GenAI4ELab/papercli-papers huggingface dataset:
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
Authors: Yupeng Chang; Yi Chang; Yuan Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Parameter-efficient fine-tuning (PEFT) has become a *de facto* standard for adapting large language models (LLMs). However, we identify a critical vulnerability within popular low-rank adaptation methods such as LoRA: they can exacerbate "Catastrophic Inheritance"---the unchecked propagation of biases, noise, and data imbalances from pre-training. This phenomenon can degrade model robustness and fairness, undermining the benefits of efficient adaptation. To address this, we introduce Bias-Alleviating Low-Rank Adaptation (BA-LoRA). Our approach is founded on a principled decomposition of Catastrophic Inheritance into three core challenges: Knowledge Drift, Representation Collapse, and Overfitting to Noise. BA-LoRA systematically mitigates these issues by incorporating a trio of targeted regularizers: consistency, diversity, and an SVD-based term, designed to preserve core knowledge, promote representational richness, and encourage robust, low-rank output representations, respectively. We conduct comprehensive evaluations on a suite of Natural Language Generation (NLG) and Natural Language Understanding (NLU) tasks using diverse, prominent open-source language models (e.g., LLaMA-2-7B and DeBERTa-v3-base). Our results show that BA-LoRA not only outperforms state-of-the-art LoRA variants in terms of performance and stability, but also demonstrates superior robustness and bias mitigation on targeted evaluations. These results provide evidence that BA-LoRA can counteract the adverse effects of Catastrophic Inheritance.
On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
Authors: Wenhao Zhang; Yuexiang Xie; Yuchang Sun; Yanxi Chen; Guoyin Wang; Yaliang Li; Bolin Ding; Jingren Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are two prominent post-training paradigms for refining the capabilities and aligning the behavior of Large Language Models (LLMs). Existing approaches that integrate SFT and RL often face the risk of disrupting established response patterns and inducing overfitting to expert data. To address this, we present a novel investigation into the unified view of SFT and RL through an off-policy versus on-policy lens. We propose CHORD, a framework for Controllable Harmonization of On- and Off-Policy Reinforcement Learning via Dynamic Weighting, which reframes SFT not as a separate stage but as a dynamically weighted auxiliary objective within the on-policy RL process. Based on an analysis of off-policy expert data's influence at both holistic and granular levels, we incorporate a dual-control mechanism in CHORD. Specifically, the framework first employs a global coefficient to holistically guide the transition from off-policy imitation to on-policy exploration, and then applies a token-wise weighting function that enables granular learning from the expert, which promotes on-policy exploration and mitigates disruption from off-policy data. We conduct extensive experiments across various practical tasks, providing empirical evidence that CHORD achieves a stable and efficient learning process. By effectively harmonizing off-policy expert data with on-policy exploration, CHORD demonstrates significant improvements over baselines. We will release the source code to inspire further research.
Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation
Authors: Minsang Kim; Seung Jun Baek
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to generate Chain-of-Thoughts for reasoning tasks. KD methods typically ask the student to mimic the teacher's distribution over the entire output. However, a student with limited capacity can be overwhelmed by such extensive supervision causing a distribution mismatch, especially in complex reasoning tasks. We propose Token-Selective Dual Knowledge Distillation (TSD-KD), a framework for student-centric distillation. TSD-KD focuses on distilling important tokens for reasoning and encourages the student to explain reasoning in its own words. TSD-KD combines indirect and direct distillation. Indirect distillation uses a weak form of feedback based on preference ranking. The student proposes candidate responses generated on its own; the teacher re-ranks those candidates as indirect feedback without enforcing its entire distribution. Direct distillation uses distribution matching; however, it selectively distills tokens based on the relative confidence between teacher and student. Finally, we add entropy regularization to maintain the student's confidence during distillation. Overall, our method provides the student with targeted and indirect feedback to support its own reasoning process and to facilitate self-improvement. The experiments show the state-of-the-art performance of TSD-KD on 10 challenging reasoning benchmarks, outperforming the baseline and runner-up in accuracy by up to 54.4% and 40.3%, respectively. Notably, a student trained by TSD-KD even outperformed its own teacher model in four cases by up to 20.3%. The source code is available at https://github.com/kmswin1/TSD-KD.
OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Authors: Yuecheng Liu; DaFeng Chi; Shiguang Wu; Zhanguang Zhang; Yuzheng Zhuang; Bowen Yang; He Zhu; Lingfeng Zhang; Pengwei Xie; David Gamaliel Arcos Bravo; Yingxue Zhang; Jianye HAO; Xingyue Quan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction, as well as continuous spatial decision-making. Nevertheless, current MLLM-based embodied systems face two critical limitations. First, **Geometric Adaptability Gap:** models trained solely on 2D inputs or with hard-coded 3D geometry injection suffer from either insufficient spatial information or restricted 2D generalization, leading to poor adaptability across tasks with diverse spatial demands. Second, **Embodiment Constraint Gap**: prior work often neglects the physical constraints of real robots, resulting in task plans that are theoretically valid but practically infeasible.To address these gaps, we introduce **OmniEVA** -- an embodied versatile planner that enables advanced embodied reasoning and task planning through two pivotal innovations: (1) a **Task-Adaptive 3D Grounding** mechanism, which uses a gated router to dynamically inject 3D features based on task context, enabling selective geometric reasoning. (2) an **Embodiment-Aware Reasoning** framework that incorporates task goals and physical constraints into the reasoning loop, ensuring executable plans. Extensive experiments show that OmniEVA achieves state-of-the-art performance on 7 of 8 embodied reasoning benchmarks and excels in downstream tasks such as object navigation and mobile manipulation. Evaluations on proposed primitive and composite benchmarks confirm its robust and versatile planning capabilities.
Bandit Learning in Matching Markets Robust to Adversarial Corruptions
Authors: Zheshun Wu; Jinhang Zuo; Zenglin Xu; Fang Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper investigates the problem of bandit learning in two-sided decentralized matching markets with adversarial corruptions. In matching markets, players on one side aim to learn their unknown preferences over arms on the other side through iterative online learning, with the goal of identifying the optimal stable match. However, in real-world applications, stochastic rewards observed by players may be corrupted by malicious adversaries, potentially misleading the learning process and causing convergence to a sub-optimal match.  We study this problem under two settings: one where the corruption level $C$ (defined as the sum of the largest adversarial alterations to the feedback across rounds) is known, and another where it is unknown. For the known corruption setting, we develop a robust variant of the classical Explore-Then-Gale-Shapley (ETGS) algorithm by incorporating widened confidence intervals. For the unknown corruption case, we propose a Multi-layer ETGS race method that adaptively mitigates adversarial effects without prior corruption knowledge.  We provide theoretical guarantees for both algorithms by establishing upper bounds on their optimal stable regret, and further derive the lower bound to demonstrate their optimality.
Leveraging Data to Say No: Memory Augmented Plug-and-Play Selective Prediction
Authors: Aditya Sarkar; Yi Li; Jiacheng Cheng; Shlok Kumar Mishra; Nuno Vasconcelos
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Selective prediction aims to endow predictors with a reject option, to avoid low confidence predictions. However, existing literature has primarily focused on closed-set tasks, such as visual question answering with predefined options or fixed-category classification. This paper considers selective  prediction for visual language foundation models, addressing a taxonomy of tasks ranging from closed to open set and from finite to unbounded vocabularies, as in image captioning. We seek training-free approaches of low-complexity, applicable to any foundation model and consider methods based on external vision-language model (VLM) embeddings, like CLIP. This is denoted as $\textit{Plug-and-Play Selective Prediction} (\textbf{\texttt{PaPSP}})$. We identify two key challenges: (1) $\textit{instability of the visual-language representations}$, leading to high variance in image-text embeddings, and (2) $\textit{poor calibration of similarity scores}$. To address these issues, we propose a $\textit{memory augmented}$ $\textbf{\texttt{PaPSP}}$ ($\textbf{\texttt{MA-PaPSP}}$) model, which augments $\textbf{\texttt{PaPSP}}$ with a retrieval dataset of image-text pairs. This is leveraged to reduce embedding variance by averaging retrieved nearest-neighbor pairs and is complemented by the use of contrastive normalization to improve score calibration. Through extensive experiments on multiple datasets, we show that $\textbf{\texttt{MA-PaPSP}}$ outperforms $\textbf{\texttt{PaPSP}}$ and other selective prediction baselines for selective captioning, image-text matching, and fine-grained classification. Source code will be made public.
ViPO: Visual Preference Optimization at Scale
Authors: Ming Li; Jie Wu; Justin Cui; Xiaojie Li; Rui Wang; Chen Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm for visual generation remains largely unexplored.
Current open-source preference datasets typically contain substantial conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naively optimizing on such noisy datasets fails to learn meaningful preferences, fundamentally hindering effective scaling. To enhance the robustness of preference algorithms against noise, we propose Poly-DPO, which extends the DPO objective with an additional polynomial term that dynamically adjusts model confidence during training based on dataset characteristics, enabling effective learning across diverse data distributions from noisy to trivially simple patterns.
Beyond biased patterns, existing datasets suffer from low resolution, limited prompt diversity, and imbalanced distributions. To facilitate large-scale visual preference optimization by tackling key data bottlenecks, we construct ViPO, a massive-scale preference dataset with 1M image pairs (1024px) across five categories and 300K video pairs (720p+) across three categories. Leveraging state-of-the-art generative models and diverse prompts ensures consistent, reliable preference signals with balanced distributions.
