GenAI4ELab/papercli-papers huggingface dataset:
Tina: Tiny Reasoning Models via LoRA
Authors: Shangshang Wang; Julian Asilis; Ömer Faruk Akgül; Enes Burak Bilgin; Ollie Liu; Willie Neiswanger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: How cost-effectively can strong reasoning abilities be achieved in language models? Driven by this question, we present Tina, a family of tiny reasoning models achieved with high cost-efficiency. Tina shows that substantial reasoning performance can be developed using only minimal resources, by applying low-rank adaptation (LoRA) during reinforcement learning (RL), to an already tiny 1.5B parameter base model. This minimalist approach produces models that are competitive with, and sometimes surpass, SOTA RL reasoning models built upon the same base model. Crucially, this is achieved at a tiny fraction of the computational cost employed by existing models. In fact, the best Tina model achieves a >20\% reasoning performance increase and 43.33\% zero-shot Pass@1 accuracy on AIME24, at only \$9 USD cost (i.e., an estimated 260x reduction). Our work reveals the surprising effectiveness of efficient RL reasoning via LoRA. We validate this across multiple open-source reasoning datasets and various ablation settings starting with a single, fixed set of hyperparameters. Furthermore, we explore the hypothesis that this effectiveness and efficiency stem from LoRA rapidly adapting the model to the structural format of reasoning rewarded by RL, while largely preserving the base model's underlying knowledge. In service of accessibility and open research, we fully open-source all code, training logs, model weights, and checkpoints.
Unified Vision–Language Modeling via Concept Space Alignment
Authors: Yifu QIU; Paul-Ambroise Duquenne; Holger Schwenk
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce V-SONAR, a vision–language embedding space extended from the
text-only embedding space SONAR (Omnilingual Embeddings Team et al., 2026),
which supports 1500 text languages and 177 speech languages. To construct
V-SONAR, we propose a post-hoc alignment pipeline that maps the representations
of an existing vision encoder into the SONAR space. We thoroughly evaluate
V-SONAR and show that its embeddings achieve competitive performance on
text-to-video retrieval. Equipped with the OMNISONAR text decoder, V-SONAR
further surpasses state-of-the-art vision–language models on video captioning tasks,
including DREAM-1K (BLEU 23.9 vs. 19.6) and PE-VIDEO (BLEU 39.0 vs. 30.0).

Leveraging V-SONAR, we first demonstrate that the Large Concept Model (LCM;
LCM team et al. 2024) operating in SONAR and trained with English text only, can
perform both single- and multi-visual concept understanding in a zero-shot manner.
Finally, we introduce V-LCM, which extends the LCM with vision–language
instruction tuning. V-LCM encodes vision and language inputs into an unified
sequence of latent embeddings via V-SONAR and SONAR, and it is trained with
the same latent diffusion objective for next-embedding prediction as in LCM’s
text-only pre-training. Experiments on a large-scale multilingual and -modal
instruction–tuning data mixture highlight the potential of V-LCM: V-LCM matches
state-of-the-art vision-language models on tasks covering image/video captioning
and question answering, while significantly outperforming them across 61 rich- to
low-resource languages out of all 62 tested languages.
Closing the Gap Between Text and Speech Understanding in LLMs
Authors: Santiago Cuervo; Skyler Seto; Maureen de Seyssel; Richard He Bai; Zijin Gu; Tatiana Likhomanenko; Navdeep Jaitly; Zakaria Aldeneh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counterparts—and even cascaded pipelines—on language understanding tasks. We term this shortfall the text–speech understanding gap: the performance drop observed when a speech-adapted LLM processes spoken inputs relative to when the original text-based LLM processes the equivalent text. Recent approaches to narrowing this gap either rely on large-scale speech synthesis of text corpora, which is costly and heavily dependent on synthetic data, or on large-scale proprietary speech datasets, which are not reproducible. As a result, there remains a need for more data-efficient alternatives for closing the text-speech understanding gap. In this work, we analyze the gap as driven by two factors: (i) forgetting of text capabilities during adaptation, and (ii) cross-modal misalignment between speech and text. Based on this analysis, we introduce SALAD—Sample-efficient Alignment with Learning through Active selection and cross-modal Distillation—which combines cross-modal distillation with targeted synthetic data to improve alignment while mitigating forgetting. Applied to 3B and 7B LLMs, SALAD achieves competitive performance with a strong open-weight model across broad-domain benchmarks in knowledge, language understanding, and reasoning, while training on over an order of magnitude less speech data from publicly available corpora.
