GenAI4ELab/papercli-papers huggingface dataset:
SpaCE-Eval: A Benchmark for Real-World Multi-Modal Reasoning
Authors: Xuyou Yang; Yucheng Zhao; Wenxuan Zhang; Immanuel Koh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-modal Large Language Models (MLLMs) represent a significant advancement in artificial intelligence. Among the growing capabilities exhibited by MLLMs, abilities to understand and reason in real-world environments stand out as particularly vital as a fundamental prerequisite for a wide array of real-world applications. The current methods for evaluating MLLMs often fall short in their ability to comprehensively assess these crucial capabilities. However, being able to reason on complex environment-scale spaces, for example, room spaces, building spaces, and even urban spaces, and to predict the future and plan actions, is essential for humans and various autonomous agents to survive in the real physical world. To address these gaps, we propose a visual-question-answering benchmark, **SpaCE-Eval** (**Spa**tial Reasoning, **C**ommonsense Knowledge and **E**nvironment Interaction) in the real world, designed to evaluate some of MLLM’s most important reasoning abilities in real-world environments. As the name suggests, it challenges the models to reason on complex spatial scenarios, invoke commonsense knowledge of the physical world, and interact with the environment. The dataset consists of all new diagrams purposefully produced by humans, where diagram-question pairs are meticulously refined and selected through a rigorous pipeline. Additionally, with the benchmark, we evaluate a selection of leading MLLMs, both proprietary and open source. The results suggest that a significant enhancement of MLLMs in reasoning in the real physical world is necessary to realise more advanced general artificial intelligence.
Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
Authors: Yulong Hui; Chao Chen; Zhihang Fu; Yihao Liu; Jieping Ye; Huanchen Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-Augmented Generation (RAG) has significantly enhanced LLMs by incorporating external information. However, prevailing agentic RAG approaches are constrained by a critical limitation: they treat the retrieval process as a black-box querying operation. 
This confines agents' actions to query issuing, hindering its ability to tackle complex information-seeking tasks. To address this, we introduce Interact-RAG, a new paradigm that elevates the LLM agent from a passive query issuer into an active manipulator of the retrieval process. We dismantle the black-box with a Corpus Interaction Engine, equipping the agent with a set of action primitives for fine-grained control over information retrieval. To further empower the agent on the entire RAG pipeline, we first develop a reasoning-enhanced workflow, which enables both zero-shot execution and the synthesis of interaction trajectories. We then leverage this synthetic data to train a fully autonomous end-to-end agent via Supervised Fine-Tuning (SFT), followed by refinement with Reinforcement Learning (RL). Extensive experiments across six benchmarks demonstrate that Interact-RAG significantly outperforms other advanced methods, validating the efficacy of our reasoning-interaction strategy.
Attend to the Active: Structure-Aware Dynamic Attention in LLMs for Compositional Instruction Following
Authors: Fangrui Lv; Yulei Qin; Ruixin Hong; Jian Liang; Jinyang Wu; Ke Li; Xing Sun; Changshui Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have exhibited strong instruction-following capabilities; however, they often struggle with compositional instructions involving multiple interleaved yet logically independent sub-tasks. These sub-tasks are typically organized in mutually exclusive structures, such as branching, chaining, or paralleling, where only one sub-task should be active at each generation step, while the others remain dormant.  Despite their inactivity, dormant sub-tasks can inadvertently attract the model's attention due to structural entanglement within the input context or intermediate representations, leading to interference that compromises output fidelity. To address this challenge, we propose ATA, a structure-aware dynamic attention mechanism grounded in compositional structures, which dynamically identifies the active sub-task during generation while suppressing attention to inactive ones. By precisely steering the model’s focus, ATA mitigates interference and explicitly enhances model adherence to the active sub-task.  Importantly, ATA operates within a single forward pass without requiring parameter updates. Extensive experiments show that ATA consistently enhances LLMs' instruction-following ability across various compositional structures, effectively mitigating attention distraction and demonstrating a strong generalization ability.
Beyond Pairwise: Empowering LLM Alignment With (Ranked) Choice Modeling
Authors: Yuxuan Tang; Yifan Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Alignment of large language models (LLMs) has predominantly relied on pairwise preference optimization, where annotators select the better of two responses to a prompt. While simple, this approach overlooks the opportunity to learn from richer forms of human feedback, such as multiway comparisons and top-$k$ rankings. We introduce \textit{Ranked Choice Preference Optimization} (RCPO), a unified framework that bridges preference optimization with (ranked) choice modeling via maximum likelihood estimation. RCPO supports both utility-based and rank-based models, subsumes several pairwise methods (such as DPO and SimPO) as special cases, and provides principled training objectives for richer feedback formats. We instantiate this framework with two representative models (Multinomial Logit and Mallows-RMJ). Experiments on Llama-3-8B-Instruct, Gemma-2-9B-it, and Mistral-7B-Instruct across in-distribution and out-of-distribution settings show that RCPO consistently outperforms competitive baselines. RCPO shows that directly leveraging ranked preference data, combined with the right choice models, yields more effective alignment. It offers an extensible foundation for incorporating (ranked) choice modeling into LLM training.
