GenAI4ELab/papercli-papers huggingface dataset:
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
Authors: Xiaoyuan Zhu; Yaowen Ye; Tianyi Alex Qiu; Hanlin Zhu; Sijun Tan; Ajraf Mannan; Jonathan Michala; Raluca Ada Popa; Willie Neiswanger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As API access becomes a primary interface to large language models (LLMs), users often interact with black-box systems that offer little transparency into the deployed model. To reduce costs or maliciously alter model behaviors, API providers may discreetly serve quantized or fine-tuned variants, which can degrade performance and compromise safety. Detecting such substitutions is difficult, as users lack access to model weights and, in most cases, even output logits. To tackle this problem, we propose a rank-based uniformity test (RUT) that can verify the behavioral equality of a black-box LLM to a locally deployed authentic model. Our method is accurate, query-efficient, and avoids detectable query patterns, making it robust to adversarial providers that reroute or mix responses upon the detection of testing attempts. We evaluate the approach across diverse query domains and threat scenarios, including quantization, harmful fine-tuning, jailbreak prompts, full model substitution, showing that it consistently achieves superior detection power over prior methods under constrained query budgets.
Multiple-Prediction-Powered Inference
Authors: Charlie Cowen-Breen; Alekh Agarwal; Stephen Bates; William W. Cohen; Jacob Eisenstein; Amir Globerson; Adam Fisch
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Statistical estimation often involves tradeoffs between expensive, high-quality measurements and a variety of lower-quality proxies. We introduce Multiple-Prediction-Powered Inference (MultiPPI): a general framework for constructing statistically efficient estimates by optimally allocating resources across these diverse data sources. This work provides theoretical guarantees about the minimax optimality, finite-sample performance, and asymptotic normality of the MultiPPI estimator, and through experiments across three diverse large language model (LLM) evaluation scenarios, we show that MultiPPI consistently achieves lower estimation error than existing baselines. This advantage stems from its budget-adaptive allocation strategy, which strategically combines subsets of models by learning their complex cost and correlation structures.
Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators
Authors: Payel Mukhopadhyay; Michael McCabe; Ruben Ohana; Miles Cranmer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer-based PDE surrogates achieve remarkable performance but face two key challenges: fixed patch sizes cause systematic error accumulation at harmonic frequencies, and computational costs remain inflexible regardless of problem complexity or available resources. We introduce Overtone, a unified solution through dynamic patch size control at inference. Overtone's key insight is that cyclically modulating patch sizes during autoregressive rollouts distributes errors across the frequency spectrum, mitigating the systematic harmonic artifact accumulation that plague fixed-patch models. We implement this through two architecture-agnostic modules—CSM (Convolutional Stride Modulation, using dynamic stride modulation) and CKM (Convolutional Kernel Modulation, using dynamic kernel resizing)—that together provide both harmonic mitigation and compute-adaptive deployment. This flexible tokenization lets users trade accuracy for speed dynamically based on computational constraints, and the cyclic rollout strategy yields up to 40% lower long rollout error in variance-normalised RMSE (VRMSE) compared to conventional, static-patch surrogates. Across challenging 2D and 3D PDE benchmarks, one Overtone model matches or exceeds fixed-patch baselines across inference compute budgets, when trained under a fixed total training budget setting.
PTNET: A PROPOSAL-CENTRIC TRANSFORMER NET- WORK FOR 3D OBJECT DETECTION
Authors: Jianping Zhong; Zhaobo Qi; Kaiwen Duan; Xinyan Liu; Beichen Zhang; Weigang Zhang; Qingming Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: 3D object detection from LiDAR point cloud data is important for autonomous driving systems. Recent two-stage 3D object detectors struggle to achieve satisfactory performance due to limitations in proposal quality, stemming from the degradation of geometric detail information in the generated proposal features caused by high sparsity and uneven distribution of point clouds, as well as a lack of effective exploitation of surrounding contextual cues in the independent proposal refinement stage. To this end, we propose a Proposal-centric
Transformer Network (PTN), which includes a Hierarchical Attentive Feature Alignment (HAFA) module and a Collaborative Proposal Refinement Module (CPRM). More concretely, to obtain multi-granularity proposal representations, HAFA employs a dual-stream architecture that extracts both coarse-grained voxel features and fine-grained point features to enhance proposal features, then harmo-
nizes them through a feature alignment network in a unified space. The CPRM first generates object queries for all objects and then establishes contextual-aware interactions to extract complementary information from semantically similar and spatially relevant proposals. PTN achieves promising performance on large-scale Waymo and KITTI benchmark, demonstrating the superiority of PTN.
