GenAI4ELab/papercli-papers huggingface dataset:
MergOPT: A Merge-Aware Optimizer for Robust Model Merging
Authors: Enneng Yang; Qun Yang; Peng Wang; Anke Tang; Guibing Guo; Xiaochun Cao; Li Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model merging aims to integrate multiple independently fine-tuned expert models into a single model while preserving the knowledge of all experts. However, existing approaches mainly address parameter conflicts at the merging stage and overlook the role of the fine-tuning process, which often leads to significant post-merge performance degradation. To address this limitation, we propose a novel merging-aware optimizer (abbreviated as MergOPT) that injects principled merge-induced parameter shifts into the weight update steps so that the fine-tuned model exhibits a more stable loss landscape under subsequent merging operations. Specifically, we first formulate model merging as a distributionally robust optimization problem in the weight space: the parameters of other experts to be merged are viewed as adversarial merge-offsets, and fine-tuning adapts to the worst-case merging scenario. Building on this formulation, we analyze the distribution of parameter updates and the effects of merging hyperparameters, from which we derive a merging-guided feasible region for weight shifts. Finally, extensive experiments across four large language models (LLMs) and one vision model show that our approach consistently outperforms standard fine-tuning, yielding an average relative gain of 3.5\% and a maximum gain of 9.5\% across four merging strategies when merging seven experts.
DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle
Authors: Yuheng Tang; Kaijie Zhu; Bonan Ruan; Chuqi Zhang; Michael Yang; Hongwei Li; Suyue Guo; Tianneng Shi; Zekun Li; Christopher Kruegel; Giovanni Vigna; Dawn Song; William Yang Wang; Lun Wang; Yangruibo Ding; Zhenkai Liang; Wenbo Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown.
Different from pure code generation, handling the DevOps cycle in real-world software, including developing, deploying, and managing, requires analyzing large-scale projects, understanding dynamic program behaviors, leveraging domain-specific tools, and making sequential decisions. 
However, existing benchmarks focus on isolated problems and lack environments and tool interfaces for DevOps.
We introduce DevOps-Gym, the first end-to-end benchmark for evaluating AI agents across core DevOps workflows: build and configuration, monitoring, issue resolving, and test generation. 
DevOps-Gym includes 700+ real-world tasks collected from 30+ projects in Java and Go.
We develop a semi-automated data collection mechanism with rigorous and non-trivial expert efforts in ensuring the task coverage and quality.
Our evaluation of state-of-the-art models and agents reveals fundamental limitations: they struggle with issue resolving and test generation in Java and Go, and remain unable to handle new tasks such as monitoring and build and configuration.
These results highlight the need for essential research in automating the full DevOps cycle with AI agents.
Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
Authors: Youssef Chaabouni; David Gamarnik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study sparse recovery when observations come from mixed-quality sources: a small collection of high-quality measurements with small noise variance and a larger collection of lower-quality measurements with higher variance. For this heterogeneous-noise setting, we establish sample-size conditions for information-theoretic and algorithmic recovery. On the information-theoretic side, we show that it is sufficient for $(n_1, n_2)$ to satisfy a linear trade-off defining the _Price of Quality_: the number of low-quality samples needed to replace one high-quality sample. In the agnostic setting, where the decoder is completely agnostic to the quality of the data, it is uniformly bounded, and in particular one high-quality sample is never worth more than two low-quality samples for this sufficient condition to hold. In the informed setting, where the decoder is informed of per-sample variances, the price of quality can grow arbitrarily large. On the algorithmic side, we analyze the LASSO in the agnostic setting and show that the recovery threshold matches the homogeneous-noise case and only depends on the average noise level, revealing a striking robustness of computational recovery to data heterogeneity. Together, these results give the first conditions for sparse recovery with mixed-quality data and expose a fundamental difference between how the information-theoretic and algorithmic thresholds adapt to changes in data quality.
Flow Caching for Autoregressive Video Generation
Authors: Yuexiao Ma; Xuzhe Zheng; Jing Xu; Xiwei Xu; Feng Ling; Xiawu Zheng; Huafeng Kuang; Huixia Li; XING WANG; Xuefeng Xiao; Fei Chao; Rongrong Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. However, this sequential generation process is notoriously slow. While caching strategies have proven effective for accelerating traditional video diffusion models, existing methods assume uniform denoising across all frames—an assumption that breaks down in autoregressive models where different video chunks exhibit varying similarity patterns at identical timesteps.
