However, its high computation overhead has prevented practical deployment at scale. In this paper, we present SpareTrain, an LLM training system that achieves complete DMR with minimal overhead by repurposing the activation checkpointing mechanism and exploiting idle GPU time. Evaluations on up to 32 H200 GPUs show that SpareTrain improves throughput by 12–35\% over naive DMR, corresponding to only 3–14\% overhead compared to unprotected training, while maintaining full DMR error detection capabilities.
CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
Authors: Zihang Li; Yangdong Ruan; Wenjun Liu; Zhengyang Wang; Tong Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computational efficiency bottlenecks, particularly in knowledge retrieval tasks involving hierarchical structures for Tree-RAG. This paper proposes a Tree-RAG acceleration method based on the improved Cuckoo Filter, which optimizes entity localization during the retrieval process to achieve significant performance improvements. Tree-RAG effectively organizes entities through the introduction of a hierarchical tree structure, while the Cuckoo Filter serves as an efficient data structure that supports rapid membership queries and dynamic updates. The experiment results demonstrate that our method is much faster than baseline methods while maintaining high levels of generative quality. For instance, our method is more than 800% faster than naive Tree-RAG on DART dataset. Our work is available at https://github.com/TUPYP7180/CFT-RAG-2025.
Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization
Authors: Junyoung Yang; Kyungmin Kim; Sangdon Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty. 
In particular, we consider the problem of online uncertainty quantification, 
where data points arrive sequentially. 
Online conformal prediction is a principled online uncertainty quantification method that dynamically constructs a prediction set at each time step.
While existing methods for online conformal prediction provide long-run coverage guarantees without any distributional assumptions, they typically assume a *full feedback* setting in which the true label is always observed. 
In this paper, we propose a novel learning method for online conformal prediction with *partial feedback* from an adaptive adversary—a more challenging setup where the true label is revealed only when it lies inside the constructed prediction set.
Specifically, we formulate online conformal prediction as an adversarial bandit problem by treating each candidate prediction set as an arm. 
Building on an existing algorithm for adversarial bandits, 
our method achieves a long-run coverage guarantee by explicitly establishing its connection to the regret of the learner.
Finally, we empirically demonstrate the effectiveness of our method in both independent and identically distributed (i.i.d.) and non-i.i.d. settings, 
showing that it successfully controls the miscoverage rate while maintaining a reasonable size of the prediction set.
PALC: Preference Alignment via Logit Calibration
Authors: SANGHYUN LEE; Hoh Peter In
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Aligning Large Language Models with human preferences typically requires computationally intensive training or complex reward architectures. We introduce PALC (Preference Alignment via Logit Calibration), a parameter-efficient framework that achieves test-time alignment through a novel intervention strategy: direct calibration in vocabulary space. Unlike existing methods that manipulate entangled hidden representations or rely on external reward models, PALC operates at the logit layer where each dimension corresponds to a distinct token, providing interpretable and efficient control. Our approach employs a bottleneck architecture that learns to compress the base model's hidden states and generate position-dependent calibration vectors, requiring only a fraction of the base model's parameters. Through this design, PALC sidesteps the superposition problem inherent in representation engineering while eliminating the computational overhead of guided decoding methods. A single scaling factor enables runtime adjustment of alignment strength without retraining, allowing practitioners to balance between preserving model capabilities and enforcing preferences. Experiments demonstrate that PALC outperforms most test-time alignment methods while maintaining near-baseline inference speed. Our ablations reveal that human preferences concentrate on surprisingly low-dimensional manifolds, validating our architectural choices. By establishing vocabulary-space intervention as an effective alignment paradigm, PALC makes preference alignment accessible for resource-constrained deployments where traditional methods are infeasible, opening new avenues for scalable and adaptive AI alignment.
