GenAI4ELab/papercli-papers huggingface dataset:
Entropic Confinement and Mode Connectivity in Overparameterized Neural Networks
Authors: Luca Di Carlo; Chase Goddard; David J. Schwab
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern neural networks exhibit a striking property: basins of attraction in the loss landscape are often connected by low-loss paths, yet optimization dynamics generally remain confined to a single convex basin and rarely explore intermediate points. We resolve this paradox by identifying entropic barriers arising from the interplay between curvature variations along these paths and noise in optimization dynamics. Empirically, we find that curvature systematically rises away from minima, producing effective forces that bias noisy dynamics back toward the endpoints — even when the loss remains nearly flat. These barriers persist longer than energetic barriers, shaping the late-time localization of solutions in parameter space. Our results highlight the role of curvature-induced entropic forces in governing both connectivity and confinement in deep learning landscapes.
CircuitSense: A Hierarchical MLLM Benchmark Bridging Visual Comprehension and Symbolic Reasoning in Engineering Design Process
Authors: Arman Akbari; Jian Gao; Yifei Zou; Mei Yang; Jinru Duan; Dmitrii Torbunov; Yanzhi Wang; Yihui Ren; Xuan Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Engineering design operates through hierarchical abstraction from system specifications to component implementations, requiring visual understanding coupled with mathematical reasoning at each level. While Multi-modal Large Language Models (MLLMs) excel at natural image tasks, their ability to extract mathematical models from technical diagrams remains unexplored. We present \textbf{CircuitSense}, a comprehensive benchmark evaluating circuit understanding across this hierarchy through 8,006+ problems spanning component-level schematics to system-level block diagrams. Our benchmark uniquely examines the complete engineering workflow: Perception, Analysis, and Design, with a particular emphasis on the critical but underexplored capability of deriving symbolic equations from visual inputs. We introduce a hierarchical synthetic generation pipeline consisting of a grid-based schematic generator and a block diagram generator with auto-derived symbolic equation labels. Comprehensive evaluation of eight state-of-the-art MLLMs, including both closed-source and open-source models, reveals fundamental limitations in visual-to-mathematical reasoning. Closed-source models achieve over 85\% accuracy on perception tasks involving component recognition and topology identification, yet their performance on symbolic derivation and analytical reasoning falls below 19\%, exposing a critical gap between visual parsing and symbolic reasoning. Models with stronger symbolic reasoning capabilities consistently achieve higher design task accuracy, confirming the fundamental role of mathematical understanding in circuit synthesis and establishing symbolic reasoning as the key metric for engineering competence. Our synthetic pipeline code is available at \href{https://anonymous.4open.science/r/CircuitSense-8AC7/README.md}{URL}.
Disentangled representation learning through unsupervised symmetry group discovery
Authors: Barthélémy Dang-Nhu; Louis Annabi; Sylvain ARGENTIERI
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Symmetry-based disentangled representation learning leverages the group structure of environment transformations to uncover the latent factors of variation. Prior approaches to symmetry-based disentanglement have required strong prior knowledge of the symmetry group's structure, or restrictive assumptions about the subgroup properties. In this work, we remove these constraints by proposing a method whereby an embodied agent autonomously discovers the group structure of its action space through unsupervised interaction with the environment. We prove the identifiability of the true action group decomposition under minimal assumptions, and derive two algorithms: one for discovering the group decomposition from interaction data, and another for learning Linear Symmetry-Based Disentangled (LSBD) representations without assuming specific subgroup properties. Our method is validated on three environments exhibiting different group decompositions, where it outperforms existing LSBD approaches.
Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
Authors: Shuofeng Zhang; Ard A. Louis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: For overparameterized linear regression with isotropic Gaussian design and minimum-$\ell_p$ interpolator $p\in(1,2]$, we give a unified, high-probability characterization for the scaling of the family of parameter norms $ \\{ \lVert \widehat{w_p}  \rVert_r \\}_{r \in [1,p]} $ with sample size.

We solve this basic,  but unresolved question through a simple dual-ray analysis, which reveals a competition between a signal 
*spike* and a *bulk* of null coordinates in $X^\top Y$,  yielding closed-form predictions for (i) a data-dependent transition 
$n_\star$ (the "elbow"), and (ii) a universal threshold $r_\star=2(p-1)$ that separates $\lVert \widehat{w_p}  \rVert_r$'s which plateau from those that continue to grow with an explicit exponent. 

This unified solution resolves the scaling of *all* $\ell_r$ norms within the family $r\in [1,p]$ under $\ell_p$-biased interpolation, 
and explains in one picture which norms saturate and which increase as $n$ grows. 

