GenAI4ELab/papercli-papers huggingface dataset:
EigenScore: OOD Detection using Posterior Covariance in Diffusion Models
Authors: Shirin Shoushtari; Yi Wang; Xiao Shi; Salman Asif; Ulugbek S. Kamilov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems in safety-sensitive domains. Diffusion models have recently emerged as powerful generative models, capable of capturing complex data distributions through iterative denoising. Building on this progress, recent work has explored their potential for OOD detection.
We propose *EigenScore*, a new OOD detection method that leverages the eigenvalue spectrum of the posterior covariance induced by a diffusion model.
We argue that posterior covariance provides a consistent signal of distribution shift, leading to larger trace and leading eigenvalues on OOD inputs, yielding a clear spectral signature. We further provide analysis explicitly linking posterior covariance to distribution mismatch, establishing it as a reliable signal for OOD detection.
To ensure tractability, we adopt a Jacobian-free subspace iteration method to estimate the leading eigenvalues using only forward evaluations of the denoiser.
Empirically, EigenScore achieves state-of-the-art  performance, with up to 2% AUROC improvement over the best baseline. Notably, it remains robust in near-OOD settings such as CIFAR-10 vs CIFAR-100, where existing diffusion-based methods often fail.
Learning Shrinks the Hard Tail: Training‑Dependent Inference Scaling in a Solvable Linear Model
Authors: Noam Itzhak Levi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We analyze neural scaling laws in a solvable model of last-layer fine-tuning where targets have intrinsic, instance-heterogeneous difficulty. In our Latent Instance Difficulty (LID) model, each input's target variance is governed by a latent ''precision'' drawn from a heavy-tailed distribution. While generalization loss recovers standard scaling laws, our main contribution connects this to inference. The pass@$k$ failure rate exhibits a power-law decay, $k^{-\beta_\mathrm{eff}}$, but the observed exponent $\beta_\mathrm{eff}$ is training-dependent. It grows with sample size $N$ before saturating at an intrinsic limit $\beta$ set by the difficulty distribution's tail. This coupling reveals that learning shrinks the ''hard tail'' of the error distribution: improvements in the model's generalization error steepen the pass@$k$ curve until irreducible target variance dominates. The LID model yields testable, closed-form predictions for this behavior, including a compute-allocation rule that favors training before saturation and inference attempts after. 
We validate these predictions in simulations and in two real‑data proxies: CIFAR‑10H (human‑label variance) and a maths teacher–student distillation task.
ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge
Authors: Zhilin Wang; Jaehun Jung; Ximing Lu; Shizhe Diao; Ellie Evans; Jiaqi Zeng; Pavlo Molchanov; Yejin Choi; Jan Kautz; Yi Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to tasks like mathematics, programming, and short-form question-answering. However, many real-world applications require evaluating LLMs in processing professional documents, synthesizing information, and generating comprehensive reports in response to user queries. We introduce ProfBench: a set of over 7000 response-criterion pairs as evaluated by human-experts with professional knowledge across Physics PhD, Chemistry PhD, Finance MBA and Consulting MBA.  We build robust and affordable LLM-Judges to evaluate ProfBench rubrics, by mitigating self-enhancement bias and reducing the cost of evaluation by 2-3 orders of magnitude, to make it fair and accessible to the broader community. Our findings reveal that ProfBench poses significant challenges even for state-of-the-art LLMs, with top-performing models like GPT-5-high achieving only 65.9\% overall performance. Furthermore, we identify notable performance disparities between proprietary and open-weight models and provide insights into the role that extended thinking plays in addressing complex, professional-domain tasks.
MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Authors: Yanghao Li; Rui Qian; Bowen Pan; Haotian Zhang; Haoshuo Huang; Bowen Zhang; Jialing Tong; Haoxuan You; Xianzhi Du; Zhe Gan; Hyunjik Kim; Chao Jia; Zhenbang Wang; Yinfei Yang; Mingfei Gao; Zi-Yi Dou; Wenze Hu; Chang Gao; Dongxu Li; Philipp Dufter; Zirui Wang; Guoli Yin; Zhengdong Zhang; Chen Chen; Yang Zhao; Ruoming Pang; Zhifeng Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddings for image-to-text understanding and discrete tokens for text-to-image generation within a common semantic space. A unified autoregressive LLM predicts high-level semantics in the form of text and image tokens, with an auxiliary diffusion decoder subsequently translating the image tokens into pixels. The architecture, together with a unified training recipe over understanding and generation data, enables scalable joint learning of both capabilities. Manzano achieves state-of-the-art results among unified models, and is competitive with specialist models, particularly on text-rich evaluation. Our studies show minimal task conflicts and consistent gains from scaling model size, validating our design choice of a hybrid tokenizer.
