GenAI4ELab/papercli-papers huggingface dataset:
Aligner, Diagnose Thyself: A Meta-Learning Paradigm for Fusing Intrinsic Feedback in Preference Alignment
Authors: Mengyang Li; Pinlong Zhao; Zhong Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The alignment of Large Language Models (LLMs) with human preferences is critically undermined by noisy labels in training datasets.
Existing robust methods often prove insufficient, as they rely on single, narrow heuristics such as perplexity or loss, failing to address the diverse nature of real-world noise.
We challenge this limited-scope approach by introducing a new paradigm where models learn to diagnose thyself, systematically fusing multiple streams of intrinsic feedback for a holistic reliability assessment of each preference pair.
We instantiate this paradigm through a meta-learning methodology that learns to adaptively reweight samples based on a rich diagnostic vector.
This vector captures three complementary perspectives: preference consistency, learning difficulty, and generation confidence.
Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods across various noise conditions.
Crucially, our work provides the first quantitative analysis of these intrinsic diagnostics, revealing that their fusion is essential for overcoming the blind spots inherent in any single heuristic.
This diagnostic-driven paradigm offers a principled path towards developing more robust and trustworthy LLMs.
Code2Bench: Scaling Source and Rigor for Dynamic Benchmark Construction
Authors: Zhe Zhang; Runlin Liu; Aishan Liu; Xingyu Liu; Xiang Gao; Hailong Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The evaluation of code-generating Large Language Models (LLMs) is fundamentally constrained by two intertwined challenges: a reliance on static, easily contaminated problem sources and the use of superficial, low-rigor testing. This paper introduces a new benchmark construction philosophy, Dual Scaling, designed to systematically address both limitations. Our approach involves continuously
scaling the source of problems from dynamic, real-world code repositories and systematically scaling the rigor of tests via automated, high-coverage Property-Based Testing (PBT). We instantiate this philosophy in CODE2BENCH, an end-to-end framework that leverages Scope Graph analysis for principled dependency classification and a 100% branch coverage quality gate to ensure test suite integrity.
Using this framework, we construct CODE2BENCH-2509, a new benchmark suite with native instances in both Python and Java. Our extensive evaluation of 10 state-of-the-art LLMs on CODE2BENCH-2509, powered by a novel "diagnostic
fingerprint" visualization, yields three key insights: (1) models exhibit a fundamental performance gap, excelling at API application (Weakly Self-Contained tasks) but struggling with algorithmic synthesis (Self-Contained tasks); (2) a model’s
performance is profoundly shaped by the target language’s ecosystem, a nuance we are the first to systematically quantify; and (3) our rigorous, scaled testing is critical in uncovering an "illusion of correctness" prevalent in simpler benchmarks. Our work presents a robust, scalable, and diagnostic paradigm for the next generation of LLM evaluation in software engineering. The code, data, and results are available at https://code2bench.github.io/.
Bayesian Evidence-Driven Prototype Evolution for Federated Domain Adaptation
Authors: Xiaoyang Yi; Li Peng; Yuru Bao; Jian Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated learning (FL), as a privacy-preserving distributed machine learning paradigm, enables clients to collaboratively train a global model without sharing local data. However, in real-world scenarios, domain shift caused by different source clients leads to structural discrepancies in the feature space, resulting in performance degradation of the global model. Although existing prototype-based FL methods offer improvements in cross-domain feature alignment, they still struggle to adapt to dynamic semantic structures and fail to continuously respond to the changing semantic separability and variance structure during training. To address this, we propose FedPTE, an FL framework with prototype topology evolution. Specifically, FedPTE treats prototype clusters as variable topological units, employing Bayesian Gaussian Mixture Models and marginal likelihood ratios on the server to perform probabilistic inference, which enables adaptive structural adjustments. Meanwhile, FedPTE introduces a stability constraint mechanism to balance the adaptability of topological evolution and training stability. By conducting prototype topology-aware contrastive learning on clients, it enhances the discriminability and cross-domain consistency of features. Experimental results demonstrate that FedPTE achieves superior performance across multiple cross-domain datasets, showcasing its strong expressiveness and generalization capability in heterogeneous domains.
Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction
Authors: Bin Cao; Yang Liu; Longhan Zhang; Yifan Wu; Zhixun Li; Yuyu Luo; Hong Cheng; Yang Ren; Tongyi ZHANG
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory. While machine learning models have emerged as efficient approximations for large-scale applications, their performance is strongly influenced by the choice of atomic representation. Although modern graph-based approaches have progressively incorporated more structural information, they often fail to capture long-range atomic interactions due to finite receptive fields and local encoding schemes. This limitation leads to distinct crystals being mapped to identical representations, hindering accurate property prediction. To address this, we introduce PRDNet that leverages unique reciprocal-space diffraction besides graph representations. To enhance sensitivity to elemental and environmental variations, we employ a data-driven pseudo-particle to generate a synthetic diffraction pattern. PRDNet ensures full invariance to crystallographic symmetries. Extensive experiments are conducted on Materials Project, JARVIS-DFT, and MatBench, demonstrating that the proposed model achieves state-of-the-art performance. The code is openly available at \url{https://github.com/Bin-Cao/PRDNet}.