LORE: Jointly Learning The Intrinsic Dimensionality and Relative Similarity Structure from Ordinal Data
Authors: Vivek Anand; Alec Helbling; Mark A. Davenport; Gordon J Berman; Sankaraleengam Alagapan; Christopher John Rozell
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem. We introduce LORE (Low Rank Ordinal Embedding), a scalable framework that jointly learns both the intrinsic dimensionality and an ordinal embedding from noisy triplet comparisons of the form, "Is A more similar to B than C?". Unlike existing methods that require the embedding dimension to be set apriori, LORE regularizes the solution using the nonconvex Schatten-$p$ quasi norm, enabling automatic joint recovery of both the ordinal embedding and its dimensionality. We optimize this joint objective via an iteratively reweighted algorithm and establish convergence guarantees. Extensive experiments on synthetic datasets, simulated perceptual spaces, and real world crowdsourced ordinal judgements show that LORE learns compact, interpretable and highly accurate low dimensional embeddings that recover the latent geometry of subjective percepts. By simultaneously inferring both the intrinsic dimensionality and ordinal embeddings, LORE enables more interpretable and data efficient perceptual modeling in psychophysics and opens new directions for scalable discovery of low dimensional structure from ordinal data in machine learning.
RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models
Authors: Yunseok Han; Yejoon Lee; Jaeyoung Do
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Reasoning Models (LRMs) exhibit strong performance, yet often produce rationales that sound plausible but fail to reflect their true decision process, undermining reliability and trust. We introduce a formal framework for *reasoning faithfulness*, defined by two testable conditions: *stance consistency* (a coherent stance linking reasoning to answer) and *causal influence* (the stated reasoning causally drives the answer under output-level interventions), explicitly decoupled from accuracy. To operationalize this, we present **RFEval**, a benchmark of 7,186 instances across seven tasks that probes faithfulness via controlled, output-level counterfactual interventions. Evaluating twelve open-source LRMs, we find unfaithfulness in 49.7% of outputs, predominantly from stance inconsistency. Failures are concentrated in brittle, convergent domains such as math and code, and correlate more with post-training regimes than with scale: within-family ablations indicate that adding current RL-style objectives on top of supervised fine-tuning can *reduce* reasoning faithfulness, even when accuracy is maintained. Crucially, *accuracy is neither a sufficient nor a reliable proxy for faithfulness*: once controlling for model and task, the accuracy–faithfulness link is weak and statistically insignificant. Our work establishes a rigorous methodology for auditing LRM reliability and shows that trustworthy AI requires optimizing not only for correct outcomes but also for the structural integrity of the reasoning process. Our code and dataset can be found at project page: https://aidaslab.github.io/RFEval/
Speculative Actions: A Lossless Framework for Faster AI Agents
Authors: Naimeng Ye; Arnav Ahuja; Georgios Liargkovas; Yunan Lu; Kostis Kaffes; Tianyi Peng
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent behavior unfolds sequentially, where each action requires an API call that can incur substantial latency. For example, a game of chess between two state-of-the-art agents can take hours. We introduce speculative actions, a lossless acceleration framework for general agentic systems. Inspired by speculative execution in microprocessors and speculative decoding in LLM inference, our method uses faster models to predict likely future actions and executes them in parallel, committing only when predictions match. We evaluate speculative actions across gaming, e-commerce, and web search environments, and additionally study a lossy extension in an operating systems setting. Across domains, we achieve up to 55% next-action prediction accuracy, translating into substantial latency reductions. Finally, we present a cost–latency analysis that formalizes the tradeoff between speculative breadth and time savings. This analysis enables principled tuning and selective branch launching, to ensure multi-branch speculation delivers practical speedups without prohibitive cost growth.
VideoJudge: Bootstrapping Enables Scalable Supervision of MLLM-as-a-Judge for Video Understanding
Authors: Abdul Waheed; Zhen Wu; Dareen Safar Alharthi; Seungone Kim; Bhiksha Raj
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Precisely evaluating video understanding models remains challenging: commonly used metrics such as BLEU, ROUGE, and BERTScore fail to capture the nuances of human judgment, while obtaining such judgments through manual evaluation is costly. Recent work has explored using large language models (LLMs) or multimodal LLMs (MLLMs) as evaluators, but their extension to video understanding remains relatively unexplored. In this work, we introduce VideoJudge, a 3B and 7B-sized MLLM judge specialized to evaluate outputs from video understanding models (\textit{i.e.}, text responses conditioned on videos). To train VideoJudge, our recipe builds on the interplay between a generator and an evaluator: the generator is prompted to produce responses conditioned on a target rating, and responses not matching the evaluator's rating are discarded. Across three out of four meta-evaluation benchmarks, VideoJudge-7B outperforms or is on par with larger MLLM judge baselines such as Qwen2.5-VL (32B and 72B). Notably, we find that LLM judges (Qwen3) models perform worse than MLLM judges (Qwen2.5-VL), and long chain-of-thought reasoning does not improve performance, indicating that providing video inputs is crucial for the evaluation of video understanding tasks.