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
Authors: Ying-Tu Chen; Wei Hung; Bing-Shu Wu; Zhang-Wei Hong; Ping-Chun Hsieh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Many sequential decision-making tasks involve optimizing multiple conflicting objectives, requiring policies that adapt to different user preferences. In multi-objective reinforcement learning (MORL), one widely studied approach addresses this by training a single policy network conditioned on preference-weighted rewards. In this paper, we explore a novel algorithmic perspective: leveraging reward-free reinforcement learning (RFRL) for MORL. While RFRL has historically been studied independently of MORL, it learns optimal policies for any possible reward function, making it a natural fit for MORL's challenge of handling unknown user preferences. We propose using the RFRL's training objective as an auxiliary task to enhance MORL, enabling more effective knowledge sharing beyond the multi-objective reward function given at training time. To this end, we adapt a state-of-the-art RFRL algorithm to the MORL setting and introduce a preference-guided exploration strategy that focuses learning on relevant parts of the environment. Through extensive experiments and ablation studies, we demonstrate that our approach significantly outperforms the state-of-the-art MORL methods across diverse MO-Gymnasium tasks, achieving superior performance and data efficiency. This work provides the first systematic adaptation of RFRL to MORL, demonstrating its potential as a scalable and empirically effective solution to multi-objective policy learning.
DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage
Authors: Haowen Gao; zhenyu zhang; Liang Pang; Fangda Guo; douhongjian; Guannan Lv; ShaoGuo Liu; Tingting Gao; Huawei Shen; Xueqi Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning (RL) with group relative policy optimization (GRPO) has become a widely adopted approach for enhancing the reasoning capabilities of multimodal large language models (MLLMs). While GRPO enables long-chain reasoning without a traditional critic model, it often suffers from sparse rewards, arising from the scarcity of positive feedback on difficult problems, and from advantage vanishing, which occurs when group-level rewards exhibit high consistency for problems that are too easy or too hard. Existing solutions fall into three categories: sample enhancement and expansion, which may aggravate vanishing advantage due to poor control of difficulty distribution; selective sample utilization, which fails to fully leverage the value of all data; and indirect reward design, which may introduce biased optimization directions due to misalignment between reasoning and the final outcome. However, these approaches overlook a fundamental question: for a given problem, how can we ensure that the within-group reward distribution of responses exhibits enough variance to yield clear optimization signals for each response? To address these issues, we propose DIVA-GRPO, a difficulty-adaptive variant augmentation advantage method that dynamically adjusts the difficulty distribution of variants for each problem from a global perspective. Our method dynamically assesses problem difficulty, samples variants with appropriate difficulty levels, and advantages are computed within both local and global(a problem and its variants) groups using difficulty-weighted and normalized scaling. This design alleviates reward sparsity and advantage vanishing, minimizes data waste, and improves training stability. Extensive experiments on six reasoning benchmarks demonstrate that DIVA-GRPO outperforms existing approaches in both training efficiency and reasoning performance.
Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors
Authors: Xin Liu; Runsong Zhao; Pengcheng Huang; Xinyu Liu; Junyi Xiao; Chunyang Xiao; Tong Xiao; Shengxiang Gao; Zhengtao Yu; JingBo Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Context compression is an advanced technique that accelerates large language model (LLM) inference by converting long inputs into compact representations.
Existing methods primarily rely on autoencoding tasks to train special compression tokens to represent contextual semantics.
While autoencoding tasks enable compression tokens to acquire compression capabilities, we remark that such capabilities potentially conflict with actual downstream
task requirements, prevent the models from learning the features more beneficial for real-world usage.
Based on this observation, we propose Semantic-Anchor Compression (SAC), a novel method that shifts from autoencoding task based compression to an architecture that is equipped with this compression capability \textit{a priori}. Instead of training models to compress contexts through autoencoding tasks, SAC directly selects so-called anchor tokens from the original context and aggregates contextual information into their key-value (KV) representations. To ensure that anchors can effectively collect information, SAC introduces two key designs: (1) anchor embedding, a learnable embedding vector attached to the selected anchor tokens to mark compression carriers and (2) bidirectional attention modification, which enables anchor tokens to integrate information from the entire context. Experimental results show that SAC consistently outperforms existing context compression methods across different compression ratios and model sizes on question-answering and long-context summarization tasks. Our data, model and code have been released at \href{https://github.com/lx-Meteors/SAC}{https://github.com/lx-Meteors/SAC}.
Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
Authors: Wenbo Gong; Meyer Scetbon; Chao Ma; Edward Meeds
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Designing efficient optimizers for large language models (LLMs) with low-memory requirements and fast convergence is an important and challenging problem. This paper makes a step towards the systematic design of such optimizers through the lens of structured Fisher information matrix (FIM) approximation. We show that many state-of-the-art efficient optimizers can be viewed as solutions to FIM approximation (under the Frobenius norm) with specific structural assumptions. Building on these insights, we propose two design recommendations of practical efficient optimizers for LLMs, involving the careful selection of structural assumptions to balance generality and efficiency, and enhancing memory efficiency of optimizers with general structures through a novel low-rank extension framework. We demonstrate how to use each design approach by deriving new memory-efficient optimizers: Row and Column Scaled SGD (RACS) and Adaptive low-dimensional subspace estimation (Alice). Experiments on LLaMA pre-training (up to 1B parameters) validate the effectiveness, showing faster and better convergence than existing memory-efficient baselines and Adam with little memory overhead. Notably, Alice achieves better than 2x faster convergence over Adam, while RACS delivers strong performance on the 1B model with SGD-like memory.
DrugTrail: Interpretable Drug Discovery via Structured Reasoning and Druggability‑Tailored Preference Optimization
Authors: Yurou Liu; Mingyang Li; Xinyuan Zhu; Rui Jiao; Yiming Dong; Xinyu Tang; Yang Liu; Jieping Ye; Bing Su; Zheng Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Machine learning promises to revolutionize drug discovery, but its "black-box" nature and narrow focus limit adoption by experts. While Large Language Models (LLMs) offer a path forward with their broad knowledge and interactivity, existing methods remain data-intensive and lack transparent reasoning. To address these issues, we present DrugTrail, an LLM-based framework for explainable drug discovery that integrates structured reasoning trajectories with a Druggability‑Tailored Preference Optimization (DTPO) strategy. It not only introduces structured reasoning traces to articulate the "how" and "why" behind its conclusions but also serve to guide task-specific reasoning pathways within the LLM's vast knowledge space, thereby enhancing its interpretability and reliability of its final outputs. Furthermore, based on the fact that optimizing for binding affinity alone does not equate to optimizing for druggability, DTPO explicitly moves beyond single-metric optimization and opens up a broader search space that balances affinity with other essential factors. Extensive experiments demonstrate the effectiveness of our approach and its generalizability to a wider range of biomolecular optimization domains, bridging the gap between LLM reasoning capabilities and trustworthy AI-assisted drug discovery.
Kimi-Dev: Agentless Training as Skill Prior for SWE-agents
Authors: Zonghan Yang; Shengjie Wang; Kelin Fu; Wenyang He; Weimin Xiong; Yibo Liu; Yibo Miao; Bofei Gao; Yejie Wang; YINGWEI MA; Yanhao Li; Yue Liu; Zhenxing Hu; kaitai zhang; Shuyi Wang; Huarong Chen; Flood Sung; Yang Liu; Yang Gao; Zhilin Yang; Tianyu Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) are increasingly applied to software engineering (SWE), with SWE-bench as a key benchmark. Solutions are split into SWE-Agent frameworks with multi-turn interactions and workflow-based Agentless methods with single-turn verifiable steps. We argue these paradigms are not mutually exclusive: reasoning-intensive Agentless training induces skill priors, including localization, code edit, and self-reflection that enable efficient and effective SWE-Agent adaptation. In this work, we first curate the Agentless training recipe and present Kimi-Dev, an open-source SWE LLM achieving 60.4\% on SWE-bench Verified, the best among workflow approaches. With additional SFT adaptation on 5k publicly-available trajectories, Kimi-Dev powers SWE-Agents to 48.6\% pass@1, on par with that of Claude 3.5 Sonnet (241022 version). These results show that structured skill priors from Agentless training can bridge workflow and agentic frameworks for transferable coding agents.
Robust Preference Alignment via Directional Neighborhood Consensus
Authors: Ruochen Mao; Yuling Shi; Xiaodong Gu; Jiaheng Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Aligning large language models with human preferences is critical for creating
reliable and controllable AI systems. A human preference can be visualized as a
high-dimensional vector where different directions represent trade-offs between
desired attributes (e.g., helpfulness vs. verbosity). Yet, because the training data
often reflects dominant, average preferences, LLMs tend to perform well on com-
mon requests but falls short in specific, individual needs. This mismatch creates
a preference coverage gap. Existing methods often address this through costly
retraining, which may not be generalized to the full spectrum of diverse preferences.
