GenAI4ELab/papercli-papers huggingface dataset:
Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models
Authors: Yuhui Wang; Changjiang Li; Guangke Chen; Jiacheng Liang; Ting Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning. However, recent studies reveal that their final answers often contradict their own reasoning traces. We hypothesize that this inconsistency stems from two competing mechanisms for generating answers: CoT reasoning and memory retrieval. To test this hypothesis, we conduct controlled experiments that challenge LRMs with misleading cues during reasoning and/or corrupted answers during retrieval. Our results across models and datasets confirm that both mechanisms operate simultaneously, with their relative dominance influenced by multiple factors: problem domains, model scales, and fine-tuning approaches (e.g., reinforcement learning vs. distillation). The findings reveal a critical limitation in current reasoning fine-tuning paradigms: models can exploit the retrieval mechanism as a shortcut, effectively "hacking" the reward signal and undermining genuine reasoning development. To address this challenge, we introduce FARL, a novel fine-tuning framework that integrates memory unlearning with reinforcement learning. By carefully suppressing retrieval shortcuts during the fine-tuning process, FARL promotes reasoning-dominant behavior and enhances generalizable reasoning capabilities. The code is available at https://github.com/ZJUWYH/FARL.
The Counting Power of Transformers
Authors: Marco Sälzer; Chris Köcher; Alexander Kozachinskiy; Georg Zetzsche; Anthony Widjaja Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Counting properties (e.g. determining whether certain tokens occur more
   than other tokens in a given input text) have played a significant role in 
    the study of expressiveness of transformers. In this paper, we provide a 
    formal 
    framework for investigating the counting power of transformers. We argue 
    that all existing results demonstrate transformers' expressivity only for 
    (semi-)linear counting properties, i.e., which are expressible as a 
    boolean combination of linear inequalities. 
    Our main result is that transformers can express counting properties that
    are highly nonlinear. More precisely, we prove that transformers can
    capture all semialgebraic counting properties, i.e., expressible as 
    a boolean combination of arbitrary multivariate polynomials (of any degree).
    Among others, these generalize the counting properties that
    can be captured by C-RASP softmax transformers, which capture only
    linear counting properties.
    To complement this result, we exhibit a natural subclass of (softmax) 
    transformers that completely characterizes semialgebraic counting 
    properties. 
    Through connections with the
    Hilbert's tenth problem, this expressivity of transformers also 
    yields a new undecidability result for analyzing an extremely simple 
    transformer model -- surprisingly with neither positional encodings 
    (i.e. NoPE-transformers) nor masking.
    We also experimentally validate trainability of such counting
    properties.
Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs
Authors: Yan Scholten; Sophie Xhonneux; Leo Schwinn; Stephan Günnemann
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their fine-tuning data. We argue this not only risks reinforcing exposure to sensitive data, but also fundamentally contradicts the principle of minimizing its use. As a remedy, we propose a novel unlearning method—Partial Model Collapse (PMC), which does not require unlearning targets in the unlearning objective. Our approach is inspired by recent observations that training generative models on their own generations leads to distribution collapse, effectively removing information from model outputs. Our central insight is that model collapse can be leveraged for machine unlearning by deliberately triggering it for data we aim to remove. We theoretically analyze that our approach converges to the desired outcome, i.e. the model unlearns the data targeted for removal. We empirically demonstrate that PMC overcomes four key limitations of existing unlearning methods that explicitly optimize on unlearning targets, and more effectively removes private information from model outputs while preserving general model utility. Overall, our contributions represent an important step toward more comprehensive unlearning that better aligns with real-world privacy constraints.
CERTIFIED VS. EMPIRICAL ADVERSARIAL ROBUSTNESS VIA HYBRID CONVOLUTIONS WITH ATTENTION STOCHASTICITY
Authors: Joy Dhar; Song Xia; Manish Kumar Pandey; Maryam Haghighat; Azadeh Alavi; Ferdous Sohel; Wenyu Zhang; Nayyar Zaidi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robustness under ℓ2 certificates and empirical robustness against strong ℓ∞ attacks, while preserving strong generalization across diverse imaging benchmarks. HyCAS unifies deterministic and randomized principles by coupling 1-Lipschitz, spectrally normalized convolutions with two stochastic components—spectral normalized random-projection filters and a randomized attention-noise mechanism—to realize a randomized defense. Injecting smoothing randomness inside the architecture yields an overall ≤ 2-Lipschitz network with formal certificates. Extensive experiments on diverse imaging benchmarks—including CIFAR-10/100, ImageNet-1k, NIH Chest X-ray, HAM10000—show that HyCAS surpasses prior leading certified and empirical defenses, boosting certified accuracy by up to ≈ 7.3% (on NIH Chest X-ray) and empirical robustness by up to ≈ 3.1% (on HAM10000), without sacrificing clean accuracy. These results show that a randomized Lipschitz constrained architecture can simultaneously improve both certified ℓ2 and empirical ℓ∞ adversarial robustness, thereby supporting safer deployment of deep models in high-stakes applications.
SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
Authors: Dachuan Shi; Abedelkadir Asi; Keying Li; Xiangchi Yuan; Leyan Pan; Wenke Lee; Wen Xiao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency. Despite this promise, latent reasoning still faces two challenges, especially in training-free settings: 1) purely latent reasoning broadens the search distribution by maintaining multiple implicit paths, which diffuses probability mass, introduces noise, and impedes convergence to a single high-confidence solution, thereby hurting accuracy; and 2) overthinking persists even without explicit text, wasting tokens and degrading efficiency. To address these issues, we introduce SwiReasoning, a training-free framework for LLM reasoning which features two key innovations: 1) SwiReasoning dynamically switches between explicit and latent reasoning, guided by block-wise confidence estimated from entropy trends in next-token distributions, to balance exploration and exploitation and promote timely convergence. 2) By limiting the maximum number of thinking-block switches, SwiReasoning curbs overthinking and improves token efficiency across varying problem difficulties. On widely used mathematics, STEM, coding, and general benchmarks, SwiReasoning consistently improves average accuracy by 1.8%–3.1% across reasoning LLMs of different model families and scales. Furthermore, under constrained budgets, SwiReasoning improves average token efficiency by 57%-79%, with larger gains as budgets tighten.
The Human Brain as a Dynamic Mixture of Expert Models in Video Understanding
Authors: Christina Sartzetaki; Anne W. Zonneveld; Pablo Oyarzo; Alessandro Thomas Gifford; Radoslaw Martin Cichy; Pascal Mettes; Iris Groen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The human brain is the most efficient and versatile system for processing dynamic visual input. By comparing representations from deep video models to brain activity, we can gain insights into mechanistic solutions for effective video processing, important to better understand the brain and to build better models. Current works in model-brain alignment primarily focus on fMRI measurements, leaving open questions about fine-grained dynamic processing. Here, we introduce the first large-scale model benchmarking on alignment to dynamic electroencephalography (EEG) recordings of short natural videos. We analyze 100+ models across the axes of temporal integration, classification task, architecture, and pretraining, using our proposed Cross-Temporal Representational Similarity Analysis (CT-RSA) which matches the best time-unfolded model features to dynamically evolving brain responses, distilling $10^7$ alignment scores. Our findings reveal novel insights on how continuous visual input is integrated in the brain, beyond the standard temporal processing hierarchy from low to high-level representations. After initial alignment to hierarchical static object processing, responses in posterior electrodes best align to mid-level temporally-integrative action features, showing high temporal correspondence to feature timings. In contrast, responses in frontal electrodes best align with high-level static action representations and show no temporal correspondence to the video. Additionally, temporally-integrating state-space models show superior alignment to intermediate posterior activity, in which self-supervised pretraining is also beneficial. We draw a metaphor to a dynamic mixture of expert models for the changing neural preference in tasks and temporal integration reflected in the alignment to different model types across time. We posit that a single best-aligned model would need such training and architecture as to allow combining and dynamically switching between these capacities.
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
Authors: John Abascal; Nicolás Berrios; Alina Oprea; Jonathan Ullman; Adam Smith; Matthew Jagielski
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The proliferation of diverse data across users and organizations has driven the development of machine learning methods that enable multiple entities to jointly train models while minimizing data sharing. Among these, *multitask learning* (MTL) is a powerful paradigm that leverages similarities among multiple tasks, each with insufficient samples to train a standalone model, to solve them simultaneously. MTL accomplishes this by learning a *shared representation* that captures common structure between tasks and generalizes well across them all. Despite being designed to be the smallest unit of shared information necessary to effectively learn patterns across multiple tasks, these shared representations can inadvertently leak sensitive information about the particular tasks they were trained~on.