Deep FlexQP: Accelerated Nonlinear Programming via Deep Unfolding
Authors: Alex Oshin; Rahul Vodeb Ghosh; Augustinos D Saravanos; Evangelos Theodorou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose FlexQP, an always-feasible convex quadratic programming (QP) solver based on an $\ell_1$ elastic relaxation of the QP constraints. If the original constraints are feasible, FlexQP provably recovers the optimal solution. If the constraints are infeasible, FlexQP identifies a solution that minimizes the constraint violation while keeping the number of violated constraints sparse. Such infeasibilities arise naturally in sequential quadratic programming (SQP) subproblems due to the linearization of the constraints. We prove the convergence of FlexQP under mild coercivity assumptions, making it robust to both feasible and infeasible QPs. We then apply deep unfolding to learn LSTM-based, dimension-agnostic feedback policies for the algorithm parameters, yielding an accelerated Deep FlexQP. To preserve the exactness guarantees of the relaxation, we propose a normalized training loss that incorporates the Lagrange multipliers. We additionally design a log-scaled loss for PAC-Bayes generalization bounds that yields substantially tighter performance certificates, which we use to construct an accelerated SQP solver with guaranteed QP subproblem performance. Deep FlexQP outperforms state-of-the-art learned QP solvers on a suite of benchmarks including portfolio optimization, classification, and regression problems, and scales to dense QPs with over 10k variables and constraints via fine-tuning. When deployed within SQP, our approach solves nonlinear trajectory optimization problems 4-16x faster than SQP with OSQP while substantially improving success rates. On predictive safety filter problems, Deep FlexQP reduces safety violations by over 70\% and increases task completion by 43\% compared to existing methods.
BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management
Authors: Kevin Bradley Dsouza; Enoch Ofosu; Daniel Chukwuemeka Amaogu; Jérôme Pigeon; Richard Boudreault; Pooneh Maghoul; Juan Moreno-Cruz; Yuri Leonenko
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Boreal forests store 30-40\% of terrestrial carbon, much in climate-vulnerable permafrost soils, making their management critical for climate mitigation. However, optimizing forest management for both carbon sequestration and permafrost preservation presents complex trade-offs that current tools cannot adequately address. We introduce BoreaRL, the first multi-objective reinforcement learning environment for climate-adaptive boreal forest management, featuring a physically-grounded simulator of coupled energy, carbon, and water fluxes. BoreaRL supports two training paradigms: site-specific mode for controlled studies and generalist mode for learning robust policies under environmental stochasticity. Through evaluation of multi-objective RL algorithms, we reveal a fundamental asymmetry in learning difficulty: carbon objectives are significantly easier to optimize than thaw (permafrost preservation) objectives, with thaw-focused policies showing minimal learning progress across both paradigms. In generalist settings, standard gradient-descent based preference-conditioned approaches fail, while a naive site selection approach achieves superior performance by strategically selecting training episodes. Analysis of learned strategies reveals distinct management philosophies, where carbon-focused policies favor aggressive high-density coniferous stands, while effective multi-objective policies balance species composition and density to protect permafrost while maintaining carbon gains. Our results demonstrate that robust climate-adaptive forest management remains challenging for current MORL methods, establishing BoreaRL as a valuable benchmark for developing more effective approaches. We open-source BoreaRL to accelerate research in multi-objective RL for climate applications.
Explainable Mixture Models through Differentiable Rule Learning
Authors: Matthias Wilms; Sascha Xu; Jilles Vreeken
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixture models excel at decomposing complex, multi-modal distributions into simpler probabilistic components, but provide no insight into the conditions under which these components arise. We introduce explainable mixture models (XMM), a framework that pairs each mixture component with a human-interpretable rule over descriptive features. This enables mixtures that are not only statistically expressive but also transparently grounded in the underlying data. We formalize the problem and examine conditions under which an XMM exactly captures a target distribution. We then propose a scalable, differentiable learning procedure for discovering sets of rules. Experiments on synthetic and real-world datasets demonstrate that our method discovers interesting sub-populations in both univariate and multivariate settings,  offering interpretable insights into the structure of complex distributions.
