GenAI4ELab/papercli-papers huggingface dataset:
Cyber-Zero: Training Cybersecurity Agents without Runtime
Authors: Terry Yue Zhuo; Dingmin Wang; Hantian Ding; Varun Kumar; Zijian Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have achieved remarkable success in software engineering tasks when trained with executable runtime environments, particularly in resolving GitHub issues. However, such runtime environments are often unavailable in other domains, especially cybersecurity, where challenge configurations and execution contexts are ephemeral or restricted. We present Cyber-Zero, the first runtime-free framework for synthesizing high-quality agent trajectories to train cybersecurity LLMs. Cyber-Zero leverages publicly available CTF writeups and employs persona-driven LLM simulation to reverse-engineer runtime behaviors and generate realistic, long-horizon interaction sequences without actual environments. Using trajectories synthesized by Cyber-Zero, we train LLM-based agents that achieve up to 13.1% absolute performance gains over baseline models on three prominent CTF benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best model, Cyber-Zero-32B, establishes new state-of-the-art performance among open-weight models, matching the capabilities of proprietary systems like DeepSeek-V3-0324 and Claude-3.5-Sonnet while offering superior cost-effectiveness, and demonstrating that runtime-free trajectory synthesis can effectively democratize the development of state-of-the-art cybersecurity agents.
SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
Authors: Yuling Gu; Oyvind Tafjord; Hyunwoo Kim; Jared Moore; Ronan Le Bras; Peter Clark; Yejin Choi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) — the ability to attribute mental states to oneself and others. Yet most evaluations stop at explicit belief attribution in classical toy stories or stylized tasks, leaving open the questions of whether LLMs can implicitly apply such knowledge to predict human behavior, or to judge an observed behavior, in diverse scenarios. We introduce SimpleToM, a benchmark that advances ToM evaluation along two novel axes. First, it probes multiple levels of ToM reasoning, from mental state inference (explicit ToM) to behavior prediction and judgment (applied ToM). Second, it situates these tasks in diverse, everyday scenarios — such as supermarkets, hospitals, schools, and offices — where information asymmetries naturally arise (e.g., hidden defects in grocery store items, incomplete information in provider–patient interactions, or restricted access to locked devices). SimpleToM contains concise stories (e.g., "The can of Pringles has moldy chips in it. Mary picks up the can in the supermarket and walks to the cashier."), each with three questions that test different degrees of ToM reasoning, asking models to predict: (a) mental states ("Is Mary aware of the mold?"), (b) behaviors ("Will Mary pay for the chips or report the mold?"), and (c) judgments ("Mary paid for the chips. Was that reasonable?"). Experiments reveal a striking gap: state-of-the-art models often reliably infer mental state (a), but fail at applying knowledge about the mental state for secondary predictions, with performance dropping sharply for behavior prediction (b) and further for behavior judgment (c). This exposes a critical fragility in LLMs’ social reasoning in terms of what they know (explicit ToM) versus how well they can implicitly apply that knowledge for predictions (applied ToM). By uniting assessment of different levels of ToM reasoning with diverse, everyday scenarios, SimpleToM opens new opportunities for rigorously evaluating and diagnosing ToM abilities in LLMs, and reveals surprising, new insights about current model capabilities, guiding efforts toward future generations of models capable of robust social understanding.
SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion
Authors: Xinjie zhu; Zijing Zhao; Hui Jin; Qingxiao Guo; Yilong Ma; YUNHAO WANG; Xiaobing Guo; Weifeng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Artificial Intelligence Generated Content (AIGC), particularly video generation with diffusion models, has been advanced rapidly. 
Invisible watermarking is a key technology for protecting AI-generated videos and tracing harmful content, and thus plays a crucial role in AI safety.
Beyond post-processing watermarks which inevitably degrade video quality, recent studies have proposed distortion-free in-generation watermarking for video diffusion models.
However, existing in-generation approaches are non-blind: they require maintaining all the message-key pairs and performing template-based matching during extraction, which incurs prohibitive computational costs at scale.
Moreover, when applied to modern video diffusion models with causal 3D Variational Autoencoders (VAEs), their robustness against temporal disturbance becomes extremely weak.
To overcome these challenges, we propose SIGMark, a Scalable In-Generation watermarking framework with blind extraction for video diffusion.
To achieve blind-extraction, we propose to generate watermarked initial noise using a Global set of Frame-wise PseudoRandom Coding keys (GF-PRC), reducing the cost of storing large-scale information while preserving noise distribution and diversity for distortion-free watermarking.
To enhance robustness, we further design a Segment Group-Ordering module (SGO) tailored to causal 3D VAEs, ensuring robust watermark inversion during extraction under temporal disturbance.