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
Authors: Andrei Rekesh; Miruna Cretu; Dmytro Shevchuk; Pietro Lio; Robert A. Batey; Mike Tyers; Michał Koziarski; Cheng-Hao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Ensuring synthesizability in generative small molecule design remains a major challenge. While recent developments in synthesizable molecule generation have demonstrated promising results, these efforts have been largely confined to 2D molecular graph representations, limiting the ability to perform geometry-based conditional generation. In this work, we present SYNCOGEN (Synthesizable Co-Generation), a single framework that combines simultaneous masked graph diffusion and flow matching for synthesizable 3D molecule generation. SYNCOGEN samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we curated SYNSPACE, a dataset containing over 600K synthesis-aware building block graphs and 3.3M conformers. SYNCOGEN achieves state-of-the-art performance in unconditional small molecule graph and conformer generation, and the model delivers competitive performance in zero-shot molecular linker design and pharmacophore conditioning for protein ligand generation in drug discovery. Overall, this multimodal formulation represents a foundation for future applications enabled by non-autoregressive molecular generation, including analog expansion, lead optimization, and direct structure conditioning.
Searching for Privacy Risks in LLM Agents via Simulation
Authors: Yanzhe Zhang; Diyi Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage others in multi-turn interactions to extract sensitive information. However, the evolving nature of such dynamic dialogues makes it challenging to anticipate emerging vulnerabilities and design effective defenses. To tackle this problem, we present a search-based framework that alternates between improving attack and defense strategies through the simulation of privacy-critical agent interactions. Specifically, we employ LLMs as optimizers to analyze simulation trajectories and iteratively propose new agent instructions. To explore the strategy space more efficiently, we further utilize parallel search with multiple threads and cross-thread propagation. Through this process, we find that attack strategies escalate from direct requests to sophisticated tactics, such as impersonation and consent forgery, while defenses evolve from simple rule-based constraints to robust identity-verification state machines. The discovered attacks and defenses generalize across diverse scenarios and backbone models, providing useful insights for developing privacy-aware agents.
Learning from Historical Activations in Graph Neural Networks
Authors: Yaniv Galron; Hadar Sinai; Haggai Maron; Moshe Eliasof
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains such as social networks, molecular chemistry, and more. A crucial component of GNNs is the pooling procedure, in which the node features calculated by the model are combined to form an informative final descriptor to be used for the downstream task. However, previous graph pooling schemes rely on the last GNN layer features as an input to the pooling or classifier layers, potentially under-utilizing important activations of previous layers produced during the forward pass of the model, which we regard as historical graph activations. This gap is particularly pronounced in cases where a node’s representation can shift significantly over the course of many graph neural layers, and worsened by graph-specific challenges such as over-smoothing in deep architectures. To bridge this gap, we introduce HistoGraph, a novel two‑stage attention‑based final aggregation layer that first applies a unified layer-wise attention over intermediate activations, followed by node-wise attention. By modeling the evolution of node representations across layers, our HistoGraph leverages both the activation history of nodes and the graph structure to refine features used for final prediction. Empirical results on multiple graph classification benchmarks demonstrate that HistoGraph offers strong performance that consistently improves traditional techniques, with particularly strong robustness in deep GNNs. Our code is at https://github.com/YanivDorGalron/HISTOGRAPH
References Improve LLM Alignment in Non-Verifiable Domains
Authors: Kejian Shi; Yixin Liu; PeiFeng Wang; Alexander Fabbri; Shafiq Joty; Arman Cohan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) has shown strong effectiveness in reasoning tasks, it cannot be directly applied to non-verifiable domains lacking ground-truth verifiers, such as LLM alignment. In this work, we investigate whether high-quality reference outputs can be effectively leveraged to bridge this gap.  First, we design evaluation protocols that enhance LLM-based evaluators for LLM alignment using reference outputs. Through comprehensive experiments, we show that a reference-guided approach substantially improves the accuracy of less capable LLM-judges using references from frontier models; stronger LLM-judges can also be enhanced by human-written references. We then demonstrate the utility of high-quality references in alignment tuning, where LLMs guided with references are used as judges to self-improve. We show that reference-guided self-improvement yields clear gains over both SFT distillation and reference-free baselines, achieving performance comparable to training with finetuned reward models. Specifically, our method achieves scores of 73.1% and 58.7% on AlpacaEval and Arena-Hard with Llama-3-8B-Instruct, and 70.0% and 74.1% with Qwen2.5-7B. These results highlight the potential of using reference-guided LLM-evaluators to enable effective post-training in non-verifiable domains.