WebDS: An End-to-End Benchmark for Web-based Data Science
Authors: Ethan Hsu; Hong Meng Yam; Ines Bouissou; Aaron Murali John; Raj Thota; Josh Koe; Vivek Sarath Putta; G K Dharesan; Alexander Spangher; Shikhar Murty; Tenghao Huang; Christopher D Manning
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Many real-world data science tasks involve complex web-based interactions: finding appropriate data available on the internet, synthesizing multimodal data from different locations, and producing summarized analyses. Existing web benchmarks often focus on simplistic interactions and often do not require diverse tool-using capabilities. Conversely, traditional data science benchmarks typically concentrate on static, highly structured datasets and do not assess end-to-end workflows that encompass data acquisition, cleaning, analysis, and insight generation. In response, we introduce WebDS, the first end-to-end web-based data science benchmark. It comprises 870 web-based data science tasks across 29 diverse websites from structured government data portals to unstructured news media, challenging agents to perform complex, multi-step, tool-based operations, across heterogeneous data formats, to better reflect the realities of modern data analytics. Evaluations of current SOTA LLM agents indicate significant performance gaps in accomplishing these tasks. For instance, Browser Use, which accomplishes 80% of tasks on Web Voyager, completes only 15% of tasks in WebDS, which our analysis suggests is due to new failure modes like poor information grounding, repetitive behavior and shortcut-taking that agents performing WebDS' tasks display. By contrast, humans achieve around 90% accuracy, highlighting a substantial gap between current agents and human performance. By providing a more robust and realistic testing ground, WebDS sets the stage for significant advances in the development of practically useful LLM-based data science.
Enhancing Vision Transformers for Object Detection via Context-Aware Token Selection and Packing
Authors: Tianyi Zhang; Baoxin Li; Jae-sun Seo; Yu Cao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In recent years, the long-range attention mechanism of vision transformers has driven significant performance breakthroughs across various computer vision tasks. However, these advancements come at the cost of inefficiency and substantial computational expense, especially when dealing with sparse data. While sparse attention mechanisms have been introduced to mitigate these issues by pruning tokens involved in attention, they often lack context-awareness and intelligence, frequently limiting the number of selected tokens uniformly across different inputs. To address these challenges, we propose a novel algorithm: Select and Pack Attention (SPA). SPA dynamically selects informative tokens using a low-cost gating layer and packs these selected tokens into new batches, allowing for a variable number of tokens to be used in GPU batch training and inference. Through extensive experiments on diverse datasets and multiple computer vision tasks, our method demonstrates superior performance and efficiency, including a 0.5-2.7 AP improvement in object detection and a 10.9%-24.9% reduction in computation.
FETAL-GAUGE: A BENCHMARK FOR ASSESSING VISION-LANGUAGE MODELS IN FETAL ULTRASOUND
Authors: Hussain Alasmawi; Numan Saeed; Mohammad Yaqub
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal health monitoring. Deep learning has the potential to enhance sonographers' efficiency and support the training of new practitioners. Vision-Language Models (VLMs) are particularly promising for ultrasound interpretation, as they can jointly process images and text to perform multiple clinical tasks within a single framework. However, despite the expansion of VLMs, no standardized benchmark exists to evaluate their performance in fetal ultrasound imaging. This gap is primarily due to the modality’s challenging nature, operator dependency, and the limited public availability of datasets. To address this gap, we present Fetal-Gauge, the first and largest visual question answering benchmark specifically designed to evaluate VLMs across various fetal ultrasound tasks. Our benchmark comprises over 42,000 images and 93,000 question-answer pairs, spanning anatomical plane identification, visual grounding of anatomical structures, fetal orientation assessment, clinical view conformity, and clinical diagnosis. We systematically evaluate several state-of-the-art VLMs, including general-purpose and medical-specific models, and reveal a substantial performance gap: the best-performing model achieves only 55\% accuracy, far below clinical requirements. Our analysis identifies critical limitations of current VLMs in fetal ultrasound interpretation, highlighting the urgent need for domain-adapted architectures and specialized training approaches. Fetal-Gauge establishes a rigorous foundation for advancing multimodal deep learning in prenatal care and provides a pathway toward addressing global healthcare accessibility challenges. Our benchmark is publicly available at https://github.com/BioMedIA-MBZUAI/FETAL-GAUGE