Comprehensive experiments on modern diffusion models show that SIGMark achieves very high bit-accuracy during extraction under both temporal and spatial disturbances with minimal overhead, demonstrating its scalability and robustness.
Our code is available at https://github.com/JeremyZhao1998/SIGMark-release.
Conformalized Hierarchical Calibration for Uncertainty-Aware Adaptive Hashing
Authors: Junyu Luo; Jinsheng Huang; Yang Xu; Lutong Zou; Xiao Luo; Bohan Wu; Yifan Wang; Wei Ju; Ming Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unsupervised domain adaptive hashing transfers knowledge from labeled source domains to unlabeled target domains, addressing domain shift challenges in real-world retrieval tasks. Existing methods face two critical limitations: target domain noise severely misleads model training, and indiscriminate domain alignment strategies treat all target samples equally, potentially distorting essential feature structures. We propose an uncertainty-aware adaptive hashing approach that addresses these challenges through a hierarchical conformal calibration framework. At the semantic level, we employ conformal inference to generate confidence prediction sets, replacing single pseudo-labels with set-based predictions whose sizes directly quantify sample reliability for weighted pseudo-label learning and domain alignment. This enables the model to focus on reliable samples while suppressing noise. At the representation level, we predict the stability of individual hash bits, where bit-level confidence guides a robust weighted quantization loss and enables dynamic weighted Hamming distance during retrieval, fundamentally enhancing hash code quality and retrieval robustness. Through this hierarchical calibration mechanism, our method achieves more adaptive and robust cross-domain knowledge transfer. Extensive experiments on multiple benchmark datasets demonstrate significant improvements over existing approaches, validating the effectiveness and superiority of our method.
Counterfactual LLM-based Framework for Measuring Rhetorical Style
Authors: Jingyi Qiu; Hong Chen; Zongyi Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rise of AI has fueled growing concerns about "hype" in machine learning papers, yet a reliable way to quantify rhetorical style independently of substantive content has remained elusive. Because bold language can stem from either strong empirical results or mere rhetorical style, it is often difficult to distinguish between the two. To disentangle rhetorical style from substantive content, we introduce a counterfactual, LLM-based framework: multiple LLM rhetorical personas generate counterfactual writings from the same substantive content, an LLM judge compares them through pairwise evaluations, and the outcomes are aggregated using a Bradley--Terry model. Applying this method to 8,485 ICLR submissions sampled from 2017 to 2025, we generate more than 250,000 counterfactual writings and provide a large-scale quantification of rhetorical style in ML papers. We find that visionary framing significantly predicts downstream attention, including citations and media attention, even after controlling for peer-review evaluations. We also observe a sharp rise in rhetorical strength after 2023, and provide empirical evidence showing that this increase is largely driven by the adoption of LLM-based writing assistance. The reliability of our framework is validated by its robustness to the choice of personas and the high correlation between LLM judgments and human annotations. Our work demonstrates that LLMs can serve as instruments to measure and improve scientific evaluation.
Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
Authors: Haoran He; Yuxiao Ye; Qingpeng Cai; Chen Hu; Binxing Jiao; Daxin Jiang; Ling Pan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: RL with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving the reasoning abilities of large language models (LLMs). Current methods rely primarily on policy optimization frameworks like PPO and GRPO, which follow generalized policy iteration that alternates between evaluating the current policy's value and improving the policy based on evaluation. While effective, they often suffer from training instability and diversity collapse, requiring complex heuristic tricks and careful tuning. We observe that standard RLVR in math reasoning can be formalized as a specialized finite-horizon Markov Decision Process with deterministic state transitions, tree-structured dynamics, and binary terminal rewards. Though large in scale, the underlying structure is simpler than general-purpose control settings for which popular RL algorithms (e.g., PPO) were developed, suggesting that several sophisticated techniques in existing methods may be reduced or even omitted. Based on this insight, we prove a surprising result: the optimal action can be recovered from the Q-function of a fixed uniformly random policy, thereby bypassing the generalized policy iteration loop and its associated heuristics. We introduce \underline{\textbf{R}}andom P\underline{\textbf{o}}licy \underline{\textbf{V}}aluation for Div\underline{\textbf{e}}rse \underline{\textbf{R}}easoning (ROVER) to translate this principle into a practical and scalable algorithm for LLM math reasoning, a minimalist yet highly effective RL method that samples actions from a softmax over these uniform-policy Q-values. ROVER preserves diversity throughout training, allowing sustained exploration of multiple valid pathways. Across multiple base models and standard math reasoning benchmarks, ROVER demonstrates superior performance in both \textbf{quality} (\textbf{+8.2} on pass@1, \textbf{+16.8} on pass@256) and \textbf{diversity} (\textbf{+20.5\%}), despite its radical simplification compared to strong, complicated existing methods.
Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection
Authors: Zhiwei Ning; Xuanang Gao; Jiaxi Cao; Runze Yang; Huiying Xu; Xinzhong Zhu; JIE YANG; Wei Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Linear modeling methods like Mamba have been merged as the effective backbone for the 3D object detection task. However, previous Mamba-based methods utilize the bidirectional encoding for the whole non-empty voxel sequence, which contains abundant useless background information in the scenes. Though directly encoding foreground voxels appears to be a plausible solution, it tends to degrade detection performance. We attribute this to the response attenuation and restricted context representation in the linear modeling for fore-only sequences. To address this problem, we propose a novel backbone, termed Fore-Mamba3D, to focus on the foreground enhancement by modifying Mamba-based encoder. The foreground voxels are first sampled according to the predicted scores. Considering the response attenuation existing in the interaction of foreground voxels across different instances, we design a regional-to-global slide window (RGSW) to propagate the information from regional split to the entire sequence. Furthermore, a semantic-assisted and state spatial fusion module (SASFMamba) is proposed to enrich contextual representation by enhancing semantic and geometric awareness within the Mamba model. Our method emphasizes foreground-only encoding and alleviates the distance-based and causal dependencies in the linear autoregression model. The superior performance across various benchmarks demonstrates the effectiveness of Fore-Mamba3D in the 3D object detection task.
ADM-v2: Pursuing Full-Horizon Roll-out in Dynamics Models for Offline Policy Learning and Evaluation
Authors: Haoxin Lin; Siyuan Xiao; Yi-Chen Li; Zhilong Zhang; Yihao Sun; Chengxing Jia; Yang Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model-based methods for offline Reinforcement Learning transfer extensive policy exploration and evaluation to data-driven dynamics models, effectively saving real-world samples in the offline setting. We expect the dynamics model to allow the policy to roll out full-horizon episodes, which is crucial for ensuring sufficient exploration and reliable evaluation. However, many previous dynamics models exhibit limited capability in long-horizon prediction. This work follows the paradigm of the Any-step Dynamics Model (ADM) that improves future predictions by reducing bootstrapping prediction to direct prediction. We structurally decouple each recurrent forward of the RNN cell from the backtracked state and propose the second version of ADM (ADM-v2), making the direct prediction more flexible. ADM-v2 not only enhances the accuracy of direct predictions for making full-horizon roll-outs but also supports parallel estimation of the any-step prediction uncertainty to improve efficiency. The results on DOPE validate the reliability of ADM-v2 for policy evaluation. Moreover, via full-horizon roll-out, ADM-v2 for policy optimization enables substantial advancements, whereas other dynamics models degrade due to long-horizon error accumulation. We are the first to achieve SOTA under the full-horizon roll-out setting on both D4RL and NeoRL. The code is available at https://github.com/LAMDA-RL/adm2.
MotionGPT3: Human Motion as a Second Modality
Authors: Bingfan Zhu; Biao Jiang; Sunyi Wang; SHIXIANG TANG; Tao Chen; Linjie Luo; Youyi Zheng; Xin Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the rapid progress of large language models (LLMs), multimodal frameworks that unify understanding and generation have become promising, yet they face increasing complexity as the number of modalities and tasks grows. We observe that motion quantization introduces approximation errors that cap motion quality, and that unifying discrete text and continuous motion within a single-stream backbone amplifies cross-modal interference. Motivated by recent multi-branch Transformer designs that separate signals from different modalities, we propose MotionGPT3, a bimodal motion–language model for both understanding and generation. MotionGPT3 encodes raw motion into a continuous latent space using a variational autoencoder (VAE), thereby avoiding quantization-induced artifacts, while leveraging the semantic prior of pretrained language models. A dual-stream Transformer with shared attention preserves modality-specific routes while enabling controlled, bidirectional information flow, which reduces interference, stabilizing optimization, and empirically accelerates convergence without degrading fidelity. For multimodal joint training, a generate-then-align three-stage schedule further improves stability and limits cross-task interference. Experiments show that MotionGPT3  achieves 2× faster convergence in training loss and up to 4× faster convergence in validation, while maintaining state-of-the-art performance on standard motion understanding and motion generation benchmarks.
DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference
Authors: Xiang Liu; Xuming Hu; Xiaowen Chu; Eunsol Choi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility is unclear. We conduct a systematic analysis across models and datasets and discover a U-shaped entropy pattern: high entropy on simple problems despite high accuracy, low entropy on medium difficulty, and high entropy on hard problems reflecting uncertainty. The 22--25\% entropy reduction from simple to optimal regions reveals a fundamental inefficiency—an \emph{overthinking} phenomenon on easy instances. Building on these insights, we introduce \textbf{DiffAdapt}, a lightweight, deployment-ready framework that predicts problem difficulty from hidden states and selects among Easy/Normal/Hard reasoning strategies to allocate computation adaptively. DiffAdapt requires no retraining of the base LLM and is compatible with common inference optimizations. Across five models and eight benchmarks, DiffAdapt achieves comparable or improved accuracy while reducing token usage by up to 22.4\%, establishing a practical path toward compute-efficient reasoning.