Remarkably, when applying Poly-DPO to our high-quality dataset, the optimal configuration converges to standard DPO. This convergence validates both our dataset quality and Poly-DPO's adaptive nature: sophisticated optimization becomes unnecessary with sufficient data quality, yet remains valuable for imperfect datasets.
We comprehensively validate our approach across various visual generation models. On noisy datasets like Pick-a-Pic V2, Poly-DPO achieves 6.87 and 2.32 gains over Diffusion-DPO on GenEval for SD1.5 and SDXL, respectively. For our high-quality ViPO dataset, models achieve performance far exceeding those trained on existing open-source preference datasets. These results confirm that addressing both algorithmic adaptability and data quality is essential for scaling visual preference optimization. Code, models and open-source datasets will be released at: https://github.com/liming-ai/ViPO
VIRTUE: Visual-Interactive Text-Image Universal Embedder
Authors: Wei-Yao Wang; Kazuya Tateishi; Qiyu Wu; Shusuke Takahashi; Yuki Mitsufuji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal representation learning models have demonstrated successful operation across complex tasks, and the integration of vision-language models (VLMs) has further enabled embedding models with instruction-following capabilities. However, existing embedding models lack visual-interactive capabilities to specify regions of interests from users (e.g., point, bounding box, mask), which have been explored in generative models to broaden their human-interactive applicability. Equipping embedding models with visual interactions not only would unlock new applications with localized grounding of user intent, which remains unexplored, but also enable the models to learn entity-level information within images to complement their global representations for conventional embedding tasks. In this paper, we propose a novel **V**isual-**I**nte**R**active **T**ext-Image **U**niversal **E**mbedder (**VIRTUE**) that extends the capabilities of the segmentation model and the vision-language model to the realm of representation learning. In VIRTUE, the segmentation model can process visual prompts that pinpoint specific regions within an image, thereby enabling the embedder to handle complex and ambiguous scenarios more precisely. To evaluate the visual-interaction ability of VIRTUE, we introduce a large-scale **S**egmentation-and-Scene **Ca**ption **R**etrieval (**SCaR**) benchmark comprising 1M samples that aims to retrieve the text caption by jointly considering the entity with a specific object and image scene. VIRTUE consistently achieves a state-of-the-art performance with significant improvements across 36 universal MMEB (**3.1\%–8.5\%**) and five visual-interactive SCaR (**15.2\%–20.3\%**) tasks. The code, models, and benchmarks are available at https://github.com/sony/virtue.
Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
Authors: Sukjun Hwang; Brandon Wang; Albert Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Major progress on language models (LMs) in recent years has largely resulted from moving away from specialized models designed for specific tasks, to general models based on powerful architectures (e.g. the Transformer) that learn everything from raw data. Despite this trend, pre-processing steps such as tokenization remain a barrier to true end-to-end foundation models. We introduce a collection of new techniques that enable a dynamic chunking mechanism which automatically learns content- and context- dependent segmentation strategies learned jointly with the rest of the model. Incorporating this into an explicit hierarchical network (H-Net) allows replacing the (implicitly hierarchical) tokenization--LM--detokenization pipeline with a single model learned fully end-to-end. When compute- and data- matched, an H-Net with one stage of hierarchy operating at the byte level outperforms a strong Transformer language model operating over BPE tokens. Iterating the hierarchy to multiple stages further increases its performance by modeling multiple levels of abstraction, demonstrating significantly better scaling with data and matching the token-based Transformer of twice its size. H-Nets pretrained on English show significantly increased character-level robustness, and qualitatively learn meaningful data-dependent chunking strategies without any heuristics or explicit supervision. Finally, the H-Net's improvement over tokenized pipelines is further increased in languages and modalities with weaker tokenization heuristics, such as Chinese and code, or DNA sequences (nearly 4x improvement in data efficiency over baselines), showing the potential of true end-to-end models that learn and scale better from unprocessed data.
SAFETY-GUIDED FLOW (SGF): A UNIFIED FRAMEWORK FOR NEGATIVE GUIDANCE IN SAFE GENERATION
Authors: Mingyu Kim; Young-Heon Kim; Mijung Park
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Safety mechanisms for diffusion and flow models have recently been developed along two distinct paths. 
In robot planning, control barrier functions are employed to guide generative trajectories away from obstacles at every denoising step by explicitly imposing geometric constraints. 
In parallel, recent data-driven, negative guidance approaches have been shown to suppress harmful content and promote diversity in generated samples. However, they rely on heuristics without clearly stating when safety guidance is actually necessary. 