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Authors: Nikhil Shivakumar Nayak; Krishnateja Killamsetty; Ligong Han; Abhishek Bhandwaldar; Prateek Chanda; Kai Xu; Oleg Silkin; Mustafa Eyceoz; Hao Wang; Aldo Pareja; Akash Srivastava
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. Existing parameter-efficient methods often limit model expressivity or introduce new parameters per task, creating scalability issues. To address these limitations, we introduce **Orthogonal Subspace Fine-Tuning (OSFT)**, a novel parameter-efficient approach for continual learning. OSFT leverages adaptive singular value decomposition (SVD) to dynamically identify and preserve critical, high-rank parameter subspaces that encode prior knowledge. All updates for new tasks are constrained to be strictly orthogonal to these preserved subspaces, which minimizes interference while maintaining a fixed parameter count and avoiding the need to store task-specific gradients. We extensively evaluate OSFT on standard continual learning benchmarks using both encoder-decoder (T5-Large) and decoder-only (LLaMA-2 7B, Mistral-7B) models across diverse tasks. Empirically, our method achieves a state-of-the-art trade-off between learnability and knowledge retention, dominating the Pareto frontier, with **up to 7\% higher** average accuracy than recent baselines like O-LoRA, and **reduces forgetting to near-negligible levels**. It notably maintains the model's general linguistic capabilities, instruction-following, and safety throughout the learning process. OSFT provides a practical, theoretically grounded, and scalable solution that effectively balances model plasticity and knowledge retention for continual learning in LLMs. Code is available at https://github.com/Red-Hat-AI-Innovation-Team/mini_trainer.
Thought Branches: Interpreting LLM Reasoning Requires Resampling
Authors: Uzay Macar; Paul C. Bogdan; Senthooran Rajamanoharan; Neel Nanda
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We argue that interpreting reasoning models from a single chain-of-thought (CoT) is fundamentally inadequate. To understand computation and causal influence, one must study reasoning as a distribution of possible trajectories elicited by a given prompt. We approximate this distribution via on-policy resampling and use it to answer concrete questions about the causes of model decisions. First, when a model states a reason for its action, does that reason actually cause the action? In agentic misalignment scenarios where models seemingly blackmail to preserve themselves, we resample specific sentences to measure their downstream effects. We find that normative self-preservation sentences have unusually small and non-resilient causal impact on the final decision across models, indicating they are not a meaningful driver of blackmail. Second, are handwritten edits to CoT sufficient for steering reasoning? We find that off-policy sentence insertions common in earlier literature yield small and unstable effects in decision-making tasks, whereas on-policy resampling produces larger and more consistent effects. Third, how do we attribute causal influence when models modify their plans or correct prior errors during reasoning? We introduce a resilience metric and counterfactual importance that repeatedly resample to remove sentences such that similar content doesn't reappear downstream. Critical planning statements resist removal but have large effects when successfully eliminated. Fourth, what can our methods, which focus on the mechanistic roles of CoT, teach us about unfaithful reasoning? Adapting causal mediation analysis, we edit hint pathways mid-trajectory and find that prompt hints exert smooth and cumulative influences rather than single-step pivots. Hidden information can influence the trajectory of reasoning by shifting what decisions are made at different junctures in a CoT, and these biases can be modeled and quantified with resampling. Overall, studying distributions via resampling enables reliable causal analysis, clearer narratives of model reasoning, and principled guidance for CoT interventions.
Sharpness-Aware Minimization in Logit Space Efficiently Enhances Direct Preference Optimization
Authors: Haocheng Luo; Zehang Deng; Thanh-Toan Do; Mehrtash Harandi; Dinh Phung; Trung Le
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Direct Preference Optimization (DPO) has emerged as a popular algorithm for aligning pretrained large language models with human preferences, owing to its simplicity and training stability. However, DPO suffers from the recently identified *squeezing effect* (also known as *likelihood displacement*), where the probability of preferred responses decreases unintentionally during training. To understand and mitigate this phenomenon, we develop a theoretical framework that models the coordinate-wise dynamics in logit space. Our analysis reveals that negative-gradient updates cause residuals to expand rapidly along high-curvature directions, which underlies the squeezing effect, whereas Sharpness-Aware Minimization (SAM) can suppress this behavior through its curvature-regularization effect. Building on this insight, we investigate *logits-SAM*, a computationally efficient variant that perturbs only the output layer with negligible overhead. Extensive experiments on Pythia-2.8B, Mistral-7B, and Gemma-2B-IT across multiple datasets and benchmarks demonstrate that logits-SAM consistently improves the effectiveness of DPO and integrates seamlessly with other DPO variants. Code is available at <https://github.com/RitianLuo/logits-sam-dpo>.