Read the Room: Video Social Reasoning with Mental-Physical Causal Chains
Authors: Lixing Niu; Jiapeng Li; Xingping Yu; Xinyi Dong; Shu Wang; Ruining Feng; Bo Wu; Ping Wei; Yisen Wang; Lifeng Fan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: "Read the room", or the ability to infer others' mental states from subtle social cues, is a hallmark of human social intelligence, but remains a major challenge for current AI systems. Existing social reasoning datasets are limited in complexity, scale, and coverage of mental states, falling short of the rich causal dynamics found in real-life interactions. In this work, we introduce R$^3$-Bench, an evaluation benchmark with fine-grained annotations of belief, intent, desire, emotion, and their causal chains in complex scenarios. Furthermore, we introduce R$^3$-FDT, a large-scale training set generated through a novel automated pipeline with the same chain structure. We conduct a comprehensive evaluation of state-of-the-art (SOTA) large vision-language models (LVLMs) on R$^3$-Bench, revealing substantial deficiencies in consistent multi-step social reasoning. We also fine-tune a 7B model on R$^3$-FDT, achieving notable improvements across multiple relevant benchmarks. Our contributions are three-fold: (i) a novel benchmark with richly annotated, multi-step causal reasoning data; (ii) systematic evidence that SOTA LVLMs fall far short of human-level reasoning; (iii) a scalable training dataset that significantly enhances social reasoning performance. The datasets and codes are available at: <https://github.com/LiXingNiu/Read-the-Room.git>.
ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization
Authors: Guoxin Chen; Wu Jing; Xinjie Chen; Xin Zhao; Ruihua Song; Chengxi Li; Kai Fan; Dayiheng Liu; Minpeng Liao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autoformalization, which translates natural language mathematics into machine-verifiable formal statements, is critical for using formal mathematical reasoning to solve math problems stated in natural language. While Large Language Models can generate syntactically correct formal statements, they often fail to preserve the original problem's semantic intent. This limitation arises from the LLM approaches' treating autoformalization as a simplistic translation task which lacks mechanisms for self-reflection and iterative refinement that human experts naturally employ. To address these issues, we propose ReForm, a Reflective Autoformalization method that tightly integrates semantic consistency evaluation into the autoformalization process. This enables the model to iteratively generate formal statements, assess its semantic fidelity, and self-correct identified errors through progressive refinement. To effectively train this reflective model, we introduce Prospective Bounded Sequence Optimization (PBSO), which employs different rewards at different sequence positions to ensure that the model develops both accurate autoformalization and correct semantic validations, preventing superficial critiques that would undermine the purpose of reflection. Extensive experiments across four autoformalization benchmarks demonstrate that ReForm achieves an average improvement of 22.6 percentage points over the strongest baselines. To further ensure evaluation reliability, we introduce ConsistencyCheck, a benchmark of 859 expert-annotated items that not only validates LLMs as judges but also reveals that autoformalization is inherently difficult: even human experts produce semantic errors in up to 38.5\% of cases.
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion Models
Authors: Min Cheng; Fatemeh Doudi; Dileep Kalathil; Mohammad Ghavamzadeh; Panganamala Kumar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning (RL) algorithms have been used recently to align diffusion models with downstream objectives such as aesthetic quality and text-image consistency by fine-tuning them to maximize a single reward function under a fixed KL regularization. However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model. We address the problem of inference-time multi-preference alignment: given a set of basis reward functions and a reference KL regularization strength, can we design a fine-tuning procedure so that, at inference time, it can generate images aligned with any user-specified linear combination of rewards and regularization, without requiring additional fine-tuning? We propose Diffusion Blend, a novel approach to solve inference-time multi-preference alignment by blending backward diffusion processes associated with fine-tuned models, and we instantiate this approach with three algorithms: DB-MPA for multi-reward alignment, DB-KLA for KL regularization control, and DB-MPA-LS for approximating DB-MPA without additional inference cost. Extensive experiments show that Diffusion Blend algorithms consistently outperform relevant baselines and closely match or exceed the performance of individually fine-tuned models, enabling efficient, user-driven alignment at inference-time.
