GenAI4ELab/papercli-papers huggingface dataset:
A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization
Authors: Hideaki Kim; Tomoharu Iwata
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The representer theorem is a cornerstone of kernel methods, which aim to estimate latent functions in reproducing kernel Hilbert spaces (RKHSs) in a nonparametric manner. Its significance lies in converting inherently infinite-dimensional optimization problems into finite-dimensional ones over dual coefficients, thereby enabling practical and computationally tractable algorithms. In this paper, we address the problem of estimating the latent triggering kernels--functions that encode the interaction structure between events--for linear multivariate Hawkes processes based on observed event sequences within an RKHS framework. We show that, under the principle of penalized least squares minimization, a novel form of representer theorem emerges: a family of transformed kernels can be defined via a system of simultaneous integral equations, and the optimal estimator of each triggering kernel is expressed as a linear combination of these transformed kernels evaluated at the data points. Remarkably, the dual coefficients are all analytically fixed to unity, obviating the need to solve a costly optimization problem to obtain the dual coefficients. This leads to a highly efficient estimator capable of handling large-scale data more effectively than conventional nonparametric approaches. Empirical evaluations on synthetic and real-world datasets reveal that the proposed method achieves competitive predictive accuracy while substantially improving computational efficiency compared to state-of-the-art kernel method-based estimators.
Scaling Goal-conditioned Reinforcement Learning with Multistep Quasimetric Distances
Authors: Bill Zheng; Vivek Myers; Benjamin Eysenbach; Sergey Levine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The problem of learning how to reach goals in an environment has been a long-
standing challenge in for AI researchers. Effective goal-conditioned reinforcement
learning (GCRL) methods promise to enable reaching distant goals without task-
specific rewards by stitching together past experiences of different complexity.
Mathematically, there is a duality between the notion of optimal goal-reaching
value functions (the likelihood of success at reaching a goal) and temporal dis-
tances (transit times states). Recent works have exploited this property by learning
quasimetric distance representations that stitch long-horizon behaviors using the in-
ductive bias of their architecture. These methods have shown promise in simulated
benchmarks, reducing value learning to a shortest-path problem. But quasimet-
ric, and more generally, goal-conditioned RL methods still struggle in complex
environments with stochasticity and high-dimensional (visual) observations. There
is a fundamental tension between the local dynamic programming (TD backups,
temporal distances) that enables optimal shortest-path reasoning in theory and the
statistical global MC updates (multistep returns, suboptimal in theory). We show
how these approaches can be integrated into a practical GCRL method that fits a
quasimetric distance using a multistep Monte-Carlo return. We show our method
outperforms existing GCRL methods on long-horizon simulated tasks with up to
4000 steps, even with visual observations. We also demonstrate that our method
can enable stitching in the real-world robotic manipulation domain (Bridge setup).
Our approach is the first end-to-end GCRL method that enables multistep stitching
in this real-world manipulation domain from an unlabeled offline dataset of visual
observations.
Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
Authors: Jakub Krajewski; Amitis Shidani; Dan Busbridge; Sam Wiseman; Jason Ramapuram
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While scaling laws for Large Language Models (LLMs) traditionally focus on proxy metrics like pretraining loss, predicting downstream task performance has been considered unreliable. This paper challenges that view by proposing a direct framework to model the scaling of downstream accuracy from the training budget. We demonstrate that for a fixed token-to-parameter ratio, a simple two-parameter scaling law accurately describes this relationship. Our findings are validated by experiments on models with up to 17B parameters trained on up to 350B tokens, showing that downstream capabilities scaling can be described using a scaling law. Furthermore, we extend this framework to extrapolate and predict accuracy of target model with up to 6.7x larger training budget based on a set of smaller experiments. We release a complete list of model losses and downstream evaluation results at various different scales to support reproducibility and encourage future research.
Making, Not Taking, the Best of N
Authors: Ammar Khairi; Daniel D'souza; Marzieh Fadaee; Julia Kreutzer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Obtaining high-quality generations in modern LLMs has largely been framed as a selection problem: identifying a single winning generation from a diverse pool of $N$ samples, the Best-of-$N$ (BoN).
Yet, this approach is inherently zero-sum, discarding diverse and potentially useful information from the pool. Instead, we explore a collaborative setup, where all candidates can potentially contribute to the final winning generation. To this end, we propose Fusion-of-$N$ (FusioN): a method that uses a general LLM judge to synthesize the most informative elements of each sample into a single final answer. 