In this paper, we present \textbf{FlowCache}, the first caching framework specifically designed for autoregressive video generation. Our key insight is that each video chunk should maintain independent caching policies, allowing fine-grained control over which chunks require recomputation at each timestep. We introduce a chunkwise caching strategy that dynamically adapts to the unique denoising characteristics of each chunk, complemented by a joint importance–redundancy optimized KV cache compression mechanism that maintains fixed memory bounds while preserving generation quality.
Our method achieves remarkable speedups of $\textbf{2.38}\times$ on MAGI-1 and $\textbf{6.7}\times$ on SkyReels-V2, with negligible quality degradation (VBench: $0.87\uparrow$ and $0.79\downarrow$ respectively). These results demonstrate that FlowCache, successfully unlocks the potential of autoregressive models for real-time, ultra-long video generation—establishing a new benchmark for efficient video synthesis at scale. The code is available at https://github.com/mikeallen39/FlowCache.
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Authors: Pranaya Jajoo; Harshit Sikchi; Siddhant Agarwal; Amy Zhang; Scott Niekum; Martha White
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Behavioral Foundation Models (BFMs) have been recently successful in producing agents with the capabilities to adapt to any unknown reward or task. In reality, these methods are only able to produce near-optimal policies for the reward functions that are in the span of some pre-existing _state features_. Naturally, their efficiency relies heavily on the choice of state features that they use. As a result, these BFMs have used a wide variety of complex objectives, often sensitive to environment coverage, to train task spanning features with different inductive properties. With this work, our aim is to examine the question: are these complex representation learning objectives necessary for zero-shot RL? Specifically, we revisit the objective of self-supervised next-state prediction in latent space for state feature learning, but observe that such an objective alone is prone to increasing state-feature similarity, and subsequently reducing span of reward functions that we can represent optimal policies for. We propose an approach, RLDP, that adds a simple regularization to maintain feature diversity and can match or surpass state-of-the-art complex representation learning methods for zero-shot RL. Furthermore, we demonstrate the prior approaches diverge in low-coverage scenarios where RLDP still succeeds.
Transductive Visual Programming: Evolving Tool Libraries from Experience for Spatial Reasoning
Authors: Shengguang Wu; Xiaohan Wang; Yuhui Zhang; Hao Zhu; Serena Yeung-Levy
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spatial reasoning in 3D scenes requires precise geometric calculations that challenge vision-language models. Visual programming addresses this by decomposing problems into steps calling specialized tools, yet existing methods rely on either fixed toolsets or speculative tool induction before solving problems, resulting in suboptimal programs and poor utilization of induced tools. We present Transductive Visual Programming (TVP), a novel framework that builds new tools from its own experience rather than speculation. TVP first solves problems using basic tools while accumulating experiential solutions into an Example Library, then abstracts recurring patterns from these programs into reusable higher-level tools for an evolving Tool Library. This allows TVP to tackle new problems with increasingly powerful tools learned from experience. On Omni3D-Bench, TVP achieves state-of-the-art performance, outperforming GPT-4o by 22% and the previous best visual programming system by 11%. Our transductively learned tools are used 5x more frequently as core program dependency than inductively created ones, demonstrating more effective tool discovery and reuse. The evolved tools also show strong generalization to unseen spatial tasks, achieving superior performance on benchmarks from SpatialScore-Hard collection without any testset-specific modification. Our work establishes experience-driven transductive tool creation as a powerful paradigm for building self-evolving visual programming agents that effectively tackle challenging spatial reasoning tasks. We release our code at https://transductive-visualprogram.github.io/.
NDAD: Negative-Direction Aware Decoding for Large Language Models via Controllable Hallucination Signal Injection
Authors: Panjia Qiu; Mingyuan Fan; Cen Chen; Daixin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have recently achieved impressive progress in knowledge-intensive and reasoning tasks. However, their tendency to produce fabricated or factually inconsistent content remains a fundamental challenge to their practical deployment. To address this issue, we propose Negative-Direction Aware Decoding (NDAD), a novel decoding method that identifies and exploits hallucination signals as repulsive directions in the model’s representation space, thereby improving factual adherence without retraining. Specifically, NDAD elicits hallucination-leaning signals by selectively masking critical attention heads, which exposes unstable hypotheses that the model would otherwise amplify during generation. To regulate the influence of these signals, NDAD employs two complementary weights: a global alignment weight measuring how well the induced signal aligns with the layer’s native activations (thus quantifying its referential utility) and a local weight estimating whether low-probability tokens in the masked distribution are likely to evolve toward the final output. Based on the weights, we derive a latent hallucination distribution that serves as the negative direction. A lightweight gradient-descent step then subtracts mass from hallucination-prone regions of the output distribution, adjusting the final logits while preserving the model’s high-confidence predictions. Extensive experiments across multiple LLMs and diverse benchmark datasets demonstrate that NDAD consistently enhances factual reliability without requiring additional training or external knowledge.
