GenAI4ELab/papercli-papers huggingface dataset:
The Alignment Waltz: Jointly Training Agents to Collaborate for Safety
Authors: Jingyu Zhang; Haozhu Wang; Eric Michael Smith; Sid Wang; Amr Sharaf; Mahesh Pasupuleti; Benjamin Van Durme; Daniel Khashabi; Jason E Weston; Hongyuan Zhan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Harnessing the power of LLMs requires a delicate dance between being helpful and harmless, leading to two critical challenges: vulnerability to adversarial attacks that elicit unsafe content, and a tendency for overrefusal on benign but sensitive prompts. Current approaches often navigate this dance with safeguard models that completely reject any content that contains unsafe portions. This approach cuts the metaphorical music entirely—it may exacerbate overrefusals and fails to provide nuanced guidance for queries it refuses. To teach models a more coordinated choreography, we propose WaltzRL, a novel multi-agent reinforcement learning framework that formulates safety alignment as a collaborative, positive-sum game. WaltzRL jointly trains a conversation agent and a feedback agent, where the latter is incentivized to provide useful suggestions that improve the safety and helpfulness of the conversation agent's responses. 
At the core of WaltzRL is a Dynamic Improvement Reward (DIR) that evolves over time based on how well the conversation agent incorporates the feedback. At inference time, unsafe responses or overrefusals from the conversation agent are improved rather than discarded. The feedback agent is deployed together with the conversation agent and only engages adaptively when needed, preserving helpfulness and low latency on safe queries. Our experiments, conducted across five diverse datasets, demonstrate that WaltzRL significantly reduces both unsafe responses (e.g., from 39.0% to 4.6% on WildJailbreak) and overrefusals (from 45.3% to 9.9% on OR-Bench) compared to various baselines. By enabling the conversation and feedback agents to co-evolve and adaptively apply feedback, WaltzRL enhances LLM safety without degrading general capabilities, thereby advancing the Pareto front between helpfulness and harmlessness.
DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
Authors: Selcuk Gurses; Aozhong Zhang; Yanxia Deng; Xun Dong; Xin Li; Naigang Wang; Penghang Yin; Zi Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning is a critical step for adapting large language models (LLMs) to domain-specific downstream tasks. To mitigate the substantial computational and memory costs of full-model fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed to update only a small subset of model parameters. However, performance gaps between PEFT approaches and full-model fine-tuning still exist. In this work, we present *DiaBlo*, a simple yet effective PEFT approach that updates only the diagonal blocks of selected model weight matrices. Unlike Low-Rank Adaptation (LoRA) and its variants, DiaBlo eliminates the need for low-rank matrix products, thereby avoiding the reliance on auxiliary initialization schemes or customized optimization strategies to improve convergence. This design leads to stable and robust convergence while maintaining comparable memory efficiency and training speed to LoRA. Moreover, we provide theoretical guarantees showing that, under mild low-rank conditions, DiaBlo is more expressive than LoRA in the linear problem and converges to a stationary point of the general nonlinear full fine-tuning. Through extensive experiments across a range of tasks—including commonsense reasoning, arithmetic reasoning, code generation, and safety alignment—we show that fine-tuning only diagonal blocks is sufficient for strong and consistent performance. DiaBlo not only achieves competitive accuracy but also preserves high memory efficiency and fast fine-tuning speed. Codes are available at https://github.com/ziyangjoy/DiaBlo.
ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Authors: Jiraphon Yenphraphai; Ashkan Mirzaei; Jianqi Chen; Jiaxu Zou; Sergey Tulyakov; Raymond A. Yeh; Peter Wonka; Chaoyang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. 
In this work, we introduce a native video-to-4D shape generation 
framework that synthesizes a single dynamic 3D representation end-to-end from the video.
Our framework introduces three key components based on large-scale pre-trained 3D models:  (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; 
(ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; 
and (iii) noise sharing across frames to enhance temporal stability. 
Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines.