Constrained Diffusion for Protein Design with Hard Structural Constraints
Authors: Jacob K Christopher; Austin Seamann; Jingyi Cui; Sagar D Khare; Ferdinando Fioretto
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches observe critical failure modes when precise constraints are necessary for functional design. To this end, we present a constrained diffusion framework for structure-guided protein design, ensuring strict adherence to functional requirements while maintaining precise stereochemical and geometric feasibility. The approach integrates proximal feasibility updates with ADMM decomposition into the generative process, scaling effectively to the complex constraint sets of this domain. We evaluate on challenging protein design tasks, including motif scaffolding and vacancy-constrained pocket design, while introducing a novel curated benchmark dataset for motif scaffolding in the PDZ domain. Our approach achieves state-of-the-art, providing perfect satisfaction of bonding and geometric constraints with no degradation in structural diversity.
Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
Authors: Yongding Tao; Tian Wang; Yihong Dong; Huanyu Liu; Kechi Zhang; Hu XiaoLong; Ge Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples may inadvertently appear in training sets, compromising the validity of reported performance. While detection methods have been developed for the pre-training and Supervised Fine-Tuning stages, a critical research gap exists for the increasingly significant phase of Reinforcement Learning (RL) post-training. 
As RL post-training becomes pivotal for advancing LLM reasoning, the absence of specialized contamination detection methods in this paradigm presents a critical vulnerability. 
To address this, we conduct the first systematic study of data detection within  RL post-training scenario and propose Self-Critique. Our method is motivated by a key observation: after RL phase, the output entropy distribution of LLMs tends to collapse into highly specific and sparse modes. Self-Critique probes for the underlying policy collapse, i.e., the model's convergence to a narrow reasoning path, which causes this entropy reduction.
To facilitate this research, we also introduce RL-MIA, a benchmark constructed to simulate this specific contamination scenario. 
Extensive experiments show that Self-Critique significantly outperforms baseline methods across multiple models and contamination tasks, achieving an AUC improvement of up to 30%. Whereas existing methods are close to a random guess for RL-phase contamination, our method makes detection possible.
Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching
Authors: Yidan Xu; Yixin Wang; XuanLong Nguyen
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Flow matching is a powerful approach for high-fidelity density estimation, but it often fails to capture the latent structure of complex data. Probabilistic models like variational autoencoders (VAEs), on the other hand, learn structured representations but underperform in sample quality. We propose Structured Flow Autoencoders (SFA), a family of probabilistic models that augments graphical models with conditional continuous normalizing flow (CNF) likelihoods, enabling flow-matching-based structured representation learning. At the core of SFA is a novel flow matching objective that explicitly accounts for latent variables, allowing joint learning of the CNF likelihood and posterior. SFA applies broadly to graphical models with continuous and mixture latents, as well as latent dynamical systems. Empirical studies across image, video, and RNA-seq data show that SFA consistently outperforms VAEs and their structured extensions in generation quality, representation utility, and scalability to large datasets. Compared to generative models like latent flow matching (LatentFM), SFA also produces more diverse samples, suggesting better coverage of the data distribution.
NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
Authors: Luke Rivard; Sun Sun; Hongyu Guo; Wenhu Chen; Yuntian Deng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a dataset of Ubuntu XFCE recordings, which include both randomly generated interactions and realistic interactions produced by AI agents. Experiments show that NeuralOS successfully renders realistic GUI sequences, accurately captures mouse interactions, and reliably predicts state transitions like application launches. Beyond reproducing existing systems, NeuralOS shows that synthesized training data can teach the model to simulate applications that were never installed, as illustrated by a Doom application, and suggests a path toward learning user interfaces purely from synthetic demonstrations.
Dimension-Free Decision Calibration for Nonlinear Loss Functions
Authors: Jingwu Tang; Jiayun Wu; Steven Wu; Jiahao Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When model predictions inform downstream decisions, a natural question is under what conditions can the decision-makers simply respond to the predictions as if they were the true outcomes. The recently proposed notion of decision calibration addresses this by requiring predictions to be unbiased conditional on the best-response actions induced by the predictions. This relaxation of classical calibration avoids the exponential sample complexity in high-dimensional outcome spaces.
However, existing guarantees are limited to linear losses. A natural strategy for nonlinear losses is to embed outcomes $y$ into an $m$-dimensional feature space $\phi(y)$ and approximate losses linearly in $\phi(y)$. Yet even simple nonlinear functions can demand exponentially large or infinite feature dimensions, raising the open question of whether decision calibration can be achieved with complexity independent of the feature dimension $m$. We begin with a negative result: even verifying decision calibration under standard deterministic best response inherently requires sample complexity polynomial in $m$. 