We then study diagonal linear networks (DLNs) trained by gradient descent. 
By calibrating the initialization scale $\alpha$ to an effective $p_{\mathrm{eff}}(\alpha)$ via the DLN separable potential, 
we show empirically that DLNs inherit the same elbow/threshold laws, 
providing a predictive bridge between explicit and implicit bias. 

Given that many generalization proxies depend on $\lVert \widehat {w_p} \rVert_r$, 
our results suggest that their predictive power will depend sensitively on which $l_r$ norm is used.
Laplacian Kernelized Bandit
Authors: Shuang Wu; Arash A. Amini
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study multi-user contextual bandits where users are related by a graph and their reward functions exhibit both non-linear behavior and graph homophily. We introduce a principled joint penalty for the collection of user reward functions $\\{f_u\\}$, combining a graph smoothness term based on RKHS distances with an individual roughness penalty. Our central contribution is proving that this penalty is equivalent to the squared norm within a single, unified _multi-user RKHS_. We explicitly derive its reproducing kernel, which elegantly fuses the graph Laplacian with the base arm kernel. This unification allows us to reframe the problem as learning a single "lifted" function, enabling the design of principled algorithms, LK-GP-UCB and LK-GP-TS, that leverage Gaussian Process posteriors over this new kernel for exploration. We provide high-probability regret bounds that scale with an _effective dimension_ of the multi-user kernel, replacing dependencies on user count or ambient dimension. Empirically, our methods outperform strong linear and non-graph-aware baselines in non-linear settings and remain competitive even when the true rewards are linear. Our work delivers a unified, theoretically grounded, and practical framework that bridges Laplacian regularization with kernelized bandits for structured exploration.
Output Supervision Can Obfuscate the Chain of Thought
Authors: jacob drori; Luke Marks; Bryce Woodworth; Alex Cloud; Alexander Matt Turner
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, OpenAI (2025) showed that training against a chain of thought (CoT) monitor can cause obfuscated CoTs, which contain bad behavior the monitor cannot detect. They proposed to keep CoTs monitorable by training only against output monitors that do not have access to CoT. We show that such training can still cause obfuscated CoTs via two mechanisms. First, when a model is trained to produce a safe-looking output, that model may generalize to making its CoTs look safe. Second, since later tokens are conditioned on earlier ones, safe‑looking CoTs may increase the likelihood of safe outputs, causing safe-looking CoTs to be reinforced. We introduce two mitigations to address these two issues, which achieve a Pareto improvement in terms of monitorability and task performance compared to regular training. To our knowledge, we are the first to identify and mitigate these problems. Our work implies that preserving CoT monitorability is more difficult than previously thought; we suggest practical guidelines for AI developers to maintain monitorable CoTs.
Influence Dynamics and Stagewise Data Attribution
Authors: Jin Hwa Lee; Matthew J. A. Smith; Maxwell Adam; Jesse Hoogland
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current training data attribution (TDA) methods treat the influence one sample has on another as static, but neural networks learn in distinct stages that exhibit changing patterns of influence. In this work, we introduce a framework for stagewise data attribution grounded in singular learning theory. We predict that influence can change non-monotonically, including sign flips and sharp peaks at developmental transitions. We first validate these predictions analytically and empirically in a toy model, showing that dynamic shifts in influence directly map to the model's progressive learning of a semantic hierarchy. Finally, we demonstrate these phenomena at scale in language models, where token-level influence changes align with known developmental stages.
Keep the Best, Forget the Rest: Reliable Alignment with Order-Aware Preference Optimization
Authors: Jiahui Zhu; Yuanjie Shi; Xiyue Peng; Xin Liu; Yan Yan; Honghao Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Direct Preference Optimization (DPO) has emerged as a powerful framework for aligning large language models (LLMs) with human preferences via pairwise comparisons. However, its performance is highly sensitive to the quality of training samples: when the reference policy is poorly aligned with human preferences, ambiguous pairs can dominate the gradient signal and degrade generalization. To address this, we propose RAPPO($\textbf{R}$eliable $\textbf{A}$lignment for $\textbf{P}$reference $\textbf{P}$olicy $\textbf{O}$ptimization), a simple sample-aware modification of the DPO loss that mitigates reference-policy misalignment by filtering out the hardest, most ambiguous samples. We theoretically show that RAPPO yields improved generalization guarantees. RAPPO is lightweight and requires only a few lines of code to be integrated into any existing DPO-type algorithm. Surprisingly, With this simple modification, our simulations across a broad suite of alignment tasks and benchmarks show consistent gains over DPO and recent state-of-the-art baselines. On the PKU-SafeRLHF benchmark, RAPPO attains helpfulness $0.693$ ($+34.8\%$ over DPO) and harmlessness $0.357$ ($-21.0\%$ vs DPO).