In this paper, we first introduce a unified probabilistic framework using a Maximum Mean Discrepancy (MMD) potential for image generation tasks that recasts both Shielded Diffusion and Safe Denoiser as instances of our energy-based negative guidance against unsafe data samples. 
Furthermore, we leverage control-barrier functions analysis to justify the existence of a critical time window in which negative guidance must be strong; outside of this window, the guidance should decay to zero to ensure safe and high-quality generation. We evaluate our unified framework on several realistic safe generation scenarios, confirming that negative guidance should be applied in the early stages of the denoising process for successful safe generation.
Mechanism of Task-oriented Information Removal in In-context Learning
Authors: Hakaze Cho; Haolin Yang; Gouki Minegishi; Naoya Inoue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In-context Learning (ICL) is an emerging few-shot learning paradigm based on modern Language Models (LMs), yet its inner mechanism remains unclear. In this paper, we investigate the mechanism through a novel perspective of information removal. Specifically, we demonstrate that in the zero-shot scenario, LMs encode queries into non-selective representations in hidden states containing information for all possible tasks, leading to arbitrary outputs without focusing on the intended task, resulting in near-zero accuracy. Meanwhile, we find that selectively removing specific information from hidden states by a low-rank filter effectively steers LMs toward the intended task. Building on these findings, by measuring the hidden states on carefully designed metrics, we observe that few-shot ICL effectively simulates such task-oriented information removal processes, selectively removing the redundant information from entangled non-selective representations, and improving the output based on the demonstrations, which constitutes a key mechanism underlying ICL. Moreover, we identify essential attention heads inducing the removal operation, termed Denoising Heads, which enables the ablation experiments blocking the information removal operation from the inference, where the ICL accuracy significantly degrades, especially when the correct label is absent from the few-shot demonstrations, confirming both the critical role of the information removal mechanism and denoising heads.
SAM 3: Segment Anything with Concepts
Authors: Nicolas Carion; Laura Gustafson; Yuan-Ting Hu; Shoubhik Debnath; Ronghang Hu; Didac Suris Coll-Vinent; Chaitanya Ryali; Kalyan Vasudev Alwala; Haitham Khedr; Andrew Huang; Jie Lei; Tengyu Ma; Baishan Guo; Arpit Kalla; Markus Marks; Joseph Greer; Meng Wang; Peize Sun; Roman Rädle; Triantafyllos Afouras; Effrosyni Mavroudi; Katherine Xu; Tsung-Han Wu; Yu Zhou; Liliane Momeni; RISHI HAZRA; Shuangrui Ding; Sagar Vaze; Francois Porcher; Feng Li; Siyuan Li; Aishwarya Kamath; Ho Kei Cheng; Piotr Dollar; Nikhila Ravi; Kate Saenko; Pengchuan Zhang; Christoph Feichtenhofer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present Segment Anything Model (SAM) 3, a unified model that detects,
segments, and tracks objects in images and videos based on concept prompts,
which we define as either short noun phrases (e.g., “yellow school bus”), image
exemplars, or a combination of both. Promptable Concept Segmentation (PCS)
takes such prompts and returns segmentation masks and unique identities for all
matching object instances. To advance PCS, we build a scalable data engine that
produces a high-quality dataset with 4M unique concept labels, including hard
negatives, across images and videos. Our model consists of an image-level detector
and a memory-based video tracker that share a single backbone. Recognition and
localization are decoupled with a presence head, which boosts detection accuracy.
SAM 3 doubles the accuracy of existing systems in both image and video PCS,
and improves previous SAM capabilities on visual segmentation tasks. We open
source SAM 3 along with our new Segment Anything with Concepts (SA-Co)
benchmark for promptable concept segmentation.
End-to-End Probabilistic Framework for Learning with Hard Constraints
Authors: Utkarsh Utkarsh; Danielle C. Maddix; Ruijun Ma; Michael W. Mahoney; Bernie Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present ProbHardE2E, a probabilistic forecasting framework that incorporates hard operational/physical constraints and provides uncertainty quantification. Our methodology uses a novel differentiable probabilistic projection layer (DPPL) that can be combined with a wide range of neural network architectures. DPPL allows the model to learn the system in an end-to-end manner, compared to other approaches where constraints are satisfied either through a post-processing step or at inference. ProbHardE2E optimizes a strictly proper scoring rule, without making any distributional assumptions on the target, which enables it to obtain robust distributional estimates (in contrast to existing approaches that generally optimize likelihood-based objectives, which can be biased by their distributional assumptions and model choices); and it can incorporate a range of non-linear constraints (increasing the power of modeling and flexibility). We apply ProbHardE2E in learning partial differential equations with uncertainty estimates and to probabilistic time-series forecasting, showcasing it as a broadly applicable general framework that connects these seemingly disparate domains.  Our code is available at https://github.com/amazon-science/probharde2e.