We compare FusioN to BoN in two settings, (i) test-time scaling, where we sample and aggregate from a single model at test-time (ii) synthetic data generation, where we fuse samples from a pool of diverse teachers to improve a student model. We extensively benchmark both setups across 11 languages, 3 diverse benchmarks and varying model scales. Across the bench, FusionN consistently outperforms BoN showing versatility and robustness both in test-time scaling and in downstream gains from synthetic data generation. We also perform extensive analysis on FusioN, where it shows surprising strengths and robustness under challenging settings.
These results show that we should shift how we think about evaluating and utilizing LLM generations from a monolithic measure of quality, to embracing their polylithic nature. This shift allows us to integrate diverse strengths, unlock latent potential, and achieve improvements that were previously inaccessible through selection alone.
Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning
Authors: Yilun Hao; Yongchao Chen; Chuchu Fan; Yang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision Language Models (VLMs) show strong potential for visual planning but struggle with precise spatial and long-horizon reasoning, while Planning Domain Definition Language (PDDL) planners excel at formal long-horizon planning but cannot interpret visual inputs. Recent works combine these complementary advantages by translating visual problems into PDDL. However, while  VLMs can generate PDDL problem files satisfactorily, accurately generating PDDL domain files, which encode planning rules, remains challenging and typically requires human expertise or environment interaction.
We propose VLMFP, a Dual-VLM-guided framework that autonomously generates both PDDL problem and domain files for formal visual planning. VLMFP combines a SimVLM that simulates action consequences with a GenVLM that generates and iteratively refines PDDL files by aligning symbolic execution with simulated outcomes, enabling multiple levels of generalization across unseen instances, visual appearances, and game rules.
We evaluate VLMFP on 6 grid-world domains and demonstrate its generalization capability. On average, SimVLM achieves 87.3\% and 86.0\% scenario understanding and action simulation for seen and unseen appearances, respectively. With the guidance of SimVLM, VLMFP attains 70.0\%, 54.1\% planning success on unseen instances in seen and unseen appearances, respectively. We further demonstrate that VLMFP scales to complex long-horizon 3D planning tasks, including multi-robot collaboration and assembly scenarios with partial observability and diverse visual variations. Project page: https://sites.google.com/view/vlmfp.
FACT: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations
Authors: Enric Boix-Adserà; Neil Rohit Mallinar; James B Simon; Mikhail Belkin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: It is a central challenge in deep learning to understand how neural networks learn representations. A leading approach is the Neural Feature Ansatz (NFA) (Radhakrishnan et al., 2024), a conjectured mechanism for how feature learning occurs. Although the NFA is empirically validated, it is an educated guess and lacks a theoretical basis, and thus it is unclear when it might fail, and how to improve it. In this paper, we take a first-principles approach to understanding why this observation holds, and when it does not. We use first-order optimality conditions to derive the Features at Convergence Theorem (FACT), an alternative to the NFA that (a) obtains greater agreement with learned features at convergence, (b) explains why the NFA holds in most settings, and (c) captures essential feature learning phenomena in neural networks such as grokking behavior in modular arithmetic and phase transitions in learning sparse parities, similarly to the NFA. Thus, our results unify theoretical first-order optimality analyses of neural networks with the empirically-driven NFA literature, and provide a principled alternative that provably and empirically holds at convergence.
Trapped by simplicity: When Transformers fail to learn from noisy features
Authors: Evan Peters; Matheus Hrabowec Zambianco; Ando Deng; Devin Blankespoor; Achim Kempf
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Noise is ubiquitous in data used to train large language models, but it is not well understood whether these models are able to correctly generalize to inputs generated without noise. Here, we study noise-robust learning: are transformers trained on data with noisy features able to find a target function that correctly predicts labels for noiseless features? We show that transformers succeed at noise-robust learning for a selection of $k$-sparse parity and majority functions, compared to LSTMs which fail at this task for even modest feature noise. However, we find that transformers typically fail at noise-robust learning of random $k$-juntas, especially when the boolean sensitivity of the optimal solution is smaller than that of the target function. We argue that this failure is due to a combination of two factors: transformers' bias toward simpler functions, combined with an observation that the empirically optimal function for noise-robust learning has lower sensitivity than the target function. We test this hypothesis by exploiting transformers' simplicity bias to trap them in an incorrect solution, but show that transformers can escape this trap by training with an additional loss term penalizing high-sensitivity solutions. Overall, we find that transformers are particularly ineffective for learning boolean functions in the presence of feature noise.
Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?
Authors: Yifan Wang; Mayank Jobanputra; Ji-Ung Lee; Soyoung Oh; Isabel Valera; Vera Demberg
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about fairness.