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
Authors: Yuanchang Zhou; Siyu Hu; Xiangyu Zhang; Hongyu Wang; Guangming Tan; Weile Jia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials science. Equivariant MLIPs achieve state-of-the-art accuracy in a wide range of benchmarks by incorporating equivariant inductive bias. However, the reliance on tensor products and high-degree representations makes them computationally costly. This raises a fundamental question: as quantum mechanical-based datasets continue to expand, can we develop a more compact model to thoroughly exploit high-dimensional atomic interactions? 
In this work, we present MatRIS (\textbf{Mat}erials \textbf{R}epresentation and \textbf{I}nteraction \textbf{S}imulation), an invariant MLIP that introduces attention-based modeling of three-body interactions. MatRIS leverages a novel separable attention mechanism with linear complexity $O(N)$, enabling both scalability and expressiveness. MatRIS delivers accuracy comparable to that of leading equivariant models on a wide range of popular benchmarks (Matbench-Discovery, MatPES, MDR phonon, Molecular dataset, etc). Taking Matbench-Discovery as an example, MatRIS achieves an F1 score of up to 0.847 and attains comparable accuracy at a lower training cost. The work indicates that our carefully designed invariant models can match or exceed the accuracy of equivariant models at a fraction of the cost, shedding light on the development of accurate and efficient MLIPs.
SK2Decompile: LLM-based Two-Phase Binary Decompilation from Skeleton to Skin
Authors: Hanzhuo Tan; Weihao Li; Xiaolong Tian; Siyi Wang; Jiaming Liu; Jing Li; Yuqun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have emerged as a promising approach for binary decompilation. However, the existing LLM-based decompilers still are somewhat limited in effectively presenting a program's source-level structure with its original identifiers.
To mitigate this, we introduce SK2Decompile, a novel two-phase approach to decompile from the skeleton (semantic structure) to the skin (identifier) of programs. Specifically, we first apply a Structure Recovery model to translate a program's binary code to an Intermediate Representation (IR) as deriving the program's "skeleton", i.e., preserving control flow and data structures while obfuscating all identifiers with generic placeholders. We also apply reinforcement learning to reward the model for producing program structures that adhere to the syntactic and semantic rules expected by compilers. Second, we apply an Identifier Naming model to produce meaningful identifiers which reflect actual program semantics as deriving the program's "skin". We train the Identifier Naming model with a separate reinforcement learning objective that rewards the semantic similarity between its predictions and the reference code. Such a two-phase decompilation process facilitates advancing the correctness and readability of decompilation independently.
Our evaluations indicate that SK2Decompile, significantly outperforms the SOTA baselines, achieving 21.6% average re-executability rate gain over GPT-5-mini on the HumanEval dataset and 29.4% average R2I improvement over Idioms on the GitHub2025 benchmark.
Incentivizing Agentic Reasoning in LLM Judges via Tool-Integrated Reinforcement Learning
Authors: Ran Xu; Jingjing Chen; Jiayu Ye; Yu Wu; Jun Yan; Carl Yang; Hongkun Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) are widely used as judges to evaluate response quality, providing a scalable alternative to human evaluation. However, most LLM judges operate solely on intrinsic text-based reasoning, limiting their ability to verify complex constraints or perform accurate computation. Motivated by the success of tool-integrated reasoning (TIR) in numerous tasks, we propose TIR-Judge, an end-to-end RL framework for training LLM judges that integrates a Python executor for precise evaluation. TIR-Judge is built on three principles: (i) diverse training across verifiable and non-verifiable domains, (ii) flexible judgment formats (pointwise, pairwise, listwise), and (iii) iterative RL that enables bootstrapping directly from a base model without distillation. On six public benchmarks, TIR-Judge surpasses strong reasoning-based judges by up to 6.4% (pointwise) and 7.7% (pairwise), and achieves listwise performance comparable to Claude-Opus-4 despite having only 8B parameters. Remarkably, TIR-Judge-Zero—trained entirely without distillation—matches the performance of the distilled variants, showing that tool-augmented judges can self-improve through iterative reinforcement learning.