h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Authors: Yanru Qu; Yijie Zhang; Wenjuan Tan; Xiangzhe Kong; Xiangxin Zhou; Chaoran Cheng; Mathieu Blanchette; Jiaxuan You; Ge Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate molecular representations are critical for drug discovery, and a central
challenge lies in capturing the chemical environment of molecular fragments,
as key interactions, such as H-bond and π stacking, which occur only under specific
local conditions. Most existing approaches represent molecules as atom-level
graphs; however, individual atoms cannot express stereochemistry, lone pairs,
conjugation, and other complex features. Fragment-based methods (e.g., principal
subgraph or functional group libraries) fail to preserve essential information such
as chirality, aromatic bond integrity, and ionic states. This work addresses these
limitations from two aspects. (i) OverlapBPE tokenization. We propose a
novel data-driven molecule tokenization method. Unlike existing approaches, our
method allows overlapping fragments, reflecting the inherently fuzzy boundaries
of small-molecule substructures and, together with enriched chemical information
at the token level, thereby preserving a more complete chemical context. (ii) h-
MINT model. We develop a hierarchical molecular interaction network capable
of jointly modeling drug–target interactions at both atom and fragment levels. By
supporting fragment overlaps, the model naturally accommodates the many-to-
many atom–fragment mappings introduced by the OverlapBPE scheme. Extensive
evaluation against state-of-the-art methods shows our method improves binding
affinity prediction by 2-4% Pearson/Spearman correlation on PDBBind and LBA,
enhances virtual screening by 1-3% in key metrics on DUD-E and LIT-PCBA, and
achieves the best overall HTS performance on PubChem assays. Further analysis
demonstrates that our method effectively captures interactive information while
maintaining good generalization.
From Neural Networks to Logical Theories: The Correspondence between Fibring Modal Logics and Fibring Neural Networks
Authors: Ouns El Harzli; Bernardo Cuenca Grau; Artur d'Avila Garcez; Ian Horrocks; Tarek R. Besold
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fibring of modal logics is a well-established formalism for combining countable families of modal logics into a single fibred language with common semantics, characterized by fibred models. Inspired by this formalism, fibring of neural networks was introduced as a neurosymbolic framework for combining learning and reasoning in neural networks. Fibring of neural networks uses the (pre-)activations of a trained network to evaluate a fibring function computing the weights of another network whose outputs are injected back into the original network. However, the exact correspondence between fibring of neural networks and fibring of modal logics was never formally established. In this paper, we close this gap by formalizing the idea of fibred models compatible with fibred neural networks. Using this correspondence, we then derive non-uniform logical expressiveness results for Graph Neural Networks (GNNs), Graph Attention Networks (GATs) and Transformer encoders. Longer-term, the goal of this paper is to open the way for the use of fibring as a formalism for interpreting the logical theories learnt by neural networks with the tools of computational logic.
How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation.
Authors: Prabhant Singh; Sibylle Hess; Joaquin Vanschoren
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset. Despite the growing interest in developing such metrics, the benchmarks used to measure their progress have gone largely unexamined. In this work, we empirically show the shortcomings of widely used benchmark setups to evaluate transferability estimation metrics. We argue that the benchmarks on which these metrics are evaluated are fundamentally flawed. We empirically demonstrate that their unrealistic model spaces and static performance hierarchies artificially inflate the perceived performance of existing metrics, to the point where simple, dataset-agnostic heuristics can outperform sophisticated methods. Our analysis reveals a critical disconnect between current evaluation protocols and the complexities of real-world model selection. To address this, we provide concrete recommendations for constructing more robust and realistic benchmarks to guide future research in a more meaningful direction.
MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
Authors: Zilin Xiao; Qi Ma; Mengting Gu; Chun-cheng Jason Chen; Xintao Chen; Vicente Ordonez; Vijai Mohan
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Universal multimodal embedding models have achieved great success in capturing semantic relevance between queries and candidates. However, current methods either condense queries and candidates into a single vector, potentially limiting the expressiveness for fine-grained information, or produce too many vectors that are prohibitively expensive for multi-vector retrieval. In this work, we introduce MetaEmbed, a new framework for multimodal retrieval that rethinks how multimodal embeddings are constructed and interacted with at scale. During training, a fixed number of learnable Meta Tokens are appended to the input sequence. At test-time, their last-layer contextualized representations serve as compact yet expressive multi-vector embeddings. Through the proposed Matryoshka Multi-Vector Retrieval training, MetaEmbed learns to organize information by granularity across multiple vectors. As a result, we enable test-time scaling in multimodal retrieval where users can balance retrieval quality against efficiency demands by selecting the number of tokens used for indexing and retrieval interactions. Extensive evaluations on the Massive Multimodal Embedding Benchmark (MMEB) and the Visual Document Retrieval Benchmark (ViDoRe) confirm that MetaEmbed achieves state-of-the-art retrieval performance while scaling robustly to models with 32B parameters. Code is available at https://github.com/facebookresearch/MetaEmbed.
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Authors: Rishabh Ranjan; Valter Hudovernik; Mark Znidar; Charilaos I. Kanatsoulis; Roshan Reddy Upendra; Mahmoud Mohammadi; Joe Meyer; Tom Palczewski; Carlos Guestrin; Jure Leskovec
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks.
The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures and functional dependencies.