Localized Concept Erasure in Text-to-Image Diffusion Models via High-Level Representation Misdirection
Authors: Uichan Lee; Jeonghyeon Kim; Sangheum Hwang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in text-to-image (T2I) diffusion models have seen rapid and widespread adoption. However, their powerful generative capabilities raise concerns about potential misuse for synthesizing harmful, private, or copyrighted content. To mitigate such risks, concept erasure techniques have emerged as a promising solution. Prior works have primarily focused on fine-tuning the denoising component (e.g., the U-Net backbone). However, recent causal tracing studies suggest that visual attribute information is localized in the early self-attention layers of the text encoder, indicating a potential alternative for concept erasing. Building on this insight, we conduct preliminary experiments and find that directly fine-tuning early layers can suppress target concepts but often degrades the generation quality of non-target concepts. To overcome this limitation, we propose High-Level Representation Misdirection (HiRM), which misdirects high-level semantic representations of target concepts in the text encoder toward designated vectors such as random directions or semantically defined directions (e.g., super-categories), while updating only early layers that contain causal states of visual attributes. Our decoupling strategy enables precise concept removal with minimal impact on unrelated concepts, as demonstrated by strong results on UnlearnCanvas and NSFW benchmarks across diverse targets (e.g., objects, styles, nudity). HiRM also preserves generative utility at low training cost, transfers to state-of-the-art architectures such as Flux without additional training, and shows synergistic effects with denoiser-based concept erasing methods.
Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation
Authors: Xu Yan; Jun Yin; Shiliang Sun; Minghua Wan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or information bottleneck theory to learn consistent representations under missing-view conditions, but loss-based alignment without explicit structural constraints limits the ability to capture stable and discriminative shared semantics. To address this issue, we introduce a more structured mechanism for consistent representation learning: we learn discrete consistent representations through a multi-view shared codebook and cross-view reconstruction, which naturally align different views within the limited shared codebook embeddings and reduce feature redundancy. At the decision level, we design a weight estimation method that evaluates the ability of each view to preserve label correlation structures, assigning weights accordingly to enhance the quality of the fused prediction. In addition, we introduce a fused-teacher self-distillation framework, where the fused prediction guides the training of view-specific classifiers and feeds the global knowledge back into the single-view branches, thereby enhancing the generalization ability of the model under missing-label conditions. The effectiveness of our proposed method is thoroughly demonstrated through extensive comparative experiments with advanced methods on five benchmark datasets. Code is available at https://github.com/xuy11/SCSD.
Computing Equilibrium beyond Unilateral Deviation
Authors: Mingyang Liu; Gabriele Farina; Asuman E. Ozdaglar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no guarantees against profitable coordinated deviations by coalitions. Although the literature proposes notions to address multilateral deviations (\emph{e.g.}, strong Nash and coalition-proof equilibrium), these generally fail to exist. In this paper, we study a solution concept that accommodates multi-player deviations and is guaranteed to exist. We prove a fixed-parameter lower bound on the complexity of computing such an equilibrium and present an algorithm that matches this bound.
Rejuvenating Cross-Entropy Loss in Knowledge Distillation for Recommender Systems
Authors: Zhangchi Zhu; Wei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper analyzes Cross-Entropy (CE) loss in knowledge distillation (KD) for recommender systems. KD for recommender systems targets at distilling rankings, especially among items most likely to be preferred, and can only be computed on a small subset of items. Considering these features, we reveal the connection between CE loss and NDCG in the field of KD. We prove that when performing KD on an item subset, minimizing CE loss maximizes the lower bound of NDCG, only if an assumption of closure is satisfied. It requires that the item subset consists of the student's top items. However, this contradicts our goal of distilling rankings of the teacher's top items. We empirically demonstrate the vast gap between these two kinds of top items. To bridge the gap between our goal and theoretical support, we propose **R**ejuvenated **C**ross-**E**ntropy for **K**nowledge **D**istillation (RCE-KD). It splits the top items given by the teacher into two subsets based on whether they are highly ranked by the student. For the subset that defies the condition, a sampling strategy is devised to use teacher-student collaboration to approximate our assumption of closure. We also combine the losses on the two subsets adaptively. Extensive experiments demonstrate the effectiveness of our method. Our code is available at https://github.com/BDML-lab/RCE-KD.
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning
Authors: Qianyue Hao; Sibo Li; Jian Yuan; Yong Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite rapid advancements in large language models (LLMs), the token-level autoregressive nature constrains their complex reasoning capabilities.
To enhance LLM reasoning, inference-time techniques, including Chain/Tree/Graph-of-Thought(s), successfully improve the performance, as they are fairly cost-effective by guiding reasoning through external logical structures without modifying LLMs' parameters.
However, these manually predefined, task-agnostic frameworks are applied uniformly across diverse tasks, lacking adaptability.