Counterfactual Structural Causal Bandits
Authors: Min Woo Park; Sanghack Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Causal reasoning lies at the heart of robust and generalizable decision-making, and the *Pearl Causal Hierarchy* provides a formal language for distinguishing between observational ($\mathcal{L}_1$), interventional ($\mathcal{L}_2$), and counterfactual ($\mathcal{L}_3$) levels of reasoning. Existing bandit algorithms that leverage causal knowledge have primarily operated within the $\mathcal{L}_1$ and $\mathcal{L}_2$ regimes, treating each realizable and physical intervention as a distinct arm. That is, they have largely excluded counterfactual quantities due to their perceived inaccessibility. In this paper, we introduce a *counterfactual structural causal bandit* (ctf-SCB) framework which expands the agent's feasible action space beyond conventional observational and interventional arms to include a class of realizable counterfactual actions. Our framework offers a principled extension of structural causal bandits and paves the way for integrating counterfactual reasoning into sequential decision-making.
RoRE: Rotary Ray Embedding for Generalised Multi-Modal Scene Understanding
Authors: Ryan Griffiths; Donald G. Dansereau
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformers have emerged as powerful implicit rendering models, capable of performing geometric reasoning and producing photorealistic novel views in a single feedforward pass. A central challenge in these architectures is how to inject camera parameters into the transformer in a way that generalises across diverse sensing conditions. In this work, we present Rotary Ray Embedding (RoRE), an approach that embeds image patches directly as rays, using a learning based rotary positional embedding (RoPE). This ray-based formulation provides a unified and general representation, improving robustness to unconventional camera geometries and sensing modalities. We evaluate our approach on conventional perspective imagery, fisheye cameras, and multi-modal RGB-thermal setups, showing that a single network can flexibly integrate arbitrary numbers of cameras and modalities into a coherent scene representation. Experiments demonstrate improved generalisation and cross-modal consistency compared to existing methods, highlighting the potential for relative ray-based embeddings to build adaptable, plug-and-play vision systems. Code available at: https://roboticimaging.github.io/RoRE
CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering
Authors: Yahan Li; Jifan Yao; John Bosco S. Bunyi; Adam C Frank; Angel Hsing-Chi Hwang; Ruishan Liu
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Medical question answering (QA) benchmarks often focus on multiple-choice or fact-based tasks, leaving open-ended answers to real patient questions underexplored. This gap is particularly critical in mental health, where patient questions often mix symptoms, treatment concerns, and emotional needs, requiring answers that balance clinical caution with contextual sensitivity.
We present CounselBench, a large-scale benchmark developed with 100 mental health professionals to evaluate and stress-test large language models (LLMs) in realistic help-seeking scenarios. The first component, CounselBench-EVAL, contains 2,000 expert evaluations of answers from GPT-4, LLaMA 3, Gemini, and online human therapists on patient questions from the public forum CounselChat. Each answer is rated across six clinically grounded dimensions, with span-level annotations and written rationales. Expert evaluations show that while LLMs achieve high scores on several dimensions, they also exhibit recurring issues, including unconstructive feedback, overgeneralization, and limited personalization or relevance. Responses were frequently flagged for safety risks, most notably unauthorized medical advice. Follow-up experiments show that LLM judges systematically overrate model responses and overlook safety concerns identified by human experts. To probe failure modes more directly, we construct CounselBench-Adv, an adversarial dataset of 120 expert-authored mental health questions designed to trigger specific model issues. Expert evaluation of 1,080 responses from nine LLMs reveals consistent, model-specific failure patterns. Together, CounselBench establishes a clinically grounded framework for benchmarking LLMs in mental health QA.
Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles
Authors: Namrita Varshney; Ashutosh Gupta; Arhaan Ahmad; Tanay Vineet Tayal; S. Akshay
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness. We study the feature sensitivity problem, which asks whether an ensemble is ``sensitive" to a specified subset of features - such as protected attributes- whose manipulation can alter model predictions. Existing approaches often yield examples of sensitivity that lie far from the training distribution, limiting their interpretability and practical value. We propose a data-aware sensitivity framework that constrains the sensitive examples to remain close to the dataset, thereby producing realistic and interpretable evidence of model weaknesses. To this end, we develop novel techniques for data-aware search using a combination of mixed-integer linear programming (MILP) and satisfibility modulo theories (SMT) encodings. Our contributions are fourfold. Firstly, we strengthen the NP-hardness result for sensitivity verification, showing it holds even for trees of depth 1. Secondly, we develop MILP-optimizations that significantly speed up sensitivity verification for single ensembles and for the first time can also handle multiclass tree ensembles. Thirdly we introduce a data-aware framework generating realistic examples near the training distribution. Finally, we conduct an extensive experimental evaluation on large tree ensembles, demonstrating scalability to ensembles with up to 800 trees of depth 8, achieving substantial improvements over the state of the art. This framework provides a practical foundation for analyzing the reliability and fairness of tree-based models in high-stakes applications.