In this paper, we present the Relational Transformer (RT) architecture,
which can be pretrained on diverse relational databases and directly applied to unseen datasets and tasks without task- or dataset-specific fine-tuning, or retrieval of in-context examples. RT (i) incorporates task specification via task table prompting, (ii) tokenizes cells with table/column metadata, (iii) is pretrained via masked token prediction, and (iv) utilizes a novel Relational Attention mechanism over columns, rows, and primary-foreign key links.
Pretrained on RelBench datasets spanning tasks such as churn and sales forecasting, RT attains strong zero-shot performance,
averaging 93% of fully supervised AUROC
on binary classification tasks
with a single forward pass of a 22M parameter model,
as opposed to 84% for a 27B LLM.
Fine-tuning yields state-of-the-art results with high sample efficiency. Our experimental analyses show that RT's zero-shot transfer leverages task context,
relational attention patterns and schema semantics. Overall, RT provides a practical path toward foundation models for relational data.
Code, models, data: https://github.com/snap-stanford/relational-transformer.
Provably Accelerated Imaging with Restarted Inertia and Score-based Image Priors
Authors: Marien Renaud; Julien Hermant; Deliang Wei; Yu Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fast convergence and high-quality image recovery are two essential features of algorithms for solving ill-posed imaging inverse problems.
Existing methods, such as regularization by denoising (RED), often focus on designing sophisticated image priors to improve reconstruction quality, while leaving convergence acceleration to heuristics. 
To bridge the gap, we propose Restarted Inertia with Score-based Priors (RISP) as a principled extension of RED.
RISP incorporates a restarting inertia for fast convergence, while still allowing score-based image priors for high-quality reconstruction.
We prove that RISP attains a faster stationary-point convergence rate than RED, without requiring the convexity of the image prior.
We further derive and analyze the associated continuous-time dynamical system, offering insight into the connection between RISP and the heavy-ball ordinary differential equation (ODE).
Experiments across a range of imaging inverse problems demonstrate that RISP enables fast convergence while achieving high-quality reconstructions.
Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
Authors: Qinyuan Wu; Soumi Das; Mahsa Amani; Bishwamittra Ghosh; Mohammad Aflah Khan; Krishna P. Gummadi; Muhammad Bilal Zafar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Rote learning is a memorization technique based on repetition. Many researchers argue that rote learning hinders generalization because it encourages verbatim memorization rather than deeper understanding. This concern extends even to factual knowledge, which inevitably requires a certain degree of memorization.
In this work, we challenge this view and demonstrate that large language models (LLMs) can, in fact, generalize over rote memorized data. We introduce a two-phase “memorize-then-generalize” framework, where the model first rote memorizes factual subject-object associations using a synthetic semantically meaningless key token and then learns to generalize by fine-tuning on a small set of semantically meaningful prompts. Extensive experiments over 8 LLMs show that the models can reinterpret rote memorized data through the semantically meaningful prompts, as evidenced by the emergence of structured, semantically aligned latent representations between the key token and the semantically meaningful prompts.
This surprising finding opens the door to both effective and efficient knowledge injection as well as possible risks of repurposing the memorized data for malicious usage.
Generalized Parallel Scaling with Interdependent Generations
Authors: Harry Dong; David Brandfonbrener; Eryk Helenowski; Yun He; Mrinal Kumar; Han Fang; Yuejie Chi; Karthik Abinav Sankararaman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Parallel LLM inference scaling involves sampling a set of $N>1$ responses for a single input prompt. However, these $N$ parallel responses tend to be generated independently from each other, partitioning compute resources and leaving potentially useful information in one generation untapped by others. This is in contrast to response length scaling where past computation is used in all future steps. For higher quality responses and response sets, we propose Bridge to generate interdependent responses in parallel by rethinking batched LLM hidden states as holistic tensors rather than independent slices. With only a small amount (2.8\%-5.1\%) of new parameters, Bridge improves the relative mean accuracy gains from reinforcement learning with verifiable rewards by up to 39\% and boosts consistency of correct responses. Trained once, Bridge scales to any generation width, all with greater performance than independent generations, unlocking a more general mode of parallel scaling that effectively leverages information between sequences, compatible with any post-generation aggregation technique.
Diversified Multinomial Logit Contextual Bandits
Authors: Heesang Ann; Taehyun Hwang; Min-hwan Oh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial bandits encode diversity in rewards but lack structured choice probabilities. We bridge this gap with the *diversified multinomial logit* (DMNL) contextual bandit, which augments MNL choice probabilities with a generally submodular diversity function, thereby formalizing the relevance—diversity trade-off within a single model.