Bilinear representation mitigates reversal curse and enables consistent model editing
Authors: Dong-Kyum Kim; Minsung Kim; Jea Kwon; Nakyeong Yang; Meeyoung Cha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The reversal curse—a language model's inability to infer an unseen fact "B is A" from a learned fact "A is B"—is widely considered a fundamental limitation. We show that this is not an inherent failure but an artifact of how models encode knowledge. Our results demonstrate that training from scratch on synthetic relational knowledge graphs leads to the emergence of a bilinear relational structure within the models' hidden representations. This structure alleviates the reversal curse and facilitates inference of unseen reverse facts. Crucially, this bilinear geometry is foundational for consistent model editing: updates to a single fact propagate correctly to its reverse and logically dependent relations. In contrast, models lacking this representation suffer from the reversal curse and fail to generalize model edits, leading to logical inconsistencies. Our results establish that training on a relational knowledge dataset induces the emergence of bilinear internal representations, which in turn support language models in behaving in a logically consistent manner after editing. This suggests that the efficacy of language model editing depends not only on the choice of algorithm but on the underlying representational geometry of the knowledge itself.
OrthoRF: Exploring Orthogonality in Object-Centric Representations
Authors: Despoina Touska; Bastiaan Onne Fagginger Auer; Alexandru Onose; Tejaswi Kasarla; Luis Armando Pérez Rey; Maximilian Lipp; Lyubov Amitonova; Martin R. Oswald; Pascal Cerfontaine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural synchrony is hypothesized to help the brain organize visual scenes into structured multi-object representations. In machine learning, synchrony-based models analogously learn object-centric representations by storing binding in the phase of complex-valued features. Rotating Features (RF) instantiate this idea with vector-valued activations, encoding object presence in magnitudes and affiliation in orientations. We propose Orthogonal Rotating Features (OrthoRF), which enforces orthogonality in RF’s orientation space via an inner-product loss and architectural modifications. This yields sharper phase alignment and more reliable grouping. In evaluations of unsupervised object discovery, including settings with overlapping objects, noise, and out-of-distribution tests, OrthoRF matches or outperforms current models while producing more interpretable representations, and it eliminates the post-hoc clustering required by many synchrony-based approaches. Unlike current models, OrthoRF also recovers occluded object parts, indicating stronger grouping under occlusion. Overall, orthogonality emerges as a simple, effective inductive bias for synchrony-based object-centric learning.
COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics
Authors: Kartik Sharma; Rakshit Trivedi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Activation steering methods enable inference-time control of large language model (LLM) behavior without retraining, but current approaches face a fundamental trade-off: sample-efficient methods suboptimally capture steering signals from labeled examples, while methods that better extract these signals require hundreds to thousands of examples. We introduce COLD-Steer, a training-free framework that steers LLM activations by approximating the representational changes that would result from gradient descent on in-context examples. Our key insight is that the effect of fine-tuning on a small set of examples can be efficiently approximated at inference time without actual parameter updates. We formalize this through two complementary approaches: (i) a unit kernel approximation method that updates the activations directly using gradients with respect to them, normalized across examples, and (ii) a finite-difference approximation requiring only two forward passes regardless of example count. Experiments across a variety of steering tasks and benchmarks demonstrate that COLD-Steer achieves upto 95\% steering effectiveness while using 50 times fewer samples compared to the best baseline. COLD-Steer enables real-time adaptation to new steering objectives and facilitates accommodating diverse perspectives without extensive demonstration data, which we validate through our experiments on pluralistic alignment tasks. Our framework opens new possibilities for adaptive, context-aware model control that can flexibly address varying loss-driven human preferences through principled approximation of learning dynamics rather than specialized training procedures.
Constantly Improving Image Models Need Constantly Improving Benchmarks
Authors: Jiaxin Ge; Grace Luo; Heekyung Lee; Nishant Malpani; Long Lian; XuDong Wang; Aleksander Holynski; Trevor Darrell; Sewon Min; David M. Chan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in image generation, often driven by proprietary systems like GPT-4o Image Gen, regularly introduce new capabilities that reshape how users interact with these models. Existing benchmarks often lag behind and fail to capture these emerging use cases, leaving a gap between community perceptions of progress and formal evaluation. To address this, we present ECHO, a framework for constructing benchmarks directly from real-world evidence of model use: social media posts that showcase novel prompts and qualitative user judgments. Applying this framework to GPT-4o Image Gen, we construct a dataset of over 35,000 prompts curated from such posts. Our analysis shows that ECHO (1) discovers creative and complex tasks absent from existing benchmarks, such as re-rendering product labels across languages or generating receipts with specified totals, (2) more clearly distinguishes state-of-the-art models from alternatives, and
(3) surfaces community feedback that we use to inform the design of metrics for model quality (e.g., measuring observed shifts in color, identity, and structure). Our website is at https://echo-bench.github.io.