To overcome this barrier, we study a smooth variant where agents follow quantal responses. This smooth relaxation admits dimension-free algorithms: given $\mathrm{poly}(|\mathcal{A}|,1/\epsilon)$ samples and any initial predictor $p$, our introducded algorithm efficiently test and achieve decision calibration for broad function classes which can be well-approximated by bounded-norm functions in (possibly infinite-dimensional) separable RKHS, including piecewise linear and Cobb–Douglas loss functions.
When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms
Authors: Qibing Ren; Zhijie Zheng; Jiaxuan Guo; Junchi Yan; Lizhuang Ma; Jing Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this work, we study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents. We investigate whether agents can collaborate in fraudulent behaviors, how such collaboration amplifies risks, and what factors influence fraud success. To support this research, we present MultiAgentFinancialFraudBench, a large-scale benchmark for simulating financial fraud scenarios based on realistic online interactions. The benchmark covers 28 typical online fraud scenarios, spanning the full fraud lifecycle across both public and private domains. We further analyze key factors affecting fraud success, including interaction depth, activity level, and fine-grained collaboration failure modes. Finally, we propose a series of mitigation strategies including adding content-level warnings to fraudulent posts and dialogues, using LLMs as monitors to block potentially malicious agents, and fostering group resilience through information sharing at the societal level. Notably, we observe that malicious agents can adapt to environmental interventions. Our findings highlight the real-world risks of multi-agent financial fraud and suggest practical measures for mitigating them. Code is available at https://github.com/zheng977/MutiAgent4Fraud.
Hierarchical Concept-based Interpretable Models
Authors: Oscar Hill; Mateo Espinosa Zarlenga; Mateja Jamnik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, and debiasing. Concept Embedding Models (CEMs) address this by mapping inputs to human-interpretable concept representations from which tasks can be predicted. Yet, CEMs fail to represent inter-concept relationships and require concept annotations at different granularities during training, limiting their applicability.
In this paper, we introduce *Hierarchical Concept Embedding Models* (HiCEMs), a new family of CEMs that explicitly model concept relationships through hierarchical structures. To enable HiCEMs in real-world settings, we propose *Concept Splitting*, a method for automatically discovering finer-grained sub-concepts from a pretrained CEM’s embedding space without requiring additional annotations. This allows HiCEMs to generate fine-grained explanations from limited concept labels, reducing annotation burdens. 
Our evaluation across multiple datasets, including a user study and experiments on *PseudoKitchens*, a newly proposed concept-based dataset of 3D kitchen renders, demonstrates that (1) Concept Splitting discovers human-interpretable sub-concepts absent during training that can be used to train highly accurate HiCEMs, and (2) HiCEMs enable powerful test-time concept interventions at different granularities, leading to improved task accuracy.
Probing to Refine: Reinforcement Distillation of LLM Reasoners via Explanatory Inversion
Authors: Zhen Tan; Chengshuai Zhao; Song Wang; Jundong Li; Tianlong Chen; huan liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent advances, distilled models frequently suffer from superficial pattern memorization and subpar generalization. To overcome these limitations, we introduce a novel distillation framework that moves beyond simple mimicry to instill a deeper conceptual understanding. Our framework features two key innovations. \underline{\textit{First}}, to address pattern memorization, Explanatory Inversion (EI) generates targeted ``explanatory probes'' that compel the student to articulate the underlying logic behind an answer, rather than just memorizing it. \underline{\textit{Second}}, to improve generalization, Explanatory GRPO (\texttt{EXGRPO}) uses a reinforcement learning algorithm with a novel Dialogue Structure Utility Bonus, which explicitly rewards the student for maintaining a coherent reasoning process across these probes. Extensive evaluations on 12 datasets demonstrate significant improvements. Using Gemma-7b as the student model, our method yields an average \textbf{20.39\%} increase over zero-shot performance and a \textbf{6.02\%} improvement over the state-of-the-art distillation baselines. Moreover, models distilled with our method show remarkable training efficiency (e.g., surpassing vanilla fine-tuning with \textbf{10-25\%} training data) and strong generalization to out-of-distribution tasks.
Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
Authors: Jiaqi Han; Austin Wang; Minkai Xu; Wenda Chu; Meihua Dang; Haotian Ye; Huayu Chen; Yisong Yue; Stefano Ermon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Discrete diffusion models have demonstrated great promise in modeling various sequence data, ranging from human language to biological sequences. Inspired by the success of RL in language models, there is growing interest in further improving the models by alignment with a certain reward. In this work, we propose an offline preference optimization method to approach trajectory alignment for discrete diffusion models. Instead of applying the reward on the final output and backpropagating the gradient to the entire denoising process, we decompose the problem into a set of stepwise alignment objectives by matching the per-step posterior. This framework enables efficient diffusion optimization, is compatible with arbitrary reward functions, and importantly, yields an equivalent optimal solution under additive factorization of the trajectory reward. Experiments across multiple domains including DNA sequence design, protein inverse folding, and language modeling consistently demonstrate the superiority of our approach. Notably, it achieves an up to 12\% improvement over the most competitive RL-based baseline in terms of predicted activity on DNA sequence design, and further improves the GSM8K score from 78.6 to 81.2 on LLaDA-8B-Instruct for language modeling.