Accelerated Parallel Tempering via Neural Transports
Authors: Leo Zhang; Peter Potaptchik; Jiajun He; Yuanqi Du; Arnaud Doucet; Francisco Vargas; Hai-Dang Dau; Saifuddin Syed
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeting high-dimensional, multimodal, or complex target distributions. Parallel Tempering (PT) enhances MCMC's sample efficiency through annealing and parallel computation, propagating samples from tractable reference distributions to intractable targets via state swapping across interpolating distributions. The effectiveness of PT is limited by the often minimal overlap between adjacent distributions in challenging problems, which requires increasing the computational resources to compensate. We introduce a framework that accelerates PT by leveraging neural samplers---including normalising flows, diffusion models, and controlled diffusions---to reduce the required overlap. Our approach utilises neural samplers in parallel, circumventing the computational burden of neural samplers while preserving the asymptotic consistency of classical PT. We demonstrate theoretically and empirically on a variety of multimodal sampling problems that our method improves sample quality, reduces the computational cost compared to classical PT, and enables efficient free energy/normalising constant estimation.
Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
Authors: Bowen Zuo; Yinglun Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typically allocate compute uniformly across all queries, overlooking variation in query difficulty. To address this inefficiency, we formulate test-time compute allocation as a novel bandit learning problem and propose adaptive algorithms that estimate query difficulty on the fly and allocate compute accordingly. Compared to uniform allocation, our algorithms allocate more compute to challenging queries while maintaining accuracy on easier ones. Among challenging queries, our algorithms further learn to prioritize solvable instances, effectively reducing excessive computing on unsolvable queries. We theoretically prove that our algorithms achieve better compute efficiency than uniform allocation and empirically validate their effectiveness on math and code benchmarks. Specifically, our algorithms achieve up to an 11.10\% performance improvement (15.04\% relative) on the MATH-500 dataset, up to 10.82\% (14.44\% relative) on the AIME25 dataset, and up to an 11.23\% performance improvement (15.29\% relative) on the LiveCodeBench dataset.
Learning Mixtures of Linear Dynamical Systems via Hybrid Tensor-EM Method
Authors: Lulu Gong; Shreya Saxena
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Linear dynamical systems (LDSs) have been powerful tools for modeling high-dimensional time-series data across many domains, including neuroscience. However, a single LDS often struggles to capture the heterogeneity of neural data, where trajectories recorded under different conditions can have variations in their dynamics. Mixtures of linear dynamical systems (MoLDS) provide a path to model these variations in temporal dynamics for different observed trajectories. 
However, MoLDS remains difficult to apply in complex and noisy settings, limiting its practical use in neural data analysis. Tensor-based moment methods can provide global identifiability guarantees for MoLDS, but their practical performance degrades in high-noise or complex scenarios. Commonly used expectation-maximization (EM) methods offer flexibility in fitting latent models but are highly sensitive to initialization and prone to poor local minima. Here, we propose a tensor-based moment method that provides identifiability guarantees for learning MoLDS, which can be followed by EM updates to combine the strengths of both approaches. The novelty in our approach lies in the construction of moment tensors using the input-output data, on which we then apply Simultaneous Matrix Diagonalization (SMD) to recover globally consistent estimates of mixture weights and system parameters. These estimates can then be refined through a full Kalman EM algorithm, with closed-form updates for all LDS parameters. We validate our framework on synthetic benchmarks and real-world datasets. On synthetic data, the proposed Tensor-EM method achieves more reliable recovery and improved robustness compared to either pure tensor or randomly initialized EM methods.  
We then apply this method to two neural datasets from non-human primates doing reaching tasks. For both datasets, our method successfully
models and clusters different conditions as separate subsystems.
These results demonstrate that MoLDS provides an effective framework for modeling complex neural data in different brain regions, and that Tensor-EM is a principled and reliable approach to MoLDS learning for these applications.
Contrastive Diffusion Guidance for Spatial Inverse Problems
Authors: Sattwik Basu; Chaitanya Amballa; Zhongweiyang Xu; Jorge Vančo Sampedro; Srihari Nelakuditi; Romit Roy Choudhury
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable.
Although generative inverse solvers have made significant progress, we find that these forward operators introduce a distinct set of challenges.
As a concrete instance, we consider the problem of reconstructing spatial layouts, such as floorplans, from human movement trajectories, where the underlying path-generation process is inherently non-differentiable and only partially known.
In such problems, direct likelihood-based guidance becomes unstable, since the underlying path-planning process does not provide reliable gradients.
We break-away from existing diffusion-based posterior samplers and reformulate likelihood-based guidance in a smoother embedding space.
This embedding space is learned using a contrastive objective to bring compatible trajectory-floorplan pairs close together while pushing mismatched pairs apart.
We show that this surrogate likelihood score in the embedding space provides a valid approximation to the true likelihood score, making it possible to steer the denoising process towards the posterior.
Across extensive experiments, our model CoGuide produces more consistent reconstructions and is more robust than existing inverse-solvers and guided diffusion.
Beyond spatial mapping, we show that our method can be applied more broadly, suggesting a route toward solving generalized blind inverse problems using diffusion models.