At the same time, their black-box nature makes it difficult for users to recognize biased predictions and for developers to effectively mitigate them.
While some studies suggest that input-based explanations can help detect and mitigate bias, others question their reliability in ensuring fairness.
Existing research on explainability in fair NLP has been predominantly qualitative, with limited large-scale quantitative analysis.
In this work, we conduct the first systematic study of the relationship between explainability and fairness in hate speech detection, focusing on both encoder- and decoder-only models.
We examine three key dimensions: (1) identifying biased predictions, (2) selecting fair models, and (3) mitigating bias during model training.
Our findings show that input-based explanations can effectively detect biased predictions and serve as useful supervision for reducing bias during training, but they are unreliable for selecting fair models among candidates.
Our code is available at https://github.com/Ewanwong/fairness_x_explainability.
Remotely Detectable Robot Policy Watermarking
Authors: Michael Amir; Manon Flageat; Amanda Prorok
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of machine learning for real-world robotic systems has created a new form of intellectual property: the trained policy. This raises a critical need for novel methods that verify ownership and detect unauthorized, possibly unsafe misuse. While watermarking is established in other domains, physical policies present a unique challenge: remote detection. Existing methods assume access to the robot’s internal state, but auditors are often limited to external observations (e.g., video footage). This “Physical Observation Gap” means the watermark must be detected from signals that are noisy, asynchronous, and filtered by unknown system dynamics. We formalize this challenge using the concept of a glimpse sequence, and introduce Colored Noise Coherency (CoNoCo), the first watermarking strategy designed for remote detection. CoNoCo embeds a spectral signal into the robot’s motions by leveraging the policy’s inherent stochasticity. To show it does not degrade performance, we prove CoNoCo preserves the marginal action distribution. Our experiments demonstrate strong, robust detection across various remote modalities—including motion capture and side-way/top-down video footage—in both simulated and real-world robot experiments. This work provides a necessary step toward protecting intellectual property in robotics, offering the first method for validating the provenance of physical policies non invasively, using purely remote observations.
MLE-Smith: Scaling MLE Tasks with Automated Multi-agent Pipeline
Authors: Rushi Qiang; Yuchen Zhuang; Anikait Singh; Percy Liang; Chao Zhang; Sherry Yang; Bo Dai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly constrained. Current MLE benchmarks suffer from low scalability and limited applicability because they rely on static, manually curated tasks that demand extensive time and manual effort to produce.
We introduce MLE-Smith, a fully automated multi-agent pipeline, to transform raw datasets into competition-style MLE challenges through an efficient generate--verify--execute paradigm for scaling MLE tasks with verifiable quality, real-world usability and rich diversity. 
The proposed multi-agent pipeline in MLE-Smith drives structured task design and standardized refactoring, coupled with a hybrid verification mechanism that enforces strict structural rules and high-level semantic soundness. It further validates empirical solvability and real-world fidelity through interactive execution.
We apply MLE-Smith to 224 of real-world datasets and generates 606 tasks spanning multiple categories, objectives, and modalities, demonstrating that MLE-Smith can work 
effectively 
across a wide range of real-world datasets.
Evaluation on generated tasks shows that the performance of eight mainstream and cutting-edge LLMs on MLE-Smith tasks is strongly correlated with their performance on carefully human-designed tasks, highlighting the effectiveness of the MLE-Smith in scaling up MLE tasks while maintaining task quality.
Causally Robust Reward Learning from Reason-Augmented Preference Feedback
Authors: Minjune Hwang; Yigit Korkmaz; Daniel Seita; Erdem Biyik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Preference‑based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback makes it especially vulnerable to causal confusion. The learned reward often latches onto spurious features that merely co‑occur with preferred trajectories during training, collapsing when those correlations disappear or reverse at test time. We introduce ReCouPLe, a lightweight framework that uses natural language rationales to provide the missing causal signal. Each rationale is treated as a guiding projection axis in an embedding space, training the model to score trajectories based on features aligned with that axis while de-emphasizing context that is unrelated to the stated reason. Because the same rationales (e.g., "_avoids collisions_", "_completes the task faster_") can appear across multiple tasks, ReCouPLe naturally reuses the same causal direction whenever tasks share semantics, and transfers preference knowledge to novel tasks without extra data or language‑model fine‑tuning. Our learned reward model can ground preferences on the articulated reason, aligning better with user intent and generalizing beyond spurious features. ReCouPLe outperforms baselines by up to 1.5x in reward accuracy under distribution shifts, and 2x in downstream policy performance in novel tasks. We have released our code at [https://github.com/mj-hwang/ReCouPLe](https://github.com/mj-hwang/ReCouPLe).