On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime
Authors: Shuai Jiang; Eric C Cyr; Ben S. Southworth; Alexey Voronin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spectral bias, the tendency of neural networks to learn low frequencies first, can be both a blessing and a curse. While it enhances the generalization capabilities by suppressing high-frequency noise, it can be a limitation in scientific tasks that require capturing fine-scale structures. The delayed generalization phenomenon known as grokking is another barrier to rapid training of neural networks. Grokking has been hypothesized to arise as learning transitions from the NTK to the feature-rich regime. This paper explores the impact of preconditioned gradient descent (PGD), such as Gauss-Newton, on spectral bias and grokking phenomena. We demonstrate through theoretical and empirical results how PGD can mitigate issues associated with spectral bias. Additionally, building on the rich learning grokking hypothesis, we study how PGD can be used to reduce delays associated with grokking. Our conjecture is that PGD, without the impediment of spectral bias, enables uniform exploration of the parameter space in the NTK regime. Our experimental results confirm this prediction, providing strong evidence that grokking represents a transitional behavior between the lazy regime characterized by the NTK and the rich regime. These findings deepen our understanding of the interplay between optimization dynamics, spectral bias, and the phases of neural network learning.
AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs
Authors: Gunho Park; Jeongin Bae; Beomseok Kwon; Byeongwook Kim; Se Jung Kwon; Dongsoo Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The deployment of large language models (LLMs) is increasingly constrained by memory and latency bottlenecks, motivating the need for quantization techniques that flexibly balance accuracy and efficiency. 
Recent work has introduced multi-precision models, which enable inference at multiple precisions within a single model depending on runtime constraints.
To support such flexibility, quantized weights are often stored as bit-planes, where hardware efficiency improves when the compute operates directly at the bit-plane level and activates only the precision required by each request.
In this work, we present AnyBCQ, a hardware-friendly multi-precision extension of Binary-Coded Quantization (BCQ) that supports direct bit-plane operations.
By representing weights as binary bit-planes with corresponding scale factors, AnyBCQ enables bit-plane–level computation and maps naturally to accelerator-friendly, bit-parallel arithmetic.
Our progressive precision expansion mechanism incrementally refines scaling factors while reusing previously assigned binary codes, yielding monotonic improvements in accuracy as additional bits are enabled. 
We further co-design a specialized kernel that exploits the BCQ structure to support dynamic per-request precision selection with negligible overhead. 
Experiments on recent LLMs demonstrate that AnyBCQ significantly narrows the accuracy drop in the low-bit regime (e.g. 2-bit), remains competitive at higher precision, and achieves throughput gains of up to $3.0\times$ over half precision and $1.2\times$ over state-of-the-art multi-precision methods. 
By aligning algorithmic flexibility with hardware efficiency, AnyBCQ provides a practical foundation for multi-precision LLM deployment across diverse service-level objectives.
VisioMath: Benchmarking Figure-based Mathematical Reasoning in LMMs
Authors: Can Li; Ying Liu; Ting Zhang; Mei Wang; Hua Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large multimodal models have achieved remarkable progress in integrating vision and language, enabling strong performance across perception, reasoning, and domain-specific tasks. However, their capacity to reason over multiple, visually similar inputs remains insufficiently explored. Such fine-grained comparative reasoning is central to real-world tasks, especially in mathematics and education, where learners must often distinguish between nearly identical diagrams to identify correct solutions. To address this gap, we present VisioMath, a curated benchmark of 1,800 high-quality K–12 mathematics problems in which all candidate answers are diagrams with subtle visual similarities. A comprehensive evaluation of state-of-the-art LMMs, covering both leading closed-source systems and widely adopted open-source models, reveals a consistent decline in accuracy as inter-image similarity increases. Analysis indicates that the dominant failure mode stems from image–text misalignment: rather than grounding reasoning in textual cues, models often resort to shallow positional heuristics, resulting in systematic errors. We further explore three alignment-oriented strategies, spanning training-free approaches and finetuning, and achieve substantial accuracy gains. We hope that VisioMath will serve as a rigorous benchmark and catalyst for developing LMMs toward deeper diagram understanding, precise comparative reasoning, and grounded multi-image–text integration. The code and dataset are available at https://github.com/Nefefilibata/VisioMath.