This brittleness means that when a user’s request reflects a nuanced preference
deviating from the training data’s central tendency, model performance can degrade
unpredictably. To address this challenge, we introduce Robust Preference Selection
(RPS), a post-hoc, training-free method by leveraging directional neighborhood
consensus. Instead of forcing a model to generate a response from a single, highly
specific preference, RPS samples multiple responses from a local neighborhood
of related preferences to create a superior candidate pool. It then selects the re-
sponse that best aligns with the user’s original intent. We provide a theoretical
framework showing that, under mild conditions where (i) nearby preference direc-
tions correspond to better-trained regions of the model and (ii) the reward-model
scores change smoothly with small angular changes in the preference vector, our
neighborhood generation strategy yields a higher expected best score than a strong
baseline that also samples multiple candidates. Comprehensive experiments across
three distinct alignment paradigms (DPA, DPO, and SFT) demonstrate that RPS
consistently improves robustness against this baseline, achieving win rates of up
to 69% on challenging preferences from under-represented regions of the space
without any model retraining. Our work presents a practical, theoretically-grounded
solution for enhancing the reliability of preference-aligned models.
Dual Distillation for Few-Shot Anomaly Detection
Authors: Le Dong; Qinzhong Tan; Chunlei Li; Jingliang Hu; Yilei Shi; Weisheng Dong; Xiao Xiang Zhu; Lichao Mou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. While recent unsupervised anomaly detection approaches show promise, they require substantial normal training data and struggle to generalize across anatomical contexts. We introduce D$^2$4FAD, a novel dual distillation framework for few-shot anomaly detection that identifies anomalies in previously unseen tasks using only a small number of normal reference images. Our approach leverages a pre-trained encoder as a teacher network to extract multi-scale features from both support and query images, while a student decoder learns to distill knowledge from the teacher on query images and self-distill on support images. We further propose a learn-to-weight mechanism that dynamically assesses the reference value of each support image conditioned on the query, optimizing anomaly detection performance. To evaluate our method, we curate a comprehensive benchmark dataset comprising 13,084 images across four organs, four imaging modalities, and five disease categories. Extensive experiments demonstrate that D$^2$4FAD significantly outperforms existing approaches, establishing a new state-of-the-art in few-shot medical anomaly detection. Code is available at https://github.com/ttttqz/D24FAD.
Token-Efficient Long-Term Interest Sketching and Internalized Reasoning for LLM-based Recommendation
Authors: Zhihao Ding; Jinming Li; Shuai Mu; Jieming Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) can solve complex real-world tasks when prompted to generate chain-of-thought (CoT) reasoning, motivating their use for preference reasoning in recommender systems. However, applying LLM reasoning on recommendation faces  two practical challenges. First, LLMs struggle to reason over long, noisy user histories that often span hundreds of items while truncation discards signals needed to capture long-term interests. Second, in decoder-only architectures, CoT requires generating rationale tokens autoregressively, leading to prohibitive inference latency for real-world deployment. To address the challenges, we propose SIREN, a framework that enables effective LLM-based rating prediction via long-term interest sketching and internalized reasoning. First, instead of prompting raw histories, we build a compact, token-bounded interest sketch that preserves persistent preferences and suppresses noise. Specifically, we encode and cluster item descriptions to discover semantic topics, then compress each user’s history into a short list of liked and disliked topics, facilitating LLM reasoning. 
Second, we develop an internalized reasoning strategy for efficient inference. We adopt a two-stage training paradigm: (i) train the LLM to reason explicitly for rating prediction with rule-based reinforcement learning, since ground-truth CoTs are unavailable in recommendation; and (ii) learn to internalize CoT into model parameters through hidden alignment. At inference, the LLM directly generates the rating with near-CoT quality.
Extensive experiments show that SIREN reduces average input tokens by $48.7\%$ compared to raw-history prompting, outperforms existing methods while delivering over $100\times$ lower inference latency than CoT-based LLM recommenders. Code and data are available at https://github.com/TommyDzh/SIREN.
Source-Guided Flow Matching
Authors: Zifan Wang; Alice Harting; Matthieu Barreau; Michael M. Zavlanos; Karl Henrik Johansson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Guidance of generative models is typically achieved by modifying the probability flow vector field through the addition of a guidance field. In this paper, we instead propose the Source-Guided Flow Matching (SGFM) framework, which modifies the source distribution directly while keeping the pre-trained vector field intact. This reduces the guidance problem to a well-defined problem of sampling from the source distribution. We theoretically show that SGFM recovers the desired target distribution exactly. Furthermore, we provide bounds on the Wasserstein error for the generated distribution when using an approximate sampler of the source distribution and an approximate vector field. The key benefit of our approach is that it allows the user to flexibly choose the sampling method depending on their specific problem. To illustrate this, we systematically compare different sampling methods and discuss conditions for asymptotically exact guidance. Moreover, our framework integrates well with optimal flow matching models since the straight transport map generated by the vector field is preserved. Experimental results on synthetic 2D benchmarks, physics-informed generative tasks, and imaging inverse problems demonstrate the effectiveness and flexibility of the proposed framework.