GenAI4ELab/papercli-papers huggingface dataset:
Self-Speculative Masked Diffusions
Authors: Andrew Campbell; Valentin De Bortoli; Jiaxin Shi; Arnaud Doucet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present self-speculative masked diffusions, a new class of masked diffusion generative models for discrete data that require significantly fewer function evaluations to generate samples. Standard masked diffusion models predict factorized logits over currently masked positions. A number of masked positions are then sampled, however, the factorization approximation means that sampling too many positions in one go leads to poor sample quality. As a result, many simulation steps and therefore neural network function evaluations are required to generate high-quality data. We reduce the computational burden by generating \emph{non-factorized} predictions over masked positions. This is achieved by modifying the final transformer attention mask from non-causal to causal, enabling draft token generation and parallel validation via a novel, model-integrated speculative sampling mechanism. This results in a non-factorized predictive distribution over masked positions in a single forward pass. We apply our method to GPT2 scale text modelling and protein sequence generation, finding that we can achieve a ~2x reduction in the required number of network forward passes relative to standard masked diffusion models.
Divide, Harmonize, Then Conquer It: Shooting Multi-Commodity Flow Problems with Multimodal Language Models
Authors: Xinyu Yuan; Yan Qiao; Zonghui Wang; Wenzhi CHEN
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The multi-commodity flow (MCF) problem is a fundamental topic in network flow and combinatorial optimization, with broad applications in transportation, communication, and logistics, etc. Nowadays, the rapid expansion of allocation systems has posed challenges for existing optimization engines in balancing optimality and tractability. In this paper, we present Pram, the first ML-based method that leverages the reasoning power of multimodal language models (MLMs) for addressing the trade-off dilemma—a great need of service providers. As part of our proposal, Pram (i) quickly computes high-quality allocations by dividing the original problem into local subproblems, which are then resolved by an MLM-powered "agent", and (ii) ensures global consistency by harmonizing these subproblems via a multi-agent reinforcement learning algorithm. Theoretically, we show that Pram, which learns to perform gradient descent in context, provably converges to the optimum within the family of MCF problems. 
Empirically, on real-world datasets and public topologies, Pram achieves performance comparable to, and in some cases even surpassing, linear programming solvers (very close to the optimal solution), and substantially lower runtimes (one to two orders of magnitude faster). 
Moreover, Pram exhibits strong robustness (<10\% performance degradation under failures or bursts), demonstrating MLM's generalization ability to unforeseen events. 
Pram  is objective-agnostic and seamlessly integrates with mainstream allocation systems, providing a practical and scalable solution for future networks.
Monocular Normal Estimation via Shading Sequence Estimation
Authors: Zongrui Li; Xinhua Ma; Minghui Hu; Yunqing Zhao; Yingchen Yu; Qian Zheng; Chang Liu; Xudong Jiang; Song Bai
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Monocular normal estimation aims to estimate the normal map from a single RGB image of an object under arbitrary lights. Existing methods rely on deep models to directly predict normal maps. However, they often suffer from 3D misalignment: while the estimated normal maps may appear to have a correct appearance, the reconstructed surfaces often fail to align with the 3D geometry. We argue that this misalignment stems from the current paradigm: the model struggles to distinguish and estimate varying geometry represented in normal maps, as the differences in underlying geometry are reflected only through relatively subtle color variations. To address this issue, we propose a new paradigm that reformulates normal estimation as shading sequence estimation, where shading sequences are more sensitive to various geometry information. By learning to infer the shading sequence of an object, the model can better capture underlying 3D geometry and thereby produce more accurate normal predictions. Building on this paradigm, we present RoSE, a method that leverages image-to-video generative models to predict shading sequences, which are then converted into normal maps by solving a simple ordinary least-squares problem. To enhance robustness and better handle complex objects, RoSE is trained on a synthetic dataset, MultiShade, with diverse shapes, materials, and light conditions. Experiments demonstrate that RoSE achieves state-of-the-art performance on both synthetic and real-world benchmark datasets for object-based monocular normal estimation.
Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People
Authors: Gabriel Grand; Valerio Pepe; Joshua B. Tenenbaum; Jacob Andreas
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Many emerging applications of AI—from scientific discovery to medical diagnosis—require agents to seek information strategically: forming hypotheses, asking targeted questions, and making decisions under uncertainty. In high-stakes settings with limited resources, do language models (LMs) behave like rational agents? Drawing on insights from human cognition, we develop methods to evaluate and enhance agentic information-seeking. First, we introduce a decision-oriented dialogue task called Collaborative Battleship, in which a Captain must balance exploration (asking questions) and action (taking shots), while a Spotter must supply accurate, contextually-grounded answers. Compared to human players (N=42), we find that many LM agents struggle to ask informative questions, produce accurate answers, and identify high-utility actions. To address these gaps, we develop novel Monte Carlo inference strategies for LMs inspired by Bayesian Experimental Design (BED). For Spotter agents, our approach boosts accuracy by up to 14.7% absolute over LM-only baselines; for Captain agents, it raises expected information gain (EIG) by up to 0.227 bits (94.2% of the achievable noise ceiling). Combined, these components yield sharper targeting (+0.303–0.374 F1), and enable weaker LMs, such as Llama-4-Scout, to outperform both humans (8% → 82% win rate) and frontier models (0% → 67% win rate vs. GPT-5) at ≈1% of GPT-5's cost. We replicate these findings on Guess Who?, where our methods significantly boost accuracy (+28.3–42.4 p.p.), demonstrating their general applicability for building information-seeking agents.
LeSTD: LLM Compression via Learning-based Sparse Tensor Decomposition
Authors: Yi Li; Zhichun Guo; Miao Yin; Bingzhe Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) achieve remarkable success, but their massive parameter counts present significant deployment challenges. Post-training tensor decomposition offers a promising, data-free compression strategy by exploiting structural redundancies within the model weights. However, existing tensor methods face a critical limitation: the dense core tensor bottleneck. While these methods find a shared low-rank basis, the resulting dense core tensor grows polynomially with the chosen ranks, becoming a new storage bottleneck and capping the maximum achievable compression. To overcome this fundamental barrier, we introduce LeSTD (\textbf{Le}arning-based \textbf{S}parse \textbf{T}ensor \textbf{D}ecomposition), a novel two-stage framework for the high-ratio compression of Multi-Head Attention (MHA) blocks. LeSTD first employs an iterative algorithm to identify a high-quality, and shared orthogonal basis that jointly represents all attention heads. Subsequently, it introduces a principled, importance-based pruning algorithm that learns an ultra-sparse core tensor by systematically removing the least salient elements and refitting the remaining ones to preserve model fidelity. By decoupling basis optimization from core sparsification, LeSTD breaks the compression ceiling imposed by the dense core, enabling significantly higher compression ratios than prior methods.
Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate
Authors: Zhiqi Bu; Shiyun Xu; Jialin Mao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep learning has non-convex loss landscape and its optimization dynamics is hard to analyze or control. Nevertheless, the dynamics can be empirically convex-like across various tasks, models, optimizers, hyperparameters, etc. In this work, we examine the applicability of convexity and Lipschitz continuity in deep learning, in order to precisely control the loss dynamics via the learning rate schedules. We illustrate that deep learning quickly becomes weakly convex after a short period of training, and the loss is predicable by an upper bound on the last iterate, which further informs the scaling of optimal learning rate. Through the lens of convexity, we build scaling laws of learning rates and losses that extrapolate as much as $80\times$ across training horizons and $70\times$ across model sizes.
Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures
Authors: Yedi Zhang; Andrew M Saxe; Peter E. Latham
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural networks trained with gradient descent often learn solutions of increasing complexity over time, a phenomenon known as simplicity bias. Despite being widely observed across architectures, existing theoretical treatments lack a unifying framework. We present a theoretical framework that explains a simplicity bias arising from saddle-to-saddle learning dynamics for a general class of neural networks, incorporating fully-connected, convolutional, and attention-based architectures. Here, simple means expressible with few hidden units, i.e., hidden neurons, convolutional kernels, or attention heads. Specifically, we show that linear networks learn solutions of increasing rank, ReLU networks learn solutions with an increasing number of kinks, convolutional networks learn solutions with an increasing number of convolutional kernels, and self-attention models learn solutions with an increasing number of attention heads. By analyzing fixed points, invariant manifolds, and dynamics of gradient descent learning, we show that saddle-to-saddle dynamics operates by iteratively evolving near an invariant manifold, approaching a saddle, and switching to another invariant manifold. Our analysis also disentangles data-induced and initialization-induced saddle-to-saddle dynamics. In particular, the former leads to low-rank weights while the latter to sparse weights. Equipped with the theory, we predict the effects of data distribution and weight initialization on the duration and number of plateaus in learning. Overall, our theory offers a framework for understanding when and why gradient descent progressively learns increasingly complex solutions.
UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videos
Authors: Mingxuan Liu; Honglin He; Elisa Ricci; Wayne Wu; Bolei Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Urban embodied AI agents, ranging from delivery robots to quadrupeds, are increasingly populating our cities, navigating chaotic streets to provide last-mile connectivity. Training such agents requires diverse, high-fidelity urban environments to scale, yet existing human-crafted or procedurally generated simulation scenes either lack scalability or fail to capture real-world complexity. We introduce UrbanVerse, a data-driven real-to-sim system that converts crowd-sourced city-tour videos into physics-aware, interactive simulation scenes. UrbanVerse consists of: (i) UrbanVerse-100K, a repository of 100k+ annotated urban 3D assets with semantic and physical attributes, and (ii) UrbanVerse-Gen, an automatic pipeline that extracts scene layouts from video and instantiates metric-scale 3D simulations using retrieved assets. Running in IsaacSim, UrbanVerse offers 160 high-quality constructed scenes from 24 countries, along with a curated benchmark of 10 artist-designed test scenes. Experiments show that UrbanVerse scenes preserve real-world semantics and layouts, achieving human-evaluated realism comparable to manually crafted scenes. In urban navigation, policies trained in UrbanVerse exhibit scaling power laws and strong generalization, improving success by +6.3% in simulation and +30.1% in zero-shot sim-to-real transfer comparing to prior methods, accomplishing a 300 m real-world mission with only two interventions.
The Softmax Bottleneck Does Not Limit the Probabilities of the Most Likely Tokens
Authors: Ronen Basri; David Jacobs
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In many popular transformer architectures, an output projection matrix linearly maps lower-dimensional embeddings into a higher-dimensional space of logits. It has been shown that this leads to a softmax bottleneck that prevents the production of arbitrary probability distributions.  It has been argued that this limits large language models (LLMs) in their ability to express next token probabilities that perfectly align with the statistics of natural language.  We focus on the ability of such models to produce accurate probabilities for just the top-$m$ tokens.  We provide theoretical bounds that show that even a randomly initialized projection matrix can successfully do this for rather large values of $m$, supported by empirical results on both random and trained matrices. This raises questions about whether the softmax bottleneck significantly limits the capabilities of LLMs. We also derive bounds on the maximum number of probabilities that any trained output projection matrix can specify.
CellDuality: Unlocking Biological Reasoning in LLMs with Self-Supervised RLVR
Authors: Yuhang Chen; Zhen Tan; Ruichen Zhang; Mufan Qiu; Tianlong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: \begin{abstract}
Developing generalist large language models (LLMs) capable of complex biological reasoning is a central challenge in computational biology. While existing LLMs excel at predictive tasks like cell type annotation and logically-constrained problems, enabling open-ended and mechanistic reasoning remains a challenge. A promising direction is Reinforcement Learning from Verifiable Rewards (RLVR), which has been shown to significantly enhance complex reasoning in general domains like mathematics and code synthesis. However, its application in biology is hindered, as most biological outcomes are non-verifiable. For example, verifying a generated gene sequence is usually infeasible. In this paper, we introduce CellDuality, a self-supervised framework that enables LLM agents for robust reasoning in single-cell biology. Our framework is built on the principle of complementary task duality, a self-verification process that leverages a bidirectional reasoning loop. First, the model performs a forward reasoning task by predicting a biological outcome (e.g., a cell's response to a drug). Then, in a complementary inverse task, it must reason backward from its own prediction to reconstruct the initial conditions (e.g., the original drug perturbation). The fidelity of this reconstruction serves as an intrinsic reward signal, creating a feedback loop that enforces logical and biological consistency. We use these intrinsic rewards to align the base LLM via reinforcement learning, without requiring ground-truth verification labels. We demonstrate that CellDuality achieves state-of-the-art performance and provides coherent biological explanations across a diverse suite of single-cell reasoning tasks. Critically, on the challenging out-of-distribution perturbation prediction benchmark, our self-supervised approach significantly outperforms the standard fine-tuning baseline and narrows the performance gap to a supervised RLVR baseline. Our work showcases a new path toward scalable training of biological foundation models.
Building a Foundational Guardrail for General Agentic Systems via Synthetic Data
Authors: Yue Huang; Hang Hua; Yujun Zhou; Pengcheng Jing; Manish Nagireddy; Inkit Padhi; Greta Dolcetti; Zhangchen Xu; Subhajit Chaudhury; Ambrish Rawat; Liubov Nedoshivina; Pin-Yu Chen; Prasanna Sattigeri; Xiangliang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While LLM agents can plan multi-step tasks, intervening at the planning stage—before any action is executed—is often the safest way to prevent harm, since certain risks can lead to severe consequences once carried out. However, existing guardrails mostly operate post-execution, which is difficult to scale and leaves little room for controllable supervision at the plan level. To address this challenge, we highlight three critical gaps in current research: data gap,  model gap, and  evaluation gap. To close the data gap, we introduce AuraGen, a controllable engine that (i) synthesizes benign trajectories, (ii) injects category-labeled risks with calibrated difficulty, and (iii) filters outputs via an automated reward model, producing large and reliable corpora for pre-execution safety. To close the guardian model gap, we propose a foundational guardrail Safiron, combining a cross-planner adapter with a compact guardian model. The adapter unifies different input formats, while Safiron flags risky cases, assigns risk types, and generates rationales; trained in two stages with a broadly explored data recipe, Safiron achieves robust transfer across settings. To close the evaluation gap, we release \texttt{Pre-Exec Bench}, a realistic benchmark covering diverse tools and branching trajectories, which measures detection, fine-grained categorization, explanation, and cross-planner generalization in human-verified scenarios. Extensive experiments demonstrate consistent gains over strong baselines on Pre-Exec Bench, and ablations further distill actionable practices, providing a practical template for safer agentic systems.
Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models
Authors: Paulius Rauba; Mihaela van der Schaar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large neural networks are typically trained for a fixed computational budget, creating a rigid trade-off between performance and efficiency that is ill-suited for deployment in resource-constrained or dynamic environments. Existing approaches to this problem present a difficult choice: training a discrete collection of specialist models is computationally prohibitive, while dynamic methods like slimmable networks often lack the flexibility to be applied to large, pre-trained foundation models. In this work, we propose *Nested Subspace Networks (NSNs)*, a novel architectural paradigm that enables a single model to be dynamically and granularly adjusted across a continuous spectrum of compute budgets at inference time. The core of our approach is to re-parameterize linear layers to satisfy a nested subspace property, such that the function computed at a given rank is a strict subspace of the function at any higher rank. We show that this entire hierarchy of models can be optimized jointly via an uncertainty-aware objective that learns to balance the contributions of different ranks based on their intrinsic difficulty. We demonstrate empirically that NSNs can be surgically applied to pre-trained LLMs and unlock a smooth and predictable compute-performance frontier. For example, a single NSN-adapted model can achieve a 50\% reduction in inference FLOPs with only a 5 percentage point loss in accuracy. Our findings establish NSNs as a powerful framework for creating the next generation of adaptive foundation models.
From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning
Authors: Honglin He; Yukai Ma; Brad Squicciarini; Wayne Wu; Bolei Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Navigation foundation models trained on massive web-scale data enable agents to generalize across diverse environments and embodiments. However, these models, which are trained solely on offline data, often lack the capacity to reason about the consequences of their actions or adapt through counterfactual understanding. They thus face significant limitations in the real-world urban navigation where interactive and safe behaviors, such as avoiding obstacles and moving pedestrians, are critical. To tackle these challenges, we introduce the Seeing-to-Experiencing (S2E) learning framework to scale the capability of navigation foundation models with reinforcement learning. S2E combines the strengths of pre-training on offline videos and post-training through reinforcement learning. It maintains the model's generalizability acquired from large-scale real-world videos while enhancing its interactivity through reinforcement learning in simulation environments. Specifically, we introduce two innovations:
1) an Anchor-Guided Distribution Matching strategy for offline pretraining, which stabilizes learning and models diverse motion patterns through anchor-based supervision; and
2) a Residual-Attention Module for reinforcement learning, which obtains reactive behaviors from simulation environments without erasing the model’s pretrained knowledge.
Moreover, we establish a comprehensive end-to-end evaluation benchmark, NavBench-GS, built on photorealistic 3D Gaussian Splatting reconstructions of real-world scenes that incorporate physical interactions. It can systematically assess the generalizability and safety of navigation foundation models.
PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm
Authors: Jing-Jing Li; Joel Mire; Eve Fleisig; Valentina Pyatkin; Anne Collins; Maarten Sap; Sydney Levine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current AI safety frameworks, which often treat harmfulness as binary, lack the flexibility to handle borderline cases where humans meaningfully disagree. To build more pluralistic systems, it is essential to move beyond consensus and instead understand where and why disagreements arise. We introduce PluriHarms, a benchmark designed to systematically study human harm judgments across two key dimensions—the harm axis (benign to harmful) and the agreement axis (agreement to disagreement). Our scalable framework generates prompts that capture diverse AI harms and human values while targeting cases with high disagreement rates, validated by human data. The benchmark includes 150 prompts with 15,000 ratings from 100 human annotators, enriched with demographic and psychological traits and prompt-level features of harmful actions, effects, and values. Our analyses show that prompts that relate to imminent risks and tangible harms amplify perceived harmfulness, while annotator traits (e.g., toxicity experience, education) and their interactions with prompt content explain systematic disagreement. We benchmark AI safety models and alignment methods on PluriHarms, finding that while personalization significantly improves prediction of human harm judgments, considerable room remains for future progress. By explicitly targeting value diversity and disagreement, our work provides a principled benchmark for moving beyond "one-size-fits-all" safety toward pluralistically safe AI.