Prompt and Parameter Co-Optimization for Large Language Models
Authors: Xiaohe Bo; Rui Li; Zexu Sun; Quanyu Dai; Zeyu Zhang; Zihang Tian; Xu Chen; Zhenhua Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Prompt optimization and fine-tuning are two major approaches to improve the performance of Large Language Models (LLMs).
They enhance the capabilities of LLMs from complementary perspectives: the former through explicit natural language, and the latter through implicit parameter updates. 
However, prior work has typically studied them in isolation, leaving their synergistic potential largely underexplored. To bridge this gap, in this paper, we introduce MetaTuner, a novel framework that jointly integrates prompt optimization and fine-tuning for LLM training.
Specifically, we introduce two neural networks to generate prompts and parameters, respectively, while allowing them to share a common bottom encoding layer to enable knowledge sharing.
By the guidance of the final supervised signals, our framework is optimized to discover the optimal combinations between the prompts and parameters.
Given that prompt learning involves discrete optimization while fine-tuning operates in a continuous parameter space, we design a supervised regularization loss to train our framework effectively.
Extensive experiments across diverse benchmarks show that our method consistently outperforms the baselines.  To benefit the research community, we have released our project at https://github.com/BoXiaohe/MetaTuner.
Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval
Authors: Yingyi Zhang; Junyi Li; Wenlin Zhang; Pengyue Jia; Xianneng Li; Yichao Wang; Derong Xu; Yi Wen; Huifeng Guo; Yong Liu; Xiangyu Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Personalized large language models (LLMs) rely on memory retrieval to incorporate user-specific histories, preferences, and contexts. Existing approaches either overload the LLM by feeding all the user's past memory into the prompt, which is costly and unscalable, or simplify retrieval into a one-shot similarity search, which captures only surface matches. Cognitive science, however, shows that human memory operates through a dual process: Familiarity, offering fast but coarse recognition, and Recollection, enabling deliberate, chain-like reconstruction for deeply recovering episodic content. 
Current systems lack both the ability to perform recollection retrieval and mechanisms to adaptively switch between the dual retrieval paths, leading to either insufficient recall or the inclusion of noise.
To address this, we propose RF-Mem (Recollection–Familiarity Memory Retrieval), a familiarity uncertainty-guided dual-path memory retriever. 
RF-Mem measures the familiarity signal through the mean score and entropy. High familiarity leads to the direct top-$K$ Familiarity retrieval path, while low familiarity activates the Recollection path. In the Recollection path, the system clusters candidate memories and applies $\alpha$-mix with the query to iteratively expand evidence in embedding space, simulating deliberate contextual reconstruction.
This design embeds human-like dual-process recognition into the retriever, avoiding full-context overhead and enabling scalable, adaptive personalization. Experiments across three benchmarks and corpus scales demonstrate that RF-Mem consistently outperforms both one-shot retrieval and full-context reasoning under fixed budget and latency constraints. 
Our code can be found in the Reproducibility Statement.
Emergent Discrete Controller Modules for Symbolic Planning in Transformers
Authors: S M Rafiuddin; Muntaha Nujat Khan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformers struggle with tasks that require symbolic planning loops, variable updates, and conditional branching, especially under length extrapolation. We introduce discrete controller modules that insert a small set of program primitives (ASSIGN, ADD, COMPARE, BRANCH) into Transformer blocks via a Gumbel–Softmax selector over operations and a compact program state of registers, flags, and optional memory. We prove that the augmented model can simulate any bounded-step program by mapping each primitive step to one controller step, and we bound the deviation of relaxed execution from its discrete trace by $O(\tau+\kappa^{-1})$ (selection temperature $\tau$, comparison sharpness $\kappa$). Empirically, the controller-augmented Transformer achieves strong length generalization on algorithmic benchmarks (Sorting, Sum-of-List, BFS), improving longest-length accuracy by up to $20$–$40$ points over strong baselines, and yields consistent gains on symbolic QA (DROP) and program-synthesis-style tasks (RobustFill) with reduced compositionality drop-off. The learned execution is interpretable: operation traces align with ground truth, register roles are linearly decodable, and targeted knockouts cause localized accuracy losses. The approach adds only $\sim$5–7% FLOPs and can be applied sparsely (every $p$-th layer).