In this work, we investigate privacy leakage in shared representations through the lens of inference attacks. Towards this, we propose a novel, *black-box task-inference* threat model where the adversary, given the embedding vectors produced by querying the shared representation on samples from a particular task, aims to determine whether the task was present in the multitask training dataset. Motivated by analysis of tracing attacks on mean estimation over mixtures of Gaussian distributions, we develop efficient, purely black-box attacks on machine learning models that exploit the dependencies between embeddings from the same task without requiring shadow models or labeled reference data. We evaluate our attacks across vision and language domains when MTL is used for personalization and for solving multiple distinct learning problems, and demonstrate that even with access only to fresh task samples rather than training data, a black-box adversary can successfully infer a task's inclusion in training.
MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models
Authors: Jonas Belouadi; Tamy Boubekeur; Adrien Kaiser
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Material node graphs are programs that generate the 2D channels of procedural materials, including geometry such as roughness and displacement maps, and reflectance such as albedo and conductivity maps. They are essential in computer graphics for representing the appearance of virtual 3D objects parametrically and at arbitrary resolution. In particular, their directed acyclic graph structure and intermediate states enable a modular, interpretable workflow for interactive appearance modeling. However, creating such graphs remains challenging and typically requires professional training. While recent neural program synthesis approaches attempt to simplify this process, they solely represent graphs as textual programs, failing to capture the inherently visual-spatial nature of node graphs that makes them accessible to humans. To address this gap, we present MultiMat, a multimodal program synthesis framework that leverages large multimodal models to process both visual and textual graph representations for improved generation of procedural material graphs. We train our models on a new dataset of production-quality procedural materials and combine them with a constrained tree search inference algorithm that ensures static correctness while efficiently navigating the program space. Our experimental results show that our multimodal program synthesis method is more efficient in both unconditional and conditional graph synthesis with higher visual quality and fidelity than text-only baselines, establishing new state-of-the-art performance.
VITA: Zero-Shot Value Functions via Test-Time Adaptation of Vision–Language Models
Authors: Christos Ziakas; Alessandra Russo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision–Language Models (VLMs) show promise as zero-shot goal-conditioned value functions, but their frozen pre-trained representations limit generalization and temporal reasoning. We introduce VITA, a zero-shot value function learning method that enhances both capabilities via test-time adaptation. At inference, a lightweight adaptation module is updated via a gradient step on a meta-learned self-supervised loss, such that each test-time update improves value estimation. By updating sequentially over a trajectory, VITA encodes history into its parameters, addressing the temporal reasoning limitations. To mitigate shortcut learning, we propose a dissimilarity-based sampling strategy that selects semantically diverse segments of the trajectory during training. In real-world robotic manipulation tasks, VITA generalizes from a single training environment to diverse out-of-distribution tasks, environments, and embodiments, outperforming the state-of-the-art zero-shot method using autoregressive VLMs. Furthermore, we demonstrate that VITA’s zero-shot value estimates can be utilized for reward shaping in offline reinforcement learning, resulting in multi-task policies on the Meta-World benchmark that exceed the performance of those trained with the simulation’s fuzzy-logic dense rewards. Project website: https://chziakas.github.io/vita/.
Displacement-Resistant Extensions of DPO with Nonconvex $f$-Divergences
Authors: Idan Pipano; Shoham Sabach; Kavosh Asadi; Mohammad Ghavamzadeh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: DPO and related algorithms align language models by directly optimizing the RLHF objective: find a policy that maximizes the Bradley-Terry reward while staying close to a reference policy through a KL divergence penalty. Previous work showed that this approach could be further generalized: the original problem remains tractable even if the KL divergence is replaced by a family of $f$-divergence with a convex generating function $f$. Our first contribution is to show that convexity of $f$ is not essential. Instead, we identify a more general condition, referred to as DPO-inducing, that precisely characterizes when the RLHF problem remains tractable. Our next contribution is to establish a second condition on $f$ that is necessary to prevent probability displacement, a known empirical phenomenon in which the probabilities of the winner and the loser responses approach zero. We refer to any $f$ that satisfies this condition as displacement-resistant. We finally focus on a specific DPO-inducing and displacement-resistant $f$, leading to our novel SquaredPO loss. Compared to DPO, this new loss offers stronger theoretical guarantees while performing competitively in practice.
DADA: Dual Averaging with Distance Adaptation
Authors: Mohammad Moshtaghifar; Anton Rodomanov; Daniil Vankov; Sebastian U Stich
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a novel parameter-free universal gradient method for solving convex optimization problems. Our algorithm—Dual Averaging with Distance Adaptation (DADA)–is based on the classical scheme of dual averaging and dynamically adjusts its coefficients based on the observed gradients and the distance between its iterates to the starting point, without the need for knowing any problem-specific parameters. DADA is a universal algorithm that simultaneously works for a wide range of problem classes as long as one is able to bound the local growth of the objective around its minimizer. Particular examples of such problem classes are nonsmooth Lipschitz functions, Lipschitz-smooth functions, Hölder-smooth functions, functions with high-order Lipschitz derivative, quasi-self-concordant functions, and (L0, L1)-smooth functions. Furthermore, in contrast to many existing methods, DADA is suitable not only for unconstrained problems, but also constrained ones, possibly with unbounded domain, and it does not require fixing neither the number of iterations nor the accuracy in advance.
SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports
Authors: Haotian Xia; Haonan Ge; Junbo Zou; Hyun Woo Choi; Xuebin Zhang; Danny Suradja; Botao Rui; Ethan Tran; Wendy Jin; Zhen Ye; Xiyang Lin; Christopher Lai; Shengjie Zhang; Junwen Miao; Shichao Chen; Rhys Tracy; Vicente Ordonez; Weining Shen; Hanjie Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Artificial Intelligence brings powerful new tools to sports, from automated officiating to tactical analysis, but these applications all depend on a core reasoning capability. 
Deeply understanding sports requires an intricate blend of fine-grained visual perception and rule-based reasoning—a challenge that pushes the limits of current multimodal models. 
To succeed, models must master three critical capabilities: perceiving nuanced visual details, applying abstract sport rule knowledge, and grounding that knowledge in specific visual evidence.
Current sports benchmarks either cover single sports or lack the detailed reasoning chains and precise visual grounding needed to robustly evaluate these core capabilities in a multi-sport context. 
To address this gap, we introduce SportR, the first multi-sports large-scale benchmark designed to train and evaluate MLLMs on the fundamental reasoning required for sports intelligence. 
Our benchmark provides a dataset of 4,789 images and 2,052 videos. To enable granular evaluation, we structure our benchmark around a progressive hierarchy of question-answer (QA) pairs designed to probe reasoning at increasing depths—from simple infraction identification to complex penalty prediction. 
For the most advanced tasks requiring multi-step reasoning, such as determining penalties or explaining tactics, we provide 6,841 high-quality, human-authored Chain-of-Thought (CoT) annotations.
In addition, our benchmark incorporates both image and video modalities and provides manual bounding box annotations to test visual grounding in the image part directly. Extensive experiments demonstrate the profound difficulty of our benchmark. State-of-the-art baseline models perform poorly on our most challenging tasks. While training on our data via Supervised Fine-Tuning and Reinforcement Learning improves these scores, they remain relatively low, highlighting a significant gap in current model capabilities. SportR presents a new challenge for the community, providing a critical resource to drive future research in multimodal sports reasoning. The dataset is available at https://github.com/chili-lab/SportR.
S3OD: Towards Generalizable Salient Object Detection with Synthetic Data
Authors: Orest Kupyn; Hirokatsu Kataoka; Christian Rupprecht
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present a method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-aware architecture. We introduce S3OD, a dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline that extracts labels from diffusion and DINO-v3 features. The iterative generation framework prioritizes challenging categories based on model performance. We propose a streamlined multi-mask decoder that handles the inherent ambiguity in salient object detection by predicting multiple valid interpretations. Models trained only on synthetic data achieve 20-50% error reduction in cross-dataset generalization, while fine-tuned versions reach state-of-the-art performance across DIS and HR-SOD benchmarks.
Quantitative Bounds for Length Generalization in Transformers
Authors: Zachary Izzo; Eshaan Nichani; Jason D. Lee
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We study the problem of length generalization (LG) in transformers: the ability of a model trained on shorter sequences to maintain performance when evaluated on much longer, previously unseen inputs. Prior work by Huang et al. (2024) established that transformers eventually achieve length generalization once the training sequence length exceeds some finite threshold, but left open the question of how large it must be. In this work, we provide the first quantitative bounds on the required training length for length generalization to occur.
Motivated by previous empirical and theoretical work, we analyze LG in several distinct problem settings: $\ell_\infty$ error control vs. average error control over an input distribution, infinite-precision softmax attention vs. finite-precision attention (which reduces to an argmax) in the transformer, as well as for one- or two-layer transformers. In all scenarios, we prove that LG occurs when the internal behavior of the transformer on longer sequences can be ``simulated'' by its behavior on shorter sequences seen during training. Our bounds give qualitative estimates for the required length of training data required for a transformer to generalize, and we verify these insights empirically. These results sharpen our theoretical understanding of the mechanisms underlying extrapolation in transformers, and formalize the intuition that richer training data is required for generalization on more complex tasks.