One Patch Doesn’t Fit All: Adaptive Patching for Native-Resolution Multimodal Large Language Models
Authors: Wenzhuo Liu; Weijie Yin; Fei Zhu; Shijie Ma; Haiyang Guo; yi chen; Xiao-Hui Li; LiangXiao; ChaoFeng; Cheng-Lin Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world visual signals are inherently variable in resolution, and it is natural to endow multimodal large language models (MLLMs) with such native-resolution perception capabilities. In principle, for general and straightforward multimodal understanding, low-resolution images are sufficient. While for images with nuanced details like documents and charts, it is crucial to preserve fine-grained details using high-resolution inputs, as naive resizing inevitably results in information loss. Recent advances employ sequence packing to process images of any resolution and aspect ratios. Despite these efforts, model performance degrades at both low and high resolutions, and high-resolution inputs incur substantial computational costs. We argue that the rigid use of a single patch size is the primary cause: when image resolution or information density varies, fixing patch size is intrinsically suboptimal.  To address this issue, we introduce Adaptive Patching (AdaPatch), a simple yet effective strategy that adjusts patch size according to image resolution and information density and could be seamlessly plugged into pre-trained fixed-patch MLLMs without any training efforts. Extensive evaluations demonstrate consistent improvements in native resolution performance without additional training. Besides, we provide a training-based method to further adapt MLLMs with dynamic patch sizes and enhance the performance.
GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs
Authors: Tao Feng; Haozhen Zhang; Zijie Lei; Peixuan Han; Jiaxuan You
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLM routing has achieved promising results in integrating the strengths of di-
verse models while balancing efficiency and performance. However, to support
more realistic and challenging applications, routing must extend into agentic LLM
settings—where task planning, multi-round cooperation among heterogeneous
agents, and memory utilization are indispensable. To address this gap, we pro-
pose GraphPlanner, a heterogeneous graph memory-augmented agentic router
for multi-agent LLMs that generates routing workflows for each query and sup-
ports both inductive and transductive inference. GraphPlanner formulates
workflow generation as a Markov Decision Process (MDP), where at each step
it selects both the LLM backbone and the agent role (Planner, Executor, Sum-
marizer). By leveraging a heterogeneous graph, denoted as GARNet, to capture
interaction memories among queries, agents, and responses, GraphPlanner
integrates historical memory and workflow memory into richer state represen-
tations. The entire pipeline is optimized with reinforcement learning, jointly
improving task-specific performance and computational efficiency. We evalu-
ate GraphPlanner across 14 diverse LLM tasks and demonstrate that: (1)
GraphPlanner outperforms strong single- and multi-round routers, improv-
ing accuracy by up to 9.3% while reducing GPU cost from 186.26 GiB to
1.04 GiB; (2) GraphPlanner generalizes robustly to unseen tasks and LLMs,
exhibiting strong zero-shot capabilities; and (3) GraphPlanner effectively
leverages historical memories, supporting both inductive and transductive infer-
ence for more adaptive routing. Our code for GraphPlanner is released at
https://github.com/ulab-uiuc/GraphPlanner.
PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
Authors: Francesco Spinnato; Cristiano Landi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing research communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets on which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable a more robust evaluation of irregular temporal data analysis methods.
Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers
Authors: Peter Shaw; James Cohan; Jacob Eisenstein; Kristina Toutanova
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Minimum Description Length (MDL) principle offers a formal framework for applying Occam's razor in machine learning. However, its application to neural networks such as Transformers is challenging due to the lack of a principled, universal measure for model complexity. This paper introduces the theoretical notion of asymptotically optimal description length objectives, grounded in the theory of Kolmogorov complexity. We establish that a minimizer of such an objective achieves optimal compression, for any dataset, up to an additive constant, in the limit as model resource bounds increase. We prove that asymptotically optimal objectives exist for Transformers, building on a new demonstration of their computational universality. We further show that such objectives can be tractable and differentiable by constructing and analyzing a variational objective based on an adaptive Gaussian mixture prior. Our empirical analysis shows that this variational objective selects for a low-complexity solution with strong generalization on an algorithmic task, but standard optimizers fail to find such solutions from a random initialization, highlighting key optimization challenges. More broadly, by providing a theoretical framework for identifying description length objectives with strong asymptotic guarantees, we outline a potential path towards training neural networks that achieve greater compression and generalization.
