GenAI4ELab/papercli-papers huggingface dataset:
t-SNE Exaggerates Clusters, Provably
Authors: Noah Bergam; Szymon Snoeck; Nakul Verma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Central to the widespread use of t-distributed stochastic neighbor embedding (t-SNE) is the conviction that it produces visualizations whose structure roughly matches that of the input. To the contrary, we prove that (1) the strength of the input clustering, and (2) the extremity of outlier points, cannot be reliably inferred from the t-SNE output. We demonstrate the prevalence of these failure modes in practice as well.
Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping
Authors: Zhenyu Lei; Qiong Wu; JIANXIONG DONG; Yinhan He; Emily Dodwell; Yushun Dong; Jundong Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and monolithic skill, applying broad training that is inefficient and unable to target specific reasoning errors. We introduce Reasoning Editing, a paradigm for selectively modifying specific reasoning patterns in LLMs while preserving other reasoning pathways. This task presents a fundamental trade-off between Generality, the ability of an edit to generalize across different tasks sharing the same reasoning pattern, and Locality, the ability to preserve other reasoning capabilities.
Through systematic investigation, we uncover the Circuit-Interference Law: edit interference between reasoning patterns is proportional to the overlap of their neural circuits. Guided by this principle, we propose REdit, the first framework to actively reshape neural circuits before editing, thereby modulating interference between reasoning patterns and mitigating the trade-off. REdit integrates three components: (i) Contrastive Circuit Reshaping, which directly addresses the generality-locality trade-off by disentangling overlapping circuits; (ii) Meta-Contrastive Learning, which extends transferability to novel reasoning patterns; and (iii) Dual-Level Protection, which preserves preexisting abilities by constraining reshaping update directions and regularizing task-level predictions.
Extensive experiments with Qwen-2.5-3B on propositional logic reasoning tasks across three difficulty levels demonstrate that REdit consistently achieves superior generality and locality compared to baselines, with additional validation in mathematics showing broader potential. Our code is available at https://github.com/LzyFischer/REdit.
Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
Authors: Yaochen Zhu; Harald Steck; Dawen Liang; Yinhan He; Vito Claudio Ostuni; Jundong Li; Nathan Kallus
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning LLMs to the recommendation task remains challenging: pretrained LLMs often generate out-of-catalog items, violate required output formats, and their ranking quality degrades sharply toward the end of the generated list. To this end, we propose ConvRec-R1, a two-stage framework for end-to-end training of LLM-based conversational recommender systems. In Stage 1, we construct a behavioral-cloning dataset with a Remap-Reflect-Adjust pipeline, which produces high-quality, catalog-grounded demonstrations from powerful blackbox LLMs to warm-start the RL training. In Stage 2, we propose Rank-GRPO, a principled extension of group relative policy optimization (GRPO) tailored to tasks with rank-style outputs. Rank-GRPO treats each rank in the recommendation list as the unit instead of token (too fine-grained) or sequence (too coarse), redefining rewards to remove non-causal credit assignment and introducing a rank-level importance ratio based on the geometric mean of rank-wise token probabilities to stabilize policy updates. Experiments on the on the Reddit-v2 and Redial datasets show that ConvRec-R1 converges faster and achieves higher Recall and NDCG than GRPO-style baselines. Code and datasets are released at https://github.com/yaochenzhu/Rank-GRPO.
ARINBEV: Bird's-Eye View Layout Estimation with Conditional Autoregressive Model
Authors: Jiyong Kwag; Charles Toth; Alper Yilmaz
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in Bird’s Eye View (BEV) layout estimation have advanced through refinements in architectural and geometric design. However, existing methods often overlook the structured relationships among traffic elements. Components such as drivable areas, lane dividers, and pedestrian crossings constitute an interdependent system governed by civil engineering standards. For instance, stop lines precede crosswalks, which align with sidewalks, while lane dividers follow road curvature. To capture these interdependencies, we propose \textbf{ARINBEV}, an autoregressive model for BEV map estimation. Unlike prior generative approaches that rely on complex multiphase training or encoder-decoder architectures, ARINBEV employs a single-stage, decoder-only autoregressive design. This architecture enables semantically consistent BEV map estimation. On nuScenes and Argoverse2, ARINBEV attains 64.3 and 65.6 mIoU, respectively, while using $1.7\times$ fewer parameters and training $1.8\times$ faster than state-of-the-art models.