Incorporating diversity renders exact MNL assortment optimization intractable. We propose a *white-box* UCB-based algorithm, `OFU-DMNL`, that constructs assortments item-wise by maximizing optimistic marginal gains, avoids black-box optimization oracles, and provides end-to-end guarantees.
We show that `OFU-DMNL` achieves at least a $(1-\tfrac{1}{e+1})$-*approximate* regret bound $\tilde{O}\big(d \sqrt{T/K}\big)$, where $d$ is the context dimension, $K$ the maximum assortment size, and $T$ the horizon, and attains an improved approximation factor over standard submodular baselines. 
Experiments demonstrate consistent gains and, relative to exhaustive enumeration, comparable regret with substantially lower runtime. Overall, DMNL bandits provide a principled and practical foundation for diversity-aware assortment optimization under uncertainty, and `OFU-DMNL` offers a statistically and computationally efficient solution.
ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment
Authors: Hongjue Zhao; Haosen Sun; Jiangtao Kong; Xiaochang Li; Qineng Wang; Liwei Jiang; Qi Zhu; Tarek F. Abdelzaher; Yejin Choi; Manling Li; Huajie Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Activation steering, or representation engineering, offers a lightweight approach to align large language models (LLMs) by manipulating their internal activations at inference time. However, current methods suffer from two key limitations: \textit{(i)} the lack of a unified theoretical framework for guiding the design of steering directions, and \textit{(ii)} an over-reliance on \textit{one-step steering} that fail to capture complex patterns of activation distributions. In this work, we propose a unified ordinary differential equations (ODEs)-based \textit{theoretical} framework for activation steering in LLM alignment. We show that conventional activation addition can be interpreted as a first-order approximation to the solution of an ODE. Based on this ODE perspective, identifying a steering direction becomes equivalent to designing a \textit{barrier function} from control theory. Derived from this framework, we introduce ODESteer, a kind of ODE-based steering guided by barrier functions, which shows \textit{empirical} advancement in LLM alignment. ODESteer identifies steering directions by defining the barrier function as the log-density ratio between positive and negative activations, and employs it to construct an ODE for \textit{multi-step and adaptive} steering. Compared to state-of-the-art activation steering methods, ODESteer achieves consistent empirical improvements on diverse LLM alignment benchmarks, a notable $5.7\%$ improvement over TruthfulQA, $2.5\%$ over UltraFeedback, and $2.4\%$ over RealToxicityPrompts. Our work establishes a principled new view of activation steering in LLM alignment by unifying its theoretical foundations via ODEs, and validating it empirically through the proposed ODESteer method.
Geometry-aware Policy Imitation
Authors: Yiming Li; Nael Darwiche; Amirreza Razmjoo; Sichao Liu; Yilun Du; Auke Ijspeert; Sylvain Calinon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a Geometry-Aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state–action samples. From these curves, GPI derives distance fields that give rise to two complementary control primitives: a progression flow that advances along expert trajectories and an attraction flow that corrects deviations. Their combination defines a controllable, non-parametric vector field that directly guides robot behavior. This formulation decouples metric learning from policy synthesis, enabling modular adaptation across low-dimensional robot states and high-dimensional perceptual inputs. GPI naturally supports multimodality by preserving distinct demonstrations as separate models and allows efficient composition of new demonstrations through simple additions to the distance field. We evaluate GPI in simulation and on real robots across diverse tasks. Experiments show that GPI achieves higher success rates than diffusion-based policies while running 20× faster, requiring less memory, and remaining robust to perturbations. These results establish GPI as an efficient, interpretable, and scalable alternative to generative approaches for robotic imitation learning.
Learning to Recall with Transformers Beyond Orthogonal Embeddings
Authors: Nuri Mert Vural; Alberto Bietti; Mahdi Soltanolkotabi; Denny Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern large language models (LLMs) excel at tasks that require storing and retrieving knowledge, such as factual recall and question answering. Transformers are central to this capability because they can encode information during training and retrieve it at inference. Existing theoretical analyses typically study transformers under idealized assumptions such as infinite data or orthogonal embeddings. In realistic settings, however, models are trained on finite datasets with non-orthogonal (random) embeddings. We address this gap by analyzing a single-layer transformer with random embeddings trained with (empirical) gradient descent on a simple token-retrieval task, where the model must identify an informative token within a length-$L$ sequence and learn a one-to-one mapping from tokens to labels. Our analysis tracks the ``early phase'' of gradient descent and yields explicit formulas for the model’s storage capacity---revealing a multiplicative dependence between sample size $N$, embedding dimension $d$, and sequence length $L$. We validate these scalings numerically and further complement them with a lower bound for the underlying statistical problem, demonstrating that this multiplicative scaling is intrinsic under non-orthogonal embeddings. Code to reproduce all experiments is publicly available.