What's the plan? Metrics for implicit planning in LLMs and their application to rhyme generation and question answering
Authors: Jim Maar; Denis Paperno; Callum Stuart McDougall; Neel Nanda
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Prior work suggests that language models, while trained on next token prediction, show implicit planning behavior: they may select the next token in preparation to a predicted future token, such as a likely rhyming word, as supported by a prior qualitative study of Claude 3.5 Haiku using a cross-layer transcoder. 
We propose much simpler techniques for assessing implicit planning in language models. With case studies on rhyme poetry generation and question answering, we demonstrate that our methodology easily scales to many models.
Across models, we find that the generated rhyme (e.g. "-ight") or answer to a question ("whale")
can be manipulated by steering at the end of the preceding line with a vector, affecting the generation of intermediate tokens leading up to the rhyme or answer word.
We show that implicit planning is a universal mechanism, present in smaller models than previously thought, starting from 1B parameters. 
Our methodology offers a widely applicable direct way to study implicit planning abilities of LLMs.
More broadly, understanding planning abilities of language models can inform decisions in AI safety and control.
Hilbert-Guided Sparse Local Attention
Authors: Yunge Li; Lanyu Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The quadratic compute and memory costs of global self-attention severely limit its use in high-resolution images. Local attention reduces complexity by restricting attention to neighborhoods. Block-sparse kernels can further improve the efficiency of local attention, but conventional local attention patterns often fail to deliver significant speedups because tokens within a window are not contiguous in the 1D sequence. This work proposes a novel method for constructing windows and neighborhoods based on the Hilbert curve. Image tokens are first reordered along a Hilbert curve, and windows and neighborhoods are then formed on the reordered 1D sequence. From a block-sparse perspective, this strategy significantly increases block sparsity and can be combined with existing block-sparse kernels to improve the efficiency of 2D local attention. Experiments show that the proposed Hilbert Window Attention and Hilbert Slide Attention can accelerate window attention and slide attention by about $4\times$ and $18\times$, respectively. To assess practicality, the strategy is instantiated as the Hilbert Window Transformer and the Hilbert Neighborhood Transformer, both of which achieve end-to-end speedups with minimal accuracy loss. Overall, combining Hilbert-guided local attention with block-sparse kernels offers a general and practical approach to enhancing the efficiency of 2D local attention for images.
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning
Authors: Wassim Bouaziz; Mathurin VIDEAU; Nicolas Usunier; El-Mahdi El-Mhamdi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The pre-training of large language models (LLMs) relies on massive text datasets sourced from diverse and difficult-to-curate origins.
Although membership inference attacks and hidden canaries have been explored to trace data usage, such methods rely on *regurgitation* of training data, which LM providers try to limit.
In this work, we demonstrate that *indirect data poisoning* (where the targeted behavior is absent from training data) is not only feasible against LLMs but also allows to effectively protect a dataset and trace its use.
Using gradient-based optimization prompt-tuning, we craft poisons to make a model learn arbitrary *secret sequences*: secret responses to secret prompts that are **absent from the training corpus**.\
We validate our approach on language models pre-trained from scratch and show that less than 0.005\% of poisoned tokens are sufficient to covertly make a LM learn a *secret* and detect it with extremely high confidence ( $p < 10^{-55}$ ) with a theoretically certifiable scheme.
Crucially, this occurs without performance degradation (on LM benchmarks) and despite secrets **never appearing in the training set**.
Is the Reversal Curse a Binding Problem? Uncovering Limitations of Transformers from a Basic Generalization Failure
Authors: Boshi Wang; Huan Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite their impressive capabilities, LLMs exhibit a basic generalization failure known as the *Reversal Curse*, where they struggle to learn reversible factual associations. Understanding why this occurs could help identify weaknesses in current models and advance their generalization and robustness. In this paper, we conjecture that the Reversal Curse in LLMs is a manifestation of the long-standing *binding problem* in cognitive science, neuroscience and AI. Specifically, we hypothesize two primary causes of the Reversal Curse stemming from transformers' limitations in conceptual binding: the *inconsistency* and *entanglements* of concept representations. We perform a series of experiments that support these conjectures. Our exploration leads to a model design based on JEPA (Joint-Embedding Predictive Architecture) that for the first time breaks the Reversal Curse without side-stepping it with specialized data augmentation or non-causal masking, and moreover, generalization could be further improved by incorporating special memory layers that support disentangled concept representations. Our research opens up the broader fundamental challenge of designing models capable of learning systematic conceptual binding with less human scaffolding.