Omni-IML: Towards Unified Interpretable Image Manipulation Localization
Authors: Chenfan Qu; Yiwu Zhong; Fengjun Guo; Lianwen Jin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing Image Manipulation Localization (IML) methods rely heavily on task-specific designs, making them perform well only on the target IML task, while joint training on multiple IML tasks causes significant performance degradation, hindering real applications. To this end, we propose Omni-IML, the first generalist model designed to unify IML across diverse tasks. Specifically, Omni-IML achieves generalization through three key components: (1) a Modal Gate Encoder, which adaptively selects the optimal encoding modality per sample, (2) a Dynamic Weight Decoder, which dynamically adjusts decoder filters to the task at hand, and (3) an Anomaly Enhancement module that leverages box supervision to highlight the tampered regions and facilitate the learning of task-agnostic features. Beyond localization, to support interpretation of the tampered images, we construct Omni-273k, a large high-quality dataset that includes natural language descriptions of tampered artifacts. It is annotated through our automatic, chain-of-thoughts annotation technique. We also design a simple-yet-effective interpretation module to better utilize these descriptive annotations. Our extensive experiments show that our single Omni-IML model achieves state-of-the-art performance across all four major IML tasks, providing a valuable solution for practical deployment and a promising direction of generalist models in image forensics. Our code and dataset are available at https://github.com/qcf-568/OmniIML.
Self-Supervised Learning from Structural Invariance
Authors: Yipeng Zhang; Hafez Ghaemi; Jungyoon Lee; Shahab Bakhtiari; Eilif B. Muller; Laurent Charlin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Joint-embedding self-supervised learning (SSL), the key paradigm for unsupervised representation learning from visual data, learns from invariances between semantically-related data pairs.
We study the one-to-many mapping problem in SSL,
where each datum may be mapped to multiple valid targets. 
This arises when data pairs come from naturally occurring generative processes, e.g., successive video frames.
We show that existing methods struggle to flexibly capture this conditional uncertainty. As a remedy, we introduce 
a latent variable to account for this uncertainty and derive a variational lower bound on the mutual information between paired embeddings.
Our derivation yields a simple regularization term for standard SSL objectives.
The resulting method, which we call
AdaSSL, applies to both contrastive and 
distillation-based
SSL objectives, and we empirically show its versatility in
causal representation learning,
fine-grained image understanding, and world modeling on videos.
DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
Authors: Fan Shu; Yite Wang; Ruofan Wu; Boyi Liu; Zhewei Yao; Yuxiong He; Feng Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create a emergent need for accurate benchmarking. There are two major gaps in existing benchmarks: (i) the lack of standardized, process-aware evaluation that captures instruction adherence and process fidelity, and (ii) the scarcity of accurately labeled training data. To bridge these gaps, we introduce DARE-bench, a benchmark designed for machine learning modeling and data science instruction following. Unlike many existing benchmarks that rely on human- or model-based judges, all tasks in DARE-bench have verifiable ground truth, ensuring objective and reproducible evaluation. To cover a broad range of tasks and support agentic tools, DARE-bench consists of 6,300 Kaggle-derived tasks and provides both large-scale training data and evaluation sets. Extensive evaluations show that even highly capable models such as gpt-o4-mini struggle to achieve good performance, especially in machine learning modeling tasks. Using DARE-bench training tasks for fine-tuning can substantially improve model performance. For example, supervised fine-tuning boosts Qwen3-32B’s accuracy by 1.83× and reinforcement learning boosts Qwen3-4B’s accuracy by more than 8×. These significant improvements verify the importance of DARE-bench both as an accurate evaluation benchmark and critical training data.
Singleton-Optimized Conformal Prediction
Authors: Tao Wang; Yan Sun; Edgar Dobriban
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conformal prediction can be used to construct prediction sets that cover the true outcome with a desired probability, but can sometimes lead to large prediction sets that are costly in practice. The most useful outcome is a singleton prediction---an unambiguous decision---yet existing efficiency-oriented methods primarily optimize average set size. Motivated by this, we propose a new non-conformity score that is motivated by minimizing the probability of producing non-singleton sets while maintaining coverage. Starting from a non-convex constrained optimization problem as a motivation, we provide a convex-geometric reformulation and associated algorithm for computing the non-conformity score and associated split conformal prediction sets in $O(K)$ time for $K$-class problems. Using this score in split conformal prediction, we introduce Singleton-Optimized Conformal Prediction (SOCOP). We evaluate our method in experiments on image classification and LLM multiple-choice answering, comparing with standard non-conformity scores such as the (negative) label probability estimates and their cumulative distribution function; both of which are motivated by aiming to optimize average length. The results show that SOCOP increases singleton frequency (sometimes by over 20\%) compared to the above scores, with minimal impact on average set size.
Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead
Authors: Feiyang Kang; Michael Kuchnik; Karthik Padthe; Marin Vlastelica; Ruoxi Jia; Carole-Jean Wu; Newsha Ardalani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In post-training for reasoning Large Language Models (LLMs), the current state of practice trains LLMs in two independent stages: Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR, shortened as "RL" below). In this work, we challenge whether high SFT scores translate to improved performance after RL. We provide extensive counter-examples where this is not true. We find high SFT scores can be biased toward simpler or more homogeneous data and are not reliably predictive of subsequent RL gains or scaled-up post-training effectiveness. In some cases, RL training on models with improved SFT performance could lead to substantially worse outcome compared to RL on the base model without SFT. We study alternative metrics and identify generalization loss on held-out reasoning examples and Pass@large k performance to provide strong proxies for the RL outcome. We trained hundreds of models up to 12B-parameter with SFT and RLVR via GRPO and ran extensive evaluations on 7 math benchmarks with up to 256 repetitions, spending $>$1M GPU hours. Experiments include models from Llama3, Mistral-Nemo, Qwen3 and multiple state-of-the-art SFT/RL datasets. Compared to directly predicting from pre-RL performance, prediction based on generalization loss and Pass@large k achieves substantial higher precision, improving $R^2$ coefficient and Spearman's rank correlation coefficient by up to 0.5 (2x). This provides strong utility for broad use cases. For example, in most experiments, we find SFT training on unique examples for a one epoch underperforms training on half examples for two epochs, either after SFT or SFT-then-RL; With the same SFT budget, training only on short examples may lead to better SFT performance, though, it often leads to worse outcome after RL compared to training on examples with varying lengths. This work develops an enhanced evaluation tool for math reasoning tasks and is open-sourced.
Sequences of Logits Reveal the Low Rank Structure of Language Models
Authors: Noah Golowich; Allen Liu; Abhishek Shetty
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: A major problem in the study of large language models  is to understand their inherent low-dimensional structure.  We introduce an approach to study the low-dimensional structure of language models at a model-agnostic level: as sequential probabilistic models. We first empirically demonstrate that a wide range of modern language models exhibit low-rank structure: in particular, matrices built from the model's logits for varying sets of prompts and responses have low approximate rank. We then show that this low-rank structure can be leveraged for generation --- in particular, we can generate a response to a target prompt using a linear combination of the model's outputs on unrelated, or even nonsensical prompts.

On the theoretical front, we observe that studying the approximate rank of language models in the sense discussed above yields a simple universal abstraction whose theoretical predictions parallel our experiments. We then analyze the representation power of the abstraction and give provable learning guarantees.
A Sharp KL Convergence Analysis for Diffusion Models under Minimal Assumptions
Authors: Nishant Jain; Tong Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion-based generative models have emerged as highly effective methods for synthesizing high-quality samples. Recent works have focused on analyzing the convergence of their generation process with minimal assumptions, either through reverse SDEs or probability flow ODEs. The best known guarantees, without any smoothness assumptions, for the KL divergence so far achieve a linear dependence on the data dimension $d$ and an inverse quadratic dependence on accuracy level $\varepsilon$. In this work, we present a refined analysis for the standard Exponential Integrator discretization that improves the dependence on $\varepsilon$, at the same time maintaining the linear dependence on $d$. Following recent works on higher order/randomized midpoint discretizations, we model the generation process as a composition of two steps: a reverse ODE step followed by a smaller noising step, which leads to better dependence on step size. We then provide a novel analysis which achieves linear dependence on $d$ for the ODE discretization error without any smoothness assumptions.
    Specifically, we introduce a general  ODE-based counterpart of the stochastic localization argument from Benton et al and develop new proof techniques to bound second-order spatial derivatives of the score function -- terms that do not arise in previous diffusion analyses and cannot be handled by existing techniques.  Leveraging this framework, we prove that $\tilde{O}\left(\tfrac{d \log^{3/2}(1/\delta)}{\varepsilon}\right)$ steps suffice to approximate the target distribution—corrupted by Gaussian noise of variance $\delta$—to within $O(\varepsilon^2)$ in KL divergence, improving upon the previous best result requiring $\tilde{O}\left(\tfrac{d \log^2(1/\delta)}{\varepsilon^2}\right)$ steps.
RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Authors: Alonso Urbano; David W. Romero; Max Zimmer; Sebastian Pokutta
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group $G$ fixed a priori. Class-pose decompositions aim to create disentangled representations by factoring inputs into invariant features and a pose $g\in G$ defined relative to a training-dependent, \emph{arbitrary} canonical representation. We introduce RECON, a class-pose agnostic \emph{canonical orientation normalization} that corrects arbitrary canonicals via a simple right translation, yielding \emph{natural}, data-aligned canonicalizations. This enables (i) unsupervised discovery of instance-specific pose distributions, (ii) detection of out-of-distribution poses and (iii) a plug-and-play \emph{test-time canonicalization layer}. This layer can be attached on top of any pre-trained model to infuse group invariance, improving its performance without retraining.  We validate on 2D (images) and 3D (molecular ensembles), demonstrating fine-grained, accurate pose discovery, and matching or outperforming label-supervised
canonicalizations in downstream classification.
BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation via Lens of Dynamic Interactions
Authors: Nan Huo; Xiaohan Xu; Jinyang Li; Per Jacobsson; Shipei Lin; Bowen Qin; Binyuan Hui; Xiaolong Li; Ge Qu; Shuzheng Si; Linheng Han; Edward Alexander; Xintong Zhu; Rui Qin; Ruihan Yu; Yiyao Jin; Feige Zhou; Weihao Zhong; Yun Chen; Hongyu Liu; Chenhao Ma; Fatma Ozcan; Yannis Papakonstantinou; Reynold Cheng
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Large language models (LLMs) have demonstrated remarkable performance on single-turn text-to-SQL tasks, but real-world database applications predominantly require multi-turn interactions to handle ambiguous queries, execution errors, and evolving user requirements. Existing multi-turn benchmarks fall short of capturing this complexity, either by treating conversation histories as static context or by limiting evaluation to narrow, read-only (SELECT-ONLY) operations, thereby potentially failing to reflect the challenges encountered in production-grade database assistant. In this work, we introduce BIRD-INTERACT, a benchmark that restores this missing realism through: (1) a **comprehensive interaction environment** that couples each database with a hierarchical knowledge base, metadata files, and a function-driven user simulator, enabling models to solicit clarifications, retrieve knowledge, and recover from execution errors without human supervision; (2) two **evaluation settings** reflecting real-world interaction settings which contain a pre-defined conversational protocol (c-Interact) and a more open-ended agentic setting (a-Interact) in which the model autonomously decides when to query the user simulator or explore the DB environment; (3) a **challenging task suite** that covers the full CRUD spectrum for both business-intelligence and operational use cases, guarded by executable test cases. Each task features ambiguous and follow-up sub-tasks, requiring LLMs to engage in dynamic interaction. The suite is organized into two sets: a full set (BIRD-INTERACT-FULL) of 600 tasks which unfold up to 11,796 dynamic interactions for a comprehensive overview of performance and a lite set (BIRD-INTERACT-LITE) of 300 tasks, with simplified databases for detailed behavioral analysis of interactions, and fast development of methods. Our empirical results highlight the difficulty of BIRD-INTERACT: the most recent flagship model GPT-5 completes only 8.67% of tasks in the c-Interact setting and 17.00% in the a-Interact setting on the full task suite. Further analysis via memory grafting and Interaction Test-time Scaling (ITS) validates the importance of effective interaction for achieving success in dynamic text-to-SQL tasks.
Robust Training of Neural Networks at Arbitrary Precision and Sparsity
Authors: Chengxi Ye; Grace Chu; Yanfeng Liu; Yichi Zhang; Lukasz Lew; Li Zhang; Mark Sandler; Andrew G. Howard
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The discontinuous operations inherent in quantization and sparsification introduce a long-standing obstacle to backpropagation, particularly in ultra-low precision and sparse regimes. While the community has long viewed quantization as unfriendly to gradient descent due to its lack of smoothness, we pinpoint—for the first time—that the key issue is the absence of a proper gradient path that allows training to learn robustness to quantization noise. The standard Straight-Through Estimator (STE) exacerbates this with its well-understood mismatch: a quantization-aware forward pass but oblivious backward pass, leading to unmanaged error and instability. We solve this by explicitly modeling quantization as additive noise, making the full forward-backward path well-defined without heuristic gradient estimation. As one natural solution, we introduce a denoising dequantization transform derived from a principled ridge regression objective, creating an explicit, corrective gradient path that makes learning robust to the noise STE bypasses. We extend this to sparsification by treating it as a special form of quantization that zeros out small values. Our unified framework trains models at arbitrary precisions and sparsity levels with off-the-shelf recipes, enabling stable A1W1 and sub-1-bit networks where others falter. It yields state-of-the-art results, mapping efficiency frontiers for modern LLMs and providing a theoretically grounded path to hyper-efficient neural networks.