To improve this, we propose **RL-of-Thoughts (RLoT)**, where we train a lightweight navigator model with reinforcement learning (RL) to generate task-adaptive logical structures at inference time, enhancing LLM reasoning.
Specifically, we design five basic logic blocks from the perspective of human cognition.
During the reasoning process, the trained RL navigator dynamically selects the suitable logic blocks and combines them into task-specific logical structures according to problem characteristics.
Experiments across multiple reasoning benchmarks (AIME, MATH, GPQA, etc.) with multiple LLMs (GPT, Llama, Qwen, and DeepSeek) illustrate that RLoT outperforms established inference-time techniques in most cases and improves up to 13.4% in challenging situations.
Remarkably, with less than 3K parameters, our RL navigator is able to make sub-10B LLMs comparable to 100B-scale counterparts.
Moreover, the RL navigator demonstrates strong transferability: a model trained on one specific LLM-task pair can effectively generalize to unseen LLMs and tasks.
Our code is open-source at https://github.com/tsinghua-fib-lab/RL-LLM-Reasoning.
Larger Datasets Can Be Repeated More: A Theoretical Analysis of Multi-Epoch Scaling in Linear Regression
Authors: Tingkai Yan; Haodong Wen; Binghui Li; Kairong Luo; Wenguang Chen; Kaifeng Lyu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While data scaling laws of large language models (LLMs) have been widely examined in the one-pass regime with massive corpora, their form under limited data and repeated epochs remains largely unexplored. This paper presents a theoretical analysis of how a common workaround, training for multiple epochs on the same dataset, reshapes the data scaling laws in linear regression.
    Concretely, we ask: to match the performance of training on a dataset of size $N$ for $K$ epochs, how much larger must a dataset be if the model is trained for only one pass?
    We quantify this using the $\textit{effective reuse rate}$ of the data, $E(K, N)$, which we define as the multiplicative factor by which the dataset must grow under one-pass training to achieve the same test loss as $K$-epoch training.
    Our analysis precisely characterizes the scaling behavior of $E(K, N)$ for SGD in linear regression under either strong convexity or Zipf-distributed data: (1) When $K$ is small, we prove that $E(K, N) \approx K$, indicating that every new epoch yields a linear gain; (2) As $K$ increases, $E(K, N)$ plateaus at a problem-dependent value that grows with $N$ ($\Theta(\log N)$ for the strongly-convex case), implying that larger datasets can be repeated more times before the marginal benefit vanishes.
    These theoretical findings point out a neglected factor in a recent empirical study by [Muennighoff et al. (2023)](https://arxiv.org/abs/2305.16264), which claimed that training LLMs for up to $4$ epochs results in negligible loss differences compared to using fresh data at each step, $\textit{i.e.}$, $E(K, N) \approx K$ for $K \le 4$ in our notation. 
    Supported by further empirical validation with LLMs,
    our results reveal that the maximum $K$ value for which $E(K, N) \approx K$ in fact depends on the data size and distribution, 
    and underscore the need to explicitly model both factors in future studies of scaling laws with data reuse.
Global-Recent Semantic Reasoning on Dynamic Text-Attributed Graphs with Large Language Models
Authors: Yunan Wang; Jianxin Li; Ziwei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dynamic Text-Attribute Graphs (DyTAGs), characterized by time-evolving graph interactions and associated text attributes, are prevalent in real-world applications. Existing methods, such as Graph Neural Networks (GNNs) and Large Language Models (LLMs), mostly focus on static TAGs. Extending these existing methods to DyTAGs is challenging as they largely neglect the *recent-global temporal semantics*: the recent semantic dependencies among interaction texts and the global semantic evolution of nodes over time. Furthermore, applying LLMs to the abundant and evolving text in DyTAGs faces efficiency issues. To tackle these challenges, we propose $\underline{Dy}$namic $\underline{G}$lobal-$\underline{R}$ecent $\underline{A}$daptive $\underline{S}$emantic $\underline{P}$rocessing (DyGRASP), a novel method that leverages LLMs and temporal GNNs to efficiently and effectively reason on DyTAGs. Specifically, we first design a node-centric implicit reasoning method together with a sliding window mechanism to efficiently capture recent temporal semantics. In addition, to capture global semantic dynamics of nodes, we leverage explicit reasoning with tailored prompts and an RNN-like chain structure to infer long-term semantics. Lastly, we intricately integrate the recent and global temporal semantics as well as the dynamic graph structural information using updating and merging layers. Extensive experiments on DyTAG benchmarks demonstrate DyGRASP's superiority, achieving up to 34\% improvement in Hit@10 for destination node retrieval task. Besides, DyGRASP exhibits strong generalization across different temporal GNNs and LLMs.