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
Authors: Lechen Zhang; Tolga Ergen; Lajanugen Logeswaran; Moontae Lee; David Jurgens
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on optimizing prompts specific to a task. However, much less attention has been given to optimizing the general instructions included in a prompt, known as a system prompt. To address this gap, we propose SPRIG, an edit-based genetic algorithm that iteratively constructs prompts from prespecified components to maximize the model's performance in general scenarios. We evaluate the performance of system prompts on a collection of 47 different types of tasks to ensure generalizability. Our study finds that a single optimized system prompt performs on par with task prompts optimized for each individual task. Moreover, combining system and task-level optimizations leads to further improvement, which showcases their complementary nature. Experiments also reveal that the optimized system prompts generalize effectively across model families, parameter sizes, and languages. This study provides insights into the role of system-level instructions in maximizing LLM potential.
Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving
Authors: Chang Su; Zhongkai Hao; Zhizhou Zhang; Zeyu Xia; Youjia Wu; Hang Su; Jun Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) with reasoning abilities have demonstrated 
growing promise for tackling complex scientific problems. Yet such tasks are inherently domain-specific, unbounded and open-ended, demanding exploration across vast and flexible solution spaces. Existing approaches, whether purely learning-based or reliant on carefully designed workflows, often suffer from limited exploration efficiency and poor generalization. 
To overcome these challenges, we present **HELIX**---a 
**H**ierarchical **E**volutionary reinforcement **L**earning framework with **I**n-context e**X**periences. HELIX introduces two key novelties: (i) a diverse yet high-quality pool of candidate solutions that broadens exploration through in-context learning, and (ii) reinforcement learning for iterative policy refinement that progressively elevates solution quality. This synergy enables the discovery of more advanced solutions. On the circle packing task, HELIX achieves state-of-the-art result with a sum of radii of 2.63598308 using only a 14B model. Across standard machine learning benchmarks, HELIX further surpasses GPT-4o with a carefully engineered pipeline, delivering an average F1 improvement of 5.95 points on the Adult and Bank Marketing datasets.
Distilling to Hybrid Attention Models via KL-Guided Layer Selection
Authors: Yanhong Li; Songlin Yang; Shawn Tan; Mayank Mishra; Rameswar Panda; Jiawei Zhou; Yoon Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Distilling pretrained softmax attention Transformers into more efficient hybrid architectures that interleave softmax and linear attention layers is a promising approach for improving the inference efficiency of LLMs  without requiring expensive pretraining from scratch. A critical factor in the conversion process is  layer selection, i.e., deciding on which layers to convert to linear attention variants. This paper describes a simple and efficient recipe for layer selection that uses layer importance scores derived from a small amount of training on generic text data.  Once the layers have been selected we use a  recent pipeline for the distillation process itself \citep[RADLADS;][]{goldstein2025radlads}, which consists of attention weight transfer, hidden state alignment, KL-based distribution matching, followed by a small amount of finetuning. We find that this approach is more effective than existing  approaches for layer selection, including heuristics that uniformly interleave linear attentions based on a fixed ratio, as well as more involved approaches that rely on specialized diagnostic datasets.
Critique-RL: Training Language Models For Critiquing Through Two-Stage Reinforcement Learning
Authors: Zhiheng Xi; Jixuan Huang; Xin Guo; Boyang Hong; Dingwen Yang; Xiaoran Fan; Shuo Li; Zehui Chen; Junjie Ye; Siyu Yuan; Zhengyin Du; Xuesong Yao; Yufei Xu; Jiecao Chen; Rui Zheng; Tao Gui; Qi Zhang; Xuanjing Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training critiquing language models to assess and provide feedback on model outputs is a promising way to improve LLMs for complex reasoning tasks. However, existing approaches typically rely on stronger supervisors for annotating critique data. To address this, we propose Critique-RL, an online RL approach for developing critiquing language models without stronger supervision. Our approach operates on a two-player paradigm: the actor generates a response, the critic provides feedback, and the actor refines the response accordingly. We first reveal that relying solely on indirect reward signals from the actor’s outputs for RL optimization often leads to unsatisfactory critics: while their helpfulness (i.e., providing constructive feedback) improves, the discriminability (i.e., determining whether a response is high-quality or not) remains poor, resulting in marginal performance gains. To overcome this, Critique-RL adopts a two-stage optimization strategy. In stage I, it reinforces the discriminability of the critic with direct rule-based reward signals; in stage II, it introduces indirect rewards based on actor refinement to improve the critic's helpfulness, while maintaining its discriminability via appropriate regularization. Extensive experiments across various tasks and models show that Critique-RL delivers substantial performance improvements. For example, it achieves a $9.02\%$ gain on in-domain tasks and a $5.70\%$ gain on out-of-domain tasks for Qwen2.5-7B, highlighting its potential.